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| library_name
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niksmer/PolicyBERTa-7d
|
niksmer
| 2022-03-24T09:19:57Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
model-index:
- name: PolicyBERTa-7d
results: []
widget:
- text: "Russia must end the war."
- text: "Democratic institutions must be supported."
- text: "The state must fight political corruption."
- text: "Our energy economy must be nationalised."
- text: "We must increase social spending."
---
# PolicyBERTa-7d
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/). It was inspired by the model from [Laurer (2020)](https://huggingface.co/MoritzLaurer/policy-distilbert-7d).
It achieves the following results on the evaluation set:
- Loss: 0.8549
- Accuracy: 0.7059
- F1-micro: 0.7059
- F1-macro: 0.6683
- F1-weighted: 0.7033
- Precision: 0.7059
- Recall: 0.7059
## Model description
This model was trained on 115,943 manually annotated sentences to classify text into one of seven political categories: "external relations", "freedom and democracy", "political system", "economy", "welfare and quality of life", "fabric of society" and "social groups".
## Intended uses & limitations
The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance.
```python
from transformers import pipeline
import pandas as pd
classifier = pipeline(
task="text-classification",
model="niksmer/PolicyBERTa-7d")
# Load text data you want to classify
text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list()
# Inference
output = classifier(text)
# Print output
pd.DataFrame(output).head()
```
## Training and evaluation data
PolicyBERTa-7d was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020.
| Country | Count manifestos | Count sentences | Time span |
|----------------|------------------|-----------------|--------------------|
| Australia | 18 | 14,887 | 2010-2016 |
| Ireland | 23 | 24,966 | 2007-2016 |
| Canada | 14 | 12,344 | 2004-2008 & 2015 |
| New Zealand | 46 | 35,079 | 1993-2017 |
| South Africa | 29 | 13,334 | 1994-2019 |
| USA | 9 | 13,188 | 1992 & 2004-2020 |
| United Kingdom | 34 | 30,936 | 1997-2019 |
Canadian manifestos between 2004 and 2008 are used as test data.
The Manifesto Project mannually annotates individual sentences from political party manifestos in 7 main political domains: 'Economy', 'External Relations', 'Fabric of Society', 'Freedom and Democracy', 'Political System', 'Welfare and Quality of Life' or 'Social Groups' - see the [codebook](https://manifesto-project.wzb.eu/down/papers/handbook_2021_version_5.pdf) for the exact definitions of each domain.
### Tain data
Train data was higly imbalanced.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | external relations | 7,640 |
| 1 | freedom and democracy | 5,880 |
| 2 | political system | 11,234 |
| 3 | economy | 29,218 |
| 4 | welfare and quality of life | 37,200 |
| 5 | fabric of society | 13,594 |
| 6 | social groups | 11,177 |
Overall count: 115,943
### Validation data
The validation was created by chance.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | external relations | 1,345 |
| 1 | freedom and democracy | 1,043 |
| 2 | political system | 2,038 |
| 3 | economy | 5,140 |
| 4 | welfare and quality of life | 6,554 |
| 5 | fabric of society | 2,384 |
| 6 | social groups | 1,957 |
Overall count: 20,461
## Test data
The test dataset contains ten canadian manifestos between 2004 and 2008.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | external relations | 824 |
| 1 | freedom and democracy | 296 |
| 2 | political system | 1,041 |
| 3 | economy | 2,188 |
| 4 | welfare and quality of life | 2,654 |
| 5 | fabric of society | 940 |
| 6 | social groups | 387 |
Overall count: 8,330
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
```
training_args = TrainingArguments(
warmup_steps=0,
weight_decay=0.1,
learning_rate=1e-05,
fp16 = True,
evaluation_strategy="epoch",
num_train_epochs=5,
per_device_train_batch_size=16,
overwrite_output_dir=True,
per_device_eval_batch_size=16,
save_strategy="no",
logging_dir='logs',
logging_strategy= 'steps',
logging_steps=10,
push_to_hub=True,
hub_strategy="end")
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
| 0.9154 | 1.0 | 1812 | 0.8984 | 0.6785 | 0.6785 | 0.6383 | 0.6772 | 0.6785 | 0.6785 |
| 0.8374 | 2.0 | 3624 | 0.8569 | 0.6957 | 0.6957 | 0.6529 | 0.6914 | 0.6957 | 0.6957 |
| 0.7053 | 3.0 | 5436 | 0.8582 | 0.7019 | 0.7019 | 0.6594 | 0.6967 | 0.7019 | 0.7019 |
| 0.7178 | 4.0 | 7248 | 0.8488 | 0.7030 | 0.7030 | 0.6662 | 0.7011 | 0.7030 | 0.7030 |
| 0.6688 | 5.0 | 9060 | 0.8549 | 0.7059 | 0.7059 | 0.6683 | 0.7033 | 0.7059 | 0.7059 |
### Validation evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| PolicyBERTa-7d | 0.71 | 0.67 | 0.70 |
### Test evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| PolicyBERTa-7d | 0.65 | 0.60 | 0.65 |
### Evaluation per category
| Label | Validation F1-Score | Test F1-Score |
|-----------------------------|---------------------|---------------|
| external relations | 0.76 | 0.70 |
| freedom and democracy | 0.61 | 0.55 |
| political system | 0.55 | 0.55 |
| economy | 0.74 | 0.67 |
| welfare and quality of life | 0.77 | 0.72 |
| fabric of society | 0.67 | 0.60 |
| social groups | 0.58 | 0.41 |
### Evaluation based on saliency theory
Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent.
Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology.
The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions). But PolicyBERTa isn't fine-tuned to predict the rile-index, if you're interested in that, check [ManiBERT](https://huggingface.co/niksmer/ManiBERT) or [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE).
In the following table, the predicted and original share of the individual policy domains are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original shares is 0.965.
| Party-ID | Year | Type | Share external relations | Share freedom and democracy | Share political system | Share economy | Share welfare and quality of life | Share fabric of society | Share social groups |
|--------------|-------------|---------------|--------------------------|-----------------------------|------------------------|----------------|-----------------------------------|-------------------------|---------------------|
| 62320 | 2004 | Predicted | 7.1% | 4.8% | 13.2% | 20.3% | 35.2% | 9.6% | 9.8% |
| | | Original | 10.2% | 2.5% | 13.7% | 23.8% | 31.7% | 11.6% | 6.4% |
| 62320 | 2006 | Predicted | 2.9% | 4.7% | 16.4% | 18.9% | 38.3% | 11.9% | 6.9% |
| | | Original | 5.6% | 5.0% | 15.8% | 20.7% | 38.7% | 9.3% | 4.9% |
| 62320 | 2008 | Predicted | 6.8% | 4.7% | 6.2% | 24.7% | 38.3% | 10.3% | 9.0% |
| | | Original | 5.6% | 3.7% | 8.2% | 33.1% | 29.5% | 11.7% | 4.3% |
| 62420 | 2004 | Predicted | 9.7% | 3.5% | 14.5% | 24.7% | 34.8% | 8.5% | 4.3% |
| | | Original | 12.6% | 1.3% | 18.8% | 23.0% | 33.2% | 9.0% | 2.0% |
| 62420 | 2006 | Predicted | 9.5% | 2.2% | 7.9% | 27.8% | 34.8% | 9.2% | 8.7% |
| | | Original | 10.6% | 2.5% | 9.6% | 29.7% | 33.1% | 8.3% | 6.2% |
| 62420 | 2008 | Predicted | 0.7% | 0.5% | 3.5% | 41.7% | 46.4% | 3.7% | 3.5% |
| | | Original | 2.0% | 0.2% | 4.4% | 33.3% | 45.9% | 7.7% | 6.4% |
| 62623 | 2004 | Predicted | 7.1% | 11.4% | 24.5% | 17.6% | 21.5% | 13.6% | 4.3% |
| | | Original | 8.4% | 6.7% | 28.8% | 17.4% | 18.7% | 15.5% | 4.5% |
| 62623 | 2006 | Predicted | 5.6% | 8.5% | 23.6% | 15.6% | 14.8% | 24.3% | 7.6% |
| | | Original | 5.0% | 8.9% | 22.2% | 17.4% | 17.2% | 25.7% | 3.6% |
| 62623 | 2008 | Predicted | 5.0% | 4.4% | 12.2% | 33.1% | 21.9% | 17.5% | 5.9% |
| | | Original | 5.6% | 2.2% | 11.6% | 37.8% | 17.8% | 20.9% | 4.1% |
| 62110 | 2008 | Predicted | 10.0% | 3.1% | 6.8% | 22.7% | 41.3% | 10.1% | 6.0% |
| | | Original | 13.4% | 3.3% | 7.7% | 26.9% | 35.6% | 8.9% | 4.3% |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3
|
niksmer/RoBERTa-RILE
|
niksmer
| 2022-03-24T09:19:40Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
metrics:
- accuracy
- precision
- recall
model-index:
- name: RoBERTa-RILE
results: []
widget:
- text: "Russia must end the war."
- text: "Democratic institutions must be supported."
- text: "The state must fight political corruption."
- text: "Our energy economy must be nationalised."
- text: "We must increase social spending."
---
# RoBERTa-RILE
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/).
## Model description
This model was trained on 115,943 manually annotated sentences to classify text into one of three political categories: "neutral", "left", "right".
## Intended uses & limitations
The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance.
```python
from transformers import pipeline
import pandas as pd
classifier = pipeline(
task="text-classification",
model="niksmer/RoBERTa-RILE")
# Load text data you want to classify
text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list()
# Inference
output = classifier(text)
# Print output
pd.DataFrame(output).head()
```
## Training and evaluation data
## Training and evaluation data
RoBERTa-RILE was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020.
| Country | Count manifestos | Count sentences | Time span |
|----------------|------------------|-----------------|--------------------|
| Australia | 18 | 14,887 | 2010-2016 |
| Ireland | 23 | 24,966 | 2007-2016 |
| Canada | 14 | 12,344 | 2004-2008 & 2015 |
| New Zealand | 46 | 35,079 | 1993-2017 |
| South Africa | 29 | 13,334 | 1994-2019 |
| USA | 9 | 13,188 | 1992 & 2004-2020 |
| United Kingdom | 34 | 30,936 | 1997-2019 |
Canadian manifestos between 2004 and 2008 are used as test data.
The Manifesto Project mannually annotates individual sentences from political party manifestos in over 50 main categories - see the [codebook](https://manifesto-project.wzb.eu/down/papers/handbook_2021_version_5.pdf) for the exact definitions of each categorie. It has created a valid left-right-scale, the rile-index, to aaggregate manifesto in a standardized, onde-dimensional political space from left to right based on saliency-theory.
RoBERTa-RILE classifies texts based on the rile index.
### Tain data
Train data was slightly imbalanced.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | neutral | 52,277 |
| 1 | left | 37,106 |
| 2 | right | 26,560 |
Overall count: 115,943
### Validation data
The validation was created by chance.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | neutral | 9,198 |
| 1 | left | 6,637 |
| 2 | right | 4,626 |
Overall count: 20,461
### Test data
The test dataset contains ten canadian manifestos between 2004 and 2008.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | neutral | 3,881 |
| 1 | left | 2,611 |
| 2 | right | 1,838 |
Overall count: 8,330
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
```
training_args = TrainingArguments(
warmup_ratio=0.05,
weight_decay=0.1,
learning_rate=1e-05,
fp16 = True,
evaluation_strategy="epoch",
num_train_epochs=5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
save_strategy="no",
logging_dir='logs',
logging_strategy= 'steps',
logging_steps=10,
push_to_hub=True,
hub_strategy="end")
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
| 0.7442 | 1.0 | 1812 | 0.6827 | 0.7120 | 0.7120 | 0.7007 | 0.7126 | 0.7120 | 0.7120 |
| 0.6447 | 2.0 | 3624 | 0.6618 | 0.7281 | 0.7281 | 0.7169 | 0.7281 | 0.7281 | 0.7281 |
| 0.5467 | 3.0 | 5436 | 0.6657 | 0.7309 | 0.7309 | 0.7176 | 0.7295 | 0.7309 | 0.7309 |
| 0.5179 | 4.0 | 7248 | 0.6654 | 0.7346 | 0.7346 | 0.7240 | 0.7345 | 0.7346 | 0.7346 |
| 0.4787 | 5.0 | 9060 | 0.6757 | 0.7350 | 0.7350 | 0.7241 | 0.7347 | 0.7350 | 0.7350 |
### Validation evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| RoBERTa-RILE | 0.74 | 0.72 | 0.73 |
### Test evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| RoBERTa-RILE | 0.69 | 0.67 | 0.69 |
### Evaluation per category
| Label | Validation F1-Score | Test F1-Score |
|-----------------------------|---------------------|---------------|
| neutral | 0.77 | 0.74 |
| left | 0.73 | 0.65 |
| right | 0.67 | 0.62 |
### Evaluation based on saliency theory
Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent.
Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology.
The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions).
In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use [ManiBERT](https://huggingface.co/niksmer/ManiBERT).
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3
|
buvnswrn/daml-t5-pretrain
|
buvnswrn
| 2022-03-24T09:08:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T07:11:08Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- imdb
model-index:
- name: daml-t5-pretrain-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# daml-t5-pretrain-imdb
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
tiennvcs/distilbert-base-uncased-finetuned-ner
|
tiennvcs
| 2022-03-24T07:29:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-24T07:17:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9264836138175376
- name: Recall
type: recall
value: 0.9361226087929299
- name: F1
type: f1
value: 0.9312781703856213
- name: Accuracy
type: accuracy
value: 0.9836529143565221
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0616
- Precision: 0.9265
- Recall: 0.9361
- F1: 0.9313
- Accuracy: 0.9837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2437 | 1.0 | 878 | 0.0745 | 0.9144 | 0.9173 | 0.9158 | 0.9799 |
| 0.0518 | 2.0 | 1756 | 0.0621 | 0.9177 | 0.9353 | 0.9264 | 0.9826 |
| 0.03 | 3.0 | 2634 | 0.0616 | 0.9265 | 0.9361 | 0.9313 | 0.9837 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
nguyenvulebinh/iwslt-asr-wav2vec-large-4500h
|
nguyenvulebinh
| 2022-03-24T07:12:52Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:common_voice",
"dataset:librispeech_asr",
"dataset:how2",
"dataset:must-c-v1",
"dataset:must-c-v2",
"dataset:europarl",
"dataset:tedlium",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-23T14:53:55Z |
---
language: en
datasets:
- common_voice
- librispeech_asr
- how2
- must-c-v1
- must-c-v2
- europarl
- tedlium
tags:
- audio
- automatic-speech-recognition
license: cc-by-nc-4.0
---
# Fine-Tune Wav2Vec2 large model for English ASR
### Data for fine-tune
| Dataset | Duration in hours |
|--------------|-------------------|
| Common Voice | 1667 |
| Europarl | 85 |
| How2 | 356 |
| Librispeech | 936 |
| MuST-C v1 | 407 |
| MuST-C v2 | 482 |
| Tedlium | 482 |
### Evaluation result
| Dataset | Duration in hours | WER w/o LM | WER with LM |
|-------------|-------------------|------------|-------------|
| Librispeech | 5.4 | 2.9 | 1.1 |
| Tedlium | 2.6 | 7.9 | 5.4 |
### Usage
[](https://colab.research.google.com/drive/1FAhtGvjRdHT4W0KeMdMMlL7sm6Hbe7dv?usp=sharing)
```python
from transformers.file_utils import cached_path, hf_bucket_url
from importlib.machinery import SourceFileLoader
from transformers import Wav2Vec2ProcessorWithLM
from IPython.lib.display import Audio
import torchaudio
import torch
# Load model & processor
model_name = "nguyenvulebinh/iwslt-asr-wav2vec-large-4500h"
model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)
# Load an example audio (16k)
audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="tst_2010_sample.wav")))
input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')
# Infer
output = model(**input_data)
# Output transcript without LM
print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))
# and of course there's teams that have a lot more tada structures and among the best are recent graduates of kindergarten
# Output transcript with LM
print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)
# and of course there are teams that have a lot more ta da structures and among the best are recent graduates of kindergarten
```
### Model Parameters License
The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
### Contact
[email protected]
[](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
|
libalabala/mt5-small-finetuned-amazon-en-es
|
libalabala
| 2022-03-24T07:00:11Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-17T08:45:00Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1997
- Rouge1: 16.7312
- Rouge2: 8.6607
- Rougel: 16.1846
- Rougelsum: 16.2411
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 7.0772 | 1.0 | 1209 | 3.3307 | 12.4644 | 4.0353 | 12.0167 | 12.0722 |
| 4.0223 | 2.0 | 2418 | 3.2257 | 15.338 | 7.0168 | 14.7769 | 14.8391 |
| 3.8018 | 3.0 | 3627 | 3.1997 | 16.7312 | 8.6607 | 16.1846 | 16.2411 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Pavithra/codeparrot-ds-sample
|
Pavithra
| 2022-03-24T06:41:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T05:12:32Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds-sample
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds-sample
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5219
- eval_runtime: 603.3856
- eval_samples_per_second: 154.402
- eval_steps_per_second: 4.826
- epoch: 0.15
- step: 10000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Prototypeu/bart-base-finetuned-tldrhq-cnn-dailymail
|
Prototypeu
| 2022-03-24T05:13:18Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-22T18:11:10Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: Prototypeu/bart-base-finetuned-tldrhq-cnn-dailymail
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Prototypeu/bart-base-finetuned-tldrhq-cnn-dailymail
This model is a fine-tuned version of [Prototypeu/bart-base-finetuned-xsum](https://huggingface.co/Prototypeu/bart-base-finetuned-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5049
- Train Logits Loss: 1.5049
- Train Rouge1: 28.1795
- Train Rouge2: 14.0392
- Train Rougel: 23.7617
- Train Rougelsum: 26.5583
- Train Gen Len: 19.0
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 3e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 255113, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 32000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.98, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Logits Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:-----------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 2.9074 | 2.9074 | 26.9164 | 12.6984 | 22.4321 | 25.2287 | 19.0 | 0 |
| 1.9368 | 1.9368 | 28.0165 | 13.8906 | 23.4187 | 26.3779 | 19.0 | 1 |
| 1.7246 | 1.7246 | 27.6022 | 13.5255 | 23.2301 | 25.9923 | 19.0 | 2 |
| 1.5945 | 1.5945 | 28.0347 | 13.7045 | 23.4851 | 26.3488 | 19.0 | 3 |
| 1.5049 | 1.5049 | 28.1795 | 14.0392 | 23.7617 | 26.5583 | 19.0 | 4 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.6.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
beston91/gpt2-xl_ft_logits_10k
|
beston91
| 2022-03-24T05:04:35Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-20T14:36:17Z |
---
tags:
- generated_from_trainer
model-index:
- name: gpt2-xl_ft_logits_10k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl_ft_logits_10k
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.3791
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100.0
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.99 | 54 | 6.1576 |
| No log | 1.99 | 108 | 6.2663 |
| No log | 2.99 | 162 | 6.3520 |
| No log | 3.99 | 216 | 6.3791 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
quincyqiang/chinese-roberta-wwm-ext
|
quincyqiang
| 2022-03-24T04:58:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-24T04:52:35Z |
---
license: apache-2.0
---
|
Yaxin/xlm-roberta-base-yelp-mlm
|
Yaxin
| 2022-03-24T04:44:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"dataset:yelp_review_full",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-24T04:10:58Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-yelp-mlm
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: yelp_review_full yelp_review_full
type: yelp_review_full
args: yelp_review_full
metrics:
- name: Accuracy
type: accuracy
value: 0.7356223359340127
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-yelp-mlm
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the yelp_review_full yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1743
- Accuracy: 0.7356
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
lazyturtl/digital
|
lazyturtl
| 2022-03-24T04:28:50Z | 68 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-15T00:21:49Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: digital
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8974359035491943
---
# digital
## Example Images
#### ansys
#### blender
#### roblox
#### sketchup
|
clisi2000/distilbert-base-uncased-distilled-clinc
|
clisi2000
| 2022-03-24T03:50:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-24T03:43:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
model-index:
- name: distilbert-base-uncased-distilled-clinc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.2+cpu
- Datasets 1.18.4
- Tokenizers 0.10.3
|
huggingtweets/btohtoh-willitbetoomuch
|
huggingtweets
| 2022-03-24T02:06:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-24T01:50:00Z |
---
language: en
thumbnail: http://www.huggingtweets.com/btohtoh-willitbetoomuch/1648087519902/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506402743296020484/X79Yfcx5_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1488467916198539265/3pTy_Kr3_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">BToh & unloading</div>
<div style="text-align: center; font-size: 14px;">@btohtoh-willitbetoomuch</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from BToh & unloading.
| Data | BToh | unloading |
| --- | --- | --- |
| Tweets downloaded | 3241 | 85 |
| Retweets | 347 | 0 |
| Short tweets | 480 | 3 |
| Tweets kept | 2414 | 82 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d3flykp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @btohtoh-willitbetoomuch's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lp51jew) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lp51jew/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/btohtoh-willitbetoomuch')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Waynehillsdev/Wayne_NLP_mT5
|
Waynehillsdev
| 2022-03-24T02:02:30Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: Wayne_NLP_mT5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wayne_NLP_mT5
This model was trained only english datasets.
if you want trained korean + english model
go to wayne_mulang_mT5.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0a0+3fd9dcf
- Datasets 1.18.3
- Tokenizers 0.11.0
|
rurupang/roberta-base-finetuned-sts
|
rurupang
| 2022-03-24T01:54:26Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-22T14:13:32Z |
---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- pearsonr
model-index:
- name: roberta-base-finetuned-sts
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: sts
metrics:
- name: Pearsonr
type: pearsonr
value: 0.956039443806831
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-sts
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1999
- Pearsonr: 0.9560
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearsonr |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 329 | 0.2462 | 0.9478 |
| 1.2505 | 2.0 | 658 | 0.1671 | 0.9530 |
| 1.2505 | 3.0 | 987 | 0.1890 | 0.9525 |
| 0.133 | 4.0 | 1316 | 0.2360 | 0.9548 |
| 0.0886 | 5.0 | 1645 | 0.2265 | 0.9528 |
| 0.0886 | 6.0 | 1974 | 0.2097 | 0.9518 |
| 0.0687 | 7.0 | 2303 | 0.2281 | 0.9523 |
| 0.0539 | 8.0 | 2632 | 0.2212 | 0.9542 |
| 0.0539 | 9.0 | 2961 | 0.1843 | 0.9532 |
| 0.045 | 10.0 | 3290 | 0.1999 | 0.9560 |
| 0.0378 | 11.0 | 3619 | 0.2357 | 0.9533 |
| 0.0378 | 12.0 | 3948 | 0.2134 | 0.9541 |
| 0.033 | 13.0 | 4277 | 0.2273 | 0.9540 |
| 0.03 | 14.0 | 4606 | 0.2148 | 0.9533 |
| 0.03 | 15.0 | 4935 | 0.2207 | 0.9534 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
negfir/distilbert-base-uncased-finetuned-squad
|
negfir
| 2022-03-24T01:39:12Z | 40 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2789 | 1.0 | 5533 | 1.2200 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
huggingtweets/btohtoh
|
huggingtweets
| 2022-03-24T01:35:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-24T01:35:48Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506402743296020484/X79Yfcx5_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">BToh</div>
<div style="text-align: center; font-size: 14px;">@btohtoh</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from BToh.
| Data | BToh |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 347 |
| Short tweets | 480 |
| Tweets kept | 2414 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xnk5832/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @btohtoh's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gdcu3k6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gdcu3k6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/btohtoh')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
microsoft/amos
|
microsoft
| 2022-03-24T01:24:38Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T01:16:31Z |
---
license: mit
---
# Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
This model card contains the AMOS model (**base++** version) proposed in [this paper](). The official GitHub repository can be found [here](https://github.com/microsoft/AMOS).
# Citation
If you find this model card useful for your research, please cite the following paper:
```
@inproceedings{meng2022amos,
title={Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators},
author={Meng, Yu and Xiong, Chenyan and Bajaj, Payal and Tiwary, Saurabh and Bennett, Paul and Han, Jiawei and Song, Xia},
booktitle={ICLR},
year={2022}
}
```
|
espnet/chai_microsoft_indian_langs_te
|
espnet
| 2022-03-24T00:36:45Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"te",
"dataset:microsoft_indian_languages_interspeech2018",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-23T23:36:26Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: te
datasets:
- microsoft_indian_languages_interspeech2018
license: cc-by-4.0
---
## ESPnet2 model
### ``
This model was trained by Chaitanya Narisetty using recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/ms_indic_is18/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/chai_microsoft_indian_langs_te
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Tue Mar 22 13:38:24 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.8.1+cu111`
- Git hash: `f91410f712d1287cd6809c5bf26b54c5a40fe314`
- Commit date: `Mon Mar 14 22:32:17 2022 -0400`
## asr_train_asr_xlsr53_conformer_raw_te_bpe150_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|28413|78.0|19.5|2.5|2.4|24.4|80.1|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.best_asr_model_valid.acc.ave/test_te|3040|28413|78.0|19.4|2.6|2.4|24.4|79.7|
|decode_transformer5_lm_lm_train_lm_transformer_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|28413|78.0|19.5|2.6|2.5|24.5|79.9|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|229419|95.6|2.2|2.2|1.6|6.1|80.1|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.best_asr_model_valid.acc.ave/test_te|3040|229419|95.6|2.2|2.2|1.6|6.0|79.7|
|decode_transformer5_lm_lm_train_lm_transformer_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|229419|95.6|2.1|2.2|1.6|6.0|79.9|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|146657|92.7|4.7|2.6|1.6|8.9|80.1|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.best_asr_model_valid.acc.ave/test_te|3040|146657|92.8|4.7|2.6|1.6|8.9|79.7|
|decode_transformer5_lm_lm_train_lm_transformer_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|146657|92.8|4.6|2.6|1.6|8.9|79.9|
## config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_xlsr53_conformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_xlsr53_conformer_raw_te_bpe150_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: 15
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 64
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_te_bpe150_sp_ssl/train/speech_shape
- exp/asr_stats_raw_te_bpe150_sp_ssl/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_te_bpe150_sp_ssl/valid/speech_shape
- exp/asr_stats_raw_te_bpe150_sp_ssl/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_te_sp/wav.scp
- speech
- sound
- - dump/raw/train_te_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_te/wav.scp
- speech
- sound
- - dump/raw/dev_te/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0005
scheduler: warmuplr
scheduler_conf:
warmup_steps: 30000
token_list:
- <blank>
- <unk>
- ా
- ు
- ి
- ం
- ే
- వ
- న
- ల
- ▁అ
- క
- ్
- ో
- మ
- ▁
- త
- ర
- ప
- ీ
- ▁మ
- య
- డ
- ▁ప
- ద
- ని
- గ
- ▁వ
- స
- కు
- ె
- ర్
- ▁స
- ▁క
- ్య
- న్న
- ట
- ▁చ
- ▁త
- ాల
- ంట
- ూ
- శ
- ంద
- ార
- ▁న
- ారు
- ▁ఉ
- లు
- ▁ఆ
- ను
- జ
- రి
- ▁ప్ర
- ించ
- ధ
- ై
- హ
- ంది
- ్ర
- ▁ఇ
- చ
- రు
- స్త
- లో
- ▁ద
- డు
- ▁ఎ
- ▁వి
- ల్ల
- ణ
- గా
- ది
- డి
- న్నారు
- దు
- ిన
- ▁ర
- త్
- ొ
- ▁గ
- ంత
- ంగా
- ▁కా
- బ
- ▁జ
- ష
- ▁తెల
- ులు
- ▁ఏ
- ట్ట
- చ్చ
- తి
- నే
- కి
- ంలో
- ▁అవును
- ▁చెప్ప
- భ
- ▁ఈ
- ప్ప
- ▁ని
- ▁రా
- క్క
- ▁బ
- ట్ల
- ▁భ
- తో
- ▁కూడా
- ▁బా
- ద్ద
- ▁చేస
- ▁లే
- ాయి
- ానికి
- త్ర
- ▁కొ
- ఖ
- ▁ఒక
- ▁చాలా
- క్ష
- ళ
- ▁చేస్త
- ృ
- థ
- ఘ
- ఫ
- ఓ
- ౌ
- ఒ
- ఐ
- ఠ
- ఢ
- అ
- ఉ
- ఏ
- ఈ
- ౦
- ఇ
- ః
- ఋ
- ఝ
- ఔ
- ఛ
- ఞ
- ఊ
- ఎ
- ఆ
- ఙ
- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: bpe
bpemodel: data/te_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: fused
frontend_conf:
frontends:
- frontend_type: default
n_fft: 512
win_length: 400
hop_length: 160
- frontend_type: s3prl
frontend_conf:
upstream: wav2vec2_xlsr
download_dir: ./hub
multilayer_feature: true
align_method: linear_projection
proj_dim: 200
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 400
output_size: 100
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 15
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
input_layer: embed
num_blocks: 6
linear_units: 2048
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.7a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/russian_commonvoice_blstm
|
espnet
| 2022-03-24T00:02:17Z | 3 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"ru",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-23T23:59:42Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: ru
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/russian_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout fa1b865352475b744c37f70440de1cc6b257ba70
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/russian_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Mar 23 19:56:59 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `fa1b865352475b744c37f70440de1cc6b257ba70`
- Commit date: `Wed Feb 16 16:42:36 2022 -0500`
## asr_blstm_specaug_num_time_mask_2_lr_0.1
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_ru|7307|71189|79.3|18.4|2.4|2.1|22.8|71.1|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_ru|7307|537025|95.0|3.0|2.0|1.1|6.1|71.1|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_ru|7307|399162|93.2|4.5|2.3|1.4|8.2|71.1|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_blstm_specaug_num_time_mask_2_lr_0.1
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_ru_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_ru_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_ru_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_ru_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_ru_sp/wav.scp
- speech
- sound
- - dump/raw/train_ru_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_ru/wav.scp
- speech
- sound
- - dump/raw/dev_ru/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- е
- о
- и
- с
- м
- а
- в
- н
- д
- т
- у
- .
- я
- ы
- л
- й
- з
- п
- к
- но
- ','
- ▁в
- ра
- б
- ж
- ю
- г
- го
- ▁по
- ▁с
- ни
- ч
- х
- р
- ко
- ре
- ш
- ли
- ть
- ▁на
- ль
- ва
- ер
- ▁и
- ет
- ст
- ро
- на
- ла
- ле
- ь
- ен
- то
- ло
- да
- ка
- ▁не
- ств
- ти
- ци
- ся
- ▁за
- ▁про
- че
- ем
- ру
- же
- та
- ▁при
- ▁со
- ▁это
- ри
- ф
- ки
- бо
- ц
- ▁С
- ста
- ения
- щ
- сти
- э
- К
- О
- А
- И
- '-'
- Т
- Я
- Б
- Д
- М
- '?'
- –
- Г
- —
- '!'
- У
- ъ
- '"'
- »
- ё
- Ф
- ':'
- Х
- Ю
- F
- ;
- O
- I
- E
- R
- −
- В
- С
- ''''
- П
- C
- L
- A
- ‐
- H
- T
- G
- S
- (
- )
- B
- K
- P
- Z
- M
- Й
- X
- Ц
- Ж
- Ч
- Ш
- «
- З
- Л
- Е
- Р
- Э
- N
- Н
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/ru_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_ru_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
public-data/dlib_face_landmark_model
|
public-data
| 2022-03-23T22:54:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-23T22:52:02Z |
# dlib face landmark model
- http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
|
radev/xlm-roberta-base-finetuned-panx-de
|
radev
| 2022-03-23T22:27:27Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-16T22:11:53Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8593216480764853
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1345
- F1: 0.8593
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 263 | 0.1807 | 0.8065 |
| 0.2218 | 2.0 | 526 | 0.1365 | 0.8485 |
| 0.2218 | 3.0 | 789 | 0.1345 | 0.8593 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
bigmorning/my-gpt-model-5
|
bigmorning
| 2022-03-23T22:11:47Z | 5 | 1 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T22:04:49Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my-gpt-model-5
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-gpt-model-5
This model is a fine-tuned version of [bigmorning/my-gpt-model-3](https://huggingface.co/bigmorning/my-gpt-model-3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.9979
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 4.9979 | 0 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/radagasttbrown
|
huggingtweets
| 2022-03-23T21:33:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T21:13:19Z |
---
language: en
thumbnail: http://www.huggingtweets.com/radagasttbrown/1648071147429/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362404255798280192/yIKMf5AN_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Radagast 🌋</div>
<div style="text-align: center; font-size: 14px;">@radagasttbrown</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Radagast 🌋.
| Data | Radagast 🌋 |
| --- | --- |
| Tweets downloaded | 3228 |
| Retweets | 457 |
| Short tweets | 230 |
| Tweets kept | 2541 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1b1t67ko/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @radagasttbrown's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/boipgvkp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/boipgvkp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/radagasttbrown')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
pere/test-t5-small
|
pere
| 2022-03-23T20:39:40Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-20T12:17:29Z |
---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
license: apache-2.0
---
## Test T5 small conversion
This is a test repo for the conversion of T5X to HuggingFace Flax.
The current model is first converted from MTF to T5X using the conversion script included in the T5X library:
```bash
python3 -m t5x.scripts.convert_tf_checkpoint --gin_file=t5x/examples/t5/t5_1_0/small.gin --gin.convert_checkpoint.model=%MODEL --gin.convert_checkpoint.tf_checkpoint_path=\"gs://t5-data/pretrained_models/small/model.ckpt-1000000\" --gin.convert_checkpoint.output_dir=\"/tmp/t5x_checkpoints/t5_small\" --logtostderr
```
After creating the T5X model, the model is converted to Huggingface Flax by a modified version of the script from @stefan-it (https://gist.githubusercontent.com/stefan-it/30e4998ef159f33696e377a46f699d9f/raw/c19da5d067dc9d31d0b8115a79e8626186e11daa/convert_t5x_checkpoint_to_flax.py). The modified version is included in this repo. The modification is basically that the wi_0 and wi_1 layers are combined into wi. This might be a difference between t5_1_0 and t5_1_1
```bash
python3 convert_t5_checkpoint_to_flax.py --t5x_checkpoint_path /tmp/t5x_checkpoints/t5_small/checkpoint_1000000/ --flax_dump_folder_path /tmp/flax_dump_folder/ --config_name t5-small
```
The tokenizer.json was copied from https://huggingface.co/t5-small/blob/main/tokenizer.json.
To be able to use the widgets in HuggingFace, the model was converted to pyTorch by running:
```python
from transformers import T5ForConditionalGeneration
model =
T5ForConditionalGeneration.from_pretrained(".", from_flax=True)
model.save_pretrained(".")
```
|
bigmorning/my-gpt-model-4
|
bigmorning
| 2022-03-23T20:00:04Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T19:52:49Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my-gpt-model-4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-gpt-model-4
This model is a fine-tuned version of [bigmorning/my-gpt-model-3](https://huggingface.co/bigmorning/my-gpt-model-3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.0556
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 5.0556 | 0 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/ryiacy
|
huggingtweets
| 2022-03-23T19:51:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T19:28:42Z |
---
language: en
thumbnail: http://www.huggingtweets.com/ryiacy/1648065062687/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1424813722011410434/73S-oYNT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">cyriac</div>
<div style="text-align: center; font-size: 14px;">@ryiacy</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from cyriac.
| Data | cyriac |
| --- | --- |
| Tweets downloaded | 1050 |
| Retweets | 32 |
| Short tweets | 60 |
| Tweets kept | 958 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26de85bt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ryiacy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2p7goxic) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2p7goxic/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ryiacy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Zarkit/bert-base-multilingual-uncased-sentiment1
|
Zarkit
| 2022-03-23T19:50:26Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-23T18:58:36Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Zarkit/bert-base-multilingual-uncased-sentiment1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Zarkit/bert-base-multilingual-uncased-sentiment1
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4891
- Validation Loss: 0.5448
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7980, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.6166 | 0.5680 | 0 |
| 0.4891 | 0.5448 | 1 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
negfir/uncased_L-12_H-128_A-2
|
negfir
| 2022-03-23T19:18:33Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T18:49:57Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: uncased_L-12_H-128_A-2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uncased_L-12_H-128_A-2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
gayanin/bart-med-term-conditional-masking
|
gayanin
| 2022-03-23T19:06:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-23T14:24:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-med-term-conditional-masking
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-med-term-conditional-masking
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5115
- Rouge2 Precision: 0.7409
- Rouge2 Recall: 0.5343
- Rouge2 Fmeasure: 0.6025
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.6278 | 1.0 | 15827 | 0.5546 | 0.7255 | 0.5244 | 0.5908 |
| 0.5356 | 2.0 | 31654 | 0.5286 | 0.7333 | 0.5293 | 0.5966 |
| 0.4757 | 3.0 | 47481 | 0.5154 | 0.7376 | 0.532 | 0.5998 |
| 0.4337 | 4.0 | 63308 | 0.5107 | 0.7406 | 0.5342 | 0.6023 |
| 0.4045 | 5.0 | 79135 | 0.5115 | 0.7409 | 0.5343 | 0.6025 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Zohar/distilgpt2-finetuned-hotel-reviews
|
Zohar
| 2022-03-23T18:42:18Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T17:17:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-hotel-reviews
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-hotel-reviews
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6253
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7533 | 1.0 | 1259 | 3.6803 |
| 3.6644 | 2.0 | 2518 | 3.6366 |
| 3.6426 | 3.0 | 3777 | 3.6253 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.11.0
|
ScandinavianMrT/gpt2_ONION_prefinetune_4.0
|
ScandinavianMrT
| 2022-03-23T18:39:51Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T18:34:47Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2_ONION_prefinetune_4.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2_ONION_prefinetune_4.0
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6484
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 153 | 4.7368 |
| No log | 2.0 | 306 | 4.6732 |
| No log | 3.0 | 459 | 4.6527 |
| 4.8529 | 4.0 | 612 | 4.6484 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1
|
DrishtiSharma
| 2022-03-23T18:35:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- sl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- sl
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-sl-with-LM-v1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sl
metrics:
- name: Test WER
type: wer
value: 0.20626555409164105
- name: Test CER
type: cer
value: 0.051648321634392154
- name: Test WER (+LM)
type: wer
value: 0.13482652613087395
- name: Test CER (+LM)
type: cer
value: 0.038838663862562475
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sl
metrics:
- name: Dev WER
type: wer
value: 0.5406156320830592
- name: Dev CER
type: cer
value: 0.22249723590310583
- name: Dev WER (+LM)
type: wer
value: 0.49783147459727384
- name: Dev CER (+LM)
type: cer
value: 0.1591062599627158
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sl
metrics:
- name: Test WER
type: wer
value: 46.17
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2756
- Wer: 0.2279
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3881 | 6.1 | 500 | 2.9710 | 1.0 |
| 2.6401 | 12.2 | 1000 | 1.7677 | 0.9734 |
| 1.5152 | 18.29 | 1500 | 0.5564 | 0.6011 |
| 1.2191 | 24.39 | 2000 | 0.4319 | 0.4390 |
| 1.0237 | 30.49 | 2500 | 0.3141 | 0.3175 |
| 0.8892 | 36.59 | 3000 | 0.2748 | 0.2689 |
| 0.8296 | 42.68 | 3500 | 0.2680 | 0.2534 |
| 0.7602 | 48.78 | 4000 | 0.2820 | 0.2506 |
| 0.7186 | 54.88 | 4500 | 0.2672 | 0.2398 |
| 0.6887 | 60.98 | 5000 | 0.2729 | 0.2402 |
| 0.6507 | 67.07 | 5500 | 0.2767 | 0.2361 |
| 0.6226 | 73.17 | 6000 | 0.2817 | 0.2332 |
| 0.6024 | 79.27 | 6500 | 0.2679 | 0.2279 |
| 0.5787 | 85.37 | 7000 | 0.2837 | 0.2316 |
| 0.5744 | 91.46 | 7500 | 0.2838 | 0.2284 |
| 0.5556 | 97.56 | 8000 | 0.2763 | 0.2281 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-maltese
|
DrishtiSharma
| 2022-03-23T18:35:17Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"mt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- mt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- mt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-maltese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: mt
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-maltese
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2994
- Wer: 0.2781
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1800
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.0174 | 9.01 | 1000 | 3.0552 | 1.0 |
| 1.0446 | 18.02 | 2000 | 0.6708 | 0.7577 |
| 0.7995 | 27.03 | 3000 | 0.4202 | 0.4770 |
| 0.6978 | 36.04 | 4000 | 0.3054 | 0.3494 |
| 0.6189 | 45.05 | 5000 | 0.2878 | 0.3154 |
| 0.5667 | 54.05 | 6000 | 0.3114 | 0.3286 |
| 0.5173 | 63.06 | 7000 | 0.3085 | 0.3021 |
| 0.4682 | 72.07 | 8000 | 0.3058 | 0.2969 |
| 0.451 | 81.08 | 9000 | 0.3146 | 0.2907 |
| 0.4213 | 90.09 | 10000 | 0.3030 | 0.2881 |
| 0.4005 | 99.1 | 11000 | 0.3001 | 0.2789 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
### Evaluation Script
!python eval.py \
--model_id DrishtiSharma/wav2vec2-large-xls-r-300m-maltese \
--dataset mozilla-foundation/common_voice_8_0 --config mt --split test --log_outputs
|
Akashpb13/Hausa_xlsr
|
Akashpb13
| 2022-03-23T18:35:09Z | 53 | 4 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"ha",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ha
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- ha
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/Hausa_xlsr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ha
metrics:
- name: Test WER
type: wer
value: 0.20614541257934219
- name: Test CER
type: cer
value: 0.04358048053214061
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ha
metrics:
- name: Test WER
type: wer
value: 0.20614541257934219
- name: Test CER
type: cer
value: 0.04358048053214061
---
# Akashpb13/Hausa_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):
- Loss: 0.275118
- Wer: 0.329955
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Hausa train.tsv, dev.tsv, invalidated.tsv, reported.tsv and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0
## Training procedure
For creating the training dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000096
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|------|---------------|-----------------|----------|
| 500 | 5.175900 | 2.750914 | 1.000000 |
| 1000 | 1.028700 | 0.338649 | 0.497999 |
| 1500 | 0.332200 | 0.246896 | 0.402241 |
| 2000 | 0.227300 | 0.239640 | 0.395839 |
| 2500 | 0.175000 | 0.239577 | 0.373966 |
| 3000 | 0.140400 | 0.243272 | 0.356095 |
| 3500 | 0.119200 | 0.263761 | 0.365164 |
| 4000 | 0.099300 | 0.265954 | 0.353428 |
| 4500 | 0.084400 | 0.276367 | 0.349693 |
| 5000 | 0.073700 | 0.282631 | 0.343825 |
| 5500 | 0.068000 | 0.282344 | 0.341158 |
| 6000 | 0.064500 | 0.281591 | 0.342491 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Akashpb13/Hausa_xlsr --dataset mozilla-foundation/common_voice_8_0 --config ha --split test
```
|
sammy786/wav2vec2-xlsr-bashkir
|
sammy786
| 2022-03-23T18:35:07Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ba",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ba
license: apache-2.0
tags:
- automatic-speech-recognition
- ba
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-bashkir
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ba
metrics:
- name: Test WER
type: wer
value: 11.32
- name: Test CER
type: cer
value: 2.34
---
# sammy786/wav2vec2-xlsr-bashkir
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ba dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss:
- Wer:
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|:----:|:-------------:|:---------------:|:--------:|
| 200 | 5.387100 | 1.982867 | 1.000000 |
| 400 | 1.269800 | 0.369958 | 0.545755 |
| 600 | 0.903600 | 0.287705 | 0.465594 |
| 800 | 0.787300 | 0.235142 | 0.417091 |
| 1000 | 0.816300 | 0.206325 | 0.390534 |
| 1200 | 0.700500 | 0.197106 | 0.383987 |
| 1400 | 0.707100 | 0.179855 | 0.381368 |
| 1600 | 0.657800 | 0.181605 | 0.370593 |
| 1800 | 0.647800 | 0.168626 | 0.358767 |
| 2000 | 0.650700 | 0.164833 | 0.351483 |
| 2200 | 0.490900 | 0.168133 | 0.363309 |
| 2400 | 0.431000 | 0.161201 | 0.344350 |
| 2600 | 0.372100 | 0.160254 | 0.338280 |
| 2800 | 0.367500 | 0.150885 | 0.329687 |
| 3000 | 0.351300 | 0.154112 | 0.331392 |
| 3200 | 0.314800 | 0.147147 | 0.326700 |
| 3400 | 0.316800 | 0.142681 | 0.325090 |
| 3600 | 0.313000 | 0.138736 | 0.319553 |
| 3800 | 0.291800 | 0.138166 | 0.315570 |
| 4000 | 0.311300 | 0.135977 | 0.322894 |
| 4200 | 0.304900 | 0.128820 | 0.308627 |
| 4400 | 0.301600 | 0.129475 | 0.307440 |
| 4600 | 0.281800 | 0.131863 | 0.305967 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-bashkir --dataset mozilla-foundation/common_voice_8_0 --config ba --split test
```
|
nouamanetazi/wav2vec2-xls-r-300m-ar
|
nouamanetazi
| 2022-03-23T18:35:04Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ar
license: apache-2.0
tags:
- ar
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: XLS-R-300M - Arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: 1.0
- name: Test CER
type: cer
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-ar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - AR dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.0191
- eval_wer: 1.0
- eval_runtime: 252.2389
- eval_samples_per_second: 30.217
- eval_steps_per_second: 0.476
- epoch: 1.0
- step: 340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
Please use the evaluation script `eval.py` included in the repo.
1. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id nouamanetazi/wav2vec2-xls-r-300m-ar --dataset speech-recognition-community-v2/dev_data --config ar --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
|
infinitejoy/wav2vec2-large-xls-r-300m-hungarian
|
infinitejoy
| 2022-03-23T18:34:54Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hu",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hu
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hu
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Hungarian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: hu
metrics:
- name: Test WER
type: wer
value: 31.099
- name: Test CER
type: cer
value: 6.737
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hu
metrics:
- name: Test WER
type: wer
value: 45.469
- name: Test CER
type: cer
value: 15.727
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: hu
metrics:
- name: Test WER
type: wer
value: 48.2
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hungarian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HU dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2562
- Wer: 0.3112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.3964 | 3.52 | 1000 | 1.2251 | 0.8781 |
| 1.3176 | 7.04 | 2000 | 0.3872 | 0.4462 |
| 1.1999 | 10.56 | 3000 | 0.3244 | 0.3922 |
| 1.1633 | 14.08 | 4000 | 0.3014 | 0.3704 |
| 1.1132 | 17.61 | 5000 | 0.2913 | 0.3623 |
| 1.0888 | 21.13 | 6000 | 0.2864 | 0.3498 |
| 1.0487 | 24.65 | 7000 | 0.2821 | 0.3435 |
| 1.0431 | 28.17 | 8000 | 0.2739 | 0.3308 |
| 0.9896 | 31.69 | 9000 | 0.2629 | 0.3243 |
| 0.9839 | 35.21 | 10000 | 0.2806 | 0.3308 |
| 0.9586 | 38.73 | 11000 | 0.2650 | 0.3235 |
| 0.9501 | 42.25 | 12000 | 0.2585 | 0.3173 |
| 0.938 | 45.77 | 13000 | 0.2561 | 0.3117 |
| 0.921 | 49.3 | 14000 | 0.2559 | 0.3115 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-hindi
|
infinitejoy
| 2022-03-23T18:34:51Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hi",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hi
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Hindi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 100
- name: Test CER
type: cer
value: 92.98
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5414
- Wer: 1.0194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.6095 | 3.38 | 500 | 4.5881 | 0.9999 |
| 3.3396 | 6.76 | 1000 | 3.3301 | 1.0001 |
| 2.0061 | 10.14 | 1500 | 1.2096 | 1.0063 |
| 1.523 | 13.51 | 2000 | 0.7836 | 1.0051 |
| 1.3868 | 16.89 | 2500 | 0.6837 | 1.0080 |
| 1.2807 | 20.27 | 3000 | 0.6568 | 1.0112 |
| 1.231 | 23.65 | 3500 | 0.6120 | 1.0105 |
| 1.1673 | 27.03 | 4000 | 0.5972 | 1.0089 |
| 1.1416 | 30.41 | 4500 | 0.5780 | 1.0132 |
| 1.0738 | 33.78 | 5000 | 0.5806 | 1.0123 |
| 1.0771 | 37.16 | 5500 | 0.5586 | 1.0067 |
| 1.0287 | 40.54 | 6000 | 0.5464 | 1.0058 |
| 1.0106 | 43.92 | 6500 | 0.5407 | 1.0062 |
| 0.9538 | 47.3 | 7000 | 0.5334 | 1.0089 |
| 0.9607 | 50.68 | 7500 | 0.5395 | 1.0110 |
| 0.9108 | 54.05 | 8000 | 0.5502 | 1.0137 |
| 0.9252 | 57.43 | 8500 | 0.5498 | 1.0062 |
| 0.8943 | 60.81 | 9000 | 0.5448 | 1.0158 |
| 0.8728 | 64.19 | 9500 | 0.5257 | 1.0113 |
| 0.8577 | 67.57 | 10000 | 0.5550 | 1.0178 |
| 0.8332 | 70.95 | 10500 | 0.5607 | 1.0166 |
| 0.8174 | 74.32 | 11000 | 0.5429 | 1.0145 |
| 0.8168 | 77.7 | 11500 | 0.5561 | 1.0116 |
| 0.7872 | 81.08 | 12000 | 0.5478 | 1.0164 |
| 0.7707 | 84.46 | 12500 | 0.5412 | 1.0216 |
| 0.7742 | 87.84 | 13000 | 0.5391 | 1.0207 |
| 0.7594 | 91.22 | 13500 | 0.5379 | 1.0208 |
| 0.7678 | 94.59 | 14000 | 0.5415 | 1.0198 |
| 0.7502 | 97.97 | 14500 | 0.5409 | 1.0191 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-galician
|
infinitejoy
| 2022-03-23T18:34:49Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"gl",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- gl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- gl
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Galician
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: gl
metrics:
- name: Test WER
type: wer
value: 101.54
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: gl
metrics:
- name: Test WER
type: wer
value: 105.69
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: gl
metrics:
- name: Test WER
type: wer
value: 101.95
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-galician
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1525
- Wer: 0.1542
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0067 | 4.35 | 500 | 2.9632 | 1.0 |
| 1.4939 | 8.7 | 1000 | 0.5005 | 0.4157 |
| 0.9982 | 13.04 | 1500 | 0.1967 | 0.1857 |
| 0.8726 | 17.39 | 2000 | 0.1587 | 0.1564 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
emre/wav2vec2-xls-r-300m-gl-CV8
|
emre
| 2022-03-23T18:34:43Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"gl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language: gl
tags:
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-gl-CV8
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice gl
type: common_voice
args: gl
metrics:
- name: Test WER
type: wer
value: 0.208
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: gl
metrics:
- name: Test WER
type: wer
value: 22.94
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: gl
metrics:
- name: Test WER
type: wer
value: 47.82
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: gl
metrics:
- name: Test WER
type: wer
value: 50.8
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-gl-CV8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2151
- Wer: 0.2080
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9427 | 4.9 | 500 | 2.8801 | 1.0 |
| 2.1594 | 9.8 | 1000 | 0.4092 | 0.4001 |
| 0.7332 | 14.71 | 1500 | 0.2151 | 0.2080 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
emre/wav2vec2-xls-r-300m-ab-CV8
|
emre
| 2022-03-23T18:34:41Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ab",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language: ab
tags:
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-ab-CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ab
metrics:
- name: Test WER
type: wer
value: 44.9
---
# wav2vec2-xls-r-300m-ab-CV8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2105
- Wer: 0.5474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.7729 | 0.63 | 500 | 3.0624 | 1.0021 |
| 2.7348 | 1.26 | 1000 | 1.0460 | 0.9815 |
| 1.2756 | 1.9 | 1500 | 0.4618 | 0.8309 |
| 1.0419 | 2.53 | 2000 | 0.3725 | 0.7449 |
| 0.9491 | 3.16 | 2500 | 0.3368 | 0.7345 |
| 0.9006 | 3.79 | 3000 | 0.3014 | 0.6936 |
| 0.8519 | 4.42 | 3500 | 0.2852 | 0.6767 |
| 0.8243 | 5.06 | 4000 | 0.2701 | 0.6504 |
| 0.7902 | 5.69 | 4500 | 0.2641 | 0.6221 |
| 0.7767 | 6.32 | 5000 | 0.2549 | 0.6192 |
| 0.7516 | 6.95 | 5500 | 0.2515 | 0.6179 |
| 0.737 | 7.59 | 6000 | 0.2408 | 0.5963 |
| 0.7217 | 8.22 | 6500 | 0.2429 | 0.6261 |
| 0.7101 | 8.85 | 7000 | 0.2366 | 0.5687 |
| 0.6922 | 9.48 | 7500 | 0.2277 | 0.5680 |
| 0.6866 | 10.11 | 8000 | 0.2242 | 0.5847 |
| 0.6703 | 10.75 | 8500 | 0.2222 | 0.5803 |
| 0.6649 | 11.38 | 9000 | 0.2247 | 0.5765 |
| 0.6513 | 12.01 | 9500 | 0.2182 | 0.5644 |
| 0.6369 | 12.64 | 10000 | 0.2128 | 0.5508 |
| 0.6425 | 13.27 | 10500 | 0.2132 | 0.5514 |
| 0.6399 | 13.91 | 11000 | 0.2116 | 0.5495 |
| 0.6208 | 14.54 | 11500 | 0.2105 | 0.5474 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
Harveenchadha/odia_large_wav2vec2
|
Harveenchadha
| 2022-03-23T18:34:27Z | 21 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"or",
"robust-speech-event",
"dataset:Harveenchadha/indic-voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
language:
- or
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- or
- robust-speech-event
datasets:
- Harveenchadha/indic-voice
model-index:
- name: Hindi Large
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
type: common_voice
args: or
metrics:
- name: Test WER
type: wer
value: 54.26
- name: Test CER
type: cer
value: 11.36
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice-7.0
type: mozilla-foundation/common_voice_7_0
args: or
metrics:
- name: Test WER
type: wer
value: 53.58
- name: Test CER
type: cer
value: 11.26
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice-8.0
type: mozilla-foundation/common_voice_8_0
args: or
metrics:
- name: Test WER
type: wer
value: 55.26
- name: Test CER
type: cer
value: 13.01
---
|
AndrewMcDowell/wav2vec2-xls-r-300m-japanese
|
AndrewMcDowell
| 2022-03-23T18:34:20Z | 38 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"ja",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- ja
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300-m
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
- name: Test WER
type: wer
value: 95.82
- name: Test CER
type: cer
value: 23.64
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: de
metrics:
- name: Test WER
type: wer
value: 100.0
- name: Test CER
type: cer
value: 30.99
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ja
metrics:
- name: Test CER
type: cer
value: 30.37
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ja
metrics:
- name: Test CER
type: cer
value: 34.42
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset.
Kanji are converted into Hiragana using the [pykakasi](https://pykakasi.readthedocs.io/en/latest/index.html) library during training and evaluation. The model can output both Hiragana and Katakana characters. Since there is no spacing, WER is not a suitable metric for evaluating performance and CER is more suitable.
On mozilla-foundation/common_voice_8_0 it achieved:
- cer: 23.64%
On speech-recognition-community-v2/dev_data it achieved:
- cer: 30.99%
It achieves the following results on the evaluation set:
- Loss: 0.5212
- Wer: 1.3068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.0974 | 4.72 | 1000 | 4.0178 | 1.9535 |
| 2.1276 | 9.43 | 2000 | 0.9301 | 1.2128 |
| 1.7622 | 14.15 | 3000 | 0.7103 | 1.5527 |
| 1.6397 | 18.87 | 4000 | 0.6729 | 1.4269 |
| 1.5468 | 23.58 | 5000 | 0.6087 | 1.2497 |
| 1.4885 | 28.3 | 6000 | 0.5786 | 1.3222 |
| 1.451 | 33.02 | 7000 | 0.5726 | 1.3768 |
| 1.3912 | 37.74 | 8000 | 0.5518 | 1.2497 |
| 1.3617 | 42.45 | 9000 | 0.5352 | 1.2694 |
| 1.3113 | 47.17 | 10000 | 0.5228 | 1.2781 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs
```
2. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
|
sammy786/wav2vec2-xlsr-finnish
|
sammy786
| 2022-03-23T18:34:11Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- fi
license: apache-2.0
tags:
- automatic-speech-recognition
- fi
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-finnish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: fi
metrics:
- name: Test WER
type: wer
value: 13.72
- name: Test CER
type: cer
value: 2.35
---
# sammy786/wav2vec2-xlsr-finnish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - fi dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss: 8.7555
- Wer: 23.0231
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv, invalidated.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 8
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|:----:|:-------------:|:---------------:|:--------:|
| 200 | 4.253700 | 0.881733 | 0.967007 |
| 400 | 0.864800 | 0.226977 | 0.420836 |
| 600 | 0.607000 | 0.157473 | 0.343375 |
| 800 | 0.380200 | 0.145640 | 0.302672 |
| 1000 | 0.318400 | 0.128028 | 0.293886 |
| 1200 | 0.261100 | 0.121414 | 0.289941 |
| 1400 | 0.232300 | 0.113451 | 0.279182 |
| 1600 | 0.216600 | 0.113649 | 0.282948 |
| 1800 | 0.202500 | 0.112375 | 0.276134 |
| 2000 | 0.190000 | 0.105725 | 0.273803 |
| 2200 | 0.171000 | 0.109715 | 0.270755 |
| 2400 | 0.156500 | 0.105042 | 0.264300 |
| 2600 | 0.155600 | 0.108337 | 0.260714 |
| 2800 | 0.149100 | 0.112435 | 0.263583 |
| 3000 | 0.145100 | 0.106193 | 0.261969 |
| 3200 | 0.131700 | 0.102860 | 0.251210 |
| 3400 | 0.129100 | 0.096058 | 0.246907 |
| 3600 | 0.121600 | 0.099932 | 0.246369 |
| 3800 | 0.112000 | 0.099041 | 0.244397 |
| 4000 | 0.114100 | 0.101566 | 0.242604 |
| 4200 | 0.111500 | 0.089498 | 0.239197 |
| 4400 | 0.099800 | 0.092835 | 0.240990 |
| 4600 | 0.095300 | 0.093518 | 0.238121 |
| 4800 | 0.094300 | 0.090783 | 0.240631 |
| 5000 | 0.089000 | 0.094046 | 0.238479 |
| 5200 | 0.088000 | 0.089342 | 0.235252 |
| 5400 | 0.083600 | 0.087770 | 0.234535 |
| 5600 | 0.083600 | 0.088804 | 0.234355 |
| 5800 | 0.080300 | 0.090168 | 0.231307 |
| 6000 | 0.078100 | 0.090163 | 0.230949 |
| 6200 | 0.075600 | 0.088876 | 0.232383 |
| 6400 | 0.078700 | 0.087235 | 0.232024 |
| 6600 | 0.074800 | 0.086825 | 0.231486 |
| 6800 | 0.076400 | 0.087308 | 0.231845 |
| 7000 | 0.070700 | 0.087695 | 0.230769 |
| 7200 | 0.075500 | 0.087555 | 0.230231 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-finnish --dataset mozilla-foundation/common_voice_8_0 --config fi --split test
```
|
polodealvarado/xls-r-300m-es
|
polodealvarado
| 2022-03-23T18:34:06Z | 17 | 4 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice_8_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"es",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- es
tags:
- common_voice_8_0
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wave2vec-xls-r-300m-es
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_8_0 es
type: mozilla-foundation/common_voice_8_0
args: es
metrics:
- name: Test WER
type: wer
value: 14.6
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: es
metrics:
- name: Test WER
type: wer
value: 28.63
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: es
metrics:
- name: Test WER
type: wer
value: 29.72
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wav2Vec2-XLSR-300m-es
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the spanish common_voice dataset thanks to the GPU credits generously given by the OVHcloud for the Speech Recognition challenge.
It achieves the following results on the evaluation set
Without LM:
- Loss : 0.1900
- Wer : 0.146
With 5-gram:
- WER: 0.109
- CER: 0.036
### Usage with 5-gram.
The model can be used with n-gram (n=5) included in the processor as follows.
```python
import re
from transformers import AutoModelForCTC,Wav2Vec2ProcessorWithLM
import torch
# Loading model and processor
processor = Wav2Vec2ProcessorWithLM.from_pretrained("polodealvarado/xls-r-300m-es")
model = AutoModelForCTC.from_pretrained("polodealvarado/xls-r-300m-es")
# Cleaning characters
def remove_extra_chars(batch):
chars_to_ignore_regex = '[^a-záéíóúñ ]'
text = batch["translation"][target_lang]
batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
return batch
# Preparing dataset
def prepare_dataset(batch):
audio = batch["audio"]
batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"],return_tensors="pt",padding=True).input_values[0]
with processor.as_target_processor():
batch["labels"] = processor(batch["sentence"]).input_ids
return batch
common_voice_test = load_dataset("mozilla-foundation/common_voice_8_0", "es", split="test",use_auth_token=True)
common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])
common_voice_test = common_voice_test.cast_column("audio", Audio(sampling_rate=16_000))
common_voice_test = common_voice_test.map(remove_extra_chars, remove_columns=dataset.column_names)
common_voice_test = common_voice_test.map(prepare_dataset)
# Testing first sample
inputs = torch_tensor(common_voice_test[0]["input_values"])
with torch.no_grad():
logits = model(inputs).logits
pred_ids = torch.argmax(logits, dim=-1)
text = processor.batch_decode(logits.numpy()).text
print(text) # 'bien y qué regalo vas a abrir primero'
```
On the other, you can execute the eval.py file for evaluation
```bash
# To use GPU: --device 0
$ python eval.py --model_id polodealvarado/xls-r-300m-es --dataset mozilla-foundation/common_voice_8_0 --config es --device 0 --split test
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6747 | 0.3 | 400 | 0.6535 | 0.5926 |
| 0.4439 | 0.6 | 800 | 0.3753 | 0.3193 |
| 0.3291 | 0.9 | 1200 | 0.3267 | 0.2721 |
| 0.2644 | 1.2 | 1600 | 0.2816 | 0.2311 |
| 0.24 | 1.5 | 2000 | 0.2647 | 0.2179 |
| 0.2265 | 1.79 | 2400 | 0.2406 | 0.2048 |
| 0.1994 | 2.09 | 2800 | 0.2357 | 0.1869 |
| 0.1613 | 2.39 | 3200 | 0.2242 | 0.1821 |
| 0.1546 | 2.69 | 3600 | 0.2123 | 0.1707 |
| 0.1441 | 2.99 | 4000 | 0.2067 | 0.1619 |
| 0.1138 | 3.29 | 4400 | 0.2044 | 0.1519 |
| 0.1072 | 3.59 | 4800 | 0.1917 | 0.1457 |
| 0.0992 | 3.89 | 5200 | 0.1900 | 0.1438 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
lgris/wav2vec2_base_10k_8khz_pt_cv7_2
|
lgris
| 2022-03-23T18:34:03Z | 7 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"pt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- pt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2_base_10k_8khz_pt_cv7_2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: pt
metrics:
- name: Test WER
type: wer
value: 36.9
- name: Test CER
type: cer
value: 14.82
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 40.53
- name: Test CER
type: cer
value: 16.95
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Test WER
type: wer
value: 37.15
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pt
metrics:
- name: Test WER
type: wer
value: 38.95
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_base_10k_8khz_pt_cv7_2
This model is a fine-tuned version of [lgris/seasr_2022_base_10k_8khz_pt](https://huggingface.co/lgris/seasr_2022_base_10k_8khz_pt) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 76.3426
- Wer: 0.1979
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 189.1362 | 0.65 | 500 | 80.6347 | 0.2139 |
| 174.2587 | 1.3 | 1000 | 80.2062 | 0.2116 |
| 164.676 | 1.95 | 1500 | 78.2161 | 0.2073 |
| 176.5856 | 2.6 | 2000 | 78.8920 | 0.2074 |
| 164.3583 | 3.25 | 2500 | 77.2865 | 0.2066 |
| 161.414 | 3.9 | 3000 | 77.8888 | 0.2048 |
| 158.283 | 4.55 | 3500 | 77.3472 | 0.2033 |
| 159.2265 | 5.19 | 4000 | 79.0953 | 0.2036 |
| 156.3967 | 5.84 | 4500 | 76.6855 | 0.2029 |
| 154.2743 | 6.49 | 5000 | 77.7785 | 0.2015 |
| 156.6497 | 7.14 | 5500 | 77.1220 | 0.2033 |
| 157.3038 | 7.79 | 6000 | 76.2926 | 0.2027 |
| 162.8151 | 8.44 | 6500 | 76.7602 | 0.2013 |
| 151.8613 | 9.09 | 7000 | 77.4777 | 0.2011 |
| 153.0225 | 9.74 | 7500 | 76.5206 | 0.2001 |
| 157.52 | 10.39 | 8000 | 76.1061 | 0.2006 |
| 145.0592 | 11.04 | 8500 | 76.7855 | 0.1992 |
| 150.0066 | 11.69 | 9000 | 76.0058 | 0.1988 |
| 146.8128 | 12.34 | 9500 | 76.2853 | 0.1987 |
| 146.9148 | 12.99 | 10000 | 76.3426 | 0.1979 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-romanian
|
infinitejoy
| 2022-03-23T18:33:55Z | 471 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"ro",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- ro
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Romanian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ro
metrics:
- name: Test WER
type: wer
value: 14.194
- name: Test CER
type: cer
value: 3.288
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ro
metrics:
- name: Test WER
type: wer
value: 40.869
- name: Test CER
type: cer
value: 12.049
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ro
metrics:
- name: Test WER
type: wer
value: 47.2
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-romanian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1167
- Wer: 0.1421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1973 | 8.89 | 2000 | 0.4481 | 0.4849 |
| 0.6005 | 17.78 | 4000 | 0.1420 | 0.1777 |
| 0.5248 | 26.67 | 6000 | 0.1303 | 0.1651 |
| 0.4871 | 35.56 | 8000 | 0.1207 | 0.1523 |
| 0.4428 | 44.44 | 10000 | 0.1143 | 0.1425 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-mongolian
|
infinitejoy
| 2022-03-23T18:33:52Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mn",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mn
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mn
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Mongolian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: mn
metrics:
- name: Test WER
type: wer
value: 44.709
- name: Test CER
type: cer
value: 13.532
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: mn
metrics:
- name: Test WER
type: wer
value: 76.643
- name: Test CER
type: cer
value: 36.997
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: mn
metrics:
- name: Test WER
type: wer
value: 78.45
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-mongolian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - MN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6003
- Wer: 0.4473
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3677 | 15.87 | 2000 | 0.6432 | 0.6198 |
| 1.1379 | 31.75 | 4000 | 0.6196 | 0.5592 |
| 1.0093 | 47.62 | 6000 | 0.5828 | 0.5117 |
| 0.8888 | 63.49 | 8000 | 0.5754 | 0.4822 |
| 0.7985 | 79.37 | 10000 | 0.5987 | 0.4690 |
| 0.697 | 95.24 | 12000 | 0.6014 | 0.4471 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-basaa
|
infinitejoy
| 2022-03-23T18:33:50Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"bas",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bas
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Basaa
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: bas
metrics:
- name: Test WER
type: wer
value: 104.08
- name: Test CER
type: cer
value: 228.48
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-basaa
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BAS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5975
- Wer: 0.4981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 200.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.9287 | 15.62 | 500 | 2.8774 | 1.0 |
| 1.1182 | 31.25 | 1000 | 0.6248 | 0.7131 |
| 0.8329 | 46.88 | 1500 | 0.5573 | 0.5792 |
| 0.7109 | 62.5 | 2000 | 0.5420 | 0.5683 |
| 0.6295 | 78.12 | 2500 | 0.5166 | 0.5395 |
| 0.5715 | 93.75 | 3000 | 0.5487 | 0.5629 |
| 0.5016 | 109.38 | 3500 | 0.5370 | 0.5471 |
| 0.4661 | 125.0 | 4000 | 0.5621 | 0.5395 |
| 0.423 | 140.62 | 4500 | 0.5658 | 0.5248 |
| 0.3793 | 156.25 | 5000 | 0.5921 | 0.4981 |
| 0.3651 | 171.88 | 5500 | 0.5987 | 0.4888 |
| 0.3351 | 187.5 | 6000 | 0.6017 | 0.4948 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
Akashpb13/xlsr_hungarian_new
|
Akashpb13
| 2022-03-23T18:33:33Z | 41 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hu",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hu
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hu
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/xlsr_hungarian_new
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hu
metrics:
- name: Test WER
type: wer
value: 0.2851621517163838
- name: Test CER
type: cer
value: 0.06112982522287432
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hu
metrics:
- name: Test WER
type: wer
value: 0.2851621517163838
- name: Test CER
type: cer
value: 0.06112982522287432
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: hu
metrics:
- name: Test WER
type: wer
value: 47.15
---
# Akashpb13/xlsr_hungarian_new
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets):
- Loss: 0.197464
- Wer: 0.330094
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice hungarian train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000095637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 16
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|------|---------------|-----------------|----------|
| 500 | 4.785300 | 0.952295 | 0.796236 |
| 1000 | 0.535800 | 0.217474 | 0.381613 |
| 1500 | 0.258400 | 0.205524 | 0.345056 |
| 2000 | 0.202800 | 0.198680 | 0.336264 |
| 2500 | 0.182700 | 0.197464 | 0.330094 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Akashpb13/xlsr_hungarian_new --dataset mozilla-foundation/common_voice_8_0 --config hu --split test
```
|
infinitejoy/wav2vec2-large-xls-r-300m-kurdish
|
infinitejoy
| 2022-03-23T18:33:23Z | 98 | 4 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"kmr",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- kmr
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- kmr
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Kurmanji Kurdish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: kmr
metrics:
- name: Test WER
type: wer
value: 102.308
- name: Test CER
type: cer
value: 538.748
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-kurdish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - KMR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2548
- Wer: 0.2688
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3161 | 12.27 | 2000 | 0.4199 | 0.4797 |
| 1.0643 | 24.54 | 4000 | 0.2982 | 0.3721 |
| 0.9718 | 36.81 | 6000 | 0.2762 | 0.3333 |
| 0.8772 | 49.08 | 8000 | 0.2586 | 0.3051 |
| 0.8236 | 61.35 | 10000 | 0.2575 | 0.2865 |
| 0.7745 | 73.62 | 12000 | 0.2603 | 0.2816 |
| 0.7297 | 85.89 | 14000 | 0.2539 | 0.2727 |
| 0.7079 | 98.16 | 16000 | 0.2554 | 0.2681 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
deepdml/wav2vec2-large-xls-r-300m-basque
|
deepdml
| 2022-03-23T18:33:20Z | 40 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"basque",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"eu",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language: eu
metrics:
- wer
- cer
tags:
- automatic-speech-recognition
- basque
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-large-xls-r-300m-basque
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: eu
metrics:
- name: Test WER
type: wer
value: 51.89
- name: Test CER
type: cer
value: 10.01
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-basque
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4276
- Wer: 0.5962
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.9902 | 1.29 | 400 | 2.1257 | 1.0 |
| 0.9625 | 2.59 | 800 | 0.5695 | 0.7452 |
| 0.4605 | 3.88 | 1200 | 0.4276 | 0.5962 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
sammy786/wav2vec2-xlsr-breton
|
sammy786
| 2022-03-23T18:33:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"br",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- br
license: apache-2.0
tags:
- automatic-speech-recognition
- br
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-breton
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: br
metrics:
- name: Test WER
type: wer
value: 48.2
- name: Test CER
type: cer
value: 15.02
---
# sammy786/wav2vec2-xlsr-breton
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - br dataset.
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 8
- eval_batch_size: 32
- seed: 13
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-breton --dataset mozilla-foundation/common_voice_8_0 --config br --split test
```
|
samitizerxu/wav2vec2-xls-r-300m-fr
|
samitizerxu
| 2022-03-23T18:33:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"fr",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- fr
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-cls-r-300m-fr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 56.62
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: fr
metrics:
- name: Test WER
type: wer
value: 58.22
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-cls-r-300m-fr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6521
- Wer: 0.4330
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.6773 | 0.8 | 500 | 1.3907 | 0.9864 |
| 0.9526 | 1.6 | 1000 | 0.7760 | 0.6448 |
| 0.6418 | 2.4 | 1500 | 0.7605 | 0.6194 |
| 0.5028 | 3.2 | 2000 | 0.6516 | 0.5322 |
| 0.4133 | 4.0 | 2500 | 0.6303 | 0.5097 |
| 0.3285 | 4.8 | 3000 | 0.6422 | 0.5062 |
| 0.2764 | 5.6 | 3500 | 0.5936 | 0.4748 |
| 0.2361 | 6.4 | 4000 | 0.6486 | 0.4683 |
| 0.2049 | 7.2 | 4500 | 0.6321 | 0.4532 |
| 0.176 | 8.0 | 5000 | 0.6230 | 0.4482 |
| 0.1393 | 8.8 | 5500 | 0.6595 | 0.4403 |
| 0.1141 | 9.6 | 6000 | 0.6552 | 0.4348 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-basaa-cv8
|
infinitejoy
| 2022-03-23T18:32:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"bas",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bas
license: apache-2.0
tags:
- automatic-speech-recognition
- bas
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Basaa
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: bas
metrics:
- name: Test WER
type: wer
value: 38.057
- name: Test CER
type: cer
value: 11.233
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-basaa-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BAS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4648
- Wer: 0.5472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9421 | 12.82 | 500 | 2.8894 | 1.0 |
| 1.1872 | 25.64 | 1000 | 0.6688 | 0.7460 |
| 0.8894 | 38.46 | 1500 | 0.4868 | 0.6516 |
| 0.769 | 51.28 | 2000 | 0.4960 | 0.6507 |
| 0.6936 | 64.1 | 2500 | 0.4781 | 0.5384 |
| 0.624 | 76.92 | 3000 | 0.4643 | 0.5430 |
| 0.5966 | 89.74 | 3500 | 0.4530 | 0.5591 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
AlexN/xls-r-300m-fr
|
AlexN
| 2022-03-23T18:32:43Z | 56 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- fr
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-300m-fr
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0 fr
type: mozilla-foundation/common_voice_8_0
args: fr
metrics:
- name: Test WER
type: wer
value: 21.58
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 36.03
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: fr
metrics:
- name: Test WER
type: wer
value: 38.86
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2700
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
sammy786/wav2vec2-xlsr-mongolian
|
sammy786
| 2022-03-23T18:30:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mn",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mn
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mn
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-mongolian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: mn
metrics:
- name: Test WER
type: wer
value: 32.63
- name: Test CER
type: cer
value: 9.26
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: mn
metrics:
- name: Test WER
type: wer
value: 91.26
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: mn
metrics:
- name: Test WER
type: wer
value: 91.37
---
# sammy786/wav2vec2-xlsr-mongolian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - mn dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss: 31.52
- Wer: 34.1522
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|:----:|:-------------:|:---------------:|:--------:|
| 200 | 4.906200 | 3.012986 | 1.000000 |
| 400 | 1.734600 | 0.704821 | 0.750497 |
| 600 | 1.132100 | 0.496223 | 0.531241 |
| 800 | 0.929300 | 0.468937 | 0.469043 |
| 1000 | 0.772300 | 0.425313 | 0.448168 |
| 1200 | 0.623900 | 0.394633 | 0.414229 |
| 1400 | 0.512400 | 0.369225 | 0.397614 |
| 1600 | 0.439900 | 0.346033 | 0.391650 |
| 1800 | 0.391300 | 0.358454 | 0.379296 |
| 2000 | 0.377000 | 0.346822 | 0.359415 |
| 2200 | 0.347500 | 0.325205 | 0.348481 |
| 2400 | 0.343600 | 0.315233 | 0.344078 |
| 2600 | 0.328000 | 0.308826 | 0.341522 |
| 2800 | 0.358200 | 0.331786 | 0.343084 |
| 3000 | 0.417200 | 0.370051 | 0.356433 |
| 3200 | 0.685300 | 0.595438 | 0.407413 |
| 3400 | 0.764100 | 0.643449 | 0.359983 |
| 3600 | 0.717100 | 0.505033 | 0.371911 |
| 3800 | 0.620900 | 0.464138 | 0.369071 |
| 4000 | 0.590700 | 0.445417 | 0.363249 |
| 4200 | 0.561000 | 0.440727 | 0.360267 |
| 4400 | 0.550600 | 0.447122 | 0.360267 |
| 4600 | 0.562100 | 0.457020 | 0.359841 |
| 4800 | 0.578800 | 0.470477 | 0.360551 |
| 5000 | 0.580400 | 0.481413 | 0.362539 |
| 5200 | 0.605500 | 0.485240 | 0.362823 |
| 5400 | 0.582900 | 0.486654 | 0.362965 |
| 5600 | 0.593900 | 0.486715 | 0.363107 |
| 5800 | 0.590900 | 0.486716 | 0.363107 |
| 6000 | 0.587200 | 0.486716 | 0.363107 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-mongolian --dataset mozilla-foundation/common_voice_8_0 --config mn --split test
```
|
infinitejoy/wav2vec2-large-xls-r-300m-urdu
|
infinitejoy
| 2022-03-23T18:30:21Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"ur",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
- ur
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Urdu
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ur
metrics:
- name: Test WER
type: wer
value: 105.66
- name: Test CER
type: cer
value: 434.011
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
infinitejoy/wav2vec2-large-xls-r-300m-urdu
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - -UR dataset.
It achieves the following results on the evaluation set:
- Loss: NA
- Wer: NA
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py \
--model_id infinitejoy/wav2vec2-large-xls-r-300m-urdu --dataset speech-recognition-community-v2/dev_data \
--config ur --split validation --chunk_length_s 10 --stride_length_s 1
```
### Inference
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "infinitejoy/wav2vec2-large-xls-r-300m-urdu"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "ur", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
```
### Eval results on Common Voice 7 "test" (WER):
|
infinitejoy/wav2vec2-large-xls-r-300m-bashkir
|
infinitejoy
| 2022-03-23T18:30:18Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"ba",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ba
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Bashkir
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ba
metrics:
- name: Test WER
type: wer
value: 24.2
- name: Test CER
type: cer
value: 5.08
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bashkir
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1892
- Wer: 0.2421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.4792 | 0.5 | 2000 | 0.4598 | 0.5404 |
| 1.449 | 1.0 | 4000 | 0.4650 | 0.5610 |
| 1.3742 | 1.49 | 6000 | 0.4001 | 0.4977 |
| 1.3375 | 1.99 | 8000 | 0.3916 | 0.4894 |
| 1.2961 | 2.49 | 10000 | 0.3641 | 0.4569 |
| 1.2714 | 2.99 | 12000 | 0.3491 | 0.4488 |
| 1.2399 | 3.48 | 14000 | 0.3151 | 0.3986 |
| 1.2067 | 3.98 | 16000 | 0.3081 | 0.3923 |
| 1.1842 | 4.48 | 18000 | 0.2875 | 0.3703 |
| 1.1644 | 4.98 | 20000 | 0.2840 | 0.3670 |
| 1.161 | 5.48 | 22000 | 0.2790 | 0.3597 |
| 1.1303 | 5.97 | 24000 | 0.2552 | 0.3272 |
| 1.0874 | 6.47 | 26000 | 0.2405 | 0.3142 |
| 1.0613 | 6.97 | 28000 | 0.2352 | 0.3055 |
| 1.0498 | 7.47 | 30000 | 0.2249 | 0.2910 |
| 1.021 | 7.96 | 32000 | 0.2118 | 0.2752 |
| 1.0002 | 8.46 | 34000 | 0.2046 | 0.2662 |
| 0.9762 | 8.96 | 36000 | 0.1969 | 0.2530 |
| 0.9568 | 9.46 | 38000 | 0.1917 | 0.2449 |
| 0.953 | 9.96 | 40000 | 0.1893 | 0.2425 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2
|
DrishtiSharma
| 2022-03-23T18:30:10Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"bg",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- bg
license: apache-2.0
tags:
- automatic-speech-recognition
- bg
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-bg-d2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: bg
metrics:
- name: Test WER
type: wer
value: 0.28775471338792613
- name: Test CER
type: cer
value: 0.06861971204625049
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: bg
metrics:
- name: Test WER
type: wer
value: 0.49783147459727384
- name: Test CER
type: cer
value: 0.1591062599627158
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: bg
metrics:
- name: Test WER
type: wer
value: 51.25
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bg-d2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3421
- Wer: 0.2860
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset mozilla-foundation/common_voice_8_0 --config bg --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 10 --stride_length_s 1
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.8791 | 1.74 | 200 | 3.1902 | 1.0 |
| 3.0441 | 3.48 | 400 | 2.8098 | 0.9864 |
| 1.1499 | 5.22 | 600 | 0.4668 | 0.5014 |
| 0.4968 | 6.96 | 800 | 0.4162 | 0.4472 |
| 0.3553 | 8.7 | 1000 | 0.3580 | 0.3777 |
| 0.3027 | 10.43 | 1200 | 0.3422 | 0.3506 |
| 0.2562 | 12.17 | 1400 | 0.3556 | 0.3639 |
| 0.2272 | 13.91 | 1600 | 0.3621 | 0.3583 |
| 0.2125 | 15.65 | 1800 | 0.3436 | 0.3358 |
| 0.1904 | 17.39 | 2000 | 0.3650 | 0.3545 |
| 0.1695 | 19.13 | 2200 | 0.3366 | 0.3241 |
| 0.1532 | 20.87 | 2400 | 0.3550 | 0.3311 |
| 0.1453 | 22.61 | 2600 | 0.3582 | 0.3131 |
| 0.1359 | 24.35 | 2800 | 0.3524 | 0.3084 |
| 0.1233 | 26.09 | 3000 | 0.3503 | 0.2973 |
| 0.1114 | 27.83 | 3200 | 0.3434 | 0.2946 |
| 0.1051 | 29.57 | 3400 | 0.3474 | 0.2956 |
| 0.0965 | 31.3 | 3600 | 0.3426 | 0.2907 |
| 0.0923 | 33.04 | 3800 | 0.3478 | 0.2894 |
| 0.0894 | 34.78 | 4000 | 0.3421 | 0.2860 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
vitouphy/wav2vec2-xls-r-300m-japanese
|
vitouphy
| 2022-03-23T18:30:07Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"ja",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"doi:10.57967/hf/0124",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- ja
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Japanese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
- name: Test WER
type: wer
value: 54.05
- name: Test CER
type: cer
value: 27.54
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ja
metrics:
- name: Validation WER
type: wer
value: 48.77
- name: Validation CER
type: cer
value: 24.87
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ja
metrics:
- name: Test CER
type: cer
value: 27.36
---
#
This model is for transcribing audio into Hiragana, one format of Japanese language.
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `mozilla-foundation/common_voice_8_0 dataset`. Note that the following results are achieved by:
- Modify `eval.py` to suit the use case.
- Since kanji and katakana shares the same sound as hiragana, we convert all texts to hiragana using [pykakasi](https://pykakasi.readthedocs.io) and tokenize them using [fugashi](https://github.com/polm/fugashi).
It achieves the following results on the evaluation set:
- Loss: 0.7751
- Cer: 0.2227
# Evaluation results (Running ./eval.py):
| Model | Metric | Common-Voice-8/test | speech-recognition-community-v2/dev-data |
|:--------:|:------:|:-------------------:|:------------------------------------------:|
| w/o LM | WER | 0.5964 | 0.5532 |
| | CER | 0.2944 | 0.2629 |
| w/ LM | WER | 0.5405 | 0.4877 |
| | CER | **0.2754** | **0.2487** |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.4081 | 1.6 | 500 | 4.0983 | 1.0 |
| 3.303 | 3.19 | 1000 | 3.3563 | 1.0 |
| 3.1538 | 4.79 | 1500 | 3.2066 | 0.9239 |
| 2.1526 | 6.39 | 2000 | 1.1597 | 0.3355 |
| 1.8726 | 7.98 | 2500 | 0.9023 | 0.2505 |
| 1.7817 | 9.58 | 3000 | 0.8219 | 0.2334 |
| 1.7488 | 11.18 | 3500 | 0.7915 | 0.2222 |
| 1.7039 | 12.78 | 4000 | 0.7751 | 0.2227 |
| Stop & Train | | | | |
| 1.6571 | 15.97 | 5000 | 0.6788 | 0.1685 |
| 1.520400 | 19.16 | 6000 | 0.6095 | 0.1409 |
| 1.448200 | 22.35 | 7000 | 0.5843 | 0.1430 |
| 1.385400 | 25.54 | 8000 | 0.5699 | 0.1263 |
| 1.354200 | 28.73 | 9000 | 0.5686 | 0.1219 |
| 1.331500 | 31.92 | 10000 | 0.5502 | 0.1144 |
| 1.290800 | 35.11 | 11000 | 0.5371 | 0.1140 |
| Stop & Train | | | | |
| 1.235200 | 38.30 | 12000 | 0.5394 | 0.1106 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
lgris/wav2vec2-xls-r-1b-cv8
|
lgris
| 2022-03-23T18:29:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"pt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- pt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-1b-cv8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: pt
metrics:
- name: Test WER
type: wer
value: 17.7
- name: Test CER
type: cer
value: 5.21
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 45.68
- name: Test CER
type: cer
value: 18.67
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Test WER
type: wer
value: 45.29
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pt
metrics:
- name: Test WER
type: wer
value: 48.03
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-1b-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2007
- Wer: 0.1838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.1172 | 0.32 | 500 | 1.2852 | 0.9783 |
| 1.4152 | 0.64 | 1000 | 0.6434 | 0.6105 |
| 1.4342 | 0.96 | 1500 | 0.4844 | 0.3989 |
| 1.4657 | 1.29 | 2000 | 0.5080 | 0.4490 |
| 1.4961 | 1.61 | 2500 | 0.4764 | 0.4264 |
| 1.4515 | 1.93 | 3000 | 0.4519 | 0.4068 |
| 1.3924 | 2.25 | 3500 | 0.4472 | 0.4132 |
| 1.4524 | 2.57 | 4000 | 0.4455 | 0.3939 |
| 1.4328 | 2.89 | 4500 | 0.4369 | 0.4069 |
| 1.3456 | 3.22 | 5000 | 0.4234 | 0.3774 |
| 1.3725 | 3.54 | 5500 | 0.4387 | 0.3789 |
| 1.3812 | 3.86 | 6000 | 0.4298 | 0.3825 |
| 1.3282 | 4.18 | 6500 | 0.4025 | 0.3703 |
| 1.3326 | 4.5 | 7000 | 0.3917 | 0.3502 |
| 1.3028 | 4.82 | 7500 | 0.3889 | 0.3582 |
| 1.293 | 5.14 | 8000 | 0.3859 | 0.3496 |
| 1.321 | 5.47 | 8500 | 0.3875 | 0.3576 |
| 1.3165 | 5.79 | 9000 | 0.3927 | 0.3589 |
| 1.2701 | 6.11 | 9500 | 0.4058 | 0.3621 |
| 1.2718 | 6.43 | 10000 | 0.4211 | 0.3916 |
| 1.2683 | 6.75 | 10500 | 0.3968 | 0.3620 |
| 1.2643 | 7.07 | 11000 | 0.4128 | 0.3848 |
| 1.2485 | 7.4 | 11500 | 0.3849 | 0.3727 |
| 1.2608 | 7.72 | 12000 | 0.3770 | 0.3474 |
| 1.2388 | 8.04 | 12500 | 0.3774 | 0.3574 |
| 1.2524 | 8.36 | 13000 | 0.3789 | 0.3550 |
| 1.2458 | 8.68 | 13500 | 0.3770 | 0.3410 |
| 1.2505 | 9.0 | 14000 | 0.3638 | 0.3403 |
| 1.2254 | 9.32 | 14500 | 0.3770 | 0.3509 |
| 1.2459 | 9.65 | 15000 | 0.3592 | 0.3349 |
| 1.2049 | 9.97 | 15500 | 0.3600 | 0.3428 |
| 1.2097 | 10.29 | 16000 | 0.3626 | 0.3347 |
| 1.1988 | 10.61 | 16500 | 0.3740 | 0.3269 |
| 1.1671 | 10.93 | 17000 | 0.3548 | 0.3245 |
| 1.1532 | 11.25 | 17500 | 0.3394 | 0.3140 |
| 1.1459 | 11.58 | 18000 | 0.3349 | 0.3156 |
| 1.1511 | 11.9 | 18500 | 0.3272 | 0.3110 |
| 1.1465 | 12.22 | 19000 | 0.3348 | 0.3084 |
| 1.1426 | 12.54 | 19500 | 0.3193 | 0.3027 |
| 1.1278 | 12.86 | 20000 | 0.3318 | 0.3021 |
| 1.149 | 13.18 | 20500 | 0.3169 | 0.2947 |
| 1.114 | 13.5 | 21000 | 0.3224 | 0.2986 |
| 1.1249 | 13.83 | 21500 | 0.3227 | 0.2921 |
| 1.0968 | 14.15 | 22000 | 0.3033 | 0.2878 |
| 1.0851 | 14.47 | 22500 | 0.2996 | 0.2863 |
| 1.0985 | 14.79 | 23000 | 0.3011 | 0.2843 |
| 1.0808 | 15.11 | 23500 | 0.2932 | 0.2759 |
| 1.069 | 15.43 | 24000 | 0.2919 | 0.2750 |
| 1.0602 | 15.76 | 24500 | 0.2959 | 0.2713 |
| 1.0369 | 16.08 | 25000 | 0.2931 | 0.2754 |
| 1.0573 | 16.4 | 25500 | 0.2920 | 0.2722 |
| 1.051 | 16.72 | 26000 | 0.2855 | 0.2632 |
| 1.0279 | 17.04 | 26500 | 0.2850 | 0.2649 |
| 1.0496 | 17.36 | 27000 | 0.2817 | 0.2585 |
| 1.0516 | 17.68 | 27500 | 0.2961 | 0.2635 |
| 1.0244 | 18.01 | 28000 | 0.2781 | 0.2589 |
| 1.0099 | 18.33 | 28500 | 0.2783 | 0.2565 |
| 1.0016 | 18.65 | 29000 | 0.2719 | 0.2537 |
| 1.0157 | 18.97 | 29500 | 0.2621 | 0.2449 |
| 0.9572 | 19.29 | 30000 | 0.2582 | 0.2427 |
| 0.9802 | 19.61 | 30500 | 0.2707 | 0.2468 |
| 0.9577 | 19.94 | 31000 | 0.2563 | 0.2389 |
| 0.9562 | 20.26 | 31500 | 0.2592 | 0.2382 |
| 0.962 | 20.58 | 32000 | 0.2539 | 0.2341 |
| 0.9541 | 20.9 | 32500 | 0.2505 | 0.2288 |
| 0.9587 | 21.22 | 33000 | 0.2486 | 0.2302 |
| 0.9146 | 21.54 | 33500 | 0.2461 | 0.2269 |
| 0.9215 | 21.86 | 34000 | 0.2387 | 0.2228 |
| 0.9105 | 22.19 | 34500 | 0.2405 | 0.2222 |
| 0.8949 | 22.51 | 35000 | 0.2316 | 0.2191 |
| 0.9153 | 22.83 | 35500 | 0.2358 | 0.2180 |
| 0.8907 | 23.15 | 36000 | 0.2369 | 0.2168 |
| 0.8973 | 23.47 | 36500 | 0.2323 | 0.2120 |
| 0.8878 | 23.79 | 37000 | 0.2293 | 0.2104 |
| 0.8818 | 24.12 | 37500 | 0.2302 | 0.2132 |
| 0.8919 | 24.44 | 38000 | 0.2262 | 0.2083 |
| 0.8473 | 24.76 | 38500 | 0.2257 | 0.2040 |
| 0.8516 | 25.08 | 39000 | 0.2246 | 0.2031 |
| 0.8451 | 25.4 | 39500 | 0.2198 | 0.2000 |
| 0.8288 | 25.72 | 40000 | 0.2199 | 0.1990 |
| 0.8465 | 26.05 | 40500 | 0.2165 | 0.1972 |
| 0.8305 | 26.37 | 41000 | 0.2128 | 0.1957 |
| 0.8202 | 26.69 | 41500 | 0.2127 | 0.1937 |
| 0.8223 | 27.01 | 42000 | 0.2100 | 0.1934 |
| 0.8322 | 27.33 | 42500 | 0.2076 | 0.1905 |
| 0.8139 | 27.65 | 43000 | 0.2054 | 0.1880 |
| 0.8299 | 27.97 | 43500 | 0.2026 | 0.1868 |
| 0.7937 | 28.3 | 44000 | 0.2045 | 0.1872 |
| 0.7972 | 28.62 | 44500 | 0.2025 | 0.1861 |
| 0.809 | 28.94 | 45000 | 0.2026 | 0.1858 |
| 0.813 | 29.26 | 45500 | 0.2013 | 0.1838 |
| 0.7718 | 29.58 | 46000 | 0.2010 | 0.1837 |
| 0.7929 | 29.9 | 46500 | 0.2008 | 0.1840 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
|
jsnfly/wav2vec2-large-xlsr-53-german-gpt2
|
jsnfly
| 2022-03-23T18:29:57Z | 21 | 2 |
transformers
|
[
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"de",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: Wav2Vec2-Large-XLSR-53-German-GPT2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: de
metrics:
- name: Test WER
type: wer
value: 10.02
- name: Test CER
type: cer
value: 4.7
---
# Wav2Vec2-Large-XLSR-53-German-GPT2
This is an encoder-decoder model for automatic speech recognition trained on on the
MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset. The encoder was initialized from
[jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) and
the decoder from [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2).
It was trained using a two step process:
* fine-tuning only the cross-attention weights and the decoder using the pre-computed outputs of the Wav2Vec-Modell
* relatively fast training
* also works on small GPU (eg. 8 GB)
* but may take a lot of disk space
* should already yield decent results
* fine-tuning the model end-to-end
* much slower
* needs a bigger GPU
There is also one trick, which seemed to improve performance significantly: adding position embeddings to the
encoder outputs and initializing them with the pre-trained position embeddings of the GPT2 model (See `eval.py`).
The training notebooks are still early drafts. Also results can probably improved a lot by using for example a learning
rate schedule.
|
ubamba98/wav2vec2-xls-r-1b-ro
|
ubamba98
| 2022-03-23T18:29:42Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"ro",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-xls-r-1b-ro
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: ro
metrics:
- name: Test WER
type: wer
value: 99.99
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ro
metrics:
- name: Test WER
type: wer
value: 99.98
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ro
metrics:
- name: Test WER
type: wer
value: 99.99
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-1b-ro
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1113
- Wer: 0.4770
- Cer: 0.0306
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.7844 | 1.67 | 1500 | 0.3412 | 0.8600 | 0.0940 |
| 0.7272 | 3.34 | 3000 | 0.1926 | 0.6409 | 0.0527 |
| 0.6924 | 5.02 | 4500 | 0.1413 | 0.5722 | 0.0401 |
| 0.6327 | 6.69 | 6000 | 0.1252 | 0.5366 | 0.0371 |
| 0.6363 | 8.36 | 7500 | 0.1235 | 0.5741 | 0.0389 |
| 0.6238 | 10.03 | 9000 | 0.1180 | 0.5542 | 0.0362 |
| 0.6018 | 11.71 | 10500 | 0.1192 | 0.5694 | 0.0369 |
| 0.583 | 13.38 | 12000 | 0.1216 | 0.5772 | 0.0385 |
| 0.5643 | 15.05 | 13500 | 0.1195 | 0.5419 | 0.0371 |
| 0.5399 | 16.72 | 15000 | 0.1240 | 0.5224 | 0.0370 |
| 0.5529 | 18.39 | 16500 | 0.1174 | 0.5555 | 0.0367 |
| 0.5246 | 20.07 | 18000 | 0.1097 | 0.5047 | 0.0339 |
| 0.4936 | 21.74 | 19500 | 0.1225 | 0.5189 | 0.0382 |
| 0.4629 | 23.41 | 21000 | 0.1142 | 0.5047 | 0.0344 |
| 0.4463 | 25.08 | 22500 | 0.1168 | 0.4887 | 0.0339 |
| 0.4671 | 26.76 | 24000 | 0.1119 | 0.5073 | 0.0338 |
| 0.4359 | 28.43 | 25500 | 0.1206 | 0.5479 | 0.0363 |
| 0.4225 | 30.1 | 27000 | 0.1122 | 0.5170 | 0.0345 |
| 0.4038 | 31.77 | 28500 | 0.1159 | 0.5032 | 0.0343 |
| 0.4271 | 33.44 | 30000 | 0.1116 | 0.5126 | 0.0339 |
| 0.3867 | 35.12 | 31500 | 0.1101 | 0.4937 | 0.0327 |
| 0.3674 | 36.79 | 33000 | 0.1142 | 0.4940 | 0.0330 |
| 0.3607 | 38.46 | 34500 | 0.1106 | 0.5145 | 0.0327 |
| 0.3651 | 40.13 | 36000 | 0.1172 | 0.4921 | 0.0317 |
| 0.3268 | 41.81 | 37500 | 0.1093 | 0.4830 | 0.0310 |
| 0.3345 | 43.48 | 39000 | 0.1131 | 0.4760 | 0.0314 |
| 0.3236 | 45.15 | 40500 | 0.1132 | 0.4864 | 0.0317 |
| 0.312 | 46.82 | 42000 | 0.1124 | 0.4861 | 0.0315 |
| 0.3106 | 48.49 | 43500 | 0.1116 | 0.4745 | 0.0306 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
shivam/xls-r-300m-marathi
|
shivam
| 2022-03-23T18:29:32Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"mr",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mr
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- mr
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: ''
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice Corpus 8.0
type: mozilla-foundation/common_voice_8_0
args: mr
metrics:
- name: Test WER
type: wer
value: 38.27
- name: Test CER
type: cer
value: 8.91
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR dataset.
It achieves the following results on the mozilla-foundation/common_voice_8_0 mr test set:
- Without LM
+ WER: 48.53
+ CER: 10.63
- With LM
+ WER: 38.27
+ CER: 8.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 400.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 4.2706 | 22.73 | 500 | 4.0174 | 1.0 |
| 3.2492 | 45.45 | 1000 | 3.2309 | 0.9908 |
| 1.9709 | 68.18 | 1500 | 1.0651 | 0.8440 |
| 1.4088 | 90.91 | 2000 | 0.5765 | 0.6550 |
| 1.1326 | 113.64 | 2500 | 0.4842 | 0.5760 |
| 0.9709 | 136.36 | 3000 | 0.4785 | 0.6013 |
| 0.8433 | 159.09 | 3500 | 0.5048 | 0.5419 |
| 0.7404 | 181.82 | 4000 | 0.5052 | 0.5339 |
| 0.6589 | 204.55 | 4500 | 0.5237 | 0.5897 |
| 0.5831 | 227.27 | 5000 | 0.5166 | 0.5447 |
| 0.5375 | 250.0 | 5500 | 0.5292 | 0.5487 |
| 0.4784 | 272.73 | 6000 | 0.5480 | 0.5596 |
| 0.4421 | 295.45 | 6500 | 0.5682 | 0.5467 |
| 0.4047 | 318.18 | 7000 | 0.5681 | 0.5447 |
| 0.3779 | 340.91 | 7500 | 0.5783 | 0.5347 |
| 0.3525 | 363.64 | 8000 | 0.5856 | 0.5367 |
| 0.3393 | 386.36 | 8500 | 0.5960 | 0.5359 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
|
anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm
|
anuragshas
| 2022-03-23T18:29:27Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- sl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Slovenian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sl
metrics:
- name: Test WER
type: wer
value: 12.736
- name: Test CER
type: cer
value: 3.605
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sl
metrics:
- name: Test WER
type: wer
value: 45.587
- name: Test CER
type: cer
value: 20.886
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sl
metrics:
- name: Test WER
type: wer
value: 45.42
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLS-R-300M - Slovenian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2578
- Wer: 0.2273
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1829 | 4.88 | 400 | 3.1228 | 1.0 |
| 2.8675 | 9.76 | 800 | 2.8616 | 0.9993 |
| 1.583 | 14.63 | 1200 | 0.6392 | 0.6239 |
| 1.1959 | 19.51 | 1600 | 0.3602 | 0.3651 |
| 1.0276 | 24.39 | 2000 | 0.3021 | 0.2981 |
| 0.9671 | 29.27 | 2400 | 0.2872 | 0.2739 |
| 0.873 | 34.15 | 2800 | 0.2593 | 0.2459 |
| 0.8513 | 39.02 | 3200 | 0.2617 | 0.2473 |
| 0.8132 | 43.9 | 3600 | 0.2548 | 0.2426 |
| 0.7935 | 48.78 | 4000 | 0.2637 | 0.2353 |
| 0.7565 | 53.66 | 4400 | 0.2629 | 0.2322 |
| 0.7359 | 58.54 | 4800 | 0.2579 | 0.2253 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config sl --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sl", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "zmago je divje od letel s helikopterjem visoko vzrak"
```
### Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 19.938 | 12.736 |
|
anantoj/wav2vec2-xls-r-1b-korean
|
anantoj
| 2022-03-23T18:29:13Z | 37 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ko",
"dataset:kresnik/zeroth_korean",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: ko
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- kresnik/zeroth_korean
model-index:
- name: Wav2Vec2 XLS-R 1B Korean
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ko
metrics:
- name: Test WER
type: wer
value: 82.07
- name: Test CER
type: cer
value: 42.12
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ko
metrics:
- name: Test WER
type: wer
value: 82.09
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0639
- Wer: 0.0449
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.603 | 0.72 | 500 | 4.6572 | 0.9985 |
| 2.6314 | 1.44 | 1000 | 2.0424 | 0.9256 |
| 2.2708 | 2.16 | 1500 | 0.9889 | 0.6989 |
| 2.1769 | 2.88 | 2000 | 0.8366 | 0.6312 |
| 2.1142 | 3.6 | 2500 | 0.7555 | 0.5998 |
| 2.0084 | 4.32 | 3000 | 0.7144 | 0.6003 |
| 1.9272 | 5.04 | 3500 | 0.6311 | 0.5461 |
| 1.8687 | 5.75 | 4000 | 0.6252 | 0.5430 |
| 1.8186 | 6.47 | 4500 | 0.5491 | 0.4988 |
| 1.7364 | 7.19 | 5000 | 0.5463 | 0.4959 |
| 1.6809 | 7.91 | 5500 | 0.4724 | 0.4484 |
| 1.641 | 8.63 | 6000 | 0.4679 | 0.4461 |
| 1.572 | 9.35 | 6500 | 0.4387 | 0.4236 |
| 1.5256 | 10.07 | 7000 | 0.3970 | 0.4003 |
| 1.5044 | 10.79 | 7500 | 0.3690 | 0.3893 |
| 1.4563 | 11.51 | 8000 | 0.3752 | 0.3875 |
| 1.394 | 12.23 | 8500 | 0.3386 | 0.3567 |
| 1.3641 | 12.95 | 9000 | 0.3290 | 0.3467 |
| 1.2878 | 13.67 | 9500 | 0.2893 | 0.3135 |
| 1.2602 | 14.39 | 10000 | 0.2723 | 0.3029 |
| 1.2302 | 15.11 | 10500 | 0.2603 | 0.2989 |
| 1.1865 | 15.83 | 11000 | 0.2440 | 0.2794 |
| 1.1491 | 16.55 | 11500 | 0.2500 | 0.2788 |
| 1.093 | 17.27 | 12000 | 0.2279 | 0.2629 |
| 1.0367 | 17.98 | 12500 | 0.2076 | 0.2443 |
| 0.9954 | 18.7 | 13000 | 0.1844 | 0.2259 |
| 0.99 | 19.42 | 13500 | 0.1794 | 0.2179 |
| 0.9385 | 20.14 | 14000 | 0.1765 | 0.2122 |
| 0.8952 | 20.86 | 14500 | 0.1706 | 0.1974 |
| 0.8841 | 21.58 | 15000 | 0.1791 | 0.1969 |
| 0.847 | 22.3 | 15500 | 0.1780 | 0.2060 |
| 0.8669 | 23.02 | 16000 | 0.1608 | 0.1862 |
| 0.8066 | 23.74 | 16500 | 0.1447 | 0.1626 |
| 0.7908 | 24.46 | 17000 | 0.1457 | 0.1655 |
| 0.7459 | 25.18 | 17500 | 0.1350 | 0.1445 |
| 0.7218 | 25.9 | 18000 | 0.1276 | 0.1421 |
| 0.703 | 26.62 | 18500 | 0.1177 | 0.1302 |
| 0.685 | 27.34 | 19000 | 0.1147 | 0.1305 |
| 0.6811 | 28.06 | 19500 | 0.1128 | 0.1244 |
| 0.6444 | 28.78 | 20000 | 0.1120 | 0.1213 |
| 0.6323 | 29.5 | 20500 | 0.1137 | 0.1166 |
| 0.5998 | 30.22 | 21000 | 0.1051 | 0.1107 |
| 0.5706 | 30.93 | 21500 | 0.1035 | 0.1037 |
| 0.5555 | 31.65 | 22000 | 0.1031 | 0.0927 |
| 0.5389 | 32.37 | 22500 | 0.0997 | 0.0900 |
| 0.5201 | 33.09 | 23000 | 0.0920 | 0.0912 |
| 0.5146 | 33.81 | 23500 | 0.0929 | 0.0947 |
| 0.515 | 34.53 | 24000 | 0.1000 | 0.0953 |
| 0.4743 | 35.25 | 24500 | 0.0922 | 0.0892 |
| 0.4707 | 35.97 | 25000 | 0.0852 | 0.0808 |
| 0.4456 | 36.69 | 25500 | 0.0855 | 0.0779 |
| 0.443 | 37.41 | 26000 | 0.0843 | 0.0738 |
| 0.4388 | 38.13 | 26500 | 0.0816 | 0.0699 |
| 0.4162 | 38.85 | 27000 | 0.0752 | 0.0645 |
| 0.3979 | 39.57 | 27500 | 0.0761 | 0.0621 |
| 0.3889 | 40.29 | 28000 | 0.0771 | 0.0625 |
| 0.3923 | 41.01 | 28500 | 0.0755 | 0.0598 |
| 0.3693 | 41.73 | 29000 | 0.0730 | 0.0578 |
| 0.3642 | 42.45 | 29500 | 0.0739 | 0.0598 |
| 0.3532 | 43.17 | 30000 | 0.0712 | 0.0553 |
| 0.3513 | 43.88 | 30500 | 0.0762 | 0.0516 |
| 0.3349 | 44.6 | 31000 | 0.0731 | 0.0504 |
| 0.3305 | 45.32 | 31500 | 0.0725 | 0.0507 |
| 0.3285 | 46.04 | 32000 | 0.0709 | 0.0489 |
| 0.3179 | 46.76 | 32500 | 0.0667 | 0.0467 |
| 0.3158 | 47.48 | 33000 | 0.0653 | 0.0494 |
| 0.3033 | 48.2 | 33500 | 0.0638 | 0.0456 |
| 0.3023 | 48.92 | 34000 | 0.0644 | 0.0464 |
| 0.2975 | 49.64 | 34500 | 0.0643 | 0.0455 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-abkhaz
|
infinitejoy
| 2022-03-23T18:28:58Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ab",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ab
license: apache-2.0
tags:
- ab
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Abkhaz
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ab
metrics:
- name: Test WER
type: wer
value: 60.07
- name: Test CER
type: cer
value: 12.5
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-abkhaz
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5359
- Wer: 0.6192
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8617 | 22.73 | 500 | 2.6264 | 1.0013 |
| 1.2716 | 45.45 | 1000 | 0.6218 | 0.6942 |
| 1.049 | 68.18 | 1500 | 0.5442 | 0.6368 |
| 0.9632 | 90.91 | 2000 | 0.5364 | 0.6242 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
Harveenchadha/hindi_large_wav2vec2
|
Harveenchadha
| 2022-03-23T18:28:53Z | 44 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"hi",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:Harveenchadha/indic-voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
language:
- hi
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- hi
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- Harveenchadha/indic-voice
model-index:
- name: Hindi Large
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
type: common_voice
args: hi
metrics:
- name: Test WER
type: wer
value: 23.08
- name: Test CER
type: cer
value: 8.11
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice-7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 23.36
- name: Test CER
type: cer
value: 8.94
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice-8.0
type: mozilla-foundation/common_voice_8_0
args: hi
metrics:
- name: Test WER
type: wer
value: 24.85
- name: Test CER
type: cer
value: 9.99
---
|
vutankiet2901/wav2vec2-large-xlsr-53-ja
|
vutankiet2901
| 2022-03-23T18:28:48Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common-voice",
"hf-asr-leaderboard",
"ja",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- ja
tags:
- automatic-speech-recognition
- common-voice
- hf-asr-leaderboard
- ja
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xlsr-53-ja
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
- name: Test WER (with LM)
type: wer
value: 15.37
- name: Test CER (with LM)
type: cer
value: 6.91
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
- name: Test WER (with LM)
type: wer
value: 16.09
- name: Test CER (with LM)
type: cer
value: 7.15
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ja
metrics:
- name: Test WER (with LM)
type: wer
value: 37.96
- name: Test CER (with LM)
type: cer
value: 21.11
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ja
metrics:
- name: Test CER
type: cer
value: 26.02
---
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset.
### Benchmark WER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|
|without LM| 15.74 | 25.10 |
|with 4-grams LM| 15.37 | 16.09 |
### Benchmark CER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|
|without LM| 9.51 | 9.95 |
|with 4-grams LM| 6.91 | 7.15 |
## Evaluation
Please use the eval.py file to run the evaluation:
```python
python eval.py --model_id vutankiet2901/wav2vec2-large-xlsr-53-ja --dataset mozilla-foundation/common_voice_7_0 --config ja --split test --log_outputs
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 4.7776 | 4.73 | 1500 | 2.9540 | 0.9772 | 0.8489 |
| 1.9076 | 9.46 | 3000 | 0.7146 | 0.5371 | 0.2484 |
| 1.507 | 14.2 | 4500 | 0.5843 | 0.4689 | 0.2196 |
| 1.3742 | 18.93 | 6000 | 0.5286 | 0.4321 | 0.1988 |
| 1.2776 | 23.66 | 7500 | 0.5007 | 0.4056 | 0.1870 |
| 1.2003 | 28.39 | 9000 | 0.4676 | 0.3848 | 0.1802 |
| 1.1281 | 33.12 | 10500 | 0.4524 | 0.3694 | 0.1720 |
| 1.0657 | 37.85 | 12000 | 0.4449 | 0.3590 | 0.1681 |
| 1.0129 | 42.59 | 13500 | 0.4266 | 0.3423 | 0.1617 |
| 0.9691 | 47.32 | 15000 | 0.4214 | 0.3375 | 0.1587 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
manifoldix/xlsr-fa-lm
|
manifoldix
| 2022-03-23T18:28:30Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"fa",
"dataset:common_voice",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: fa
datasets:
- common_voice
tags:
- hf-asr-leaderboard
- robust-speech-event
widget:
- example_title: Common Voice sample 2978
src: https://huggingface.co/manifoldix/xlsr-fa-lm/resolve/main/sample2978.flac
- example_title: Common Voice sample 5168
src: https://huggingface.co/manifoldix/xlsr-fa-lm/resolve/main/sample5168.flac
model-index:
- name: XLS-R-300m Wav2Vec2 Persian
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice fa
type: common_voice
args: fa
metrics:
- name: Test WER without LM
type: wer
value: 26%
- name: Test WER with LM
type: wer
value: 23%
---
## XLSR-300m Persian
Fine-tuned on commom voice FA
|
emre/wav2vec2-xls-r-300m-Russian-small
|
emre
| 2022-03-23T18:28:22Z | 19 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ru",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- ru
tags:
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-Russian-small
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ru
type: common_voice
args: ru
metrics:
- name: Test WER
type: wer
value: 48.38
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ru
metrics:
- name: Test WER
type: wer
value: 58.25
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ru
metrics:
- name: Test WER
type: wer
value: 56.83
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-Russian-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3514
- Wer: 0.4838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.512 | 1.32 | 400 | 3.2207 | 1.0 |
| 3.1562 | 2.65 | 800 | 3.0166 | 1.0 |
| 1.5211 | 3.97 | 1200 | 0.7134 | 0.8275 |
| 0.6724 | 5.3 | 1600 | 0.4713 | 0.6402 |
| 0.4693 | 6.62 | 2000 | 0.3904 | 0.5668 |
| 0.3693 | 7.95 | 2400 | 0.3609 | 0.5121 |
| 0.3004 | 9.27 | 2800 | 0.3514 | 0.4838 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
edugp/wav2vec2-xls-r-300m-36-tokens-with-lm-es
|
edugp
| 2022-03-23T18:28:19Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- es
tags:
- es
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-36-tokens-with-lm-es
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice es
type: common_voice
args: es
metrics:
- name: Test WER
type: wer
value: 0.08677014042867702
- name: Test CER
type: cer
value: 0.02810974186831335
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: es
metrics:
- name: Test WER
type: wer
value: 31.68
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: es
metrics:
- name: Test WER
type: wer
value: 34.45
---
# Wav2Vec2-xls-r-300m-36-tokens-with-lm-es
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Wer: 0.0868
- Cer: 0.0281
This model consists of a Wav2Vec2 model with an additional KenLM 5-gram language model for CTC decoding.
The model is trained removing all characters that are not lower-case unaccented letters between `a-z` or the Spanish accented vowels `á`, `é`, `í`, `ó`, `ú` or the dieresis on the vowel `u` - `ü`.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 3.6512 | 0.07 | 400 | 0.5734 | 0.4325 |
| 0.4404 | 0.14 | 800 | 0.3329 | 0.3021 |
| 0.3465 | 0.22 | 1200 | 0.3067 | 0.2871 |
| 0.3214 | 0.29 | 1600 | 0.2808 | 0.2694 |
| 0.319 | 0.36 | 2000 | 0.2755 | 0.2677 |
| 0.3015 | 0.43 | 2400 | 0.2667 | 0.2437 |
| 0.3102 | 0.51 | 2800 | 0.2679 | 0.2475 |
| 0.2955 | 0.58 | 3200 | 0.2591 | 0.2421 |
| 0.292 | 0.65 | 3600 | 0.2547 | 0.2404 |
| 0.2961 | 0.72 | 4000 | 0.2824 | 0.2716 |
| 0.2906 | 0.8 | 4400 | 0.2531 | 0.2321 |
| 0.2886 | 0.87 | 4800 | 0.2668 | 0.2573 |
| 0.2934 | 0.94 | 5200 | 0.2608 | 0.2454 |
| 0.2844 | 1.01 | 5600 | 0.2414 | 0.2233 |
| 0.2649 | 1.09 | 6000 | 0.2412 | 0.2198 |
| 0.2587 | 1.16 | 6400 | 0.2432 | 0.2211 |
| 0.2631 | 1.23 | 6800 | 0.2414 | 0.2225 |
| 0.2584 | 1.3 | 7200 | 0.2489 | 0.2290 |
| 0.2588 | 1.37 | 7600 | 0.2341 | 0.2156 |
| 0.2581 | 1.45 | 8000 | 0.2323 | 0.2155 |
| 0.2603 | 1.52 | 8400 | 0.2423 | 0.2231 |
| 0.2527 | 1.59 | 8800 | 0.2381 | 0.2192 |
| 0.2588 | 1.66 | 9200 | 0.2323 | 0.2176 |
| 0.2543 | 1.74 | 9600 | 0.2391 | 0.2151 |
| 0.2528 | 1.81 | 10000 | 0.2295 | 0.2091 |
| 0.2535 | 1.88 | 10400 | 0.2317 | 0.2099 |
| 0.2501 | 1.95 | 10800 | 0.2225 | 0.2105 |
| 0.2441 | 2.03 | 11200 | 0.2356 | 0.2180 |
| 0.2275 | 2.1 | 11600 | 0.2341 | 0.2115 |
| 0.2281 | 2.17 | 12000 | 0.2269 | 0.2117 |
| 0.227 | 2.24 | 12400 | 0.2367 | 0.2125 |
| 0.2471 | 2.32 | 12800 | 0.2307 | 0.2090 |
| 0.229 | 2.39 | 13200 | 0.2231 | 0.2005 |
| 0.2325 | 2.46 | 13600 | 0.2243 | 0.2100 |
| 0.2314 | 2.53 | 14000 | 0.2252 | 0.2098 |
| 0.2309 | 2.6 | 14400 | 0.2269 | 0.2089 |
| 0.2267 | 2.68 | 14800 | 0.2155 | 0.1976 |
| 0.225 | 2.75 | 15200 | 0.2263 | 0.2067 |
| 0.2309 | 2.82 | 15600 | 0.2196 | 0.2041 |
| 0.225 | 2.89 | 16000 | 0.2212 | 0.2052 |
| 0.228 | 2.97 | 16400 | 0.2192 | 0.2028 |
| 0.2136 | 3.04 | 16800 | 0.2169 | 0.2042 |
| 0.2038 | 3.11 | 17200 | 0.2173 | 0.1998 |
| 0.2035 | 3.18 | 17600 | 0.2185 | 0.2002 |
| 0.207 | 3.26 | 18000 | 0.2358 | 0.2120 |
| 0.2102 | 3.33 | 18400 | 0.2213 | 0.2019 |
| 0.211 | 3.4 | 18800 | 0.2176 | 0.1980 |
| 0.2099 | 3.47 | 19200 | 0.2186 | 0.1960 |
| 0.2093 | 3.55 | 19600 | 0.2208 | 0.2016 |
| 0.2046 | 3.62 | 20000 | 0.2138 | 0.1960 |
| 0.2095 | 3.69 | 20400 | 0.2222 | 0.2023 |
| 0.2106 | 3.76 | 20800 | 0.2159 | 0.1964 |
| 0.2066 | 3.83 | 21200 | 0.2083 | 0.1931 |
| 0.2119 | 3.91 | 21600 | 0.2130 | 0.1957 |
| 0.2167 | 3.98 | 22000 | 0.2210 | 0.1987 |
| 0.1973 | 4.05 | 22400 | 0.2112 | 0.1930 |
| 0.1917 | 4.12 | 22800 | 0.2107 | 0.1891 |
| 0.1903 | 4.2 | 23200 | 0.2132 | 0.1911 |
| 0.1903 | 4.27 | 23600 | 0.2077 | 0.1883 |
| 0.1914 | 4.34 | 24000 | 0.2054 | 0.1901 |
| 0.1943 | 4.41 | 24400 | 0.2059 | 0.1885 |
| 0.1943 | 4.49 | 24800 | 0.2095 | 0.1899 |
| 0.1936 | 4.56 | 25200 | 0.2078 | 0.1879 |
| 0.1963 | 4.63 | 25600 | 0.2018 | 0.1884 |
| 0.1934 | 4.7 | 26000 | 0.2034 | 0.1872 |
| 0.2011 | 4.78 | 26400 | 0.2051 | 0.1896 |
| 0.1901 | 4.85 | 26800 | 0.2059 | 0.1858 |
| 0.1934 | 4.92 | 27200 | 0.2028 | 0.1832 |
| 0.191 | 4.99 | 27600 | 0.2046 | 0.1870 |
| 0.1775 | 5.07 | 28000 | 0.2081 | 0.1891 |
| 0.175 | 5.14 | 28400 | 0.2084 | 0.1904 |
| 0.19 | 5.21 | 28800 | 0.2086 | 0.1920 |
| 0.1798 | 5.28 | 29200 | 0.2079 | 0.1935 |
| 0.1765 | 5.35 | 29600 | 0.2145 | 0.1930 |
| 0.181 | 5.43 | 30000 | 0.2062 | 0.1918 |
| 0.1808 | 5.5 | 30400 | 0.2083 | 0.1875 |
| 0.1769 | 5.57 | 30800 | 0.2117 | 0.1895 |
| 0.1788 | 5.64 | 31200 | 0.2055 | 0.1857 |
| 0.181 | 5.72 | 31600 | 0.2057 | 0.1870 |
| 0.1781 | 5.79 | 32000 | 0.2053 | 0.1872 |
| 0.1852 | 5.86 | 32400 | 0.2077 | 0.1904 |
| 0.1832 | 5.93 | 32800 | 0.1979 | 0.1821 |
| 0.1758 | 6.01 | 33200 | 0.1957 | 0.1754 |
| 0.1611 | 6.08 | 33600 | 0.2028 | 0.1773 |
| 0.1606 | 6.15 | 34000 | 0.2018 | 0.1780 |
| 0.1702 | 6.22 | 34400 | 0.1977 | 0.1759 |
| 0.1649 | 6.3 | 34800 | 0.2073 | 0.1845 |
| 0.1641 | 6.37 | 35200 | 0.1947 | 0.1774 |
| 0.1703 | 6.44 | 35600 | 0.2009 | 0.1811 |
| 0.1716 | 6.51 | 36000 | 0.2091 | 0.1817 |
| 0.1732 | 6.58 | 36400 | 0.1942 | 0.1743 |
| 0.1642 | 6.66 | 36800 | 0.1930 | 0.1749 |
| 0.1685 | 6.73 | 37200 | 0.1962 | 0.1716 |
| 0.1647 | 6.8 | 37600 | 0.1977 | 0.1822 |
| 0.1647 | 6.87 | 38000 | 0.1917 | 0.1748 |
| 0.1667 | 6.95 | 38400 | 0.1948 | 0.1774 |
| 0.1647 | 7.02 | 38800 | 0.2018 | 0.1783 |
| 0.15 | 7.09 | 39200 | 0.2010 | 0.1796 |
| 0.1663 | 7.16 | 39600 | 0.1969 | 0.1731 |
| 0.1536 | 7.24 | 40000 | 0.1935 | 0.1726 |
| 0.1544 | 7.31 | 40400 | 0.2030 | 0.1799 |
| 0.1536 | 7.38 | 40800 | 0.1973 | 0.1772 |
| 0.1559 | 7.45 | 41200 | 0.1973 | 0.1763 |
| 0.1547 | 7.53 | 41600 | 0.2052 | 0.1782 |
| 0.1584 | 7.6 | 42000 | 0.1965 | 0.1737 |
| 0.1542 | 7.67 | 42400 | 0.1878 | 0.1725 |
| 0.1525 | 7.74 | 42800 | 0.1946 | 0.1750 |
| 0.1547 | 7.81 | 43200 | 0.1934 | 0.1691 |
| 0.1534 | 7.89 | 43600 | 0.1919 | 0.1711 |
| 0.1574 | 7.96 | 44000 | 0.1935 | 0.1745 |
| 0.1471 | 8.03 | 44400 | 0.1915 | 0.1689 |
| 0.1433 | 8.1 | 44800 | 0.1956 | 0.1719 |
| 0.1433 | 8.18 | 45200 | 0.1980 | 0.1720 |
| 0.1424 | 8.25 | 45600 | 0.1906 | 0.1681 |
| 0.1428 | 8.32 | 46000 | 0.1892 | 0.1649 |
| 0.1424 | 8.39 | 46400 | 0.1916 | 0.1698 |
| 0.1466 | 8.47 | 46800 | 0.1970 | 0.1739 |
| 0.1496 | 8.54 | 47200 | 0.1902 | 0.1662 |
| 0.1408 | 8.61 | 47600 | 0.1858 | 0.1649 |
| 0.1445 | 8.68 | 48000 | 0.1893 | 0.1648 |
| 0.1459 | 8.76 | 48400 | 0.1875 | 0.1686 |
| 0.1433 | 8.83 | 48800 | 0.1920 | 0.1673 |
| 0.1448 | 8.9 | 49200 | 0.1833 | 0.1631 |
| 0.1461 | 8.97 | 49600 | 0.1904 | 0.1693 |
| 0.1451 | 9.04 | 50000 | 0.1969 | 0.1661 |
| 0.1336 | 9.12 | 50400 | 0.1950 | 0.1674 |
| 0.1362 | 9.19 | 50800 | 0.1971 | 0.1685 |
| 0.1316 | 9.26 | 51200 | 0.1928 | 0.1648 |
| 0.132 | 9.33 | 51600 | 0.1908 | 0.1615 |
| 0.1301 | 9.41 | 52000 | 0.1842 | 0.1569 |
| 0.1322 | 9.48 | 52400 | 0.1892 | 0.1616 |
| 0.1391 | 9.55 | 52800 | 0.1956 | 0.1656 |
| 0.132 | 9.62 | 53200 | 0.1876 | 0.1598 |
| 0.1349 | 9.7 | 53600 | 0.1870 | 0.1624 |
| 0.1325 | 9.77 | 54000 | 0.1834 | 0.1586 |
| 0.1389 | 9.84 | 54400 | 0.1892 | 0.1647 |
| 0.1364 | 9.91 | 54800 | 0.1840 | 0.1597 |
| 0.1339 | 9.99 | 55200 | 0.1858 | 0.1626 |
| 0.1269 | 10.06 | 55600 | 0.1875 | 0.1619 |
| 0.1229 | 10.13 | 56000 | 0.1909 | 0.1619 |
| 0.1258 | 10.2 | 56400 | 0.1933 | 0.1631 |
| 0.1256 | 10.27 | 56800 | 0.1930 | 0.1640 |
| 0.1207 | 10.35 | 57200 | 0.1823 | 0.1585 |
| 0.1248 | 10.42 | 57600 | 0.1889 | 0.1596 |
| 0.1264 | 10.49 | 58000 | 0.1845 | 0.1584 |
| 0.1251 | 10.56 | 58400 | 0.1869 | 0.1588 |
| 0.1251 | 10.64 | 58800 | 0.1885 | 0.1613 |
| 0.1276 | 10.71 | 59200 | 0.1855 | 0.1575 |
| 0.1303 | 10.78 | 59600 | 0.1836 | 0.1597 |
| 0.1246 | 10.85 | 60000 | 0.1810 | 0.1573 |
| 0.1283 | 10.93 | 60400 | 0.1830 | 0.1581 |
| 0.1273 | 11.0 | 60800 | 0.1837 | 0.1619 |
| 0.1202 | 11.07 | 61200 | 0.1865 | 0.1588 |
| 0.119 | 11.14 | 61600 | 0.1889 | 0.1580 |
| 0.1179 | 11.22 | 62000 | 0.1884 | 0.1592 |
| 0.1187 | 11.29 | 62400 | 0.1824 | 0.1565 |
| 0.1198 | 11.36 | 62800 | 0.1848 | 0.1552 |
| 0.1154 | 11.43 | 63200 | 0.1866 | 0.1565 |
| 0.1211 | 11.51 | 63600 | 0.1862 | 0.1563 |
| 0.1177 | 11.58 | 64000 | 0.1816 | 0.1527 |
| 0.1156 | 11.65 | 64400 | 0.1834 | 0.1540 |
| 0.1144 | 11.72 | 64800 | 0.1837 | 0.1524 |
| 0.119 | 11.79 | 65200 | 0.1859 | 0.1538 |
| 0.1183 | 11.87 | 65600 | 0.1869 | 0.1558 |
| 0.122 | 11.94 | 66000 | 0.1853 | 0.1535 |
| 0.1197 | 12.01 | 66400 | 0.1871 | 0.1586 |
| 0.1096 | 12.08 | 66800 | 0.1838 | 0.1540 |
| 0.1074 | 12.16 | 67200 | 0.1915 | 0.1592 |
| 0.1084 | 12.23 | 67600 | 0.1845 | 0.1545 |
| 0.1097 | 12.3 | 68000 | 0.1904 | 0.1552 |
| 0.112 | 12.37 | 68400 | 0.1846 | 0.1578 |
| 0.1109 | 12.45 | 68800 | 0.1862 | 0.1549 |
| 0.1114 | 12.52 | 69200 | 0.1889 | 0.1552 |
| 0.1119 | 12.59 | 69600 | 0.1828 | 0.1530 |
| 0.1124 | 12.66 | 70000 | 0.1822 | 0.1540 |
| 0.1127 | 12.74 | 70400 | 0.1865 | 0.1589 |
| 0.1128 | 12.81 | 70800 | 0.1786 | 0.1498 |
| 0.1069 | 12.88 | 71200 | 0.1813 | 0.1522 |
| 0.1069 | 12.95 | 71600 | 0.1895 | 0.1558 |
| 0.1083 | 13.02 | 72000 | 0.1925 | 0.1557 |
| 0.1009 | 13.1 | 72400 | 0.1883 | 0.1522 |
| 0.1007 | 13.17 | 72800 | 0.1829 | 0.1480 |
| 0.1014 | 13.24 | 73200 | 0.1861 | 0.1510 |
| 0.0974 | 13.31 | 73600 | 0.1836 | 0.1486 |
| 0.1006 | 13.39 | 74000 | 0.1821 | 0.1462 |
| 0.0973 | 13.46 | 74400 | 0.1857 | 0.1484 |
| 0.1011 | 13.53 | 74800 | 0.1822 | 0.1471 |
| 0.1031 | 13.6 | 75200 | 0.1823 | 0.1489 |
| 0.1034 | 13.68 | 75600 | 0.1809 | 0.1452 |
| 0.0998 | 13.75 | 76000 | 0.1817 | 0.1490 |
| 0.1071 | 13.82 | 76400 | 0.1808 | 0.1501 |
| 0.1083 | 13.89 | 76800 | 0.1796 | 0.1475 |
| 0.1053 | 13.97 | 77200 | 0.1785 | 0.1470 |
| 0.0978 | 14.04 | 77600 | 0.1886 | 0.1495 |
| 0.094 | 14.11 | 78000 | 0.1854 | 0.1489 |
| 0.0915 | 14.18 | 78400 | 0.1854 | 0.1498 |
| 0.0947 | 14.25 | 78800 | 0.1888 | 0.1500 |
| 0.0939 | 14.33 | 79200 | 0.1885 | 0.1494 |
| 0.0973 | 14.4 | 79600 | 0.1877 | 0.1466 |
| 0.0946 | 14.47 | 80000 | 0.1904 | 0.1494 |
| 0.0931 | 14.54 | 80400 | 0.1815 | 0.1473 |
| 0.0958 | 14.62 | 80800 | 0.1905 | 0.1508 |
| 0.0982 | 14.69 | 81200 | 0.1881 | 0.1511 |
| 0.0963 | 14.76 | 81600 | 0.1823 | 0.1449 |
| 0.0943 | 14.83 | 82000 | 0.1782 | 0.1458 |
| 0.0981 | 14.91 | 82400 | 0.1795 | 0.1465 |
| 0.0995 | 14.98 | 82800 | 0.1811 | 0.1484 |
| 0.0909 | 15.05 | 83200 | 0.1822 | 0.1450 |
| 0.0872 | 15.12 | 83600 | 0.1890 | 0.1466 |
| 0.0878 | 15.2 | 84000 | 0.1859 | 0.1468 |
| 0.0884 | 15.27 | 84400 | 0.1825 | 0.1429 |
| 0.0871 | 15.34 | 84800 | 0.1816 | 0.1438 |
| 0.0883 | 15.41 | 85200 | 0.1817 | 0.1433 |
| 0.0844 | 15.48 | 85600 | 0.1821 | 0.1412 |
| 0.0843 | 15.56 | 86000 | 0.1863 | 0.1411 |
| 0.0805 | 15.63 | 86400 | 0.1863 | 0.1441 |
| 0.085 | 15.7 | 86800 | 0.1808 | 0.1440 |
| 0.0848 | 15.77 | 87200 | 0.1808 | 0.1421 |
| 0.0844 | 15.85 | 87600 | 0.1841 | 0.1406 |
| 0.082 | 15.92 | 88000 | 0.1850 | 0.1442 |
| 0.0854 | 15.99 | 88400 | 0.1773 | 0.1426 |
| 0.0835 | 16.06 | 88800 | 0.1888 | 0.1436 |
| 0.0789 | 16.14 | 89200 | 0.1922 | 0.1434 |
| 0.081 | 16.21 | 89600 | 0.1864 | 0.1448 |
| 0.0799 | 16.28 | 90000 | 0.1902 | 0.1428 |
| 0.0848 | 16.35 | 90400 | 0.1873 | 0.1422 |
| 0.084 | 16.43 | 90800 | 0.1835 | 0.1421 |
| 0.083 | 16.5 | 91200 | 0.1878 | 0.1390 |
| 0.0794 | 16.57 | 91600 | 0.1877 | 0.1398 |
| 0.0807 | 16.64 | 92000 | 0.1800 | 0.1385 |
| 0.0829 | 16.71 | 92400 | 0.1910 | 0.1434 |
| 0.0839 | 16.79 | 92800 | 0.1843 | 0.1381 |
| 0.0815 | 16.86 | 93200 | 0.1812 | 0.1365 |
| 0.0831 | 16.93 | 93600 | 0.1889 | 0.1383 |
| 0.0803 | 17.0 | 94000 | 0.1902 | 0.1403 |
| 0.0724 | 17.08 | 94400 | 0.1934 | 0.1380 |
| 0.0734 | 17.15 | 94800 | 0.1865 | 0.1394 |
| 0.0739 | 17.22 | 95200 | 0.1876 | 0.1395 |
| 0.0758 | 17.29 | 95600 | 0.1938 | 0.1411 |
| 0.0733 | 17.37 | 96000 | 0.1933 | 0.1410 |
| 0.077 | 17.44 | 96400 | 0.1848 | 0.1385 |
| 0.0754 | 17.51 | 96800 | 0.1876 | 0.1407 |
| 0.0746 | 17.58 | 97200 | 0.1863 | 0.1371 |
| 0.0732 | 17.66 | 97600 | 0.1927 | 0.1401 |
| 0.0746 | 17.73 | 98000 | 0.1874 | 0.1390 |
| 0.0755 | 17.8 | 98400 | 0.1853 | 0.1381 |
| 0.0724 | 17.87 | 98800 | 0.1849 | 0.1365 |
| 0.0716 | 17.94 | 99200 | 0.1848 | 0.1380 |
| 0.074 | 18.02 | 99600 | 0.1891 | 0.1362 |
| 0.0687 | 18.09 | 100000 | 0.1974 | 0.1357 |
| 0.0651 | 18.16 | 100400 | 0.1942 | 0.1353 |
| 0.0672 | 18.23 | 100800 | 0.1823 | 0.1363 |
| 0.0671 | 18.31 | 101200 | 0.1959 | 0.1357 |
| 0.0684 | 18.38 | 101600 | 0.1959 | 0.1374 |
| 0.0688 | 18.45 | 102000 | 0.1904 | 0.1353 |
| 0.0696 | 18.52 | 102400 | 0.1926 | 0.1364 |
| 0.0661 | 18.6 | 102800 | 0.1905 | 0.1351 |
| 0.0684 | 18.67 | 103200 | 0.1955 | 0.1343 |
| 0.0712 | 18.74 | 103600 | 0.1873 | 0.1353 |
| 0.0701 | 18.81 | 104000 | 0.1822 | 0.1354 |
| 0.0688 | 18.89 | 104400 | 0.1905 | 0.1373 |
| 0.0695 | 18.96 | 104800 | 0.1879 | 0.1335 |
| 0.0661 | 19.03 | 105200 | 0.2005 | 0.1351 |
| 0.0644 | 19.1 | 105600 | 0.1972 | 0.1351 |
| 0.0627 | 19.18 | 106000 | 0.1956 | 0.1340 |
| 0.0633 | 19.25 | 106400 | 0.1962 | 0.1340 |
| 0.0629 | 19.32 | 106800 | 0.1937 | 0.1342 |
| 0.0636 | 19.39 | 107200 | 0.1905 | 0.1355 |
| 0.0631 | 19.46 | 107600 | 0.1917 | 0.1326 |
| 0.0624 | 19.54 | 108000 | 0.1977 | 0.1355 |
| 0.0621 | 19.61 | 108400 | 0.1941 | 0.1345 |
| 0.0635 | 19.68 | 108800 | 0.1949 | 0.1336 |
| 0.063 | 19.75 | 109200 | 0.1919 | 0.1317 |
| 0.0636 | 19.83 | 109600 | 0.1928 | 0.1317 |
| 0.0612 | 19.9 | 110000 | 0.1923 | 0.1314 |
| 0.0636 | 19.97 | 110400 | 0.1923 | 0.1343 |
| 0.0581 | 20.04 | 110800 | 0.2036 | 0.1332 |
| 0.0573 | 20.12 | 111200 | 0.2007 | 0.1315 |
| 0.0566 | 20.19 | 111600 | 0.1974 | 0.1319 |
| 0.0589 | 20.26 | 112000 | 0.1958 | 0.1322 |
| 0.0577 | 20.33 | 112400 | 0.1946 | 0.1307 |
| 0.0587 | 20.41 | 112800 | 0.1957 | 0.1295 |
| 0.0588 | 20.48 | 113200 | 0.2013 | 0.1306 |
| 0.0594 | 20.55 | 113600 | 0.2010 | 0.1312 |
| 0.0602 | 20.62 | 114000 | 0.1993 | 0.1314 |
| 0.0583 | 20.69 | 114400 | 0.1931 | 0.1297 |
| 0.059 | 20.77 | 114800 | 0.1974 | 0.1305 |
| 0.0566 | 20.84 | 115200 | 0.1979 | 0.1294 |
| 0.0588 | 20.91 | 115600 | 0.1944 | 0.1292 |
| 0.0569 | 20.98 | 116000 | 0.1974 | 0.1309 |
| 0.0554 | 21.06 | 116400 | 0.2080 | 0.1307 |
| 0.0542 | 21.13 | 116800 | 0.2056 | 0.1301 |
| 0.0532 | 21.2 | 117200 | 0.2027 | 0.1309 |
| 0.0535 | 21.27 | 117600 | 0.1970 | 0.1287 |
| 0.0533 | 21.35 | 118000 | 0.2124 | 0.1310 |
| 0.0546 | 21.42 | 118400 | 0.2043 | 0.1300 |
| 0.0544 | 21.49 | 118800 | 0.2056 | 0.1281 |
| 0.0562 | 21.56 | 119200 | 0.1986 | 0.1273 |
| 0.0549 | 21.64 | 119600 | 0.2075 | 0.1283 |
| 0.0522 | 21.71 | 120000 | 0.2058 | 0.1278 |
| 0.052 | 21.78 | 120400 | 0.2057 | 0.1280 |
| 0.0563 | 21.85 | 120800 | 0.1966 | 0.1295 |
| 0.0546 | 21.92 | 121200 | 0.2002 | 0.1285 |
| 0.0539 | 22.0 | 121600 | 0.1996 | 0.1279 |
| 0.0504 | 22.07 | 122000 | 0.2077 | 0.1273 |
| 0.0602 | 22.14 | 122400 | 0.2055 | 0.1278 |
| 0.0503 | 22.21 | 122800 | 0.2037 | 0.1283 |
| 0.0496 | 22.29 | 123200 | 0.2109 | 0.1279 |
| 0.0523 | 22.36 | 123600 | 0.2068 | 0.1276 |
| 0.0508 | 22.43 | 124000 | 0.2051 | 0.1257 |
| 0.0505 | 22.5 | 124400 | 0.2056 | 0.1269 |
| 0.05 | 22.58 | 124800 | 0.1995 | 0.1268 |
| 0.0496 | 22.65 | 125200 | 0.2022 | 0.1290 |
| 0.0484 | 22.72 | 125600 | 0.2095 | 0.1291 |
| 0.0518 | 22.79 | 126000 | 0.2132 | 0.1271 |
| 0.0499 | 22.87 | 126400 | 0.2124 | 0.1263 |
| 0.0485 | 22.94 | 126800 | 0.2092 | 0.1252 |
| 0.0476 | 23.01 | 127200 | 0.2138 | 0.1256 |
| 0.0467 | 23.08 | 127600 | 0.2119 | 0.1256 |
| 0.048 | 23.15 | 128000 | 0.2138 | 0.1269 |
| 0.0461 | 23.23 | 128400 | 0.2036 | 0.1244 |
| 0.0467 | 23.3 | 128800 | 0.2163 | 0.1255 |
| 0.0475 | 23.37 | 129200 | 0.2180 | 0.1258 |
| 0.0468 | 23.44 | 129600 | 0.2129 | 0.1245 |
| 0.0456 | 23.52 | 130000 | 0.2122 | 0.1250 |
| 0.0458 | 23.59 | 130400 | 0.2157 | 0.1257 |
| 0.0453 | 23.66 | 130800 | 0.2088 | 0.1242 |
| 0.045 | 23.73 | 131200 | 0.2144 | 0.1247 |
| 0.0469 | 23.81 | 131600 | 0.2113 | 0.1246 |
| 0.0453 | 23.88 | 132000 | 0.2151 | 0.1234 |
| 0.0471 | 23.95 | 132400 | 0.2130 | 0.1229 |
| 0.0443 | 24.02 | 132800 | 0.2150 | 0.1225 |
| 0.0446 | 24.1 | 133200 | 0.2166 | 0.1235 |
| 0.0435 | 24.17 | 133600 | 0.2143 | 0.1222 |
| 0.0407 | 24.24 | 134000 | 0.2175 | 0.1218 |
| 0.0421 | 24.31 | 134400 | 0.2147 | 0.1227 |
| 0.0435 | 24.38 | 134800 | 0.2193 | 0.1233 |
| 0.0414 | 24.46 | 135200 | 0.2172 | 0.1225 |
| 0.0419 | 24.53 | 135600 | 0.2156 | 0.1225 |
| 0.0419 | 24.6 | 136000 | 0.2143 | 0.1235 |
| 0.0423 | 24.67 | 136400 | 0.2179 | 0.1226 |
| 0.0423 | 24.75 | 136800 | 0.2144 | 0.1221 |
| 0.0424 | 24.82 | 137200 | 0.2135 | 0.1210 |
| 0.0419 | 24.89 | 137600 | 0.2166 | 0.1218 |
| 0.0408 | 24.96 | 138000 | 0.2151 | 0.1211 |
| 0.0433 | 25.04 | 138400 | 0.2174 | 0.1214 |
| 0.0395 | 25.11 | 138800 | 0.2242 | 0.1210 |
| 0.0403 | 25.18 | 139200 | 0.2219 | 0.1215 |
| 0.0413 | 25.25 | 139600 | 0.2225 | 0.1207 |
| 0.0389 | 25.33 | 140000 | 0.2187 | 0.1202 |
| 0.0395 | 25.4 | 140400 | 0.2244 | 0.1204 |
| 0.0398 | 25.47 | 140800 | 0.2263 | 0.1199 |
| 0.0386 | 25.54 | 141200 | 0.2165 | 0.1187 |
| 0.0396 | 25.61 | 141600 | 0.2171 | 0.1187 |
| 0.0406 | 25.69 | 142000 | 0.2199 | 0.1190 |
| 0.0404 | 25.76 | 142400 | 0.2224 | 0.1190 |
| 0.0391 | 25.83 | 142800 | 0.2230 | 0.1185 |
| 0.04 | 25.9 | 143200 | 0.2208 | 0.1200 |
| 0.0396 | 25.98 | 143600 | 0.2179 | 0.1191 |
| 0.0353 | 26.05 | 144000 | 0.2285 | 0.1178 |
| 0.0368 | 26.12 | 144400 | 0.2273 | 0.1186 |
| 0.0393 | 26.19 | 144800 | 0.2247 | 0.1196 |
| 0.0368 | 26.27 | 145200 | 0.2314 | 0.1181 |
| 0.0373 | 26.34 | 145600 | 0.2215 | 0.1188 |
| 0.038 | 26.41 | 146000 | 0.2262 | 0.1180 |
| 0.0363 | 26.48 | 146400 | 0.2250 | 0.1172 |
| 0.0365 | 26.56 | 146800 | 0.2299 | 0.1174 |
| 0.0382 | 26.63 | 147200 | 0.2292 | 0.1165 |
| 0.0365 | 26.7 | 147600 | 0.2282 | 0.1165 |
| 0.0371 | 26.77 | 148000 | 0.2276 | 0.1172 |
| 0.0365 | 26.85 | 148400 | 0.2280 | 0.1173 |
| 0.0376 | 26.92 | 148800 | 0.2248 | 0.1164 |
| 0.0365 | 26.99 | 149200 | 0.2230 | 0.1158 |
| 0.0343 | 27.06 | 149600 | 0.2300 | 0.1157 |
| 0.0354 | 27.13 | 150000 | 0.2298 | 0.1166 |
| 0.0333 | 27.21 | 150400 | 0.2307 | 0.1158 |
| 0.0353 | 27.28 | 150800 | 0.2300 | 0.1157 |
| 0.036 | 27.35 | 151200 | 0.2335 | 0.1160 |
| 0.0343 | 27.42 | 151600 | 0.2324 | 0.1155 |
| 0.0361 | 27.5 | 152000 | 0.2300 | 0.1150 |
| 0.0352 | 27.57 | 152400 | 0.2279 | 0.1146 |
| 0.0353 | 27.64 | 152800 | 0.2307 | 0.1149 |
| 0.0342 | 27.71 | 153200 | 0.2315 | 0.1152 |
| 0.0345 | 27.79 | 153600 | 0.2290 | 0.1146 |
| 0.034 | 27.86 | 154000 | 0.2319 | 0.1141 |
| 0.0347 | 27.93 | 154400 | 0.2312 | 0.1144 |
| 0.0338 | 28.0 | 154800 | 0.2328 | 0.1146 |
| 0.0347 | 28.08 | 155200 | 0.2352 | 0.1151 |
| 0.033 | 28.15 | 155600 | 0.2337 | 0.1142 |
| 0.0336 | 28.22 | 156000 | 0.2345 | 0.1141 |
| 0.0337 | 28.29 | 156400 | 0.2315 | 0.1143 |
| 0.0314 | 28.36 | 156800 | 0.2353 | 0.1140 |
| 0.0333 | 28.44 | 157200 | 0.2338 | 0.1146 |
| 0.0317 | 28.51 | 157600 | 0.2345 | 0.1139 |
| 0.0326 | 28.58 | 158000 | 0.2336 | 0.1143 |
| 0.033 | 28.65 | 158400 | 0.2352 | 0.1137 |
| 0.0325 | 28.73 | 158800 | 0.2312 | 0.1130 |
| 0.0321 | 28.8 | 159200 | 0.2338 | 0.1133 |
| 0.0334 | 28.87 | 159600 | 0.2335 | 0.1130 |
| 0.0317 | 28.94 | 160000 | 0.2340 | 0.1126 |
| 0.0321 | 29.02 | 160400 | 0.2349 | 0.1126 |
| 0.032 | 29.09 | 160800 | 0.2369 | 0.1127 |
| 0.0312 | 29.16 | 161200 | 0.2363 | 0.1124 |
| 0.0303 | 29.23 | 161600 | 0.2363 | 0.1123 |
| 0.0322 | 29.31 | 162000 | 0.2354 | 0.1124 |
| 0.03 | 29.38 | 162400 | 0.2360 | 0.1122 |
| 0.0299 | 29.45 | 162800 | 0.2378 | 0.1124 |
| 0.0313 | 29.52 | 163200 | 0.2377 | 0.1120 |
| 0.0299 | 29.59 | 163600 | 0.2367 | 0.1124 |
| 0.0313 | 29.67 | 164000 | 0.2380 | 0.1120 |
| 0.031 | 29.74 | 164400 | 0.2369 | 0.1120 |
| 0.0327 | 29.81 | 164800 | 0.2358 | 0.1117 |
| 0.0316 | 29.88 | 165200 | 0.2358 | 0.1118 |
| 0.0307 | 29.96 | 165600 | 0.2362 | 0.1118 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell
|
FremyCompany
| 2022-03-23T18:28:16Z | 9 | 4 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"nl",
"nl_BE",
"nl_NL",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- nl
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- nl
- nl_BE
- nl_NL
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-nl-v1-cv8-lm
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: nl
metrics:
- name: Test WER
type: wer
value: 3.93
- name: Test CER
type: cer
value: 1.22
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: nl
metrics:
- name: Test WER
type: wer
value: 16.35
- name: Test CER
type: cer
value: 9.64
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: nl
metrics:
- name: Test WER
type: wer
value: 15.81
---
# XLS-R-based CTC model with 5-gram language model from Open Subtitles
This model is a version of [facebook/wav2vec2-xls-r-2b-22-to-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16) fine-tuned mainly on the [CGN dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/), as well as the [MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL](https://commonvoice.mozilla.org) dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.03931
- Cer: 0.01224
> **IMPORTANT NOTE**: The `hunspell` typo fixer is **not enabled** on the website, which returns raw CTC+LM results. Hunspell reranking is only available in the `eval.py` decoding script. For best results, please use the code in that file while using the model locally for inference.
> **IMPORTANT NOTE**: Evaluating this model requires `apt install libhunspell-dev` and a pip install of `hunspell` in addition to pip installs of `pipy-kenlm` and `pyctcdecode` (see `install_requirements.sh`); in addition, the chunking lengths and strides were optimized for the model as `12s` and `2s` respectively (see `eval.sh`).
> **QUICK REMARK**: The "Robust Speech Event" set does not contain cleaned transcription text, so its WER/CER are vastly over-estimated. For instance `2014` in the dev set is left as a number but will be recognized as `tweeduizend veertien`, which counts as 3 mistakes (`2014` missing, and both `tweeduizend` and `veertien` wrongly inserted). Other normalization problems in the dev set include the presence of single quotes around some words, that then end up as non-match despite being the correct word (but without quotes), and the removal of some speech words in the final transcript (`ja`, etc...). As a result, our real error rate on the dev set is significantly lower than reported.
>
> 
>
> You can compare the [predictions](https://huggingface.co/FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell/blob/main/log_speech-recognition-community-v2_dev_data_nl_validation_predictions.txt) with the [targets](https://huggingface.co/FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell/blob/main/log_speech-recognition-community-v2_dev_data_nl_validation_targets.txt) on the validation dev set yourself, for example using [this diffing tool](https://countwordsfree.com/comparetexts).
> **WE DO SPEECH RECOGNITION**: Hello reader! If you are considering using this (or another) model in production, but would benefit from a model fine-tuned specifically for your use case (using text and/or labelled speech), feel free to [contact our team](https://www.ugent.be/ea/idlab/en/research/semantic-intelligence/speech-and-audio-processing.htm). This model was developped during the [Robust Speech Recognition challenge](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) event by [François REMY](https://www.linkedin.com/in/fremycompany/) [(twitter)](https://twitter.com/FremyCompany) and [Geoffroy VANDERREYDT](https://be.linkedin.com/in/geoffroy-vanderreydt-a4421460).
> We would like to thank [OVH](https://www.ovhcloud.com/en/public-cloud/ai-training/) for providing us with a V100S GPU.
## Model description
The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame.
To improve accuracy, a beam-search decoder based on `pyctcdecode` is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.
To further deal with typos, `hunspell` is used to propose alternative spellings for words not in the unigrams of the language model. These alternatives are then reranked based on the language model trained above, and a penalty proportional to the levenshtein edit distance between the alternative and the recognized word. This for examples enables to correct `collegas` into `collega's` or `gogol` into `google`.
## Intended uses & limitations
This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).
## Training and evaluation data
The model was:
0. initialized with [the 2B parameter model from Facebook](facebook/wav2vec2-xls-r-2b-22-to-16).
1. trained `5` epochs (6000 iterations of batch size 32) on [the `cv8/nl` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
2. trained `1` epoch (36000 iterations of batch size 32) on [the `cgn` dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/).
3. trained `5` epochs (6000 iterations of batch size 32) on [the `cv8/nl` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
### Framework versions
- Transformers 4.16.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
FremyCompany/xls-r-2b-nl-v2_lm-5gram-os
|
FremyCompany
| 2022-03-23T18:28:14Z | 5 | 3 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"nl",
"nl_BE",
"nl_NL",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- nl
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- nl
- nl_BE
- nl_NL
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-nl-v1-cv8-lm
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: nl
metrics:
- name: Test WER
type: wer
value: 4.06
- name: Test CER
type: cer
value: 1.22
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: nl
metrics:
- name: Test WER
type: wer
value: 17.77
- name: Test CER
type: cer
value: 9.77
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: nl
metrics:
- name: Test WER
type: wer
value: 16.32
---
# XLS-R-based CTC model with 5-gram language model from Open Subtitles
This model is a version of [facebook/wav2vec2-xls-r-2b-22-to-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16) fine-tuned mainly on the [CGN dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/), as well as the [MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL](https://commonvoice.mozilla.org) dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.04057
- Cer: 0.01222
## Model description
The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame.
To improve accuracy, a beam-search decoder based on `pyctcdecode` is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.
## Intended uses & limitations
This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).
## Training and evaluation data
The model was:
0. initialized with [the 2B parameter model from Facebook](facebook/wav2vec2-xls-r-2b-22-to-16).
1. trained `5` epochs (6000 iterations of batch size 32) on [the `cv8/nl` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
2. trained `1` epoch (36000 iterations of batch size 32) on [the `cgn` dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/).
3. trained `5` epochs (6000 iterations of batch size 32) on [the `cv8/nl` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
### Framework versions
- Transformers 4.16.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
yaswanth/xls-r-300m-yaswanth-hindi2
|
yaswanth
| 2022-03-23T18:28:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: xls-r-300m-yaswanth-hindi2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-300m-yaswanth-hindi2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7163
- Wer: 0.6951
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0007
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.986 | 4.46 | 500 | 2.0194 | 1.1857 |
| 0.9232 | 8.93 | 1000 | 1.2665 | 0.8435 |
| 0.5094 | 13.39 | 1500 | 1.2473 | 0.7893 |
| 0.3618 | 17.86 | 2000 | 1.3675 | 0.7789 |
| 0.2914 | 22.32 | 2500 | 1.3725 | 0.7914 |
| 0.2462 | 26.79 | 3000 | 1.4567 | 0.7795 |
| 0.228 | 31.25 | 3500 | 1.6179 | 0.7872 |
| 0.1995 | 35.71 | 4000 | 1.4932 | 0.7555 |
| 0.1878 | 40.18 | 4500 | 1.5352 | 0.7480 |
| 0.165 | 44.64 | 5000 | 1.5238 | 0.7440 |
| 0.1514 | 49.11 | 5500 | 1.5842 | 0.7498 |
| 0.1416 | 53.57 | 6000 | 1.6662 | 0.7524 |
| 0.1351 | 58.04 | 6500 | 1.6280 | 0.7356 |
| 0.1196 | 62.5 | 7000 | 1.6329 | 0.7250 |
| 0.1109 | 66.96 | 7500 | 1.6435 | 0.7302 |
| 0.1008 | 71.43 | 8000 | 1.7058 | 0.7170 |
| 0.0907 | 75.89 | 8500 | 1.6880 | 0.7387 |
| 0.0816 | 80.36 | 9000 | 1.6957 | 0.7031 |
| 0.0743 | 84.82 | 9500 | 1.7547 | 0.7222 |
| 0.0694 | 89.29 | 10000 | 1.6974 | 0.7117 |
| 0.0612 | 93.75 | 10500 | 1.7251 | 0.7020 |
| 0.0577 | 98.21 | 11000 | 1.7163 | 0.6951 |
### Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
geninhu/xls-asr-vi-40h-1B
|
geninhu
| 2022-03-23T18:27:57Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common-voice",
"hf-asr-leaderboard",
"robust-speech-event",
"vi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- vi
tags:
- automatic-speech-recognition
- common-voice
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: xls-asr-vi-40h-1B
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: vi
metrics:
- name: Test WER (with LM)
type: wer
value: 25.846
- name: Test CER (with LM)
type: cer
value: 12.961
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: vi
metrics:
- name: Test WER (with LM)
type: wer
value: 31.158
- name: Test CER (with LM)
type: cer
value: 16.179
---
# xls-asr-vi-40h-1B
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on 40 hours of FPT Open Speech Dataset (FOSD) and Common Voice 7.0.
### Benchmark WER result:
| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|---|
|without LM| 25.93 | 34.21 |
|with 4-grams LM| 24.11 | 25.84 | 31.158 |
### Benchmark CER result:
| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|---|
|without LM| 9.24 | 19.94 |
|with 4-grams LM| 10.37 | 12.96 | 16.179 |
## Evaluation
Please use the eval.py file to run the evaluation
```python
python eval.py --model_id geninhu/xls-asr-vi-40h-1B --dataset mozilla-foundation/common_voice_7_0 --config vi --split test --log_outputs
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.6222 | 1.85 | 1500 | 5.9479 | 0.5474 |
| 1.1362 | 3.7 | 3000 | 7.9799 | 0.5094 |
| 0.7814 | 5.56 | 4500 | 5.0330 | 0.4724 |
| 0.6281 | 7.41 | 6000 | 2.3484 | 0.5020 |
| 0.5472 | 9.26 | 7500 | 2.2495 | 0.4793 |
| 0.4827 | 11.11 | 9000 | 1.1530 | 0.4768 |
| 0.4327 | 12.96 | 10500 | 1.6160 | 0.4646 |
| 0.3989 | 14.81 | 12000 | 3.2633 | 0.4703 |
| 0.3522 | 16.67 | 13500 | 2.2337 | 0.4708 |
| 0.3201 | 18.52 | 15000 | 3.6879 | 0.4565 |
| 0.2899 | 20.37 | 16500 | 5.4389 | 0.4599 |
| 0.2776 | 22.22 | 18000 | 3.5284 | 0.4537 |
| 0.2574 | 24.07 | 19500 | 2.1759 | 0.4649 |
| 0.2378 | 25.93 | 21000 | 3.3901 | 0.4448 |
| 0.217 | 27.78 | 22500 | 1.1632 | 0.4565 |
| 0.2115 | 29.63 | 24000 | 1.7441 | 0.4232 |
| 0.1959 | 31.48 | 25500 | 3.4992 | 0.4304 |
| 0.187 | 33.33 | 27000 | 3.6163 | 0.4369 |
| 0.1748 | 35.19 | 28500 | 3.6038 | 0.4467 |
| 0.17 | 37.04 | 30000 | 2.9708 | 0.4362 |
| 0.159 | 38.89 | 31500 | 3.2045 | 0.4279 |
| 0.153 | 40.74 | 33000 | 3.2427 | 0.4287 |
| 0.1463 | 42.59 | 34500 | 3.5439 | 0.4270 |
| 0.139 | 44.44 | 36000 | 3.9381 | 0.4150 |
| 0.1352 | 46.3 | 37500 | 4.1744 | 0.4092 |
| 0.1369 | 48.15 | 39000 | 4.2279 | 0.4154 |
| 0.1273 | 50.0 | 40500 | 4.1691 | 0.4133 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
nouamanetazi/wav2vec2-xls-r-300m-ar-with-lm
|
nouamanetazi
| 2022-03-23T18:27:54Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ar
license: apache-2.0
tags:
- ar
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: XLS-R-300M - Arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: 1.0
- name: Test CER
type: cer
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-ar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - AR dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.0191
- eval_wer: 1.0
- eval_runtime: 252.2389
- eval_samples_per_second: 30.217
- eval_steps_per_second: 0.476
- epoch: 1.0
- step: 340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
Please use the evaluation script `eval.py` included in the repo.
1. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id nouamanetazi/wav2vec2-xls-r-300m-ar --dataset speech-recognition-community-v2/dev_data --config ar --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
|
lgris/sew-tiny-portuguese-cv
|
lgris
| 2022-03-23T18:27:49Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"sew",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"pt",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- pt
license: apache-2.0
tags:
- generated_from_trainer
- hf-asr-leaderboard
- pt
- robust-speech-event
datasets:
- common_voice
model-index:
- name: sew-tiny-portuguese-cv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 30.02
- name: Test CER
type: cer
value: 10.34
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 56.46
- name: Test CER
type: cer
value: 22.94
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Test WER
type: wer
value: 57.17
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pt
metrics:
- name: Test WER
type: wer
value: 61.3
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sew-tiny-portuguese-cv
This model is a fine-tuned version of [lgris/sew-tiny-pt](https://huggingface.co/lgris/sew-tiny-pt) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5110
- Wer: 0.2842
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 40000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| No log | 4.92 | 1000 | 0.8468 | 0.6494 |
| 3.4638 | 9.85 | 2000 | 0.4978 | 0.3815 |
| 3.4638 | 14.78 | 3000 | 0.4734 | 0.3417 |
| 0.9904 | 19.7 | 4000 | 0.4577 | 0.3344 |
| 0.9904 | 24.63 | 5000 | 0.4376 | 0.3170 |
| 0.8849 | 29.55 | 6000 | 0.4225 | 0.3118 |
| 0.8849 | 34.48 | 7000 | 0.4354 | 0.3080 |
| 0.819 | 39.41 | 8000 | 0.4434 | 0.3004 |
| 0.819 | 44.33 | 9000 | 0.4710 | 0.3132 |
| 0.7706 | 49.26 | 10000 | 0.4497 | 0.3064 |
| 0.7706 | 54.19 | 11000 | 0.4598 | 0.3100 |
| 0.7264 | 59.11 | 12000 | 0.4271 | 0.3013 |
| 0.7264 | 64.04 | 13000 | 0.4333 | 0.2959 |
| 0.6909 | 68.96 | 14000 | 0.4554 | 0.3019 |
| 0.6909 | 73.89 | 15000 | 0.4444 | 0.2888 |
| 0.6614 | 78.81 | 16000 | 0.4734 | 0.3081 |
| 0.6614 | 83.74 | 17000 | 0.4820 | 0.3058 |
| 0.6379 | 88.67 | 18000 | 0.4416 | 0.2950 |
| 0.6379 | 93.59 | 19000 | 0.4614 | 0.2974 |
| 0.6055 | 98.52 | 20000 | 0.4812 | 0.3018 |
| 0.6055 | 103.45 | 21000 | 0.4700 | 0.3018 |
| 0.5823 | 108.37 | 22000 | 0.4726 | 0.2999 |
| 0.5823 | 113.3 | 23000 | 0.4979 | 0.2887 |
| 0.5597 | 118.23 | 24000 | 0.4813 | 0.2980 |
| 0.5597 | 123.15 | 25000 | 0.4968 | 0.2972 |
| 0.542 | 128.08 | 26000 | 0.5331 | 0.3059 |
| 0.542 | 133.0 | 27000 | 0.5046 | 0.2978 |
| 0.5185 | 137.93 | 28000 | 0.4882 | 0.2922 |
| 0.5185 | 142.85 | 29000 | 0.4945 | 0.2938 |
| 0.499 | 147.78 | 30000 | 0.4971 | 0.2913 |
| 0.499 | 152.71 | 31000 | 0.4948 | 0.2873 |
| 0.4811 | 157.63 | 32000 | 0.4924 | 0.2918 |
| 0.4811 | 162.56 | 33000 | 0.5128 | 0.2911 |
| 0.4679 | 167.49 | 34000 | 0.5098 | 0.2892 |
| 0.4679 | 172.41 | 35000 | 0.4966 | 0.2863 |
| 0.456 | 177.34 | 36000 | 0.5033 | 0.2839 |
| 0.456 | 182.27 | 37000 | 0.5114 | 0.2875 |
| 0.4453 | 187.19 | 38000 | 0.5154 | 0.2859 |
| 0.4453 | 192.12 | 39000 | 0.5102 | 0.2847 |
| 0.4366 | 197.04 | 40000 | 0.5110 | 0.2842 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
pablouribe/xls-r-spanish-test
|
pablouribe
| 2022-03-23T18:27:46Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: xls-r-spanish-test
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: es
metrics:
- name: Test WER
type: wer
value: 13.89
- name: Test CER
type: cer
value: 3.85
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: es
metrics:
- name: Test WER
type: wer
value: 37.66
- name: Test CER
type: cer
value: 15.32
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: es
metrics:
- name: Test WER
type: wer
value: 41.17
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ES dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1461
- Wer: 1.0063
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.953 | 0.15 | 1000 | 2.9528 | 1.0 |
| 1.1519 | 0.3 | 2000 | 0.3735 | 1.0357 |
| 1.0278 | 0.45 | 3000 | 0.2529 | 1.0390 |
| 0.9922 | 0.61 | 4000 | 0.2208 | 1.0270 |
| 0.9618 | 0.76 | 5000 | 0.2088 | 1.0294 |
| 0.9364 | 0.91 | 6000 | 0.2019 | 1.0214 |
| 0.9179 | 1.06 | 7000 | 0.1940 | 1.0294 |
| 0.9154 | 1.21 | 8000 | 0.1915 | 1.0290 |
| 0.8985 | 1.36 | 9000 | 0.1837 | 1.0211 |
| 0.9055 | 1.51 | 10000 | 0.1838 | 1.0273 |
| 0.8861 | 1.67 | 11000 | 0.1765 | 1.0139 |
| 0.892 | 1.82 | 12000 | 0.1723 | 1.0188 |
| 0.8778 | 1.97 | 13000 | 0.1735 | 1.0092 |
| 0.8645 | 2.12 | 14000 | 0.1707 | 1.0106 |
| 0.8595 | 2.27 | 15000 | 0.1713 | 1.0186 |
| 0.8392 | 2.42 | 16000 | 0.1686 | 1.0053 |
| 0.8436 | 2.57 | 17000 | 0.1653 | 1.0096 |
| 0.8405 | 2.73 | 18000 | 0.1689 | 1.0077 |
| 0.8382 | 2.88 | 19000 | 0.1645 | 1.0114 |
| 0.8247 | 3.03 | 20000 | 0.1647 | 1.0078 |
| 0.8219 | 3.18 | 21000 | 0.1611 | 1.0026 |
| 0.8024 | 3.33 | 22000 | 0.1580 | 1.0062 |
| 0.8087 | 3.48 | 23000 | 0.1578 | 1.0038 |
| 0.8097 | 3.63 | 24000 | 0.1556 | 1.0057 |
| 0.8094 | 3.79 | 25000 | 0.1552 | 1.0035 |
| 0.7836 | 3.94 | 26000 | 0.1516 | 1.0052 |
| 0.8042 | 4.09 | 27000 | 0.1515 | 1.0054 |
| 0.7925 | 4.24 | 28000 | 0.1499 | 1.0031 |
| 0.7855 | 4.39 | 29000 | 0.1490 | 1.0041 |
| 0.7814 | 4.54 | 30000 | 0.1482 | 1.0068 |
| 0.7859 | 4.69 | 31000 | 0.1460 | 1.0066 |
| 0.7819 | 4.85 | 32000 | 0.1464 | 1.0062 |
| 0.7784 | 5.0 | 33000 | 0.1460 | 1.0063 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
|
w11wo/wav2vec2-xls-r-300m-zh-HK-v2
|
w11wo
| 2022-03-23T18:27:41Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: zh-HK
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: Wav2Vec2 XLS-R 300M Cantonese (zh-HK)
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
type: common_voice
args: zh-HK
metrics:
- name: Test CER
type: cer
value: 31.73
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: zh-HK
metrics:
- name: Test CER
type: cer
value: 23.11
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: zh-HK
metrics:
- name: Test CER
type: cer
value: 23.02
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: zh-HK
metrics:
- name: Test CER
type: cer
value: 56.6
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: zh-HK
metrics:
- name: Test CER
type: cer
value: 55.11
---
# Wav2Vec2 XLS-R 300M Cantonese (zh-HK)
Wav2Vec2 XLS-R 300M Cantonese (zh-HK) is an automatic speech recognition model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `zh-HK` subset of the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
This model was trained using HuggingFace's PyTorch framework and is part of the [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by HuggingFace. All training was done on a Tesla V100, sponsored by OVH.
All necessary scripts used for training could be found in the [Files and versions](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-zh-HK-v2/tree/main) tab, as well as the [Training metrics](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-zh-HK-v2/tensorboard) logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------------ | ------- | ----- | ------------------------------- |
| `wav2vec2-xls-r-300m-zh-HK-v2` | 300M | XLS-R | `Common Voice zh-HK` Dataset |
## Evaluation Results
The model achieves the following results on evaluation:
| Dataset | Loss | CER |
| -------------------------------- | ------ | ------ |
| `Common Voice` | 0.8089 | 31.73% |
| `Common Voice 7` | N/A | 23.11% |
| `Common Voice 8` | N/A | 23.02% |
| `Robust Speech Event - Dev Data` | N/A | 56.60% |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- `learning_rate`: 0.0001
- `train_batch_size`: 8
- `eval_batch_size`: 8
- `seed`: 42
- `gradient_accumulation_steps`: 4
- `total_train_batch_size`: 32
- `optimizer`: Adam with `betas=(0.9, 0.999)` and `epsilon=1e-08`
- `lr_scheduler_type`: linear
- `lr_scheduler_warmup_steps`: 2000
- `num_epochs`: 100.0
- `mixed_precision_training`: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
| :-----------: | :---: | :---: | :-------------: | :----: | :----: |
| 69.8341 | 1.34 | 500 | 80.0722 | 1.0 | 1.0 |
| 6.6418 | 2.68 | 1000 | 6.6346 | 1.0 | 1.0 |
| 6.2419 | 4.02 | 1500 | 6.2909 | 1.0 | 1.0 |
| 6.0813 | 5.36 | 2000 | 6.1150 | 1.0 | 1.0 |
| 5.9677 | 6.7 | 2500 | 6.0301 | 1.1386 | 1.0028 |
| 5.9296 | 8.04 | 3000 | 5.8975 | 1.2113 | 1.0058 |
| 5.6434 | 9.38 | 3500 | 5.5404 | 2.1624 | 1.0171 |
| 5.1974 | 10.72 | 4000 | 4.5440 | 2.1702 | 0.9366 |
| 4.3601 | 12.06 | 4500 | 3.3839 | 2.2464 | 0.8998 |
| 3.9321 | 13.4 | 5000 | 2.8785 | 2.3097 | 0.8400 |
| 3.6462 | 14.74 | 5500 | 2.5108 | 1.9623 | 0.6663 |
| 3.5156 | 16.09 | 6000 | 2.2790 | 1.6479 | 0.5706 |
| 3.32 | 17.43 | 6500 | 2.1450 | 1.8337 | 0.6244 |
| 3.1918 | 18.77 | 7000 | 1.8536 | 1.9394 | 0.6017 |
| 3.1139 | 20.11 | 7500 | 1.7205 | 1.9112 | 0.5638 |
| 2.8995 | 21.45 | 8000 | 1.5478 | 1.0624 | 0.3250 |
| 2.7572 | 22.79 | 8500 | 1.4068 | 1.1412 | 0.3367 |
| 2.6881 | 24.13 | 9000 | 1.3312 | 2.0100 | 0.5683 |
| 2.5993 | 25.47 | 9500 | 1.2553 | 2.0039 | 0.6450 |
| 2.5304 | 26.81 | 10000 | 1.2422 | 2.0394 | 0.5789 |
| 2.4352 | 28.15 | 10500 | 1.1582 | 1.9970 | 0.5507 |
| 2.3795 | 29.49 | 11000 | 1.1160 | 1.8255 | 0.4844 |
| 2.3287 | 30.83 | 11500 | 1.0775 | 1.4123 | 0.3780 |
| 2.2622 | 32.17 | 12000 | 1.0704 | 1.7445 | 0.4894 |
| 2.2225 | 33.51 | 12500 | 1.0272 | 1.7237 | 0.5058 |
| 2.1843 | 34.85 | 13000 | 0.9756 | 1.8042 | 0.5028 |
| 2.1 | 36.19 | 13500 | 0.9527 | 1.8909 | 0.6055 |
| 2.0741 | 37.53 | 14000 | 0.9418 | 1.9026 | 0.5880 |
| 2.0179 | 38.87 | 14500 | 0.9363 | 1.7977 | 0.5246 |
| 2.0615 | 40.21 | 15000 | 0.9635 | 1.8112 | 0.5599 |
| 1.9448 | 41.55 | 15500 | 0.9249 | 1.7250 | 0.4914 |
| 1.8966 | 42.89 | 16000 | 0.9023 | 1.5829 | 0.4319 |
| 1.8662 | 44.24 | 16500 | 0.9002 | 1.4833 | 0.4230 |
| 1.8136 | 45.58 | 17000 | 0.9076 | 1.1828 | 0.2987 |
| 1.7908 | 46.92 | 17500 | 0.8774 | 1.5773 | 0.4258 |
| 1.7354 | 48.26 | 18000 | 0.8727 | 1.5037 | 0.4024 |
| 1.6739 | 49.6 | 18500 | 0.8636 | 1.1239 | 0.2789 |
| 1.6457 | 50.94 | 19000 | 0.8516 | 1.2269 | 0.3104 |
| 1.5847 | 52.28 | 19500 | 0.8399 | 1.3309 | 0.3360 |
| 1.5971 | 53.62 | 20000 | 0.8441 | 1.3153 | 0.3335 |
| 1.602 | 54.96 | 20500 | 0.8590 | 1.2932 | 0.3433 |
| 1.5063 | 56.3 | 21000 | 0.8334 | 1.1312 | 0.2875 |
| 1.4631 | 57.64 | 21500 | 0.8474 | 1.1698 | 0.2999 |
| 1.4997 | 58.98 | 22000 | 0.8638 | 1.4279 | 0.3854 |
| 1.4301 | 60.32 | 22500 | 0.8550 | 1.2737 | 0.3300 |
| 1.3798 | 61.66 | 23000 | 0.8266 | 1.1802 | 0.2934 |
| 1.3454 | 63.0 | 23500 | 0.8235 | 1.3816 | 0.3711 |
| 1.3678 | 64.34 | 24000 | 0.8550 | 1.6427 | 0.5035 |
| 1.3761 | 65.68 | 24500 | 0.8510 | 1.6709 | 0.4907 |
| 1.2668 | 67.02 | 25000 | 0.8515 | 1.5842 | 0.4505 |
| 1.2835 | 68.36 | 25500 | 0.8283 | 1.5353 | 0.4221 |
| 1.2961 | 69.7 | 26000 | 0.8339 | 1.5743 | 0.4369 |
| 1.2656 | 71.05 | 26500 | 0.8331 | 1.5331 | 0.4217 |
| 1.2556 | 72.39 | 27000 | 0.8242 | 1.4708 | 0.4109 |
| 1.2043 | 73.73 | 27500 | 0.8245 | 1.4469 | 0.4031 |
| 1.2722 | 75.07 | 28000 | 0.8202 | 1.4924 | 0.4096 |
| 1.202 | 76.41 | 28500 | 0.8290 | 1.3807 | 0.3719 |
| 1.1679 | 77.75 | 29000 | 0.8195 | 1.4097 | 0.3749 |
| 1.1967 | 79.09 | 29500 | 0.8059 | 1.2074 | 0.3077 |
| 1.1241 | 80.43 | 30000 | 0.8137 | 1.2451 | 0.3270 |
| 1.1414 | 81.77 | 30500 | 0.8117 | 1.2031 | 0.3121 |
| 1.132 | 83.11 | 31000 | 0.8234 | 1.4266 | 0.3901 |
| 1.0982 | 84.45 | 31500 | 0.8064 | 1.3712 | 0.3607 |
| 1.0797 | 85.79 | 32000 | 0.8167 | 1.3356 | 0.3562 |
| 1.0119 | 87.13 | 32500 | 0.8215 | 1.2754 | 0.3268 |
| 1.0216 | 88.47 | 33000 | 0.8163 | 1.2512 | 0.3184 |
| 1.0375 | 89.81 | 33500 | 0.8137 | 1.2685 | 0.3290 |
| 0.9794 | 91.15 | 34000 | 0.8220 | 1.2724 | 0.3255 |
| 1.0207 | 92.49 | 34500 | 0.8165 | 1.2906 | 0.3361 |
| 1.0169 | 93.83 | 35000 | 0.8153 | 1.2819 | 0.3305 |
| 1.0127 | 95.17 | 35500 | 0.8187 | 1.2832 | 0.3252 |
| 0.9978 | 96.51 | 36000 | 0.8111 | 1.2612 | 0.3210 |
| 0.9923 | 97.85 | 36500 | 0.8076 | 1.2278 | 0.3122 |
| 1.0451 | 99.2 | 37000 | 0.8086 | 1.2451 | 0.3156 |
## Disclaimer
Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.
## Authors
Wav2Vec2 XLS-R 300M Cantonese (zh-HK) was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on OVH Cloud.
## Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
|
lgris/sew-tiny-portuguese-cv7
|
lgris
| 2022-03-23T18:27:38Z | 24 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"sew",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"pt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- pt
license: apache-2.0
tags:
- generated_from_trainer
- hf-asr-leaderboard
- pt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: sew-tiny-portuguese-cv7
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: pt
metrics:
- name: Test WER
type: wer
value: 28.9
- name: Test CER
type: cer
value: 9.41
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 47.27
- name: Test CER
type: cer
value: 19.62
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Test WER
type: wer
value: 47.3
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pt
metrics:
- name: Test WER
type: wer
value: 49.83
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sew-tiny-portuguese-cv7
This model is a fine-tuned version of [lgris/sew-tiny-pt](https://huggingface.co/lgris/sew-tiny-pt) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4232
- Wer: 0.2745
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 40000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| No log | 2.6 | 1000 | 1.0034 | 0.7308 |
| 4.1307 | 5.19 | 2000 | 0.6274 | 0.4721 |
| 4.1307 | 7.79 | 3000 | 0.5541 | 0.4130 |
| 1.3117 | 10.39 | 4000 | 0.5302 | 0.3880 |
| 1.3117 | 12.99 | 5000 | 0.5082 | 0.3644 |
| 1.2047 | 15.58 | 6000 | 0.4818 | 0.3539 |
| 1.2047 | 18.18 | 7000 | 0.4822 | 0.3477 |
| 1.14 | 20.78 | 8000 | 0.4781 | 0.3428 |
| 1.14 | 23.38 | 9000 | 0.4840 | 0.3401 |
| 1.0818 | 25.97 | 10000 | 0.4613 | 0.3251 |
| 1.0818 | 28.57 | 11000 | 0.4569 | 0.3257 |
| 1.0451 | 31.17 | 12000 | 0.4494 | 0.3132 |
| 1.0451 | 33.77 | 13000 | 0.4560 | 0.3201 |
| 1.011 | 36.36 | 14000 | 0.4687 | 0.3174 |
| 1.011 | 38.96 | 15000 | 0.4397 | 0.3122 |
| 0.9785 | 41.56 | 16000 | 0.4605 | 0.3173 |
| 0.9785 | 44.16 | 17000 | 0.4380 | 0.3064 |
| 0.9458 | 46.75 | 18000 | 0.4372 | 0.3048 |
| 0.9458 | 49.35 | 19000 | 0.4426 | 0.3039 |
| 0.9126 | 51.95 | 20000 | 0.4317 | 0.2962 |
| 0.9126 | 54.54 | 21000 | 0.4345 | 0.2960 |
| 0.8926 | 57.14 | 22000 | 0.4365 | 0.2948 |
| 0.8926 | 59.74 | 23000 | 0.4306 | 0.2940 |
| 0.8654 | 62.34 | 24000 | 0.4303 | 0.2928 |
| 0.8654 | 64.93 | 25000 | 0.4351 | 0.2915 |
| 0.8373 | 67.53 | 26000 | 0.4340 | 0.2909 |
| 0.8373 | 70.13 | 27000 | 0.4279 | 0.2907 |
| 0.83 | 72.73 | 28000 | 0.4214 | 0.2867 |
| 0.83 | 75.32 | 29000 | 0.4256 | 0.2849 |
| 0.8062 | 77.92 | 30000 | 0.4281 | 0.2826 |
| 0.8062 | 80.52 | 31000 | 0.4398 | 0.2865 |
| 0.7846 | 83.12 | 32000 | 0.4218 | 0.2812 |
| 0.7846 | 85.71 | 33000 | 0.4227 | 0.2791 |
| 0.7697 | 88.31 | 34000 | 0.4200 | 0.2767 |
| 0.7697 | 90.91 | 35000 | 0.4285 | 0.2791 |
| 0.7539 | 93.51 | 36000 | 0.4238 | 0.2777 |
| 0.7539 | 96.1 | 37000 | 0.4288 | 0.2757 |
| 0.7413 | 98.7 | 38000 | 0.4205 | 0.2748 |
| 0.7413 | 101.3 | 39000 | 0.4241 | 0.2761 |
| 0.7348 | 103.89 | 40000 | 0.4232 | 0.2745 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
cahya/wav2vec2-luganda
|
cahya
| 2022-03-23T18:27:18Z | 27 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"common_voice",
"hf-asr-leaderboard",
"lg",
"robust-speech-event",
"speech",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: lg
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- common_voice
- hf-asr-leaderboard
- lg
- robust-speech-event
- speech
license: apache-2.0
model-index:
- name: Wav2Vec2 Luganda by Indonesian-NLP
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice lg
type: common_voice
args: lg
metrics:
- name: Test WER
type: wer
value: 9.332
- name: Test CER
type: cer
value: 1.987
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: lg
metrics:
- name: Test WER
type: wer
value: 13.844
- name: Test CER
type: cer
value: 2.68
---
# Automatic Speech Recognition for Luganda
This is the model built for the
[Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition).
It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Luganda Common Voice dataset](https://huggingface.co/datasets/common_voice) version 7.0.
We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/luganda-asr) to test the model.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "lg", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "lg", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model.to("cuda")
chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "‘", "’", "’"]
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
WER without KenLM: 15.38 %
WER With KenLM:
**Test Result**: 7.53 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/indonesian-nlp/luganda-asr)
|
DrishtiSharma/wav2vec2-large-xls-r-300m-bg-v1
|
DrishtiSharma
| 2022-03-23T18:27:15Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"bg",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- bg
license: apache-2.0
tags:
- automatic-speech-recognition
- bg
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-bg-v1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: bg
metrics:
- name: Test WER
type: wer
value: 0.4709579127785184
- name: Test CER
type: cer
value: 0.10205125354383235
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: bg
metrics:
- name: Test WER
type: wer
value: 0.7053128872366791
- name: Test CER
type: cer
value: 0.210804311998487
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: bg
metrics:
- name: Test WER
type: wer
value: 72.6
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5197
- Wer: 0.4689
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-v1 --dataset mozilla-foundation/common_voice_8_0 --config bg --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-v1 --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 10 --stride_length_s 1
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3711 | 2.61 | 300 | 4.3122 | 1.0 |
| 3.1653 | 5.22 | 600 | 3.1156 | 1.0 |
| 2.8904 | 7.83 | 900 | 2.8421 | 0.9918 |
| 0.9207 | 10.43 | 1200 | 0.9895 | 0.8689 |
| 0.6384 | 13.04 | 1500 | 0.6994 | 0.7700 |
| 0.5215 | 15.65 | 1800 | 0.5628 | 0.6443 |
| 0.4573 | 18.26 | 2100 | 0.5316 | 0.6174 |
| 0.3875 | 20.87 | 2400 | 0.4932 | 0.5779 |
| 0.3562 | 23.48 | 2700 | 0.4972 | 0.5475 |
| 0.3218 | 26.09 | 3000 | 0.4895 | 0.5219 |
| 0.2954 | 28.7 | 3300 | 0.5226 | 0.5192 |
| 0.287 | 31.3 | 3600 | 0.4957 | 0.5146 |
| 0.2587 | 33.91 | 3900 | 0.4944 | 0.4893 |
| 0.2496 | 36.52 | 4200 | 0.4976 | 0.4895 |
| 0.2365 | 39.13 | 4500 | 0.5185 | 0.4819 |
| 0.2264 | 41.74 | 4800 | 0.5152 | 0.4776 |
| 0.2224 | 44.35 | 5100 | 0.5031 | 0.4746 |
| 0.2096 | 46.96 | 5400 | 0.5062 | 0.4708 |
| 0.2038 | 49.57 | 5700 | 0.5217 | 0.4698 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
geninhu/xls-asr-vi-40h
|
geninhu
| 2022-03-23T18:27:13Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common-voice",
"hf-asr-leaderboard",
"robust-speech-event",
"vi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- vi
tags:
- automatic-speech-recognition
- common-voice
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: xls-asr-vi-40h
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: vi
metrics:
- name: Test WER (with Language model)
type: wer
value: 56.57
---
# xls-asr-vi-40h
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common voice 7.0 vi & private dataset.
It achieves the following results on the evaluation set (Without Language Model):
- Loss: 1.1177
- Wer: 60.58
## Evaluation
Please run the eval.py file
```bash
!python eval_custom.py --model_id geninhu/xls-asr-vi-40h --dataset mozilla-foundation/common_voice_7_0 --config vi --split test
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 23.3878 | 0.93 | 1500 | 21.9179 | 1.0 |
| 8.8862 | 1.85 | 3000 | 6.0599 | 1.0 |
| 4.3701 | 2.78 | 4500 | 4.3837 | 1.0 |
| 4.113 | 3.7 | 6000 | 4.2698 | 0.9982 |
| 3.9666 | 4.63 | 7500 | 3.9726 | 0.9989 |
| 3.5965 | 5.56 | 9000 | 3.7124 | 0.9975 |
| 3.3944 | 6.48 | 10500 | 3.5005 | 1.0057 |
| 3.304 | 7.41 | 12000 | 3.3710 | 1.0043 |
| 3.2482 | 8.33 | 13500 | 3.4201 | 1.0155 |
| 3.212 | 9.26 | 15000 | 3.3732 | 1.0151 |
| 3.1778 | 10.19 | 16500 | 3.2763 | 1.0009 |
| 3.1027 | 11.11 | 18000 | 3.1943 | 1.0025 |
| 2.9905 | 12.04 | 19500 | 2.8082 | 0.9703 |
| 2.7095 | 12.96 | 21000 | 2.4993 | 0.9302 |
| 2.4862 | 13.89 | 22500 | 2.3072 | 0.9140 |
| 2.3271 | 14.81 | 24000 | 2.1398 | 0.8949 |
| 2.1968 | 15.74 | 25500 | 2.0594 | 0.8817 |
| 2.111 | 16.67 | 27000 | 1.9404 | 0.8630 |
| 2.0387 | 17.59 | 28500 | 1.8895 | 0.8497 |
| 1.9504 | 18.52 | 30000 | 1.7961 | 0.8315 |
| 1.9039 | 19.44 | 31500 | 1.7433 | 0.8213 |
| 1.8342 | 20.37 | 33000 | 1.6790 | 0.7994 |
| 1.7824 | 21.3 | 34500 | 1.6291 | 0.7825 |
| 1.7359 | 22.22 | 36000 | 1.5783 | 0.7706 |
| 1.7053 | 23.15 | 37500 | 1.5248 | 0.7492 |
| 1.6504 | 24.07 | 39000 | 1.4930 | 0.7406 |
| 1.6263 | 25.0 | 40500 | 1.4572 | 0.7348 |
| 1.5893 | 25.93 | 42000 | 1.4202 | 0.7161 |
| 1.5669 | 26.85 | 43500 | 1.3987 | 0.7143 |
| 1.5277 | 27.78 | 45000 | 1.3512 | 0.6991 |
| 1.501 | 28.7 | 46500 | 1.3320 | 0.6879 |
| 1.4781 | 29.63 | 48000 | 1.3112 | 0.6788 |
| 1.4477 | 30.56 | 49500 | 1.2850 | 0.6657 |
| 1.4483 | 31.48 | 51000 | 1.2813 | 0.6633 |
| 1.4065 | 32.41 | 52500 | 1.2475 | 0.6541 |
| 1.3779 | 33.33 | 54000 | 1.2244 | 0.6503 |
| 1.3788 | 34.26 | 55500 | 1.2116 | 0.6407 |
| 1.3428 | 35.19 | 57000 | 1.1938 | 0.6352 |
| 1.3453 | 36.11 | 58500 | 1.1927 | 0.6340 |
| 1.3137 | 37.04 | 60000 | 1.1699 | 0.6252 |
| 1.2984 | 37.96 | 61500 | 1.1666 | 0.6229 |
| 1.2927 | 38.89 | 63000 | 1.1585 | 0.6188 |
| 1.2919 | 39.81 | 64500 | 1.1618 | 0.6190 |
| 1.293 | 40.74 | 66000 | 1.1479 | 0.6181 |
| 1.2853 | 41.67 | 67500 | 1.1423 | 0.6202 |
| 1.2687 | 42.59 | 69000 | 1.1315 | 0.6131 |
| 1.2603 | 43.52 | 70500 | 1.1333 | 0.6128 |
| 1.2577 | 44.44 | 72000 | 1.1191 | 0.6079 |
| 1.2435 | 45.37 | 73500 | 1.1177 | 0.6079 |
| 1.251 | 46.3 | 75000 | 1.1211 | 0.6092 |
| 1.2482 | 47.22 | 76500 | 1.1177 | 0.6060 |
| 1.2422 | 48.15 | 78000 | 1.1227 | 0.6097 |
| 1.2485 | 49.07 | 79500 | 1.1187 | 0.6071 |
| 1.2425 | 50.0 | 81000 | 1.1177 | 0.6058 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
patrickvonplaten/xls-r-300-sv-cv7
|
patrickvonplaten
| 2022-03-23T18:27:10Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"sv",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
- sv
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Swedish - CV7 - v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: sv-SE
metrics:
- name: Test WER
type: wer
value: 15.99
- name: Test CER
type: cer
value: 5.2
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 24.41
- name: Test CER
type: cer
value: 11.88
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2604
- Wer: 0.2334
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 1
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
See Tensorboard
### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py --model_id patrickvonplaten/xls-r-300-sv-cv7 --dataset mozilla-foundation/common_voice_7_0 --config sv-SE --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id patrickvonplaten/xls-r-300-sv-cv7 --dataset speech-recognition-community-v2/dev_data --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.18.4.dev0
- Tokenizers 0.10.3
|
anuragshas/wav2vec2-xls-r-1b-hi-with-lm
|
anuragshas
| 2022-03-23T18:26:47Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: XLS-R-1B - Hindi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hi
metrics:
- name: Test WER
type: wer
value: 15.899
- name: Test CER
type: cer
value: 5.83
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLS-R-1B - Hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6921
- Wer: 0.3547
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.0674 | 2.07 | 400 | 1.3411 | 0.8835 |
| 1.324 | 4.15 | 800 | 0.9311 | 0.7142 |
| 1.2023 | 6.22 | 1200 | 0.8060 | 0.6170 |
| 1.1573 | 8.29 | 1600 | 0.7415 | 0.4972 |
| 1.1117 | 10.36 | 2000 | 0.7248 | 0.4588 |
| 1.0672 | 12.44 | 2400 | 0.6729 | 0.4350 |
| 1.0336 | 14.51 | 2800 | 0.7117 | 0.4346 |
| 1.0025 | 16.58 | 3200 | 0.7019 | 0.4272 |
| 0.9578 | 18.65 | 3600 | 0.6792 | 0.4118 |
| 0.9272 | 20.73 | 4000 | 0.6863 | 0.4156 |
| 0.9321 | 22.8 | 4400 | 0.6535 | 0.3972 |
| 0.8802 | 24.87 | 4800 | 0.6766 | 0.3906 |
| 0.844 | 26.94 | 5200 | 0.6782 | 0.3949 |
| 0.8387 | 29.02 | 5600 | 0.6916 | 0.3921 |
| 0.8042 | 31.09 | 6000 | 0.6806 | 0.3797 |
| 0.793 | 33.16 | 6400 | 0.7120 | 0.3831 |
| 0.7567 | 35.23 | 6800 | 0.6862 | 0.3808 |
| 0.7463 | 37.31 | 7200 | 0.6893 | 0.3709 |
| 0.7053 | 39.38 | 7600 | 0.7096 | 0.3701 |
| 0.6906 | 41.45 | 8000 | 0.6921 | 0.3676 |
| 0.6891 | 43.52 | 8400 | 0.7167 | 0.3663 |
| 0.658 | 45.6 | 8800 | 0.6833 | 0.3580 |
| 0.6576 | 47.67 | 9200 | 0.6914 | 0.3569 |
| 0.6358 | 49.74 | 9600 | 0.6922 | 0.3551 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-1b-hi-with-lm --dataset mozilla-foundation/common_voice_8_0 --config hi --split test
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-xls-r-1b-hi-with-lm"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "hi", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "तुम्हारे पास तीन महीने बचे हैं"
```
### Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 26.209 | 15.899 |
|
Wikidepia/wav2vec2-xls-r-300m-indonesian
|
Wikidepia
| 2022-03-23T18:26:42Z | 2,077 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"id",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- id
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- id
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
model-index:
- name: XLS-R-300M - Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: id
metrics:
- name: Test WER
type: wer
value: 5.046
- name: Test CER
type: cer
value: 1.699
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: id
metrics:
- name: Test WER
type: wer
value: 41.31
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: id
metrics:
- name: Test WER
type: wer
value: 52.23
---
# Wav2Vec2 XLS-R-300M - Indonesian
This model is a fine-tuned version of `facebook/wav2vec2-xls-r-300m` on the `mozilla-foundation/common_voice_8_0` and [MagicHub Indonesian Conversational Speech Corpus](https://magichub.com/datasets/indonesian-conversational-speech-corpus/).
|
jsnfly/wav2vec2-xls-r-1b-de-cv8
|
jsnfly
| 2022-03-23T18:26:40Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-1B - German
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: de
metrics:
- name: Test WER
type: wer
value: 11.37
- name: Test CER
type: cer
value: 2.89
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: de
metrics:
- name: Dev WER
type: wer
value: 31.16
- name: Dev CER
type: cer
value: 13.41
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: de
metrics:
- name: Test WER
type: wer
value: 36.79
---
# XLS-R-1b-DE
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - DE dataset. (See `run.sh` for training parameters).
|
sammy786/wav2vec2-xlsr-czech
|
sammy786
| 2022-03-23T18:26:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"cs",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- cs
license: apache-2.0
tags:
- automatic-speech-recognition
- cs
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-czech
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: cs
metrics:
- name: Test WER
type: wer
value: 11.22
- name: Test CER
type: cer
value: 2.52
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: cs
metrics:
- name: Test WER
type: wer
value: 97.02
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: cs
metrics:
- name: Test WER
type: wer
value: 69.7
---
# sammy786/wav2vec2-xlsr-czech
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - cs dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss: 7.26
- Wer: 19.32
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv, invalidated.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 8
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|:----:|:-------------:|:---------------:|:--------:|
| 200 | 6.654600 | 3.329486 | 1.000000 |
| 400 | 1.700600 | 0.317266 | 0.409446 |
| 600 | 0.767400 | 0.211371 | 0.313981 |
| 800 | 0.718600 | 0.167771 | 0.280676 |
| 1000 | 0.661700 | 0.142229 | 0.258938 |
| 1200 | 0.594400 | 0.137321 | 0.256275 |
| 1400 | 0.583900 | 0.132922 | 0.248418 |
| 1600 | 0.565100 | 0.117214 | 0.238640 |
| 1800 | 0.369600 | 0.116954 | 0.238291 |
| 2000 | 0.292800 | 0.109973 | 0.227509 |
| 2200 | 0.255400 | 0.104955 | 0.228120 |
| 2400 | 0.266800 | 0.097268 | 0.220525 |
| 2600 | 0.232700 | 0.096055 | 0.213584 |
| 2800 | 0.213700 | 0.097770 | 0.218866 |
| 3000 | 0.209900 | 0.091633 | 0.210485 |
| 3200 | 0.196800 | 0.090342 | 0.208739 |
| 3400 | 0.200500 | 0.082326 | 0.204767 |
| 3600 | 0.176800 | 0.085491 | 0.204068 |
| 3800 | 0.170000 | 0.081289 | 0.201231 |
| 4000 | 0.166200 | 0.080762 | 0.200227 |
| 4200 | 0.161700 | 0.076671 | 0.198001 |
| 4400 | 0.147000 | 0.077383 | 0.196997 |
| 4600 | 0.141900 | 0.076057 | 0.195862 |
| 4800 | 0.144800 | 0.074612 | 0.195120 |
| 5000 | 0.138900 | 0.073138 | 0.193985 |
| 5200 | 0.143900 | 0.072802 | 0.192894 |
| 5400 | 0.131100 | 0.072764 | 0.193723 |
| 5600 | 0.137000 | 0.072697 | 0.193679 |
| 5800 | 0.133300 | 0.072651 | 0.193286 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-czech --dataset mozilla-foundation/common_voice_8_0 --config cs --split test
```
|
arampacha/wav2vec2-xls-r-1b-uk
|
arampacha
| 2022-03-23T18:26:29Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"uk",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-1b-hy
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice uk
args: uk
metrics:
- type: wer
value: 10.406342913776015
name: WER LM
- type: cer
value: 2.0387492208601703
name: CER LM
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: uk
metrics:
- name: Test WER
type: wer
value: 40.57
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: uk
metrics:
- name: Test WER
type: wer
value: 28.95
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /WORKSPACE/DATA/UK/COMPOSED_DATASET/ - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1092
- Wer: 0.1752
- Cer: 0.0323
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 12000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 1.7005 | 1.61 | 500 | 0.4082 | 0.5584 | 0.1164 |
| 1.1555 | 3.22 | 1000 | 0.2020 | 0.2953 | 0.0557 |
| 1.0927 | 4.82 | 1500 | 0.1708 | 0.2584 | 0.0480 |
| 1.0707 | 6.43 | 2000 | 0.1563 | 0.2405 | 0.0450 |
| 1.0728 | 8.04 | 2500 | 0.1620 | 0.2442 | 0.0463 |
| 1.0268 | 9.65 | 3000 | 0.1588 | 0.2378 | 0.0458 |
| 1.0328 | 11.25 | 3500 | 0.1466 | 0.2352 | 0.0442 |
| 1.0249 | 12.86 | 4000 | 0.1552 | 0.2341 | 0.0449 |
| 1.016 | 14.47 | 4500 | 0.1602 | 0.2435 | 0.0473 |
| 1.0164 | 16.08 | 5000 | 0.1491 | 0.2337 | 0.0444 |
| 0.9935 | 17.68 | 5500 | 0.1539 | 0.2373 | 0.0458 |
| 0.9626 | 19.29 | 6000 | 0.1458 | 0.2305 | 0.0434 |
| 0.9505 | 20.9 | 6500 | 0.1368 | 0.2157 | 0.0407 |
| 0.9389 | 22.51 | 7000 | 0.1437 | 0.2231 | 0.0426 |
| 0.9129 | 24.12 | 7500 | 0.1313 | 0.2076 | 0.0394 |
| 0.9118 | 25.72 | 8000 | 0.1292 | 0.2040 | 0.0384 |
| 0.8848 | 27.33 | 8500 | 0.1299 | 0.2028 | 0.0384 |
| 0.8667 | 28.94 | 9000 | 0.1228 | 0.1945 | 0.0367 |
| 0.8641 | 30.55 | 9500 | 0.1223 | 0.1939 | 0.0364 |
| 0.8516 | 32.15 | 10000 | 0.1184 | 0.1876 | 0.0349 |
| 0.8379 | 33.76 | 10500 | 0.1137 | 0.1821 | 0.0338 |
| 0.8235 | 35.37 | 11000 | 0.1127 | 0.1779 | 0.0331 |
| 0.8112 | 36.98 | 11500 | 0.1103 | 0.1766 | 0.0327 |
| 0.8069 | 38.59 | 12000 | 0.1092 | 0.1752 | 0.0323 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
|
mpoyraz/wav2vec2-xls-r-300m-cv6-turkish
|
mpoyraz
| 2022-03-23T18:26:27Z | 9 | 7 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"hf-asr-leaderboard",
"robust-speech-event",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language: tr
tags:
- automatic-speech-recognition
- common_voice
- hf-asr-leaderboard
- robust-speech-event
- tr
datasets:
- common_voice
model-index:
- name: mpoyraz/wav2vec2-xls-r-300m-cv6-turkish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6.1
type: common_voice
args: tr
metrics:
- name: Test WER
type: wer
value: 8.83
- name: Test CER
type: cer
value: 2.37
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: tr
metrics:
- name: Test WER
type: wer
value: 32.81
- name: Test CER
type: cer
value: 11.22
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: tr
metrics:
- name: Test WER
type: wer
value: 34.86
---
# wav2vec2-xls-r-300m-cv6-turkish
## Model description
This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 6.1 TR](https://huggingface.co/datasets/common_voice) All `validated` split except `test` split was used for training.
- [MediaSpeech](https://www.openslr.org/108/)
## Training procedure
To support both of the datasets above, custom pre-processing and loading steps was performed and [wav2vec2-turkish](https://github.com/mpoyraz/wav2vec2-turkish) repo was used for that purpose.
### Training hyperparameters
The following hypermaters were used for finetuning:
- learning_rate 2e-4
- num_train_epochs 10
- warmup_steps 500
- freeze_feature_extractor
- mask_time_prob 0.1
- mask_feature_prob 0.1
- feat_proj_dropout 0.05
- attention_dropout 0.05
- final_dropout 0.1
- activation_dropout 0.05
- per_device_train_batch_size 8
- per_device_eval_batch_size 8
- gradient_accumulation_steps 8
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 1.18.3
- Tokenizers 0.10.3
## Language Model
N-gram language model is trained on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format.
## Evaluation Commands
Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing.
1. To evaluate on `common_voice` with split `test`
```bash
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv6-turkish --dataset common_voice --config tr --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv6-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
## Evaluation results:
| Dataset | WER | CER |
|---|---|---|
|Common Voice 6.1 TR test split| 8.83 | 2.37 |
|Speech Recognition Community dev data| 32.81 | 11.22 |
|
cahya/wav2vec2-base-turkish
|
cahya
| 2022-03-23T18:26:22Z | 57 | 4 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"tr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
- tr
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: Wav2Vec2 Base Turkish by Cahya
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6.1
type: mozilla-foundation/common_voice_7_0
args: tr
metrics:
- name: Test WER
type: wer
value: 9.437
- name: Test CER
type: cer
value: 3.325
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: tr
metrics:
- name: Test WER
type: wer
value: 8.147
- name: Test CER
type: cer
value: 2.802
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: tr
metrics:
- name: Test WER
type: wer
value: 28.011
- name: Test CER
type: cer
value: 10.66
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: tr
metrics:
- name: Test WER
type: wer
value: 33.62
---
#
This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial-cv) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
| | Dataset | WER | CER |
|---|-------------------------------|---------|----------|
| 1 | Common Voice 6.1 | 9.437 | 3.325 |
| 2 | Common Voice 7.0 | 8.147 | 2.802 |
| 3 | Common Voice 8.0 | 8.335 | 2.336 |
| 4 | Speech Recognition Community | 28.011 | 10.66 |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 7.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) 'train', 'validation' and 'other' split were used for training.
- [Media Speech](https://www.openslr.org/108/)
- [Magic Hub](https://magichub.com/)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-06
- train_batch_size: 6
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1224 | 3.45 | 500 | 0.1641 | 0.1396 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
bakrianoo/sinai-voice-ar-stt
|
bakrianoo
| 2022-03-23T18:25:21Z | 54 | 11 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"ar",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
model-index:
- name: Sinai Voice Arabic Speech Recognition Model
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice ar
args: ar
metrics:
- type: wer
value: 0.181
name: Test WER
- type: cer
value: 0.049
name: Test CER
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: 93.03
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ar
metrics:
- name: Test WER
type: wer
value: 90.79
widget:
- example_title: Example 1
src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19077324.mp3
- example_title: Example 2
src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19205138.mp3
- example_title: Example 3
src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19331711.mp3
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Sinai Voice Arabic Speech Recognition Model
# نموذج **صوت سيناء** للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2141
- Wer: 0.1808
It achieves the following results on the evaluation set:
- eval_loss = 0.2141
- eval_samples = 10388
- eval_wer = 0.181
- eval_cer = 0.049
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id bakrianoo/sinai-voice-ar-stt --dataset mozilla-foundation/common_voice_8_0 --config ar --split test
```
### Inference Without LM
```python
from transformers import (Wav2Vec2Processor, Wav2Vec2ForCTC)
import torchaudio
import torch
def speech_file_to_array_fn(voice_path, resampling_to=16000):
speech_array, sampling_rate = torchaudio.load(voice_path)
resampler = torchaudio.transforms.Resample(sampling_rate, resampling_to)
return resampler(speech_array)[0].numpy(), sampling_rate
# load the model
cp = "bakrianoo/sinai-voice-ar-stt"
processor = Wav2Vec2Processor.from_pretrained(cp)
model = Wav2Vec2ForCTC.from_pretrained(cp)
# recognize the text in a sample sound file
sound_path = './my_voice.mp3'
sample, sr = speech_file_to_array_fn(sound_path)
inputs = processor([sample], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values,).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 10
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.354 | 0.64 | 1000 | 0.4109 | 0.4493 |
| 0.5886 | 1.28 | 2000 | 0.2798 | 0.3099 |
| 0.4977 | 1.92 | 3000 | 0.2387 | 0.2673 |
| 0.4253 | 2.56 | 4000 | 0.2266 | 0.2523 |
| 0.3942 | 3.2 | 5000 | 0.2171 | 0.2437 |
| 0.3619 | 3.84 | 6000 | 0.2076 | 0.2253 |
| 0.3245 | 4.48 | 7000 | 0.2088 | 0.2186 |
| 0.308 | 5.12 | 8000 | 0.2086 | 0.2206 |
| 0.2881 | 5.76 | 9000 | 0.2089 | 0.2105 |
| 0.2557 | 6.4 | 10000 | 0.2015 | 0.2004 |
| 0.248 | 7.04 | 11000 | 0.2044 | 0.1953 |
| 0.2251 | 7.68 | 12000 | 0.2058 | 0.1932 |
| 0.2052 | 8.32 | 13000 | 0.2117 | 0.1878 |
| 0.1976 | 8.96 | 14000 | 0.2104 | 0.1825 |
| 0.1845 | 9.6 | 15000 | 0.2156 | 0.1821 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
huggingtweets/lucca_dev
|
huggingtweets
| 2022-03-23T18:20:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T18:07:47Z |
---
language: en
thumbnail: http://www.huggingtweets.com/lucca_dev/1648059357338/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1475818681628246021/sf4z2j_9_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Lucca</div>
<div style="text-align: center; font-size: 14px;">@lucca_dev</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Lucca.
| Data | Lucca |
| --- | --- |
| Tweets downloaded | 2525 |
| Retweets | 17 |
| Short tweets | 100 |
| Tweets kept | 2408 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3bq4zgob/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lucca_dev's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2kuasht1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2kuasht1/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/lucca_dev')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
muhammedshihebi/bert-base-multilingual-cased-squad
|
muhammedshihebi
| 2022-03-23T17:48:47Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-23T17:48:32Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bert-base-multilingual-cased-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5271
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18600, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.1256 | 0 |
| 0.7252 | 1 |
| 0.5271 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/stedmanhalliday
|
huggingtweets
| 2022-03-23T17:16:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T17:16:37Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500999718331199496/yhpq7J8H_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">SODI</div>
<div style="text-align: center; font-size: 14px;">@stedmanhalliday</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from SODI.
| Data | SODI |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 59 |
| Short tweets | 559 |
| Tweets kept | 2632 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4ry6l5q3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @stedmanhalliday's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1lxo4zkg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1lxo4zkg/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/stedmanhalliday')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/pierreavdb
|
huggingtweets
| 2022-03-23T16:50:02Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T16:43:47Z |
---
language: en
thumbnail: http://www.huggingtweets.com/pierreavdb/1648054135143/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1479780096483512323/LmKFSR3X_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Pierre</div>
<div style="text-align: center; font-size: 14px;">@pierreavdb</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Pierre.
| Data | Pierre |
| --- | --- |
| Tweets downloaded | 1064 |
| Retweets | 172 |
| Short tweets | 133 |
| Tweets kept | 759 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21bimkjn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @pierreavdb's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ji40nkbv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ji40nkbv/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/pierreavdb')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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