Instructions to use scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv") - Notebooks
- Google Colab
- Kaggle
wav2vec2-base_toy_train_data_augment_0.1.csv
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.3933
- Wer: 0.9997
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: 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: 1000
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.2787 | 0.84 | 200 | 3.5920 | 1.0 |
| 3.0613 | 1.68 | 400 | 3.4069 | 1.0 |
| 3.0481 | 2.52 | 600 | 3.4811 | 1.0 |
| 2.896 | 3.36 | 800 | 2.3933 | 0.9997 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
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