metadata

license: cc-by-4.0
language:
  - en
pipeline_tag: automatic-speech-recognition
library_name: nemo
datasets:
  - librispeech_asr
  - fisher_corpus
  - mozilla-foundation/common_voice_8_0
  - National-Singapore-Corpus-Part-1
  - vctk
  - voxpopuli
  - europarl
  - multilingual_librispeech
thumbnail: null
tags:
  - automatic-speech-recognition
  - speech
  - audio
  - Transducer
  - TDT
  - FastConformer
  - Conformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
widget:
  - example_title: Librispeech sample 1
    src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
  - example_title: Librispeech sample 2
    src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
  - name: parakeet-tdt-0.6b-v2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: AMI (Meetings test)
          type: edinburghcstr/ami
          config: ihm
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 11.16
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Earnings-22
          type: revdotcom/earnings22
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 11.15
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: GigaSpeech
          type: speechcolab/gigaspeech
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 9.74
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: LibriSpeech (clean)
          type: librispeech_asr
          config: other
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 1.69
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: LibriSpeech (other)
          type: librispeech_asr
          config: other
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 3.19
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: SPGI Speech
          type: kensho/spgispeech
          config: test
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 2.17
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: tedlium-v3
          type: LIUM/tedlium
          config: release1
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 3.38
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Vox Populi
          type: facebook/voxpopuli
          config: en
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 5.95
metrics:
  - wer

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]