speaker-verification-v2

Speaker-verification-v2

How to Use this Model

The model is available for use in the and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

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Input

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Output

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Model Architecture

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Training

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Datasets

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NOTE

An example for the manifest section is provided below for ASR datasets

datasets:
- librispeech_asr
- fisher_corpus
- Switchboard-1
- WSJ-0
- WSJ-1
- National-Singapore-Corpus-Part-1
- National-Singapore-Corpus-Part-6
- vctk
- voxpopuli
- europarl
- multilingual_librispeech
- mozilla-foundation/common_voice_8_0
- MLCommons/peoples_speech

The corresponding text in this section for those datasets is stated below -

The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets:

- Librispeech 960 hours of English speech
- Fisher Corpus
- Switchboard-1 Dataset
- WSJ-0 and WSJ-1
- National Speech Corpus (Part 1, Part 6)
- VCTK
- VoxPopuli (EN)
- Europarl-ASR (EN)
- Multilingual Librispeech (MLS EN) - 2,000 hour subset
- Mozilla Common Voice (v7.0)
- People's Speech  - 12,000 hour subset

Performance

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NOTE

An example is provided below for ASR metrics list that can be added to the top of the README

model-index:
- name: PUT_MODEL_NAME
  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: 17.10
  - 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: 14.11

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Limitations

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Note

An example is provided below 

Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.