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whisper-medium-id-augmented

Automatic Speech Recognition
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PyTorch
TensorBoard
Indonesian
whisper
whisper-event
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metadata
language:
  - id
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - google/fleurs
  - indonesian-nlp/librivox-indonesia
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
  - cer
model-index:
  - name: Whisper Medium ID - FLEURS-CV-LBV - Augmented
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: id_id
          split: test
        metrics:
          - type: wer
            value: 7.17
            name: WER
          - type: cer
            value: 2.39
            name: CER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0
          type: mozilla-foundation/common_voice_11_0
          config: id
          split: test
        metrics:
          - type: wer
            value: 7.59
            name: WER
          - type: cer
            value: 2.33
            name: CER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: indonesian-nlp/librivox-indonesia
          type: indonesian-nlp/librivox-indonesia
          config: ind
          split: test
        metrics:
          - type: wer
            value: 6.07
            name: WER
          - type: cer
            value: 1.84
            name: CER

Whisper Medium ID - FLEURS-CV-LBV - Augmented

This model is a fine-tuned version of openai/whisper-medium on the following datasets:

It achieves the following results on the evaluation set (Common Voice 11.0):

  • Loss: 0.2788
  • Wer: 7.6132
  • Cer: 2.3332

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Training:

Evaluation:

Training procedure

Datasets were augmented on-the-fly using audiomentations via PitchShift, AddGaussianNoise and TimeStretch transformations at p=0.3.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-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
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.3002 1.9 1000 0.1659 8.1850 2.5333
0.0514 3.8 2000 0.1818 8.0559 2.5244
0.0145 5.7 3000 0.2150 7.8945 2.5281
0.0037 7.6 4000 0.2248 7.7100 2.3738
0.0016 9.51 5000 0.2402 7.6224 2.3591
0.0009 11.41 6000 0.2525 7.7654 2.3952
0.0005 13.31 7000 0.2609 7.5994 2.3487
0.0008 15.21 8000 0.2682 7.5855 2.3347
0.0002 17.11 9000 0.2756 7.6178 2.3288
0.0002 19.01 10000 0.2788 7.6132 2.3332

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.1+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2