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wav2vec2-Irish-common-voice-Fleurs-living-audio-300m

Automatic Speech Recognition
Transformers
PyTorch
Irish
wav2vec2
audio
ga-IE
speech
Irish
Eval Results
Inference Endpoints
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wav2vec2-Irish-common-voice-Fleurs-living-audio-300m / README.md
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metadata
language: ga
datasets:
  - common_voice
  - google/fleurs
  - living_audio_irish
metrics:
  - wer
tags:
  - audio
  - automatic-speech-recognition
  - ga-IE
  - speech
  - Irish
model-index:
  - name: Wav2vec 2.0 300m XLS-R
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 10.0
          type: common_voice
          args: ga-IE
        metrics:
          - name: Test WER (Without LM)
            type: wer
            value: 19.98
          - name: Test WER (With LM)
            type: wer
            value: 13.87
          - name: Common Voice Irish Invalidated 281 utterances (Without LM)
            type: wer
            value: 39.19
          - name: Common Voice Irish Invalidated 281 utterances (With LM)
            type: wer
            value: 30.85

wav2vec2-Irish-common-voice-Fleurs-living-audio-300m

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/FLEURS - GA-IE, Common Voice Irish (Validated - (minus) Test) and Living audio Irish Speech dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3361
  • Wer: 0.1963

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 3
  • 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: 500
  • num_epochs: 18.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.56 200 2.8832 1.0
No log 1.11 400 1.1705 0.7788
3.3987 1.67 600 0.7739 0.5895
3.3987 2.23 800 0.6045 0.4902
0.8313 2.78 1000 0.5235 0.4394
0.8313 3.34 1200 0.4824 0.4002
0.8313 3.9 1400 0.4378 0.3754
0.5342 4.46 1600 0.4433 0.3634
0.5342 5.01 1800 0.4103 0.3485
0.3792 5.57 2000 0.3816 0.3310
0.3792 6.13 2200 0.3953 0.3225
0.3792 6.68 2400 0.3995 0.3132
0.2924 7.24 2600 0.3907 0.2930
0.2924 7.8 2800 0.3517 0.2740
0.2217 8.36 3000 0.3361 0.2591
0.2217 8.91 3200 0.3340 0.2451
0.2217 9.47 3400 0.3126 0.2448
0.1714 10.03 3600 0.3441 0.2556
0.1714 10.58 3800 0.3404 0.2521
0.1395 11.14 4000 0.3728 0.2518
0.1395 11.7 4200 0.3829 0.2396
0.1395 12.26 4400 0.3466 0.2361
0.1069 12.81 4600 0.3188 0.2241
0.1069 13.37 4800 0.3396 0.2197
0.0845 13.93 5000 0.3365 0.2206
0.0845 14.48 5200 0.3459 0.2209
0.0845 15.04 5400 0.3429 0.2194
0.0675 15.6 5600 0.3434 0.2182
0.0675 16.16 5800 0.3434 0.2083
0.0561 16.71 6000 0.3375 0.2036
0.0561 17.27 6200 0.3446 0.1987
0.0561 17.83 6400 0.3362 0.1978

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1
  • Tokenizers 0.13.2