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xls-asr-vi-40h-1B

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Vietnamese
wav2vec2
common-voice
hf-asr-leaderboard
robust-speech-event
Eval Results
Inference Endpoints
Model card Files Metrics Community
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metadata
license: apache-2.0
language:
  - vi
tags:
  - automatic-speech-recognition
  - common-voice
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: xls-asr-vi-40h-1B
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7.0
          type: mozilla-foundation/common_voice_7_0
          args: vi
        metrics:
          - name: Test WER (with LM)
            type: wer
            value: 25.846
          - name: Test CER (with LM)
            type: cer
            value: 12.961
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8.0
          type: mozilla-foundation/common_voice_8_0
          args: vi
        metrics:
          - name: Test WER (with LM)
            type: wer
            value: 31.158
          - name: Test CER (with LM)
            type: cer
            value: 16.179

xls-asr-vi-40h-1B

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on 40 hours of FPT Open Speech Dataset (FOSD) and Common Voice 7.0.

Benchmark WER result:

VIVOS COMMON VOICE 7.0 COMMON VOICE 8.0
without LM 25.93 34.21
with 4-grams LM 24.11 25.84 31.158

Benchmark CER result:

VIVOS COMMON VOICE 7.0 COMMON VOICE 8.0
without LM 9.24 19.94
with 4-grams LM 10.37 12.96 16.179

Evaluation

Please use the eval.py file to run the evaluation

python eval.py --model_id geninhu/xls-asr-vi-40h-1B --dataset mozilla-foundation/common_voice_7_0 --config vi --split test --log_outputs

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1500
  • num_epochs: 10.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.6222 1.85 1500 5.9479 0.5474
1.1362 3.7 3000 7.9799 0.5094
0.7814 5.56 4500 5.0330 0.4724
0.6281 7.41 6000 2.3484 0.5020
0.5472 9.26 7500 2.2495 0.4793
0.4827 11.11 9000 1.1530 0.4768
0.4327 12.96 10500 1.6160 0.4646
0.3989 14.81 12000 3.2633 0.4703
0.3522 16.67 13500 2.2337 0.4708
0.3201 18.52 15000 3.6879 0.4565
0.2899 20.37 16500 5.4389 0.4599
0.2776 22.22 18000 3.5284 0.4537
0.2574 24.07 19500 2.1759 0.4649
0.2378 25.93 21000 3.3901 0.4448
0.217 27.78 22500 1.1632 0.4565
0.2115 29.63 24000 1.7441 0.4232
0.1959 31.48 25500 3.4992 0.4304
0.187 33.33 27000 3.6163 0.4369
0.1748 35.19 28500 3.6038 0.4467
0.17 37.04 30000 2.9708 0.4362
0.159 38.89 31500 3.2045 0.4279
0.153 40.74 33000 3.2427 0.4287
0.1463 42.59 34500 3.5439 0.4270
0.139 44.44 36000 3.9381 0.4150
0.1352 46.3 37500 4.1744 0.4092
0.1369 48.15 39000 4.2279 0.4154
0.1273 50.0 40500 4.1691 0.4133

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.11.0