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geninhu
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xls-asr-vi-40h

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
    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 Language model)
            type: wer
            value: 56.57

xls-asr-vi-40h

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common voice 7.0 vi & private dataset. It achieves the following results on the evaluation set (Without Language Model):

  • Loss: 1.1177
  • Wer: 60.58

Evaluation

Please run the eval.py file

!python eval_custom.py --model_id geninhu/xls-asr-vi-40h --dataset mozilla-foundation/common_voice_7_0 --config vi --split test

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-06
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1500
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
23.3878 0.93 1500 21.9179 1.0
8.8862 1.85 3000 6.0599 1.0
4.3701 2.78 4500 4.3837 1.0
4.113 3.7 6000 4.2698 0.9982
3.9666 4.63 7500 3.9726 0.9989
3.5965 5.56 9000 3.7124 0.9975
3.3944 6.48 10500 3.5005 1.0057
3.304 7.41 12000 3.3710 1.0043
3.2482 8.33 13500 3.4201 1.0155
3.212 9.26 15000 3.3732 1.0151
3.1778 10.19 16500 3.2763 1.0009
3.1027 11.11 18000 3.1943 1.0025
2.9905 12.04 19500 2.8082 0.9703
2.7095 12.96 21000 2.4993 0.9302
2.4862 13.89 22500 2.3072 0.9140
2.3271 14.81 24000 2.1398 0.8949
2.1968 15.74 25500 2.0594 0.8817
2.111 16.67 27000 1.9404 0.8630
2.0387 17.59 28500 1.8895 0.8497
1.9504 18.52 30000 1.7961 0.8315
1.9039 19.44 31500 1.7433 0.8213
1.8342 20.37 33000 1.6790 0.7994
1.7824 21.3 34500 1.6291 0.7825
1.7359 22.22 36000 1.5783 0.7706
1.7053 23.15 37500 1.5248 0.7492
1.6504 24.07 39000 1.4930 0.7406
1.6263 25.0 40500 1.4572 0.7348
1.5893 25.93 42000 1.4202 0.7161
1.5669 26.85 43500 1.3987 0.7143
1.5277 27.78 45000 1.3512 0.6991
1.501 28.7 46500 1.3320 0.6879
1.4781 29.63 48000 1.3112 0.6788
1.4477 30.56 49500 1.2850 0.6657
1.4483 31.48 51000 1.2813 0.6633
1.4065 32.41 52500 1.2475 0.6541
1.3779 33.33 54000 1.2244 0.6503
1.3788 34.26 55500 1.2116 0.6407
1.3428 35.19 57000 1.1938 0.6352
1.3453 36.11 58500 1.1927 0.6340
1.3137 37.04 60000 1.1699 0.6252
1.2984 37.96 61500 1.1666 0.6229
1.2927 38.89 63000 1.1585 0.6188
1.2919 39.81 64500 1.1618 0.6190
1.293 40.74 66000 1.1479 0.6181
1.2853 41.67 67500 1.1423 0.6202
1.2687 42.59 69000 1.1315 0.6131
1.2603 43.52 70500 1.1333 0.6128
1.2577 44.44 72000 1.1191 0.6079
1.2435 45.37 73500 1.1177 0.6079
1.251 46.3 75000 1.1211 0.6092
1.2482 47.22 76500 1.1177 0.6060
1.2422 48.15 78000 1.1227 0.6097
1.2485 49.07 79500 1.1187 0.6071
1.2425 50.0 81000 1.1177 0.6058

Framework versions

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