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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
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metadata
license: apache-2.0
language:
  - vi
tags:
  - automatic-speech-recognition
  - robust-speech-event
  - common-voice
model-index:
  - name: xls-asr-vi-40h-1B
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7.0 vi
          type: common_voice
          args: vi
        metrics:
          - name: Test WER
            type: wer
            value: 34.21
          - name: Test CER
            type: cer
            value: 19.94

xls-asr-vi-40h-1B

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common voice 7.0 vi & private dataset.

Benchmark WER result:

VIVOS COMMON VOICE 7.0 VI
without LM 25.92 34.21

Benchmark CER result:

VIVOS COMMON VOICE 7.0 VI
without LM 9.24 19.94

Training and evaluation data

More information needed

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: 50.0
  • mixed_precision_training: Native AMP
  • attention_dropout: 0.2
  • activation_dropout: 0.1
  • warmup_steps: 1500
  • mask_time_prob: .15
  • mask_time_length: 10
  • mask_feature_prob: 0.25
  • mask_feature_length: 64

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