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Wav2Vec2_xls_r_lm_300m_hi

Automatic Speech Recognition
Transformers
PyTorch
Hindi
wav2vec2
Openslr Multilingual
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_7_0
robust-speech-event
Eval Results
Inference Endpoints
Model card Files Community
Wav2Vec2_xls_r_lm_300m_hi / README.md
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metadata
language:
  - hi
license: apache-2.0
tags:
  - Openslr Multilingual
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: Wav2Vec2_xls_r_300m_hi_final
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7.0
          type: mozilla-foundation/common_voice_7_0
          args: hi
        metrics:
          - name: Test WER
            type: wer
            value: 34.21

Wav2Vec2_xls_r_300m_hi_final

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the 'Openslr Multilingual and code-switching ASR challenge' dataset and 'mozilla-foundation/common_voice_7_0' dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3035
  • Wer: 0.3137
  • Cer: 0.0972

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 8
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.9821 0.64 400 0.5059 0.4783 0.1573
0.6861 1.28 800 0.4201 0.4247 0.1356
0.585 1.92 1200 0.3797 0.3811 0.1210
0.5193 2.56 1600 0.3577 0.3652 0.1152
0.4583 3.21 2000 0.3422 0.3519 0.1111
0.4282 3.85 2400 0.3261 0.3450 0.1071
0.3951 4.49 2800 0.3201 0.3325 0.1048
0.3619 5.13 3200 0.3167 0.3296 0.1030
0.345 5.77 3600 0.3157 0.3210 0.1013
0.338 6.41 4000 0.3051 0.3143 0.0982
0.3155 7.05 4400 0.3059 0.3154 0.0986
0.3057 7.69 4800 0.3035 0.3137 0.0972

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3
  • Tokenizers 0.11.0