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wav2vec2-large-xls-r-300m-hindi

Automatic Speech Recognition Transformers PyTorch TensorBoard
Hindi wav2vec2 generated_from_trainer hf-asr-leaderboard model_for_talk mozilla-foundation/common_voice_7_0 robust-speech-event Eval Results Inference Endpoints
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metadata
language:
  - hi
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - hi
  - model_for_talk
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-300M - Hindi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: hi
        metrics:
          - name: Test WER
            type: wer
            value: 100
          - name: Test CER
            type: cer
            value: 92.98

wav2vec2-large-xls-r-300m-hindi

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5414
  • Wer: 1.0194

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: 7.5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.6095 3.38 500 4.5881 0.9999
3.3396 6.76 1000 3.3301 1.0001
2.0061 10.14 1500 1.2096 1.0063
1.523 13.51 2000 0.7836 1.0051
1.3868 16.89 2500 0.6837 1.0080
1.2807 20.27 3000 0.6568 1.0112
1.231 23.65 3500 0.6120 1.0105
1.1673 27.03 4000 0.5972 1.0089
1.1416 30.41 4500 0.5780 1.0132
1.0738 33.78 5000 0.5806 1.0123
1.0771 37.16 5500 0.5586 1.0067
1.0287 40.54 6000 0.5464 1.0058
1.0106 43.92 6500 0.5407 1.0062
0.9538 47.3 7000 0.5334 1.0089
0.9607 50.68 7500 0.5395 1.0110
0.9108 54.05 8000 0.5502 1.0137
0.9252 57.43 8500 0.5498 1.0062
0.8943 60.81 9000 0.5448 1.0158
0.8728 64.19 9500 0.5257 1.0113
0.8577 67.57 10000 0.5550 1.0178
0.8332 70.95 10500 0.5607 1.0166
0.8174 74.32 11000 0.5429 1.0145
0.8168 77.7 11500 0.5561 1.0116
0.7872 81.08 12000 0.5478 1.0164
0.7707 84.46 12500 0.5412 1.0216
0.7742 87.84 13000 0.5391 1.0207
0.7594 91.22 13500 0.5379 1.0208
0.7678 94.59 14000 0.5415 1.0198
0.7502 97.97 14500 0.5409 1.0191

Framework versions

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