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wav2vec2-large-xls-r-300m-hi-CV7

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
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wav2vec2-large-xls-r-300m-hi-CV7 / README.md
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metadata
language:
  - hi
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - hi
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-hi-CV7
    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: []
          - name: Test CER
            type: cer
            value: []

wav2vec2-large-xls-r-300m-hi-test123

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: 0.6588
  • Wer: 0.2987

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: #

  • train_batch_size: 16
  • eval_batch_size: 8
  • 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: 2000
  • num_epochs: 60
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
12.809 1.36 200 6.2066 1.0
4.3402 2.72 400 3.5184 1.0
3.4365 4.08 600 3.2779 1.0
1.8643 5.44 800 0.9875 0.6270
0.7504 6.8 1000 0.6382 0.4666
0.5328 8.16 1200 0.6075 0.4505
0.4364 9.52 1400 0.5785 0.4215
0.3777 10.88 1600 0.6279 0.4227
0.3374 12.24 1800 0.6536 0.4192
0.3236 13.6 2000 0.5911 0.4047
0.2877 14.96 2200 0.5955 0.4097
0.2643 16.33 2400 0.5923 0.3744
0.2421 17.68 2600 0.6307 0.3814
0.2218 19.05 2800 0.6036 0.3764
0.2046 20.41 3000 0.6286 0.3797
0.191 21.77 3200 0.6517 0.3889
0.1856 23.13 3400 0.6193 0.3661
0.1721 24.49 3600 0.7034 0.3727
0.1656 25.85 3800 0.6293 0.3591
0.1532 27.21 4000 0.6075 0.3611
0.1507 28.57 4200 0.6313 0.3565
0.1381 29.93 4400 0.6564 0.3578
0.1359 31.29 4600 0.6724 0.3543
0.1248 32.65 4800 0.6789 0.3512
0.1198 34.01 5000 0.6442 0.3539
0.1125 35.37 5200 0.6676 0.3419
0.1036 36.73 5400 0.7017 0.3435
0.0982 38.09 5600 0.6828 0.3319
0.0971 39.45 5800 0.6112 0.3351
0.0968 40.81 6000 0.6424 0.3252
0.0893 42.18 6200 0.6707 0.3304
0.0878 43.54 6400 0.6432 0.3236
0.0827 44.89 6600 0.6696 0.3240
0.0788 46.26 6800 0.6564 0.3180
0.0753 47.62 7000 0.6574 0.3130
0.0674 48.98 7200 0.6698 0.3175
0.0676 50.34 7400 0.6441 0.3142
0.0626 51.7 7600 0.6642 0.3121
0.0617 53.06 7800 0.6615 0.3117
0.0599 54.42 8000 0.6634 0.3059
0.0538 55.78 8200 0.6464 0.3033
0.0571 57.14 8400 0.6503 0.3018
0.0491 58.5 8600 0.6625 0.3025
0.0511 59.86 8800 0.6588 0.2987

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0