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DrishtiSharma
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wav2vec2-large-xls-r-300m-br-d10

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Breton
wav2vec2
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Metrics Community
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metadata
language:
  - br
license: apache-2.0
tags:
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-br-d10
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice 8
          args: br
        metrics:
          - type: wer
            value: 0.5230357484228637
            name: Test WER
          - name: Test CER
            type: cer
            value: 0.1880661144228536
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: br
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

wav2vec2-large-xls-r-300m-br-d10

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BR dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1382
  • Wer: 0.4895

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d10 --dataset mozilla-foundation/common_voice_8_0 --config br --split test --log_outputs

  1. To evaluate on speech-recognition-community-v2/dev_data

Breton language isn't available in speech-recognition-community-v2/dev_data

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0004
  • 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: 800
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
13.611 0.68 100 5.8492 1.0
3.8176 1.35 200 3.2181 1.0
3.0457 2.03 300 3.0902 1.0
2.2632 2.7 400 1.4882 0.9426
1.1965 3.38 500 1.1396 0.7950
0.984 4.05 600 1.0216 0.7583
0.8036 4.73 700 1.0258 0.7202
0.7061 5.41 800 0.9710 0.6820
0.689 6.08 900 0.9731 0.6488
0.6063 6.76 1000 0.9442 0.6569
0.5215 7.43 1100 1.0221 0.6671
0.4965 8.11 1200 0.9266 0.6181
0.4321 8.78 1300 0.9050 0.5991
0.3762 9.46 1400 0.9801 0.6134
0.3747 10.14 1500 0.9210 0.5747
0.3554 10.81 1600 0.9720 0.6051
0.3148 11.49 1700 0.9672 0.6099
0.3176 12.16 1800 1.0120 0.5966
0.2915 12.84 1900 0.9490 0.5653
0.2696 13.51 2000 0.9394 0.5819
0.2569 14.19 2100 1.0197 0.5667
0.2395 14.86 2200 0.9771 0.5608
0.2367 15.54 2300 1.0516 0.5678
0.2153 16.22 2400 1.0097 0.5679
0.2092 16.89 2500 1.0143 0.5430
0.2046 17.57 2600 1.0884 0.5631
0.1937 18.24 2700 1.0113 0.5648
0.1752 18.92 2800 1.0056 0.5470
0.164 19.59 2900 1.0340 0.5508
0.1723 20.27 3000 1.0743 0.5615
0.1535 20.95 3100 1.0495 0.5465
0.1432 21.62 3200 1.0390 0.5333
0.1561 22.3 3300 1.0798 0.5590
0.1384 22.97 3400 1.1716 0.5449
0.1359 23.65 3500 1.1154 0.5420
0.1356 24.32 3600 1.0883 0.5387
0.1355 25.0 3700 1.1114 0.5504
0.1158 25.68 3800 1.1171 0.5388
0.1166 26.35 3900 1.1335 0.5403
0.1165 27.03 4000 1.1374 0.5248
0.1064 27.7 4100 1.0336 0.5298
0.0987 28.38 4200 1.0407 0.5216
0.104 29.05 4300 1.1012 0.5350
0.0894 29.73 4400 1.1016 0.5310
0.0912 30.41 4500 1.1383 0.5302
0.0972 31.08 4600 1.0851 0.5214
0.0832 31.76 4700 1.1705 0.5311
0.0859 32.43 4800 1.0750 0.5192
0.0811 33.11 4900 1.0900 0.5180
0.0825 33.78 5000 1.1271 0.5196
0.07 34.46 5100 1.1289 0.5141
0.0689 35.14 5200 1.0960 0.5101
0.068 35.81 5300 1.1377 0.5050
0.0776 36.49 5400 1.0880 0.5194
0.0642 37.16 5500 1.1027 0.5076
0.0607 37.84 5600 1.1293 0.5119
0.0607 38.51 5700 1.1229 0.5103
0.0545 39.19 5800 1.1168 0.5103
0.0562 39.86 5900 1.1206 0.5073
0.0484 40.54 6000 1.1710 0.5019
0.0499 41.22 6100 1.1511 0.5100
0.0455 41.89 6200 1.1488 0.5009
0.0475 42.57 6300 1.1196 0.4944
0.0413 43.24 6400 1.1654 0.4996
0.0389 43.92 6500 1.0961 0.4930
0.0428 44.59 6600 1.0955 0.4938
0.039 45.27 6700 1.1323 0.4955
0.0352 45.95 6800 1.1040 0.4930
0.0334 46.62 6900 1.1382 0.4942
0.0338 47.3 7000 1.1264 0.4911
0.0307 47.97 7100 1.1216 0.4881
0.0286 48.65 7200 1.1459 0.4894
0.0348 49.32 7300 1.1419 0.4906
0.0329 50.0 7400 1.1382 0.4895

Framework versions

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