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DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3

🤗 Hugging Face 来源automatic-speech-recognitionapache-2.05.0 GBother✓ 4 个校验和今天更新
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curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3 ./model-folder
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wav2vec2-large-xls-r-300m-hi-d3

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: 0.7988
  • Wer: 0.3713

###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-hi-d3 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs

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

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

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.000388
  • 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: 750
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
8.2826 1.36 200 3.5253 1.0
2.7019 2.72 400 1.1744 0.7360
0.7358 4.08 600 0.7781 0.5501
0.4942 5.44 800 0.7590 0.5345
0.4056 6.8 1000 0.6885 0.4776
0.3243 8.16 1200 0.7195 0.4861
0.2785 9.52 1400 0.7473 0.4930
0.2448 10.88 1600 0.7201 0.4574
0.2155 12.24 1800 0.7686 0.4648
0.2039 13.6 2000 0.7440 0.4624
0.1792 14.96 2200 0.7815 0.4658
0.1695 16.33 2400 0.7678 0.4557
0.1598 17.68 2600 0.7468 0.4393
0.1568 19.05 2800 0.7440 0.4422
0.1391 20.41 3000 0.7656 0.4317
0.1283 21.77 3200 0.7892 0.4299
0.1194 23.13 3400 0.7646 0.4192
0.1116 24.49 3600 0.8156 0.4330
0.1111 25.85 3800 0.7661 0.4322
0.1023 27.21 4000 0.7419 0.4276
0.1007 28.57 4200 0.8488 0.4245
0.0925 29.93 4400 0.8062 0.4070
0.0918 31.29 4600 0.8412 0.4218
0.0813 32.65 4800 0.8045 0.4087
0.0805 34.01 5000 0.8411 0.4113
0.0774 35.37 5200 0.7664 0.3943
0.0666 36.73 5400 0.8082 0.3939
0.0655 38.09 5600 0.7948 0.4000
0.0617 39.45 5800 0.8084 0.3932
0.0606 40.81 6000 0.8223 0.3841
0.0569 42.18 6200 0.7892 0.3832
0.0544 43.54 6400 0.8326 0.3834
0.0508 44.89 6600 0.7952 0.3774
0.0492 46.26 6800 0.7923 0.3756
0.0459 47.62 7000 0.7925 0.3701
0.0423 48.98 7200 0.7988 0.3713

Framework versions

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