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DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2

🤗 Hugging Face 来源automatic-speech-recognitionapache-2.05.0 GBother✓ 4 个校验和今天更新
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在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对，然后提交。你只需开始做种，并粘贴你账户中的密钥。它只读取你的文件，绝不修改。如果愿意，可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2 ./model-folder
需要做种者 →

wav2vec2-large-xls-r-300m-hi-cv8-b2

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

  • Loss: 0.7322
  • Wer: 0.3469

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-cv8-b2 --dataset mozilla-foundation/common_voice_8_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: 0.00025
  • 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: 700
  • num_epochs: 35
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
9.6226 1.04 200 3.8855 1.0
3.4678 2.07 400 3.4283 1.0
2.3668 3.11 600 1.0743 0.7175
0.7308 4.15 800 0.7663 0.5498
0.4985 5.18 1000 0.6957 0.5001
0.3817 6.22 1200 0.6932 0.4866
0.3281 7.25 1400 0.7034 0.4983
0.2752 8.29 1600 0.6588 0.4606
0.2475 9.33 1800 0.6514 0.4328
0.219 10.36 2000 0.6396 0.4176
0.2036 11.4 2200 0.6867 0.4162
0.1793 12.44 2400 0.6943 0.4196
0.1724 13.47 2600 0.6862 0.4260
0.1554 14.51 2800 0.7615 0.4222
0.151 15.54 3000 0.7058 0.4110
0.1335 16.58 3200 0.7172 0.3986
0.1326 17.62 3400 0.7182 0.3923
0.1225 18.65 3600 0.6995 0.3910
0.1146 19.69 3800 0.7075 0.3875
0.108 20.73 4000 0.7297 0.3858
0.1048 21.76 4200 0.7413 0.3850
0.0979 22.8 4400 0.7452 0.3793
0.0946 23.83 4600 0.7436 0.3759
0.0897 24.87 4800 0.7289 0.3754
0.0854 25.91 5000 0.7271 0.3667
0.0803 26.94 5200 0.7378 0.3656
0.0752 27.98 5400 0.7488 0.3680
0.0718 29.02 5600 0.7185 0.3619
0.0702 30.05 5800 0.7428 0.3554
0.0653 31.09 6000 0.7447 0.3559
0.0638 32.12 6200 0.7327 0.3523
0.058 33.16 6400 0.7339 0.3488
0.0594 34.2 6600 0.7322 0.3469

Framework versions

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