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DrishtiSharma/wav2vec2-large-xls-r-300m-as-g1

🤗 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-as-g1 ./model-folder
需要做种者 →

wav2vec2-large-xls-r-300m-as-g1

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

  • Loss: 1.3327
  • Wer: 0.5744

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-as-g1 --dataset mozilla-foundation/common_voice_8_0 --config as --split test --log_outputs

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

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

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • 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: 1000
  • num_epochs: 200
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
14.1958 5.26 100 7.1919 1.0
5.0035 10.51 200 3.9362 1.0
3.6193 15.77 300 3.4451 1.0
3.4852 21.05 400 3.3536 1.0
2.8489 26.31 500 1.6451 0.9100
0.9568 31.56 600 1.0514 0.7561
0.4865 36.82 700 1.0434 0.7184
0.322 42.1 800 1.0825 0.7210
0.2383 47.36 900 1.1304 0.6897
0.2136 52.62 1000 1.1150 0.6854
0.179 57.87 1100 1.2453 0.6875
0.1539 63.15 1200 1.2211 0.6704
0.1303 68.41 1300 1.2859 0.6747
0.1183 73.67 1400 1.2775 0.6721
0.0994 78.92 1500 1.2321 0.6404
0.0991 84.21 1600 1.2766 0.6524
0.0887 89.46 1700 1.3026 0.6344
0.0754 94.72 1800 1.3199 0.6704
0.0693 99.97 1900 1.3044 0.6361
0.0568 105.26 2000 1.3541 0.6254
0.0536 110.51 2100 1.3320 0.6249
0.0529 115.77 2200 1.3370 0.6271
0.048 121.05 2300 1.2757 0.6031
0.0419 126.31 2400 1.2661 0.6172
0.0349 131.56 2500 1.2897 0.6048
0.0309 136.82 2600 1.2688 0.5962
0.0278 142.1 2700 1.2885 0.5954
0.0254 147.36 2800 1.2988 0.5915
0.0223 152.62 2900 1.3153 0.5941
0.0216 157.87 3000 1.2936 0.5937
0.0186 163.15 3100 1.2906 0.5877
0.0156 168.41 3200 1.3476 0.5962
0.0158 173.67 3300 1.3363 0.5847
0.0142 178.92 3400 1.3367 0.5847
0.0153 184.21 3500 1.3105 0.5757
0.0119 189.46 3600 1.3255 0.5705
0.0115 194.72 3700 1.3340 0.5787
0.0103 199.97 3800 1.3327 0.5744

Framework versions

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