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DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4

🤗 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-sr-v4 ./model-folder
需要做种者 →

wav2vec2-large-xls-r-300m-sr-v4

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

  • Loss: 0.5570
  • Wer: 0.3038

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-sr-v4 --dataset mozilla-foundation/common_voice_8_0 --config sr --split test --log_outputs

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

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4 --dataset speech-recognition-community-v2/dev_data --config sr --split validation --chunk_length_s 10 --stride_length_s 1

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: 800
  • num_epochs: 200
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
8.2934 7.5 300 2.9777 0.9995
1.5049 15.0 600 0.5036 0.4806
0.3263 22.5 900 0.5822 0.4055
0.2008 30.0 1200 0.5609 0.4032
0.1543 37.5 1500 0.5203 0.3710
0.1158 45.0 1800 0.6458 0.3985
0.0997 52.5 2100 0.6227 0.4013
0.0834 60.0 2400 0.6048 0.3836
0.0665 67.5 2700 0.6197 0.3686
0.0602 75.0 3000 0.5418 0.3453
0.0524 82.5 3300 0.5310 0.3486
0.0445 90.0 3600 0.5599 0.3374
0.0406 97.5 3900 0.5958 0.3327
0.0358 105.0 4200 0.6017 0.3262
0.0302 112.5 4500 0.5613 0.3248
0.0285 120.0 4800 0.5659 0.3462
0.0213 127.5 5100 0.5568 0.3206
0.0215 135.0 5400 0.6524 0.3472
0.0162 142.5 5700 0.6223 0.3458
0.0137 150.0 6000 0.6625 0.3313
0.0114 157.5 6300 0.5739 0.3336
0.0101 165.0 6600 0.5906 0.3285
0.008 172.5 6900 0.5982 0.3112
0.0076 180.0 7200 0.5399 0.3094
0.0071 187.5 7500 0.5387 0.2991
0.0057 195.0 7800 0.5570 0.3038

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.2
  • Tokenizers 0.11.0