DrishtiSharma/wav2vec2-xls-r-myv-a1

🤗 Hugging Face 来源automatic-speech-recognitionapache-2.028 GBother✓ 2 个校验和今天更新
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This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MYV dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0356
  • Wer: 0.6524

Evaluation Commands

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

python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-myv-a1 --dataset mozilla-foundation/common_voice_8_0 --config myv --split test --log_outputs

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

Erzya language not found 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: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 800
  • num_epochs: 200.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
5.649 9.62 500 3.0038 1.0
1.6272 19.23 1000 0.7362 0.7819
1.1354 28.85 1500 0.6410 0.7111
1.0424 38.46 2000 0.6907 0.7431
0.9293 48.08 2500 0.7249 0.7102
0.8246 57.69 3000 0.7422 0.6966
0.7837 67.31 3500 0.7413 0.6813
0.7147 76.92 4000 0.7873 0.6930
0.6276 86.54 4500 0.8038 0.6677
0.6041 96.15 5000 0.8240 0.6831
0.5336 105.77 5500 0.8748 0.6749
0.4705 115.38 6000 0.9006 0.6497
0.43 125.0 6500 0.8954 0.6551
0.3859 134.62 7000 0.9074 0.6614
0.3342 144.23 7500 0.9693 0.6560
0.3155 153.85 8000 1.0073 0.6691
0.2673 163.46 8500 1.0170 0.6632
0.2409 173.08 9000 1.0304 0.6709
0.2189 182.69 9500 0.9965 0.6546
0.1973 192.31 10000 1.0360 0.6551

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0

Evaluation Command

!python eval.py
--model_id DrishtiSharma/wav2vec2-large-xls-r-300m-myv-v1
--dataset mozilla-foundation/common_voice_8_0 --config myv --split test --log_outputs