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anton-l/xtreme_s_xlsr_300m_fleurs_asr

🤗 Hugging Face 来源automatic-speech-recognitionapache-2.049 GBother✓ 8 个校验和今天更新
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xtreme_s_xlsr_300m_fleurs_asr

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

  • Cer: 0.3330
  • Loss: 1.2864
  • Wer: 0.8344

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 32
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 5.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Cer Validation Loss Wer
4.677 0.13 1000 1.0 3.2323 1.0
4.1512 0.26 2000 0.5098 1.7858 0.9869
1.119 0.39 3000 0.4412 1.6628 0.9063
0.8573 0.52 4000 0.3588 1.3440 0.9016
1.0232 0.65 5000 0.3690 1.3004 0.8775
0.6328 0.78 6000 0.3354 1.2219 0.8331
0.6636 0.91 7000 0.3604 1.2839 0.8637
0.6536 1.04 8000 0.3420 1.2481 0.8504
0.5002 1.17 9000 0.3518 1.2514 0.8403
0.4785 1.3 10000 0.3399 1.2409 0.8570
0.517 1.43 11000 0.3599 1.3058 0.8654
0.506 1.56 12000 0.3484 1.2350 0.8441
0.4013 1.69 13000 0.3327 1.1982 0.8246
0.3521 1.82 14000 0.3270 1.1653 0.8265
0.4265 1.95 15000 0.3562 1.2647 0.8564
0.3949 2.08 16000 0.3490 1.2988 0.8480
0.3059 2.21 17000 0.3327 1.2332 0.8323
0.3618 2.34 18000 0.3480 1.2394 0.8517
0.2567 2.47 19000 0.3365 1.2294 0.8394
0.3501 2.6 20000 0.3271 1.1853 0.8250
0.2766 2.73 21000 0.3425 1.2339 0.8443
0.3396 2.86 22000 0.3501 1.2768 0.8669
0.3566 2.99 23000 0.3477 1.2648 0.8710
0.3166 3.12 24000 0.3550 1.3773 0.8641
0.2388 3.25 25000 0.3301 1.2374 0.8316
0.2057 3.38 26000 0.3429 1.2846 0.8560
0.2264 3.51 27000 0.3469 1.2676 0.8542
0.1998 3.64 28000 0.3531 1.3365 0.8655
0.2701 3.77 29000 0.3518 1.3124 0.8711
0.18 3.9 30000 0.3498 1.3095 0.8648
0.1337 4.03 31000 0.3397 1.2941 0.8452
0.162 4.16 32000 0.3320 1.2942 0.8295
0.2776 4.29 33000 0.3275 1.2690 0.8276
0.1634 4.42 34000 0.3307 1.3145 0.8331
0.2172 4.54 35000 0.3334 1.3031 0.8435
0.1305 4.67 36000 0.3303 1.2768 0.8321
0.1436 4.8 37000 0.3353 1.2968 0.8416
0.134 4.93 38000 0.3330 1.2864 0.8344

Framework versions

  • Transformers 4.18.0.dev0
  • Pytorch 1.10.1+cu111
  • Datasets 1.18.4.dev0
  • Tokenizers 0.11.6