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jjyaoao/Echotune_clean_test

This model is a fine-tuned version of facebook/data2vec-audio-base-960h on the librispeech_asr dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0679
  • Wer Ortho: 0.0369
  • Wer: 0.0374

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: 6e-05
  • train_batch_size: 12
  • eval_batch_size: 12
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 34246.8
  • num_epochs: 12

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.0602 0.21 500 0.0476 0.0435 0.0439
0.0478 0.42 1000 0.0436 0.0411 0.0414
0.0492 0.63 1500 0.0443 0.0412 0.0415
0.0426 0.84 2000 0.0439 0.0401 0.0403
0.0386 1.05 2500 0.0445 0.0391 0.0395
0.0409 1.26 3000 0.0438 0.0394 0.0399
0.0437 1.47 3500 0.0444 0.0389 0.0393
0.0349 1.68 4000 0.0450 0.0392 0.0396
0.0469 1.89 4500 0.0442 0.0374 0.0378
0.033 2.1 5000 0.0454 0.0359 0.0363
0.0395 2.31 5500 0.0462 0.0363 0.0367
0.0321 2.52 6000 0.0457 0.0365 0.0369
0.0385 2.73 6500 0.0455 0.0355 0.0358
0.0378 2.94 7000 0.0449 0.0361 0.0366
0.0435 3.15 7500 0.0440 0.0355 0.0360
0.0436 3.36 8000 0.0466 0.0339 0.0344
0.0394 3.57 8500 0.0480 0.0345 0.0350
0.0448 3.78 9000 0.0478 0.0338 0.0342
0.0451 3.99 9500 0.0460 0.0355 0.0361
0.035 4.2 10000 0.0485 0.0369 0.0374
0.0387 4.41 10500 0.0487 0.0358 0.0362
0.0479 4.62 11000 0.0496 0.0363 0.0368
0.0456 4.83 11500 0.0491 0.0359 0.0365
0.0372 5.04 12000 0.0507 0.0355 0.0360
0.0395 5.25 12500 0.0526 0.0353 0.0356
0.0323 5.46 13000 0.0515 0.0368 0.0373
0.0354 5.67 13500 0.0524 0.0338 0.0343
0.031 5.88 14000 0.0531 0.0349 0.0357
0.0295 6.09 14500 0.0560 0.0344 0.0349
0.032 6.31 15000 0.0564 0.0364 0.0369
0.0462 6.52 15500 0.0548 0.0358 0.0365
0.0467 6.73 16000 0.0562 0.0347 0.0352
0.0437 6.94 16500 0.0573 0.0354 0.0359
0.0357 7.15 17000 0.0561 0.0359 0.0362
0.0297 7.36 17500 0.0602 0.0347 0.0351
0.0388 7.57 18000 0.0552 0.0341 0.0345
0.0392 7.78 18500 0.0533 0.0326 0.0331
0.0419 7.99 19000 0.0535 0.0343 0.0349
0.0326 8.2 19500 0.0614 0.0374 0.0378
0.0423 8.41 20000 0.0585 0.0341 0.0346
0.0326 8.62 20500 0.0586 0.0356 0.0362
0.0448 8.83 21000 0.0637 0.0371 0.0375
0.0763 9.04 21500 0.0607 0.0359 0.0364
0.0317 9.25 22000 0.0635 0.0400 0.0405
0.0326 9.46 22500 0.0603 0.0368 0.0372
0.0393 9.67 23000 0.0665 0.0380 0.0385
0.0341 9.88 23500 0.0664 0.0408 0.0413
0.0351 10.09 24000 0.0638 0.0384 0.0388
0.0412 10.3 24500 0.0687 0.0380 0.0384
0.0359 10.51 25000 0.0634 0.0379 0.0385
0.047 10.72 25500 0.0652 0.0373 0.0378
0.0346 10.93 26000 0.0671 0.0390 0.0396
0.0366 11.14 26500 0.0664 0.0387 0.0393
0.0359 11.35 27000 0.0669 0.0369 0.0374
0.0366 11.56 27500 0.0705 0.0358 0.0364
0.054 11.77 28000 0.0659 0.0383 0.0390
0.0335 11.98 28500 0.0679 0.0369 0.0374

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.0+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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