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---
license: apache-2.0
base_model: facebook/data2vec-audio-base-960h
tags:
- generated_from_trainer
datasets:
- librispeech_asr
metrics:
- wer
model-index:
- name: jjyaoao/Echotune_clean_test
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: librispeech_asr
type: librispeech_asr
config: clean
split: test
args: clean
metrics:
- name: Wer
type: wer
value: 0.037368222891566265
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# jjyaoao/Echotune_clean_test
This model is a fine-tuned version of [facebook/data2vec-audio-base-960h](https://huggingface.co/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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