---
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