---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: wav2vec2-large_phoneme-timit_english_timit-4k_001
  results: []
language:
- en
metrics:
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---

<!-- 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. -->

# wav2vec2-large_phoneme-timit_english_timit-4k_001

This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the timit dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4952
- Per: 0.1134

## Model description

The wav2vec 2.0 large model is pre-trained on 960 hours of the LibriSpeech dataset.
- 24 Transformer blocks (Each block: 1024 dimensions & 16 attention heads)

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Per    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.5458        | 3.46  | 1000  | 0.9087          | 0.2354 |
| 0.7877        | 6.92  | 2000  | 0.4441          | 0.1506 |
| 0.5125        | 10.38 | 3000  | 0.4241          | 0.1451 |
| 0.4485        | 13.84 | 4000  | 0.4244          | 0.1461 |
| 0.4193        | 17.3  | 5000  | 0.4618          | 0.1510 |
| 0.3899        | 20.76 | 6000  | 0.4700          | 0.1469 |
| 0.3244        | 24.22 | 7000  | 0.4496          | 0.1438 |
| 0.2717        | 27.68 | 8000  | 0.4988          | 0.1455 |
| 0.2222        | 31.14 | 9000  | 0.5182          | 0.1414 |
| 0.1872        | 34.6  | 10000 | 0.5320          | 0.1411 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.13.3