---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-fine-tuned
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: TIMIT_ASR - NA
      type: timit_asr
      config: clean
      split: test
      args: 'Config: na, Training split: train, Eval split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.41728125284530637
---

<!-- 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-base-timit-fine-tuned

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4275
- Wer: 0.4173

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.1618        | 0.8621  | 100  | 3.1117          | 1.0    |
| 2.9798        | 1.7241  | 200  | 2.9736          | 1.0    |
| 2.9144        | 2.5862  | 300  | 2.9075          | 1.0    |
| 2.1714        | 3.4483  | 400  | 2.0945          | 1.0325 |
| 1.1579        | 4.3103  | 500  | 1.0451          | 0.8299 |
| 0.6087        | 5.1724  | 600  | 0.6754          | 0.6441 |
| 0.481         | 6.0345  | 700  | 0.5275          | 0.5761 |
| 0.3072        | 6.8966  | 800  | 0.4836          | 0.5264 |
| 0.332         | 7.7586  | 900  | 0.4403          | 0.5234 |
| 0.1876        | 8.6207  | 1000 | 0.4758          | 0.5222 |
| 0.2232        | 9.4828  | 1100 | 0.4508          | 0.4892 |
| 0.1332        | 10.3448 | 1200 | 0.4394          | 0.4740 |
| 0.1085        | 11.2069 | 1300 | 0.4466          | 0.4621 |
| 0.098         | 12.0690 | 1400 | 0.4230          | 0.4493 |
| 0.1219        | 12.9310 | 1500 | 0.4180          | 0.4460 |
| 0.1021        | 13.7931 | 1600 | 0.4179          | 0.4406 |
| 0.0741        | 14.6552 | 1700 | 0.4113          | 0.4309 |
| 0.0896        | 15.5172 | 1800 | 0.4392          | 0.4308 |
| 0.0492        | 16.3793 | 1900 | 0.4202          | 0.4313 |
| 0.0759        | 17.2414 | 2000 | 0.4348          | 0.4207 |
| 0.0406        | 18.1034 | 2100 | 0.4419          | 0.4205 |
| 0.074         | 18.9655 | 2200 | 0.4306          | 0.4200 |
| 0.0378        | 19.8276 | 2300 | 0.4273          | 0.4173 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0.post300
- Datasets 2.19.1
- Tokenizers 0.19.1