---
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: 0.4090867704634435
---

<!-- 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: 0.4218
- Wer: 0.4091

## 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.1612        | 0.8621  | 100  | 3.1181          | 1.0    |
| 2.978         | 1.7241  | 200  | 2.9722          | 1.0    |
| 2.9185        | 2.5862  | 300  | 2.9098          | 1.0    |
| 2.1282        | 3.4483  | 400  | 2.0066          | 1.0247 |
| 1.1234        | 4.3103  | 500  | 1.0197          | 0.8393 |
| 0.602         | 5.1724  | 600  | 0.6714          | 0.6600 |
| 0.5032        | 6.0345  | 700  | 0.5285          | 0.5659 |
| 0.3101        | 6.8966  | 800  | 0.4819          | 0.5282 |
| 0.3432        | 7.7586  | 900  | 0.4653          | 0.5272 |
| 0.1922        | 8.6207  | 1000 | 0.4672          | 0.4918 |
| 0.2284        | 9.4828  | 1100 | 0.4834          | 0.4870 |
| 0.1372        | 10.3448 | 1200 | 0.4380          | 0.4727 |
| 0.1105        | 11.2069 | 1300 | 0.4509          | 0.4594 |
| 0.0992        | 12.0690 | 1400 | 0.4196          | 0.4544 |
| 0.1226        | 12.9310 | 1500 | 0.4237          | 0.4321 |
| 0.1013        | 13.7931 | 1600 | 0.4113          | 0.4298 |
| 0.0661        | 14.6552 | 1700 | 0.4038          | 0.4276 |
| 0.0901        | 15.5172 | 1800 | 0.4321          | 0.4225 |
| 0.053         | 16.3793 | 1900 | 0.4076          | 0.4236 |
| 0.0805        | 17.2414 | 2000 | 0.4336          | 0.4156 |
| 0.049         | 18.1034 | 2100 | 0.4193          | 0.4114 |
| 0.0717        | 18.9655 | 2200 | 0.4139          | 0.4091 |
| 0.0389        | 19.8276 | 2300 | 0.4216          | 0.4087 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0a0+git71dd2de
- Datasets 2.19.1
- Tokenizers 0.19.1