pytorch tensorboard wav2vec2 automatic-speech-recognition transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ctrlv-speechrecognition-model
  results: []
---

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# ctrlv-speechrecognition-model

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4730
- Wer: 0.3031

## Test WER in TIMIT dataset
- Wer: 0.189


[Google Colab Notebook](https://colab.research.google.com/drive/1M9ZbqvoRqshEccIlpTQGsgptpiGVgauH)

## 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: 8
- 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: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.53          | 3.45  | 500  | 1.4021          | 0.9307 |
| 0.6077        | 6.9   | 1000 | 0.4255          | 0.4353 |
| 0.2331        | 10.34 | 1500 | 0.3887          | 0.3650 |
| 0.1436        | 13.79 | 2000 | 0.3579          | 0.3393 |
| 0.1021        | 17.24 | 2500 | 0.4447          | 0.3440 |
| 0.0797        | 20.69 | 3000 | 0.4041          | 0.3291 |
| 0.0657        | 24.14 | 3500 | 0.4262          | 0.3368 |
| 0.0525        | 27.59 | 4000 | 0.4937          | 0.3429 |
| 0.0454        | 31.03 | 4500 | 0.4449          | 0.3244 |
| 0.0373        | 34.48 | 5000 | 0.4363          | 0.3288 |
| 0.0321        | 37.93 | 5500 | 0.4519          | 0.3204 |
| 0.0288        | 41.38 | 6000 | 0.4440          | 0.3145 |
| 0.0259        | 44.83 | 6500 | 0.4691          | 0.3182 |
| 0.0203        | 48.28 | 7000 | 0.5062          | 0.3162 |
| 0.0171        | 51.72 | 7500 | 0.4762          | 0.3129 |
| 0.0166        | 55.17 | 8000 | 0.4772          | 0.3090 |
| 0.0147        | 58.62 | 8500 | 0.4730          | 0.3031 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3