pytorch wav2vec2 automatic-speech-recognition dataset:common_voice transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-timit-demo-google-colab
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-base-timit-demo-google-colab

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6342
- Wer: 0.5808

## 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: 8
- 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 9.1358        | 1.19  | 500   | 3.2710          | 1.0    |
| 3.0499        | 2.38  | 1000  | 1.8976          | 1.0    |
| 1.279         | 3.56  | 1500  | 0.7502          | 0.8228 |
| 0.7953        | 4.75  | 2000  | 0.5914          | 0.7343 |
| 0.6451        | 5.94  | 2500  | 0.6152          | 0.7280 |
| 0.5351        | 7.13  | 3000  | 0.5948          | 0.7041 |
| 0.4633        | 8.31  | 3500  | 0.5585          | 0.6712 |
| 0.4272        | 9.5   | 4000  | 0.5372          | 0.6457 |
| 0.3803        | 10.69 | 4500  | 0.5404          | 0.6402 |
| 0.3462        | 11.88 | 5000  | 0.5862          | 0.6484 |
| 0.3302        | 13.06 | 5500  | 0.5991          | 0.6426 |
| 0.3096        | 14.25 | 6000  | 0.5687          | 0.6287 |
| 0.2839        | 15.44 | 6500  | 0.5798          | 0.6384 |
| 0.2701        | 16.63 | 7000  | 0.5775          | 0.6047 |
| 0.2507        | 17.81 | 7500  | 0.5638          | 0.6065 |
| 0.2376        | 19.0  | 8000  | 0.5937          | 0.6094 |
| 0.2264        | 20.19 | 8500  | 0.5944          | 0.6065 |
| 0.2146        | 21.38 | 9000  | 0.6050          | 0.6122 |
| 0.1947        | 22.57 | 9500  | 0.6283          | 0.5992 |
| 0.1982        | 23.75 | 10000 | 0.6126          | 0.6018 |
| 0.1924        | 24.94 | 10500 | 0.6075          | 0.5962 |
| 0.1855        | 26.13 | 11000 | 0.6344          | 0.5938 |
| 0.1839        | 27.32 | 11500 | 0.6118          | 0.5880 |
| 0.1741        | 28.5  | 12000 | 0.6381          | 0.5878 |
| 0.1726        | 29.69 | 12500 | 0.6342          | 0.5808 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
