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: wav2vec2-base-MIR_ST500-demo-colab
  results: []
---

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# wav2vec2-base-MIR_ST500-demo-colab

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7360
- Wer: 0.9837

## 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: 16
- 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: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 101.0917      | 16.67  | 100  | 18.8979         | 0.8208 |
| 15.5054       | 33.33  | 200  | 10.9184         | 0.8208 |
| 10.1879       | 50.0   | 300  | 7.6480          | 0.8208 |
| 6.777         | 66.67  | 400  | 3.5386          | 1.0    |
| 3.0546        | 83.33  | 500  | 2.8794          | 1.0    |
| 2.8661        | 100.0  | 600  | 2.8405          | 1.0    |
| 2.847         | 116.67 | 700  | 2.8554          | 1.0    |
| 2.7661        | 133.33 | 800  | 2.6343          | 1.0    |
| 2.3474        | 150.0  | 900  | 2.7464          | 1.0    |
| 2.2464        | 166.67 | 1000 | 2.3565          | 1.0    |
| 2.207         | 183.33 | 1100 | 2.8854          | 1.0    |
| 2.3138        | 200.0  | 1200 | 2.5868          | 1.0    |
| 2.259         | 216.67 | 1300 | 2.6530          | 1.0    |
| 2.1667        | 233.33 | 1400 | 2.4921          | 1.0    |
| 2.1268        | 250.0  | 1500 | 2.5435          | 1.0    |
| 2.1089        | 266.67 | 1600 | 2.5444          | 1.0    |
| 2.0845        | 283.33 | 1700 | 2.6796          | 1.0    |
| 2.0672        | 300.0  | 1800 | 2.5824          | 1.0    |
| 2.055         | 316.67 | 1900 | 2.4631          | 1.0    |
| 2.0317        | 333.33 | 2000 | 2.5751          | 1.0    |
| 2.0141        | 350.0  | 2100 | 2.5627          | 1.0    |
| 1.9914        | 366.67 | 2200 | 2.6132          | 1.0    |
| 1.9489        | 383.33 | 2300 | 2.7527          | 1.0    |
| 1.9146        | 400.0  | 2400 | 2.6121          | 0.9935 |
| 1.893         | 416.67 | 2500 | 2.7110          | 0.9902 |
| 1.845         | 433.33 | 2600 | 2.7410          | 0.9967 |
| 1.8095        | 450.0  | 2700 | 2.7013          | 0.9935 |
| 1.7708        | 466.67 | 2800 | 2.7719          | 0.9935 |
| 1.7224        | 483.33 | 2900 | 2.7740          | 0.9837 |
| 1.6961        | 500.0  | 3000 | 2.7360          | 0.9837 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
