---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-base-self-331-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: test
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.15007215007215008
---

<!-- 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-self-331-colab

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3282
- Wer: 0.1501

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 300
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.3444        | 30.77  | 200  | 2.1940          | 0.9841 |
| 1.972         | 61.54  | 400  | 1.4582          | 0.8167 |
| 1.3875        | 92.31  | 600  | 0.8476          | 0.5902 |
| 0.9092        | 123.08 | 800  | 0.5445          | 0.3636 |
| 0.6382        | 153.85 | 1000 | 0.4129          | 0.2641 |
| 0.5789        | 184.62 | 1200 | 0.3497          | 0.1876 |
| 0.4632        | 215.38 | 1400 | 0.3478          | 0.1616 |
| 0.4474        | 246.15 | 1600 | 0.3394          | 0.1486 |
| 0.429         | 276.92 | 1800 | 0.3282          | 0.1501 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2