---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: whisper-small-300v2
  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: 67.56756756756756
---

<!-- 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. -->

# whisper-small-300v2

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9642
- Wer Ortho: 67.5676
- Wer: 67.5676

## 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: constant_with_warmup
- lr_scheduler_warmup_steps: 30
- training_steps: 300

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.8789        | 20.0  | 60   | 1.2473          | 70.2703   | 70.2703 |
| 0.0015        | 40.0  | 120  | 0.9230          | 72.9730   | 72.9730 |
| 0.0           | 60.0  | 180  | 0.9398          | 67.5676   | 67.5676 |
| 0.0           | 80.0  | 240  | 0.9529          | 67.5676   | 67.5676 |
| 0.0           | 100.0 | 300  | 0.9642          | 67.5676   | 67.5676 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1