---
library_name: transformers
license: mit
base_model: ayameRushia/whisper-v3-turbo-id
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-v3-turbo-id
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: id
      split: test
      args: id
    metrics:
    - name: Wer
      type: wer
      value: 9.17372101582628
---

<!-- 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-v3-turbo-id

This model is a fine-tuned version of [ayameRushia/whisper-v3-turbo-id](https://huggingface.co/ayameRushia/whisper-v3-turbo-id) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1760
- Wer: 9.1737

## Model description

Fine tuned from openai/whisper-v3-turbo

## Intended uses & limitations

This model only trained using common voice version 17

## Training procedure
Preprocess data
```
import re

chars_to_ignore_regex = '[\,\?\.\!\;\:\"\”\’\'\“\(\)\[\\\\&/！\‘]' # delete following chars
chars_to_space_regex = '[\–\—\-]' # replace the following chars into space

def remove_special_characters(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
    batch["sentence"] = re.sub(chars_to_space_regex, ' ', batch["sentence"]) + " "
    # replacing some character
    batch["sentence"] = batch["sentence"].replace("é", "e").replace("á", "a").replace("ł", "l").replace("ń", "n").replace("ō", "o").strip()
    return batch

common_voice = common_voice.map(remove_special_characters)
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0706        | 1.9231 | 1000 | 0.2361          | 18.0484 |
| 0.0099        | 3.8462 | 2000 | 0.1875          | 10.3607 |
| 0.001         | 5.7692 | 3000 | 0.1760          | 9.1737  |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1