README.md

metadata

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

whisper-v3-turbo-id

This model is a fine-tuned version of 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