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
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Evaluation results