Whisper Th Large V3 Combined

🚀 Whisper Large V3 (Thai): Combined V1

This model is a fine - tuned version of the [openai/whisper - medium](https://huggingface.co/openai/whisper - large - v3). It is trained on augmented versions of the mozilla - foundation/common_voice_13_0 th, google/fleurs, and curated datasets. It provides an effective solution for Thai automatic speech recognition, achieving high accuracy on relevant test sets.

🚀 Quick Start

Use the model with huggingface's transformers as follows:

Basic Usage

from transformers import pipeline

MODEL_NAME = "biodatlab/whisper - th - large - v3 - combined"  # specify the model name
lang = "th"  # change to Thai langauge

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic - speech - recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(
  language=lang,
  task="transcribe"
)
text = pipe("audio.mp3")["text"] # give audio mp3 and transcribe text

✨ Features

This fine - tuned model of Whisper Large V3 for Thai achieves a WER of 6.59 (with Deepcut Tokenizer) on the common - voice - 13 test set, showing high accuracy in Thai automatic speech recognition.

📚 Documentation

Model description

This model is a fine - tuned version of [openai/whisper - medium](https://huggingface.co/openai/whisper - large - v3) on augmented versions of the mozilla - foundation/common_voice_13_0 th, google/fleurs, and curated datasets. It achieves the following results on the common - voice - 13 test set:

• WER: 6.59 (with Deepcut Tokenizer)

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 10000
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0
• Datasets 2.16.1
• Tokenizers 0.15.1

📄 License

This model is licensed under the Apache 2.0 license.

📦 Information Table

📚 Citation

Cite using Bibtex:

@misc {thonburian_whisper_med,
    author       = { Atirut Boribalburephan, Zaw Htet Aung, Knot Pipatsrisawat, Titipat Achakulvisut },
    title        = { Thonburian Whisper: A fine - tuned Whisper model for Thai automatic speech recognition },
    year         = 2022,
    url          = { https://huggingface.co/biodatlab/whisper - th - medium - combined },
    doi          = { 10.57967/hf/0226 },
    publisher    = { Hugging Face }
}