Whisper Large V3 (Thai): Combined V1

This model is a fine-tuned version of openai/whisper-medium 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)

Model description

Use the model with huggingface's transformers as follows:

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

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

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 }
}

Base model