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 }
}