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Thonburian Whisper is an Automatic Speech Recognition (ASR) model for Thai, fine-tuned using Whisper model originally from OpenAI. The model is released as a part of Huggingface's Whisper fine-tuning event (December 2022). We fine-tuned Whisper models for Thai using Commonvoice 13, Gowajee corpus, Thai Elderly Speech, Thai Dialect datasets. Our models demonstrate robustness under environmental noise and fine-tuned abilities to domain-specific audio such as financial and medical domains. We release models and distilled models on Huggingface model hubs (see below).
Use the model with Huggingface's transformers as follows:
import torch
from transformers import pipeline
MODEL_NAME = "biodatlab/whisper-th-medium-combined" # see alternative model names below
lang = "th"
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
# Perform ASR with the created pipe.
pipe("audio.mp3", generate_kwargs={"language":"<|th|>", "task":"transcribe"}, batch_size=16)["text"]Use pip to install the requirements as follows:
!pip install git+https://github.com/huggingface/transformers
!pip install librosa
!sudo apt install ffmpegWe measure word error rate (WER) of the model with deepcut tokenizer after normalizing special tokens (▁ to _ and — to -) and simple text-postprocessing (เเ to แ and ํา to ำ).
| Model | WER (Commonvoice 13) |
|---|---|
| Thonburian Whisper (small) Link | 13.14 |
| Thonburian Whisper (medium) Link | 7.42 |
| Thonburian Whisper (large-v2) Link | 7.69 |
| Thonburian Whisper (large-v3) Link | 6.59 |
Thonburian Whisper is fine-tuned with a combined dataset of Thai speech including common voice, google fleurs, and curated datasets.
The common voice test splitting is based on original splitting from datasets.
Inference time
We have performed benchmark average inference speed on 1 minute audio with different model sizes (small, medium, and large) on NVIDIA A100 with 32 fp, batch size of 32. The medium model presents a balanced trade-off between WER and computational costs.
| Model | Memory usage (Mb) | Inference time (sec / 1 min) | Number of Parameters |
|---|---|---|---|
| Thonburian Whisper (small) Link | 7,194 | 4.83 | 242M |
| Thonburian Whisper (medium) Link | 10,878 | 7.11 | 764M |
| Thonburian Whisper (large) Link | 18,246 | 9.61 | 1540M |
| Distilled Thonburian Whisper (small) Link | 4,944 | TBA | 166M |
| Distilled Thonburian Whisper (medium) Link | 7,084 | TBA | 428M |
Thonburian Whisper can be used for long-form audio transcription by combining VAD, Thai-word tokenizer, and chunking for word-level alignment.
We found that this is more robust and produce less insertion error rate (IER) comparing to using Whisper with timestamp. See README.md in longform_transcription folder for detail usage.
If you use the model, you can cite it with the following bibtex.
@misc {thonburian_whisper_med,
author = { Zaw Htet Aung, Thanachot Thavornmongkol, Atirut Boribalburephan, Vittavas Tangsriworakan, 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 }
}