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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). See an example evaluation script here.
| Model | WER (Commonvoice 13) | Model URL |
|---|---|---|
| Thonburian Whisper (small) | 11.0 | Link |
| Thonburian Whisper (medium) | 7.42 | Link |
| Thonburian Whisper (large-v2) | 7.69 | Link |
| Thonburian Whisper (large-v3) | 6.59 | Link |
| Distilled Thonburian Whisper (small) | 11.2 | Link |
| Distilled Thonburian Whisper (medium) | 7.6 | Link |
| Thonburian Whisper (medium-timestamps) | 15.57 | Link |
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 fp32 precision, batch size of 1. The medium model presents a balanced trade-off between WER and computational costs. (Note that the distilled models due to their smaller size and a batch size of 1 are not fully saturating the GPU. With higher batch size, the inference time will be lower substantially.)
| Model | Memory usage (Mb) | Inference time (sec / 1 min) | Number of Parameters | Model URL |
|---|---|---|---|---|
| Thonburian Whisper (small) | 931.93 | 0.50 | 242M | Link |
| Thonburian Whisper (medium) | 2923.05 | 0.83 | 764M | Link |
| Thonburian Whisper (large) | 6025.84 | 1.89 | 1540M | Link |
| Distilled Thonburian Whisper (small) | 650.27 | 4.42 | 166M | Link |
| Distilled Thonburian Whisper (medium) | 1642.15 | 4.36 | 428M | Link |
These models are fine-tuned versions of OpenAI's Whisper, optimized for Thai ASR:
- Small: Balanced performance with lower resource requirements.
- Medium: Best trade-off between accuracy and computational cost.
- Large-v2/v3: Highest accuracy, but more resource-intensive.
Use these for general Thai ASR tasks where timestamps are not required.
Model: biodatlab/whisper-th-medium-timestamp
This model is specifically designed for Thai ASR with timestamp generation. It's based on the Whisper medium architecture and fine-tuned on a custom longform dataset.
Key Features:
- Generates timestamps for transcribed text
- WER: 15.57 (with Deepcut Tokenizer)
- Suitable for subtitle creation or audio-text alignment tasks
Usage:
from transformers import pipeline
import torch
MODEL_NAME = "biodatlab/whisper-th-medium-timestamp"
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,
return_timestamps=True,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(
language=lang,
task="transcribe"
)
result = pipe("audio.mp3", return_timestamps=True)
text, timestamps = result["text"], result["chunks"]Note: While this model provides timestamp information, its accuracy may be lower than non-timestamped versions due to several factors.
These models are distilled versions of the larger Thonburian Whisper models, offering improved efficiency:
-
Distilled Medium:
- 4 decoder layers (vs 24 in teacher model)
- Distilled from the Medium Whisper ASR model
-
Distilled Small:
- 4 decoder layers (vs 12 in teacher model)
- Distilled from the Small Whisper ASR model
Both distilled models were trained on a combination of Common Voice v13, Gowajee, Thai Elderly Speech Corpus, custom scraped data, and Thai-Central Dialect from SLSCU Thai Dialect Corpus.
Use these models for efficient Thai ASR in resource-constrained environments or for faster inference times.
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 }
}