Model reference · open weights

whisper-th-small

Available as managed deployment Audio biodatlab Speech→text 1 variants 1k dl/mo

whisper-th-small is an open-weight audio or speech model from biodatlab. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released bybiodatlab
TypeAudio & music
TaskSpeech→text
Parameters (lead)206M
Runs withtransformers
Released2024-01-16
Popularity1k downloads / month
LicenceOpen weights

About

What whisper-th-small is

Distilled Small Whisper ASR Model for Thai

Model Description

This is a distilled Automatic Speech Recognition (ASR) model, based on the Whisper architecture. It has been specifically tailored for Thai language speech recognition. The model features 4 decoder layers (vs 12 in teacher model) and has been distilled from a larger teacher model, focusing on enhancing performance and efficiency.

Distillation Details
  • Teacher Model: Small Whisper ASR model
  • Datasets Used for Distillation:
    • Common Voice v13
    • Gowajee
    • Thai Elderly Speech Corpus
    • Custom Scraped Data
    • Thai-Central Dialect from SLSCU Thai Dialect Corpus

Model Performance

  • DeepCut Tokenized WER on Common Voice 13 Test Set:
    • Distilled Model: 11.23%
    • Teacher Model: 13.14%

This shows an improvement in Word Error Rate (WER), indicating enhanced accuracy in speech recognition tasks for the Thai language.

Read the full model card

Intended Use

This model is intended for use in applications requiring Thai language speech recognition.

Limitations

  • The model is specifically trained for the Thai language and may not perform well with other languages.
  • Performance might vary across different Thai dialects and accents.
  • As with any ASR system, background noise and speech clarity can impact recognition accuracy.

Acknowledgments

This model was developed using resources and datasets provided by the speech and language technology community. Special thanks to the teams behind Common Voice, Gowajee, SLSCU, and the Thai Elderly Speech Corpus for their valuable datasets.

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.2
  • Datasets 2.16.1
  • Tokenizers 0.15.0

Contributors

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/distil-whisper-th-small },
    doi          = { 10.57967/hf/0226 },
    publisher    = { Hugging Face }
}

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys whisper-th-small for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (whisper-th-small below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="whisper-th-small" -F file=@audio.mp3

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms