Typhoon Whisper Turbo is a state-of-the-art Thai Automatic Speech Recognition (ASR) model fine-tuned on the OpenAI Whisper Large v3 Turbo architecture. It is engineered to deliver a balance of high accuracy and low latency for offline transcription, significantly outperforming standard Whisper models in throughput while maintaining robust performance on Thai speech.
The model was trained on approximately 11,000 hours of Thai audio, curated and normalized using the SCB 10X Typhoon data pipeline to ensure consistent handling of Thai numbers, repetition markers, and context-dependent ambiguities.
Model Overview
Architecture: Whisper Large v3 Turbo (4 decoder layers vs 32 in standard Large v3)
Language: Thai
Dataset: ~11,000 hours of normalized Thai speech (Gigaspeech2, CommonVoice, Internal Curated Public Media)
Task: Automatic Speech Recognition (ASR)
License: MIT (inherited from OpenAI Whisper)
Performance
Typhoon Whisper Turbo achieves competitive performance against large-scale offline models on Thai speech recognition tasks.
Thai ASR Model Performance - Pareto Frontier comparing accuracy vs inference speed
Note: Lower CER (Character Error Rate) is better. Results on Gigaspeech2 (Clean/Academic), TVSpeech (Noisy/In-the-wild), and Google Fleurs (Thai) testset.
Usage
You can use this model directly with the Hugging Face transformers library.
The model was trained on approximately 11,000 hours of Thai audio data, including:
Gigaspeech2: Clean, academic-style speech
CommonVoice: Crowd-sourced diverse speech samples
Internal Curated Public Media: Proprietary datasets curated by Typhoon Team, SCB 10X
All data was normalized using the Typhoon data pipeline to ensure:
Consistent handling of Thai numbers
Proper treatment of repetition markers
Resolution of context-dependent ambiguities
Model Architecture
Typhoon Whisper Turbo is based on OpenAI Whisper Large v3 Turbo, achieving performance comparable to state-of-the-art models while being significantly more efficient:
Reduced model size: Only 4 decoder layers (compared to 32 in standard Large v3)
Lower memory footprint: Requires substantially less GPU/CPU memory during inference
Maintained accuracy: Competitive performance with minimal trade-off despite the compact architecture
Limitations
The model is optimized specifically for Thai language speech recognition
Performance may vary on dialects or accents not well-represented in the training data
Best suited for offline transcription rather than real-time streaming applications
License
This model is released under the MIT License, inherited from OpenAI Whisper.
Citation
If you use this model in your research or application, please cite our technical report:
