PyThaiASR

Python Thai Automatic Speech Recognition

PyThaiASR is a Python package for Automatic Speech Recognition with focus on Thai language. It have offline thai automatic speech recognition model.

License: Apache-2.0 License

Google Colab: Link Google colab

Install

pip install pythaiasr

By default, PyThaiASR uses Typhoon ASR powered by ONNX Runtime for low-latency, lightweight offline and realtime speech recognition on CPU and GPU. On first use, Typhoon model files are automatically downloaded to ~/pythaiasr-data/typhoon-asr-realtime/.

For PyTorch & Transformers models (Wav2Vec2 / Whisper): If you want to use the Wav2Vec2 or Whisper models:

pip install pythaiasr[torch]

For Wav2Vec2 with language model: If you want to use wannaphong/wav2vec2-large-xlsr-53-th-cv8-* with a language model:

pip install pythaiasr[lm]
pip install https://github.com/kpu/kenlm/archive/refs/heads/master.zip

For live audio streaming: If you want to stream live audio from your microphone:

pip install pythaiasr[stream]

Usage

File-based ASR

from pythaiasr import asr

file = "sample.wav"

# Uses Typhoon ASR (FastConformer RNN-T ONNX) by default
print(asr(file))

# Or explicitly select another model (requires pythaiasr[torch])
# print(asr(file, model="airesearch/wav2vec2-large-xlsr-53-th"))
# print(asr(file, model="biodatlab/whisper-small-th-combined"))

Live Audio Streaming

Stream audio directly from your microphone/soundcard in real-time:

from pythaiasr import stream_asr

# Streams audio in real-time using Typhoon ASR by default
for transcription in stream_asr():
    print(transcription, end=" ", flush=True)
    # Press Ctrl+C to stop

And examples/stream_example.py

Real-Time Streaming from File or Microphone

from pythaiasr import FastConformerRNNT, RealtimeStreamASR, stream_from_file, stream_from_mic

model = FastConformerRNNT(device="auto")
streamer = RealtimeStreamASR(model=model, step_sec=0.48)

# Simulate streaming from a pre-recorded audio file
stream_from_file(streamer, "sample.wav")

# Or stream live from microphone with sounddevice
# stream_from_mic(streamer)

API

asr

asr(data: str, model: str = _model_name, lm: bool=False, device: str=None, sampling_rate: int=16_000)

• data: path of sound file or numpy array of the voice
• model: The ASR model (default: typhoon_asr)
• lm: Use language model (for wav2vec2 models with LM)
• device: device (auto, cpu, cuda)
• sampling_rate: The sample rate
• return: thai text from ASR

stream_asr

stream_asr(model: str = _model_name, lm: bool=False, device: str=None, chunk_duration: float=None, sampling_rate: int=16_000)

• model: The ASR model (default: typhoon_asr)
• lm: Use language model (for wav2vec2 models with LM)
• device: device for running model
• chunk_duration: Duration of each audio chunk in seconds (default: 0.48s for Typhoon, 5.0s for others)
• sampling_rate: The sample rate (default: 16000)
• yield: Thai text transcription from each audio chunk

Options for model

• typhoon_asr / typhoon-asr-realtime (default) - Typhoon FastConformer RNN-T ONNX model (offline & realtime)
• airesearch/wav2vec2-large-xlsr-53-th - AI RESEARCH - PyThaiNLP model (requires pythaiasr[torch])
• wannaphong/wav2vec2-large-xlsr-53-th-cv8-newmm - Thai Wav2Vec2 with CommonVoice V8 (newmm tokenizer) (requires pythaiasr[torch])
• wannaphong/wav2vec2-large-xlsr-53-th-cv8-deepcut - Thai Wav2Vec2 with CommonVoice V8 (deepcut tokenizer) (requires pythaiasr[torch])
• biodatlab/whisper-small-th-combined - Thai Whisper small model (requires pythaiasr[torch])
• biodatlab/whisper-th-medium-combined - Thai Whisper medium model (requires pythaiasr[torch])
• biodatlab/whisper-th-large-combined - Thai Whisper large model (requires pythaiasr[torch])

You can read about models from the list:

• typhoon-ai/typhoon-asr-realtime / wannaphong/typhoon-asr-realtime-onnx - Typhoon FastConformer RNN-T ONNX model
• airesearch/wav2vec2-large-xlsr-53-th - AI RESEARCH - PyThaiNLP model
• annaphong/wav2vec2-large-xlsr-53-th-cv8-newmm - Thai Wav2Vec2 with CommonVoice V8 (newmm tokenizer) + language model
• wannaphong/wav2vec2-large-xlsr-53-th-cv8-deepcut - Thai Wav2Vec2 with CommonVoice V8 (deepcut tokenizer) + language model
• biodatlab/whisper-small-th-combined - Thai Whisper small model
• biodatlab/whisper-th-medium-combined - Thai Whisper medium model
• biodatlab/whisper-th-large-combined - Thai Whisper large model

Docker

To use this inside of Docker do the following:

docker build -t <Your Tag name> .
docker run docker run --entrypoint /bin/bash -it <Your Tag name>

You will then get access to a interactive shell environment where you can use python with all packages installed.