pythaiasr 1.3.0

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Description:

pythaiasr 1.3.0

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
Model homepage: https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th
Install
pip install pythaiasr

For Wav2Vec2 with language model:
if you want to use wannaphong/wav2vec2-large-xlsr-53-th-cv8-* model with language model, you needs to install by the step.
pip install pythaiasr[lm]
pip install https://github.com/kpu/kenlm/archive/refs/heads/master.zip

Usage
from pythaiasr import asr

file = "a.wav"
print(asr(file))

API
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
lm: Use language model (except airesearch/wav2vec2-large-xlsr-53-th model)
device: device
sampling_rate: The sample rate
return: thai text from ASR

Options for model

airesearch/wav2vec2-large-xlsr-53-th (default) - AI RESEARCH - PyThaiNLP model
wannaphong/wav2vec2-large-xlsr-53-th-cv8-newmm - Thai Wav2Vec2 with CommonVoice V8 (newmm tokenizer)
wannaphong/wav2vec2-large-xlsr-53-th-cv8-deepcut - Thai Wav2Vec2 with CommonVoice V8 (deepcut tokenizer)

You can read about models from the list:

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

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.

License:

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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