offline-fire-red-asr-decode-files.py
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#!/usr/bin/env python3
"""
This file shows how to use a non-streaming FireRedAsr AED model from
https://github.com/FireRedTeam/FireRedASR
to decode files.
Please download model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
For instance,
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16.tar.bz2
tar xvf sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16.tar.bz2
rm sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16.tar.bz2
"""
from pathlib import Path
import sherpa_onnx
import soundfile as sf
def create_recognizer():
encoder = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/encoder.int8.onnx"
decoder = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/decoder.int8.onnx"
tokens = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/tokens.txt"
test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/0.wav"
# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/1.wav"
# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/2.wav"
# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/3.wav"
# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/8k.wav"
# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/3-sichuan.wav"
# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/4-tianjin.wav"
# test_wav = "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/5-henan.wav"
if (
not Path(encoder).is_file()
or not Path(decoder).is_file()
or not Path(test_wav).is_file()
):
raise ValueError(
"""Please download model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
"""
)
return (
sherpa_onnx.OfflineRecognizer.from_fire_red_asr(
encoder=encoder,
decoder=decoder,
tokens=tokens,
debug=True,
),
test_wav,
)
def main():
recognizer, wave_filename = create_recognizer()
audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
audio = audio[:, 0] # only use the first channel
# audio is a 1-D float32 numpy array normalized to the range [-1, 1]
# sample_rate does not need to be 16000 Hz
stream = recognizer.create_stream()
stream.accept_waveform(sample_rate, audio)
recognizer.decode_stream(stream)
print(wave_filename)
print(stream.result)
if __name__ == "__main__":
main()