92 lines
2.7 KiB
Python
Executable File
92 lines
2.7 KiB
Python
Executable File
#!/usr/bin/env python3
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#
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# Copyright (c) 2024 Xiaomi Corporation
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"""
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This script shows how to use inverse text normalization with streaming ASR.
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Usage:
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(1) Download the test model
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20.tar.bz2
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(2) Download rule fst
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/itn_zh_number.fst
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Please refer to
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https://github.com/k2-fsa/colab/blob/master/sherpa-onnx/itn_zh_number.ipynb
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for how itn_zh_number.fst is generated.
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(3) Download test wave
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/itn-zh-number.wav
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(4) Run this script
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python3 ./python-api-examples/inverse-text-normalization-online-asr.py
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"""
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from pathlib import Path
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import sherpa_onnx
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import soundfile as sf
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def create_recognizer():
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encoder = "./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.int8.onnx"
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decoder = "./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx"
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joiner = "./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.int8.onnx"
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tokens = "./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt"
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rule_fsts = "./itn_zh_number.fst"
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if (
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not Path(encoder).is_file()
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or not Path(decoder).is_file()
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or not Path(joiner).is_file()
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or not Path(tokens).is_file()
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or not Path(rule_fsts).is_file()
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):
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raise ValueError(
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"""Please download model files from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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"""
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)
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return sherpa_onnx.OnlineRecognizer.from_transducer(
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encoder=encoder,
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decoder=decoder,
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joiner=joiner,
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tokens=tokens,
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debug=True,
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rule_fsts=rule_fsts,
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)
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def main():
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recognizer = create_recognizer()
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wave_filename = "./itn-zh-number.wav"
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if not Path(wave_filename).is_file():
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raise ValueError(
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"""Please download model files from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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"""
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)
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audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
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audio = audio[:, 0] # only use the first channel
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stream = recognizer.create_stream()
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stream.accept_waveform(sample_rate, audio)
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tail_padding = [0] * int(0.3 * sample_rate)
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stream.accept_waveform(sample_rate, tail_padding)
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while recognizer.is_ready(stream):
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recognizer.decode_stream(stream)
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print(wave_filename)
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print(recognizer.get_result_all(stream))
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if __name__ == "__main__":
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main()
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