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858b5052a2
This PR adds support for T-one streaming Russian ASR models in both C++ and Python APIs. The T-one model is a CTC-based Russian speech recognition model with specific characteristics including float16 state handling, 300ms frame lengths, and 8kHz sampling rate. - Added new OnlineToneCtcModel implementation with specialized processing for T-one models - Integrated T-one support into the existing CTC model pipeline and Python bindings - Added Python example and test scripts for the new functionality
76 lines
2.2 KiB
Python
Executable File
76 lines
2.2 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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This file shows how to use a streaming CTC model from T-one
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to decode files.
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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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The example model is converted from
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https://github.com/voicekit-team/T-one
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using
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https://github.com/k2-fsa/sherpa-onnx/tree/master/scripts/t-one
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-streaming-t-one-russian-2025-09-08.tar.bz2
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tar xvf sherpa-onnx-streaming-t-one-russian-2025-09-08.tar.bz2
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rm sherpa-onnx-streaming-t-one-russian-2025-09-08.tar.bz2
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"""
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from pathlib import Path
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import numpy as np
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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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model = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/model.onnx"
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tokens = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/tokens.txt"
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test_wav = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/0.wav"
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if not Path(model).is_file() or not Path(test_wav).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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return (
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sherpa_onnx.OnlineRecognizer.from_t_one_ctc(
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model=model,
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tokens=tokens,
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debug=True,
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),
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test_wav,
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)
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def main():
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recognizer, wave_filename = create_recognizer()
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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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# audio is a 1-D float32 numpy array normalized to the range [-1, 1]
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# sample_rate does not need to be 8000 Hz
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stream = recognizer.create_stream()
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left_paddings = np.zeros(int(0.3 * sample_rate), dtype=np.float32)
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stream.accept_waveform(sample_rate, left_paddings)
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stream.accept_waveform(sample_rate, audio)
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tail_paddings = np.zeros(int(0.66 * sample_rate), dtype=np.float32)
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stream.accept_waveform(sample_rate, tail_paddings)
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stream.input_finished()
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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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