online-t-one-ctc-decode-files.py
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#!/usr/bin/env python3
"""
This file shows how to use a streaming CTC model from T-one
to decode files.
Please download model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
The example model is converted from
https://github.com/voicekit-team/T-one
using
https://github.com/k2-fsa/sherpa-onnx/tree/master/scripts/t-one
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-streaming-t-one-russian-2025-09-08.tar.bz2
tar xvf sherpa-onnx-streaming-t-one-russian-2025-09-08.tar.bz2
rm sherpa-onnx-streaming-t-one-russian-2025-09-08.tar.bz2
"""
from pathlib import Path
import numpy as np
import sherpa_onnx
import soundfile as sf
def create_recognizer():
model = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/model.onnx"
tokens = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/tokens.txt"
test_wav = "./sherpa-onnx-streaming-t-one-russian-2025-09-08/0.wav"
if not Path(model).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.OnlineRecognizer.from_t_one_ctc(
model=model,
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 8000 Hz
stream = recognizer.create_stream()
left_paddings = np.zeros(int(0.3 * sample_rate), dtype=np.float32)
stream.accept_waveform(sample_rate, left_paddings)
stream.accept_waveform(sample_rate, audio)
tail_paddings = np.zeros(int(0.66 * sample_rate), dtype=np.float32)
stream.accept_waveform(sample_rate, tail_paddings)
stream.input_finished()
while recognizer.is_ready(stream):
recognizer.decode_stream(stream)
print(wave_filename)
print(recognizer.get_result_all(stream))
if __name__ == "__main__":
main()