Add C++ support for streaming NeMo CTC models. (#857)

This commit is contained in:
Fangjun Kuang
2024-05-10 16:26:43 +08:00
committed by GitHub
parent 1eb60e8711
commit 46e4e5b7ac
22 changed files with 782 additions and 41 deletions

View File

@@ -0,0 +1,67 @@
#!/usr/bin/env python3
"""
This file shows how to use a streaming CTC model from NeMo
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://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_streaming_80ms
"""
from pathlib import Path
import numpy as np
import sherpa_onnx
import soundfile as sf
def create_recognizer():
model = "./sherpa-onnx-nemo-streaming-fast-conformer-ctc-en-80ms/model.onnx"
tokens = "./sherpa-onnx-nemo-streaming-fast-conformer-ctc-en-80ms/tokens.txt"
test_wav = "./sherpa-onnx-nemo-streaming-fast-conformer-ctc-en-80ms/test_wavs/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_nemo_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 16000 Hz
stream = recognizer.create_stream()
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()