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Add Python API for keyword spotting (#576)

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* Add alsa & microphone support for keyword spotting

* Add python wrapper
This commit is contained in:
Wei Kang
2024-03-01 09:31:11 +08:00
committed by GitHub
parent 8b7928e7d6
commit 734bbd91dc
15 changed files with 1191 additions and 1 deletions
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1
sherpa-onnx/python/tests/CMakeLists.txt
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@@ -20,6 +20,7 @@ endfunction()
# please sort the files in alphabetic order
set(py_test_files
test_feature_extractor_config.py
test_keyword_spotter.py
test_offline_recognizer.py
test_online_recognizer.py
test_online_transducer_model_config.py

170
sherpa-onnx/python/tests/test_keyword_spotter.py Executable file
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@@ -0,0 +1,170 @@
# sherpa-onnx/python/tests/test_keyword_spotter.py
#
# Copyright (c) 2024 Xiaomi Corporation
#
# To run this single test, use
#
# ctest --verbose -R test_keyword_spotter_py
import unittest
import wave
from pathlib import Path
from typing import Tuple
import numpy as np
import sherpa_onnx
d = "/tmp/onnx-models"
# Please refer to
# https://k2-fsa.github.io/sherpa/onnx/kws/pretrained_models/index.html
# to download pre-trained models for testing
def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:
"""
Args:
wave_filename:
Path to a wave file. It should be single channel and each sample should
be 16-bit. Its sample rate does not need to be 16kHz.
Returns:
Return a tuple containing:
- A 1-D array of dtype np.float32 containing the samples, which are
normalized to the range [-1, 1].
- sample rate of the wave file
"""
with wave.open(wave_filename) as f:
assert f.getnchannels() == 1, f.getnchannels()
assert f.getsampwidth() == 2, f.getsampwidth() # it is in bytes
num_samples = f.getnframes()
samples = f.readframes(num_samples)
samples_int16 = np.frombuffer(samples, dtype=np.int16)
samples_float32 = samples_int16.astype(np.float32)
samples_float32 = samples_float32 / 32768
return samples_float32, f.getframerate()
class TestKeywordSpotter(unittest.TestCase):
def test_zipformer_transducer_en(self):
for use_int8 in [True, False]:
if use_int8:
encoder = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/encoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
decoder = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
joiner = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/joiner-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
else:
encoder = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/encoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
decoder = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
joiner = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/joiner-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
tokens = (
f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/tokens.txt"
)
keywords_file = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/test_wavs/test_keywords.txt"
wave0 = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/test_wavs/0.wav"
wave1 = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/test_wavs/1.wav"
if not Path(encoder).is_file():
print("skipping test_zipformer_transducer_en()")
return
keyword_spotter = sherpa_onnx.KeywordSpotter(
encoder=encoder,
decoder=decoder,
joiner=joiner,
tokens=tokens,
num_threads=1,
keywords_file=keywords_file,
provider="cpu",
)
streams = []
waves = [wave0, wave1]
for wave in waves:
s = keyword_spotter.create_stream()
samples, sample_rate = read_wave(wave)
s.accept_waveform(sample_rate, samples)
tail_paddings = np.zeros(int(0.2 * sample_rate), dtype=np.float32)
s.accept_waveform(sample_rate, tail_paddings)
s.input_finished()
streams.append(s)
results = [""] * len(streams)
while True:
ready_list = []
for i, s in enumerate(streams):
if keyword_spotter.is_ready(s):
ready_list.append(s)
r = keyword_spotter.get_result(s)
if r:
print(f"{r} is detected.")
results[i] += f"{r}/"
if len(ready_list) == 0:
break
keyword_spotter.decode_streams(ready_list)
for wave_filename, result in zip(waves, results):
print(f"{wave_filename}\n{result[0:-1]}")
print("-" * 10)
def test_zipformer_transducer_cn(self):
for use_int8 in [True, False]:
if use_int8:
encoder = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/encoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
decoder = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
joiner = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/joiner-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
else:
encoder = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/encoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
decoder = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
joiner = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/joiner-epoch-12-avg-2-chunk-16-left-64.int8.onnx"
tokens = (
f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/tokens.txt"
)
keywords_file = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/test_wavs/test_keywords.txt"
wave0 = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/test_wavs/3.wav"
wave1 = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/test_wavs/4.wav"
wave2 = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/test_wavs/5.wav"
if not Path(encoder).is_file():
print("skipping test_zipformer_transducer_cn()")
return
keyword_spotter = sherpa_onnx.KeywordSpotter(
encoder=encoder,
decoder=decoder,
joiner=joiner,
tokens=tokens,
num_threads=1,
keywords_file=keywords_file,
provider="cpu",
)
streams = []
waves = [wave0, wave1, wave2]
for wave in waves:
s = keyword_spotter.create_stream()
samples, sample_rate = read_wave(wave)
s.accept_waveform(sample_rate, samples)
tail_paddings = np.zeros(int(0.2 * sample_rate), dtype=np.float32)
s.accept_waveform(sample_rate, tail_paddings)
s.input_finished()
streams.append(s)
results = [""] * len(streams)
while True:
ready_list = []
for i, s in enumerate(streams):
if keyword_spotter.is_ready(s):
ready_list.append(s)
r = keyword_spotter.get_result(s)
if r:
print(f"{r} is detected.")
results[i] += f"{r}/"
if len(ready_list) == 0:
break
keyword_spotter.decode_streams(ready_list)
for wave_filename, result in zip(waves, results):
print(f"{wave_filename}\n{result[0:-1]}")
print("-" * 10)
if __name__ == "__main__":
unittest.main()