177 lines
7.4 KiB
Python
Executable File
177 lines
7.4 KiB
Python
Executable File
# 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.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.onnx"
|
|
decoder = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.onnx"
|
|
joiner = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/joiner-epoch-12-avg-2-chunk-16-left-64.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}/"
|
|
|
|
keyword_spotter.reset_stream(s)
|
|
|
|
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.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.onnx"
|
|
decoder = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.onnx"
|
|
joiner = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/joiner-epoch-12-avg-2-chunk-16-left-64.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}/"
|
|
|
|
keyword_spotter.reset_stream(s)
|
|
|
|
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()
|