test_online_recognizer.py
10.7 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
# sherpa-onnx/python/tests/test_online_recognizer.py
#
# Copyright (c) 2023 Xiaomi Corporation
#
# To run this single test, use
#
# ctest --verbose -R test_online_recognizer_py
import unittest
import wave
from pathlib import Path
from typing import Tuple
import numpy as np
import sherpa_onnx
d = "/tmp/icefall-models"
# Please refer to
# https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/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 TestOnlineRecognizer(unittest.TestCase):
def test_transducer_single_file(self):
for use_int8 in [True, False]:
if use_int8:
encoder = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.int8.onnx"
decoder = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx"
joiner = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.int8.onnx"
else:
encoder = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.onnx"
decoder = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx"
joiner = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx"
tokens = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt"
wave0 = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/0.wav"
if not Path(encoder).is_file():
print("skipping test_transducer_single_file()")
return
for decoding_method in ["greedy_search", "modified_beam_search"]:
recognizer = sherpa_onnx.OnlineRecognizer.from_transducer(
encoder=encoder,
decoder=decoder,
joiner=joiner,
tokens=tokens,
num_threads=1,
decoding_method=decoding_method,
provider="cpu",
)
s = recognizer.create_stream()
samples, sample_rate = read_wave(wave0)
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()
while recognizer.is_ready(s):
recognizer.decode_stream(s)
print(recognizer.get_result(s))
def test_transducer_multiple_files(self):
for use_int8 in [True, False]:
if use_int8:
encoder = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.int8.onnx"
decoder = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx"
joiner = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.int8.onnx"
else:
encoder = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.onnx"
decoder = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx"
joiner = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx"
tokens = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt"
wave0 = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/0.wav"
wave1 = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/1.wav"
wave2 = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/2.wav"
wave3 = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/3.wav"
wave4 = f"{d}/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/8k.wav"
if not Path(encoder).is_file():
print("skipping test_transducer_multiple_files()")
return
for decoding_method in ["greedy_search", "modified_beam_search"]:
recognizer = sherpa_onnx.OnlineRecognizer.from_transducer(
encoder=encoder,
decoder=decoder,
joiner=joiner,
tokens=tokens,
num_threads=1,
decoding_method=decoding_method,
provider="cpu",
)
streams = []
waves = [wave0, wave1, wave2, wave3, wave4]
for wave in waves:
s = recognizer.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)
while True:
ready_list = []
for s in streams:
if recognizer.is_ready(s):
ready_list.append(s)
if len(ready_list) == 0:
break
recognizer.decode_streams(ready_list)
results = [recognizer.get_result(s) for s in streams]
for wave_filename, result in zip(waves, results):
print(f"{wave_filename}\n{result}")
print("-" * 10)
def test_zipformer2_ctc(self):
m = "sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13"
for use_int8 in [True, False]:
name = (
"ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx"
if use_int8
else "ctc-epoch-20-avg-1-chunk-16-left-128.onnx"
)
model = f"{d}/{m}/{name}"
tokens = f"{d}/{m}/tokens.txt"
wave0 = f"{d}/{m}/test_wavs/DEV_T0000000000.wav"
wave1 = f"{d}/{m}/test_wavs/DEV_T0000000001.wav"
wave2 = f"{d}/{m}/test_wavs/DEV_T0000000002.wav"
if not Path(model).is_file():
print("skipping test_zipformer2_ctc()")
return
print(f"testing {model}")
recognizer = sherpa_onnx.OnlineRecognizer.from_zipformer2_ctc(
model=model,
tokens=tokens,
num_threads=1,
provider="cpu",
)
streams = []
waves = [wave0, wave1, wave2]
for wave in waves:
s = recognizer.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)
while True:
ready_list = []
for s in streams:
if recognizer.is_ready(s):
ready_list.append(s)
if len(ready_list) == 0:
break
recognizer.decode_streams(ready_list)
results = [recognizer.get_result(s) for s in streams]
for wave_filename, result in zip(waves, results):
print(f"{wave_filename}\n{result}")
print("-" * 10)
def test_wenet_ctc(self):
models = [
"sherpa-onnx-zh-wenet-aishell",
"sherpa-onnx-zh-wenet-aishell2",
"sherpa-onnx-zh-wenet-wenetspeech",
"sherpa-onnx-zh-wenet-multi-cn",
"sherpa-onnx-en-wenet-librispeech",
"sherpa-onnx-en-wenet-gigaspeech",
]
for m in models:
for use_int8 in [True, False]:
name = (
"model-streaming.int8.onnx" if use_int8 else "model-streaming.onnx"
)
model = f"{d}/{m}/{name}"
tokens = f"{d}/{m}/tokens.txt"
wave0 = f"{d}/{m}/test_wavs/0.wav"
wave1 = f"{d}/{m}/test_wavs/1.wav"
wave2 = f"{d}/{m}/test_wavs/8k.wav"
if not Path(model).is_file():
print("skipping test_wenet_ctc()")
return
recognizer = sherpa_onnx.OnlineRecognizer.from_wenet_ctc(
model=model,
tokens=tokens,
num_threads=1,
provider="cpu",
)
streams = []
waves = [wave0, wave1, wave2]
for wave in waves:
s = recognizer.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)
while True:
ready_list = []
for s in streams:
if recognizer.is_ready(s):
ready_list.append(s)
if len(ready_list) == 0:
break
recognizer.decode_streams(ready_list)
results = [recognizer.get_result(s) for s in streams]
for wave_filename, result in zip(waves, results):
print(f"{wave_filename}\n{result}")
print("-" * 10)
if __name__ == "__main__":
unittest.main()