EngineX-Iluvatar/enginex_bi_series-sherpa-onnx
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add python tests (#111)

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This commit is contained in:
Fangjun Kuang
2023-04-02 23:05:30 +08:00
committed by GitHub
parent 3f7e0c23ac
commit 5d3c8edbc9
11 changed files with 488 additions and 48 deletions
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95
.github/scripts/test-python.sh vendored
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@@ -8,15 +8,20 @@ log() {
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}
mkdir -p /tmp/icefall-models
dir=/tmp/icefall-models
log "Test streaming transducer models"
pushd $dir
repo_url=https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20
log "Start testing ${repo_url}"
repo=$(basename $repo_url)
repo=$dir/$(basename $repo_url)
log "Download pretrained model and test-data from $repo_url"
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
pushd $repo
cd $repo
git lfs pull --include "*.onnx"
popd
@@ -38,4 +43,88 @@ python3 ./python-api-examples/online-decode-files.py \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav \
$repo/test_wavs/3.wav
$repo/test_wavs/3.wav \
$repo/test_wavs/8k.wav
python3 ./python-api-examples/online-decode-files.py \
--tokens=$repo/tokens.txt \
--encoder=$repo/encoder-epoch-99-avg-1.int8.onnx \
--decoder=$repo/decoder-epoch-99-avg-1.int8.onnx \
--joiner=$repo/joiner-epoch-99-avg-1.int8.onnx \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav \
$repo/test_wavs/3.wav \
$repo/test_wavs/8k.wav
python3 sherpa-onnx/python/tests/test_online_recognizer.py --verbose
log "Test non-streaming transducer models"
pushd $dir
repo_url=https://huggingface.co/csukuangfj/sherpa-onnx-zipformer-en-2023-04-01
log "Start testing ${repo_url}"
repo=$dir/$(basename $repo_url)
log "Download pretrained model and test-data from $repo_url"
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
cd $repo
git lfs pull --include "*.onnx"
popd
ls -lh $repo
python3 ./python-api-examples/offline-decode-files.py \
--tokens=$repo/tokens.txt \
--encoder=$repo/encoder-epoch-99-avg-1.onnx \
--decoder=$repo/decoder-epoch-99-avg-1.onnx \
--joiner=$repo/joiner-epoch-99-avg-1.onnx \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/8k.wav
python3 ./python-api-examples/offline-decode-files.py \
--tokens=$repo/tokens.txt \
--encoder=$repo/encoder-epoch-99-avg-1.int8.onnx \
--decoder=$repo/decoder-epoch-99-avg-1.int8.onnx \
--joiner=$repo/joiner-epoch-99-avg-1.int8.onnx \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/8k.wav
python3 sherpa-onnx/python/tests/test_offline_recognizer.py --verbose
log "Test non-streaming paraformer models"
pushd $dir
repo_url=https://huggingface.co/csukuangfj/sherpa-onnx-paraformer-zh-2023-03-28
log "Start testing ${repo_url}"
repo=$dir/$(basename $repo_url)
log "Download pretrained model and test-data from $repo_url"
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
cd $repo
git lfs pull --include "*.onnx"
popd
ls -lh $repo
python3 ./python-api-examples/offline-decode-files.py \
--tokens=$repo/tokens.txt \
--paraformer=$repo/model.onnx \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav \
$repo/test_wavs/8k.wav
python3 ./python-api-examples/offline-decode-files.py \
--tokens=$repo/tokens.txt \
--paraformer=$repo/model.int8.onnx \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav \
$repo/test_wavs/8k.wav
python3 sherpa-onnx/python/tests/test_offline_recognizer.py --verbose

1
.gitignore vendored
View File

@@ -51,3 +51,4 @@ a.sh
run-offline-websocket-client-*.sh
run-sherpa-onnx-*.sh
sherpa-onnx-zipformer-en-2023-03-30
sherpa-onnx-zipformer-en-2023-04-01

11
python-api-examples/offline-decode-files.py Normal file → Executable file
View File

@@ -46,6 +46,7 @@ from typing import Tuple
import numpy as np
import sherpa_onnx
def get_args():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -165,6 +166,7 @@ def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:
samples_float32 = samples_float32 / 32768
return samples_float32, f.getframerate()
def main():
args = get_args()
assert_file_exists(args.tokens)
@@ -183,7 +185,7 @@ def main():
sample_rate=args.sample_rate,
feature_dim=args.feature_dim,
decoding_method=args.decoding_method,
debug=args.debug
debug=args.debug,
)
else:
assert_file_exists(args.paraformer)
@@ -194,10 +196,9 @@ def main():
sample_rate=args.sample_rate,
feature_dim=args.feature_dim,
decoding_method=args.decoding_method,
debug=args.debug
debug=args.debug,
)
print("Started!")
start_time = time.time()
@@ -212,12 +213,8 @@ def main():
s = recognizer.create_stream()
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)
streams.append(s)
recognizer.decode_streams(streams)
results = [s.result.text for s in streams]
end_time = time.time()

4
sherpa-onnx/csrc/features.cc
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@@ -18,8 +18,8 @@ namespace sherpa_onnx {
void FeatureExtractorConfig::Register(ParseOptions *po) {
po->Register("sample-rate", &sampling_rate,
"Sampling rate of the input waveform. Must match the one "
"expected by the model. Note: You can have a different "
"Sampling rate of the input waveform. "
"Note: You can have a different "
"sample rate for the input waveform. We will do resampling "
"inside the feature extractor");

4
sherpa-onnx/csrc/offline-stream.cc
View File

@@ -17,8 +17,8 @@ namespace sherpa_onnx {
void OfflineFeatureExtractorConfig::Register(ParseOptions *po) {
po->Register("sample-rate", &sampling_rate,
"Sampling rate of the input waveform. Must match the one "
"expected by the model. Note: You can have a different "
"Sampling rate of the input waveform. "
"Note: You can have a different "
"sample rate for the input waveform. We will do resampling "
"inside the feature extractor");

1
sherpa-onnx/csrc/offline-websocket-server.cc
View File

@@ -65,6 +65,7 @@ int32_t main(int32_t argc, char *argv[]) {
po.Register("port", &port, "The port on which the server will listen.");
config.Register(&po);
po.DisableOption("sample-rate");
if (argc == 1) {
po.PrintUsage();

28
sherpa-onnx/python/sherpa_onnx/offline_recognizer.py
View File

@@ -18,7 +18,12 @@ def _assert_file_exists(f: str):
class OfflineRecognizer(object):
"""A class for offline speech recognition."""
"""A class for offline speech recognition.
Please refer to the following files for usages
- https://github.com/k2-fsa/sherpa-onnx/blob/master/sherpa-onnx/python/tests/test_offline_recognizer.py
- https://github.com/k2-fsa/sherpa-onnx/blob/master/python-api-examples/offline-decode-files.py
"""
@classmethod
def from_transducer(
@@ -59,7 +64,7 @@ class OfflineRecognizer(object):
feature_dim:
Dimension of the feature used to train the model.
decoding_method:
Valid values are greedy_search, modified_beam_search.
Support only greedy_search for now.
debug:
True to show debug messages.
"""
@@ -68,14 +73,12 @@ class OfflineRecognizer(object):
transducer=OfflineTransducerModelConfig(
encoder_filename=encoder,
decoder_filename=decoder,
joiner_filename=joiner
),
paraformer=OfflineParaformerModelConfig(
model=""
joiner_filename=joiner,
),
paraformer=OfflineParaformerModelConfig(model=""),
tokens=tokens,
num_threads=num_threads,
debug=debug
debug=debug,
)
feat_config = OfflineFeatureExtractorConfig(
@@ -131,16 +134,12 @@ class OfflineRecognizer(object):
self = cls.__new__(cls)
model_config = OfflineModelConfig(
transducer=OfflineTransducerModelConfig(
encoder_filename="",
decoder_filename="",
joiner_filename=""
),
paraformer=OfflineParaformerModelConfig(
model=paraformer
encoder_filename="", decoder_filename="", joiner_filename=""
),
paraformer=OfflineParaformerModelConfig(model=paraformer),
tokens=tokens,
num_threads=num_threads,
debug=debug
debug=debug,
)
feat_config = OfflineFeatureExtractorConfig(
@@ -164,4 +163,3 @@ class OfflineRecognizer(object):
def decode_streams(self, ss: List[OfflineStream]):
self.recognizer.decode_streams(ss)

7
sherpa-onnx/python/sherpa_onnx/online_recognizer.py
View File

@@ -17,7 +17,12 @@ def _assert_file_exists(f: str):
class OnlineRecognizer(object):
"""A class for streaming speech recognition."""
"""A class for streaming speech recognition.
Please refer to the following files for usages
- https://github.com/k2-fsa/sherpa-onnx/blob/master/sherpa-onnx/python/tests/test_online_recognizer.py
- https://github.com/k2-fsa/sherpa-onnx/blob/master/python-api-examples/online-decode-files.py
"""
def __init__(
self,

2
sherpa-onnx/python/tests/CMakeLists.txt
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@@ -18,6 +18,8 @@ endfunction()
# please sort the files in alphabetic order
set(py_test_files
test_feature_extractor_config.py
test_offline_recognizer.py
test_online_recognizer.py
test_online_transducer_model_config.py
)

201
sherpa-onnx/python/tests/test_offline_recognizer.py Executable file
View File

@@ -0,0 +1,201 @@
# sherpa-onnx/python/tests/test_offline_recognizer.py
#
# Copyright (c) 2023 Xiaomi Corporation
#
# To run this single test, use
#
# ctest --verbose -R test_offline_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/offline-transducer/index.html
# and
# https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/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 TestOfflineRecognizer(unittest.TestCase):
def test_transducer_single_file(self):
for use_int8 in [True, False]:
if use_int8:
encoder = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/encoder-epoch-99-avg-1.int8.onnx"
decoder = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/decoder-epoch-99-avg-1.int8.onnx"
joiner = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/joiner-epoch-99-avg-1.int8.onnx"
else:
encoder = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/encoder-epoch-99-avg-1.onnx"
decoder = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/decoder-epoch-99-avg-1.onnx"
joiner = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/joiner-epoch-99-avg-1.onnx"
tokens = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/tokens.txt"
wave0 = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/test_wavs/0.wav"
if not Path(encoder).is_file():
print("skipping test_transducer_single_file()")
return
recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
encoder=encoder,
decoder=decoder,
joiner=joiner,
tokens=tokens,
num_threads=1,
)
s = recognizer.create_stream()
samples, sample_rate = read_wave(wave0)
s.accept_waveform(sample_rate, samples)
recognizer.decode_stream(s)
print(s.result.text)
def test_transducer_multiple_files(self):
for use_int8 in [True, False]:
if use_int8:
encoder = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/encoder-epoch-99-avg-1.int8.onnx"
decoder = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/decoder-epoch-99-avg-1.int8.onnx"
joiner = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/joiner-epoch-99-avg-1.int8.onnx"
else:
encoder = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/encoder-epoch-99-avg-1.onnx"
decoder = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/decoder-epoch-99-avg-1.onnx"
joiner = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/joiner-epoch-99-avg-1.onnx"
tokens = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/tokens.txt"
wave0 = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/test_wavs/0.wav"
wave1 = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/test_wavs/1.wav"
wave2 = f"{d}/sherpa-onnx-zipformer-en-2023-04-01/test_wavs/8k.wav"
if not Path(encoder).is_file():
print("skipping test_transducer_multiple_files()")
return
recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
encoder=encoder,
decoder=decoder,
joiner=joiner,
tokens=tokens,
num_threads=1,
)
s0 = recognizer.create_stream()
samples0, sample_rate0 = read_wave(wave0)
s0.accept_waveform(sample_rate0, samples0)
s1 = recognizer.create_stream()
samples1, sample_rate1 = read_wave(wave1)
s1.accept_waveform(sample_rate1, samples1)
s2 = recognizer.create_stream()
samples2, sample_rate2 = read_wave(wave2)
s2.accept_waveform(sample_rate2, samples2)
recognizer.decode_streams([s0, s1, s2])
print(s0.result.text)
print(s1.result.text)
print(s2.result.text)
def test_paraformer_single_file(self):
for use_int8 in [True, False]:
if use_int8:
model = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/model.int8.onnx"
else:
model = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/model.onnx"
tokens = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/tokens.txt"
wave0 = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/test_wavs/0.wav"
if not Path(model).is_file():
print("skipping test_paraformer_single_file()")
return
recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
paraformer=model,
tokens=tokens,
num_threads=1,
)
s = recognizer.create_stream()
samples, sample_rate = read_wave(wave0)
s.accept_waveform(sample_rate, samples)
recognizer.decode_stream(s)
print(s.result.text)
def test_paraformer_multiple_files(self):
for use_int8 in [True, False]:
if use_int8:
model = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/model.int8.onnx"
else:
model = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/model.onnx"
tokens = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/tokens.txt"
wave0 = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/test_wavs/0.wav"
wave1 = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/test_wavs/1.wav"
wave2 = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/test_wavs/2.wav"
wave3 = f"{d}/sherpa-onnx-paraformer-zh-2023-03-28/test_wavs/8k.wav"
if not Path(model).is_file():
print("skipping test_paraformer_multiple_files()")
return
recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
paraformer=model,
tokens=tokens,
num_threads=1,
)
s0 = recognizer.create_stream()
samples0, sample_rate0 = read_wave(wave0)
s0.accept_waveform(sample_rate0, samples0)
s1 = recognizer.create_stream()
samples1, sample_rate1 = read_wave(wave1)
s1.accept_waveform(sample_rate1, samples1)
s2 = recognizer.create_stream()
samples2, sample_rate2 = read_wave(wave2)
s2.accept_waveform(sample_rate2, samples2)
s3 = recognizer.create_stream()
samples3, sample_rate3 = read_wave(wave3)
s3.accept_waveform(sample_rate3, samples3)
recognizer.decode_streams([s0, s1, s2, s3])
print(s0.result.text)
print(s1.result.text)
print(s2.result.text)
print(s3.result.text)
if __name__ == "__main__":
unittest.main()

146
sherpa-onnx/python/tests/test_online_recognizer.py Executable file
View File

@@ -0,0 +1,146 @@
# 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.int8.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(
encoder=encoder,
decoder=decoder,
joiner=joiner,
tokens=tokens,
num_threads=1,
decoding_method=decoding_method,
)
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.int8.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(
encoder=encoder,
decoder=decoder,
joiner=joiner,
tokens=tokens,
num_threads=1,
decoding_method=decoding_method,
)
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)
if __name__ == "__main__":
unittest.main()