test_offline_recognizer.py
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# 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.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,
provider="cpu",
)
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.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,
provider="cpu",
)
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):
model = f"{d}/sherpa-onnx-paraformer-zh-2023-09-14/model.int8.onnx"
tokens = f"{d}/sherpa-onnx-paraformer-zh-2023-09-14/tokens.txt"
wave0 = f"{d}/sherpa-onnx-paraformer-zh-2023-09-14/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,
provider="cpu",
)
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):
model = f"{d}/sherpa-onnx-paraformer-zh-2023-09-14/model.int8.onnx"
tokens = f"{d}/sherpa-onnx-paraformer-zh-2023-09-14/tokens.txt"
wave0 = f"{d}/sherpa-onnx-paraformer-zh-2023-09-14/test_wavs/0.wav"
wave1 = f"{d}/sherpa-onnx-paraformer-zh-2023-09-14/test_wavs/1.wav"
wave2 = f"{d}/sherpa-onnx-paraformer-zh-2023-09-14/test_wavs/2.wav"
wave3 = f"{d}/sherpa-onnx-paraformer-zh-2023-09-14/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,
provider="cpu",
)
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)
def test_nemo_ctc_single_file(self):
for use_int8 in [True, False]:
if use_int8:
model = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/model.int8.onnx"
else:
model = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/model.onnx"
tokens = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/tokens.txt"
wave0 = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/0.wav"
if not Path(model).is_file():
print("skipping test_nemo_ctc_single_file()")
return
recognizer = sherpa_onnx.OfflineRecognizer.from_nemo_ctc(
model=model,
tokens=tokens,
num_threads=1,
provider="cpu",
)
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_nemo_ctc_multiple_files(self):
for use_int8 in [True, False]:
if use_int8:
model = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/model.int8.onnx"
else:
model = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/model.onnx"
tokens = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/tokens.txt"
wave0 = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/0.wav"
wave1 = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/1.wav"
wave2 = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/8k.wav"
if not Path(model).is_file():
print("skipping test_nemo_ctc_multiple_files()")
return
recognizer = sherpa_onnx.OfflineRecognizer.from_nemo_ctc(
model=model,
tokens=tokens,
num_threads=1,
provider="cpu",
)
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_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.int8.onnx" if use_int8 else "model.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.OfflineRecognizer.from_wenet_ctc(
model=model,
tokens=tokens,
num_threads=1,
provider="cpu",
)
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)
if __name__ == "__main__":
unittest.main()