test_online_recognizer.py
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# 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.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.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.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)
if __name__ == "__main__":
unittest.main()