-
Notifications
You must be signed in to change notification settings - Fork 501
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
6 changed files
with
252 additions
and
21 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,170 @@ | ||
| # 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.int8.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.int8.onnx" | ||
| decoder = f"{d}/sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.int8.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" | ||
|
|
||
| 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}/" | ||
| 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.int8.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.int8.onnx" | ||
| decoder = f"{d}/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.int8.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" | ||
|
|
||
| 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}/" | ||
| 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() |