# 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) 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()