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| #!/usr/bin/python3 | |
| # -*- coding: utf-8 -*- | |
| import argparse | |
| import os | |
| from pathlib import Path | |
| import sys | |
| import tempfile | |
| pwd = os.path.abspath(os.path.dirname(__file__)) | |
| sys.path.append(os.path.join(pwd, "../../")) | |
| import librosa | |
| import numpy as np | |
| import sherpa | |
| from scipy.io import wavfile | |
| import torch | |
| import torchaudio | |
| from project_settings import project_path, temp_directory | |
| from toolbox.k2_sherpa.utils import audio_convert | |
| from toolbox.k2_sherpa import decode, models | |
| def get_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--model_dir", | |
| default=(project_path / "pretrained_models/huggingface/csukuangfj/wenet-chinese-model").as_posix(), | |
| type=str | |
| ) | |
| parser.add_argument( | |
| "--in_filename", | |
| default=(project_path / "data/test_wavs/paraformer-zh/si_chuan_hua.wav").as_posix(), | |
| type=str | |
| ) | |
| parser.add_argument("--sample_rate", default=16000, type=int) | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| args = get_args() | |
| # audio convert | |
| signal, sample_rate = librosa.load(args.in_filename, sr=args.sample_rate) | |
| signal *= 32768.0 | |
| signal = np.array(signal, dtype=np.int16) | |
| temp_file = temp_directory / "temp.wav" | |
| wavfile.write( | |
| temp_file.as_posix(), | |
| rate=args.sample_rate, | |
| data=signal | |
| ) | |
| # audio convert | |
| # in_filename = Path(args.in_filename) | |
| # out_filename = Path(tempfile.gettempdir()) / "asr" / in_filename.name | |
| # out_filename.parent.mkdir(parents=True, exist_ok=True) | |
| # | |
| # audio_convert(in_filename=in_filename.as_posix(), | |
| # out_filename=out_filename.as_posix(), | |
| # ) | |
| # load recognizer | |
| m_dict = models.model_map["Chinese"][0] | |
| local_model_dir = Path(args.model_dir) | |
| nn_model_file = local_model_dir / m_dict["nn_model_file"] | |
| tokens_file = local_model_dir / m_dict["tokens_file"] | |
| # recognizer = models.load_recognizer( | |
| # repo_id=m_dict["repo_id"], | |
| # nn_model_file=nn_model_file.as_posix(), | |
| # tokens_file=tokens_file.as_posix(), | |
| # sub_folder=m_dict["sub_folder"], | |
| # local_model_dir=local_model_dir, | |
| # recognizer_type=m_dict["recognizer_type"], | |
| # decoding_method="greedy_search", | |
| # num_active_paths=2, | |
| # ) | |
| feat_config = sherpa.FeatureConfig(normalize_samples=False) | |
| feat_config.fbank_opts.frame_opts.samp_freq = args.sample_rate | |
| feat_config.fbank_opts.mel_opts.num_bins = 80 | |
| feat_config.fbank_opts.frame_opts.dither = 0 | |
| config = sherpa.OfflineRecognizerConfig( | |
| nn_model=nn_model_file.as_posix(), | |
| tokens=tokens_file.as_posix(), | |
| use_gpu=False, | |
| feat_config=feat_config, | |
| decoding_method="greedy_search", | |
| num_active_paths=2, | |
| ) | |
| recognizer = sherpa.OfflineRecognizer(config) | |
| # s = recognizer.create_stream() | |
| # s.accept_wave_file( | |
| # temp_file.as_posix() | |
| # ) | |
| # recognizer.decode_stream(s) | |
| # text = s.result.text.strip() | |
| # text = text.lower() | |
| # print("text: {}".format(text)) | |
| text = decode.decode_by_recognizer(recognizer=recognizer, | |
| filename=temp_file.as_posix(), | |
| ) | |
| print("text: {}".format(text)) | |
| return | |
| if __name__ == "__main__": | |
| main() | |