Datasets:
License:
| #!/usr/bin/python3 | |
| # -*- coding: utf-8 -*- | |
| import argparse | |
| from collections import Counter, defaultdict | |
| from glob import glob | |
| from itertools import chain | |
| import json | |
| import os | |
| from pathlib import Path | |
| import sys | |
| pwd = os.path.abspath(os.path.dirname(__file__)) | |
| sys.path.append(os.path.join(pwd, '../../')) | |
| import numpy as np | |
| from scipy.io import wavfile | |
| import torch | |
| import torch.nn as nn | |
| import shutil | |
| from tqdm import tqdm | |
| from project_settings import project_path | |
| from toolbox.cv2.misc import show_image | |
| from toolbox.python_speech_features.misc import wave2spectrum_image | |
| area_code = 234 | |
| def get_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--model_dir", | |
| default=(project_path / "trained_models/early_media_20220721").as_posix(), | |
| type=str | |
| ) | |
| parser.add_argument( | |
| "--wav_dir", | |
| default=(project_path / "data/early_media/{area_code}/wav".format(area_code=area_code)).as_posix(), | |
| type=str | |
| ) | |
| args = parser.parse_args() | |
| return args | |
| def demo1(): | |
| args = get_args() | |
| model_dir = Path(args.model_dir) | |
| wav_dir = Path(args.wav_dir) | |
| # model | |
| seq2seq_encoder = torch.jit.load(model_dir / "seq2seq_encoder.pth") | |
| seq2vec_encoder = torch.jit.load(model_dir / "seq2vec_encoder.pth") | |
| classification_layer = torch.jit.load(model_dir / "classification_layer.pth") | |
| with open(model_dir / "index2token.json", "r", encoding="utf-8") as f: | |
| index2token = json.load(f) | |
| # 读取文件 | |
| for filename in tqdm(wav_dir.glob("*.wav")): | |
| filename: Path = filename | |
| # path, fn = os.path.split(filename) | |
| try: | |
| sample_rate, wave = wavfile.read(filename) | |
| except UnboundLocalError: | |
| os.remove(filename) | |
| continue | |
| if sample_rate != 8000: | |
| raise AssertionError | |
| if len(wave) < 1.0 * sample_rate: | |
| os.remove(filename.as_posix()) | |
| continue | |
| max_wave_value = 32768.0 | |
| wave = wave / max_wave_value | |
| array = wave2spectrum_image( | |
| wave, | |
| sample_rate=8000, | |
| xmax=10, | |
| xmin=-50, | |
| winlen=0.025, | |
| winstep=0.01, | |
| nfft=512, | |
| n_low_freq=100, | |
| ) | |
| # show_image(array.T) | |
| array = np.array([array], dtype=np.float32) | |
| array = torch.tensor(array, dtype=torch.float32) | |
| mask: torch.IntTensor = torch.ones(size=array.shape[:-1], device=array.device, dtype=torch.int32) | |
| array = seq2seq_encoder.forward(array, mask) | |
| length = array.shape[-2] | |
| m_win_size = 50 | |
| m_win_step = 25 | |
| labels = list() | |
| idx = 0 | |
| while True: | |
| begin = idx * m_win_step | |
| end = begin + m_win_size | |
| if end > length: | |
| break | |
| window = array[:, begin:end, :] | |
| window = seq2vec_encoder.forward(window) | |
| logits = classification_layer(window) | |
| probs = torch.nn.functional.softmax(logits, dim=-1) | |
| label_idx = probs.argmax(dim=-1).item() | |
| label_str = index2token[str(label_idx)] | |
| labels.append(label_str) | |
| idx += 1 | |
| counter = Counter(labels) | |
| total = sum(counter.values()) | |
| rate_dict = defaultdict(float) | |
| for k, v in counter.items(): | |
| rate_dict[k] = v / total | |
| if rate_dict["voice"] > 0.1: | |
| tgt = filename.parent / "voice" | |
| elif rate_dict["music"] > 0.1: | |
| tgt = filename.parent / "music" | |
| elif rate_dict["bell"] > 0.1: | |
| tgt = filename.parent / "bell" | |
| else: | |
| tgt = filename.parent / "mute" | |
| tgt.mkdir(exist_ok=True) | |
| try: | |
| shutil.move(filename.as_posix(), tgt.as_posix()) | |
| except shutil.Error: | |
| fn = tgt / "{}_2.wav".format(filename.stem) | |
| shutil.move(filename.as_posix(), fn) | |
| return | |
| if __name__ == '__main__': | |
| demo1() | |