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''' |
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TAMPLEATE = { |
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"path": "" |
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"duration": "" |
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"sample_rate": "" |
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"amplitude": null, |
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"weight": null, |
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"info_path": null |
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} |
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''' |
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import torchaudio |
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import json |
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from tqdm import tqdm |
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|
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import torchaudio |
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import numpy as np |
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import torch, torch.nn as nn, random |
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from torchaudio import transforms |
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import os |
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import argparse |
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from tqdm import tqdm |
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import torchaudio |
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from torchaudio.transforms import Resample |
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from multiprocessing import Pool |
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def preprocess(args, wav_json, thread_id): |
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f = open("out.{}".format(thread_id), 'w') |
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for line in tqdm(wav_json): |
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try: |
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line = line.strip() |
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wav_info = json.loads(line) |
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meta = torchaudio.info(wav_info["path"]) |
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wav_info["num_channels"] = meta.num_channels |
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json_string = json.dumps(wav_info) |
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f.write("{}\n".format(json_string)) |
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except: |
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print(line) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description='Deep Speaker Embedding Inference') |
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parser.add_argument('--wav_json', type=str) |
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parser.add_argument('--num_thread', default=10, type=int, help='random seed') |
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args = parser.parse_args() |
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|
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wav_json_total = open(args.wav_json).readlines() |
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args.num_thread = min(len(wav_json_total), args.num_thread) |
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wav_json_list = np.array_split(wav_json_total, args.num_thread) |
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p = Pool(args.num_thread) |
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for thread_id, wav_json in enumerate(wav_json_list): |
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r = p.apply_async(preprocess, (args, wav_json, thread_id)) |
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p.close() |
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p.join() |
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r.get() |
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|
