forked from mrq/DL-Art-School
Move gen_similarities and rename
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@ -38,8 +38,10 @@ def process_subdir(subdir, options, clip_sz):
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global clip_model
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if clip_model is None:
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print('Loading CLIP model..')
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clip_model = load_model_from_config(preloaded_options=options, model_name='clip', also_load_savepoint=True)
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clip_model = load_model_from_config(preloaded_options=options, model_name='clip', also_load_savepoint=True).cuda()
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clip_model.eval()
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with torch.no_grad():
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root, paths = subdir
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if len(paths) == 0:
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return
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@ -63,7 +65,7 @@ def process_subdir(subdir, options, clip_sz):
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while len(clips) > 0:
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stacked = torch.stack(clips[:256], dim=0).cuda()
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clips = clips[256:]
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mels = wav_to_mel(stacked)
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mels = wav_to_mel(stacked).cuda()
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outp = clip_model.inference(mels).cpu()
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if sims is None:
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sims = outp
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@ -94,12 +96,13 @@ if __name__ == '__main__':
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"""
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This script iterates within a directory filled with subdirs. Each subdir contains a list of audio files from the same
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source. The script uses an speech-to-speech clip model to find the <n> most similar audio clips within each subdir for
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each clip within that subdir.
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each clip within that subdir. These similar files are recorded in a "similarities.pth" file in each subdirectory, which
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is consumed during training when the dataset searches for conditioning clips.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument('-o', type=str, help='Path to the options YAML file used to train the CLIP model', default='../options/train_voice_voice_clip.yml')
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parser.add_argument('--num_workers', type=int, help='Number concurrent processes to use', default=6)
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parser.add_argument('--root_path', type=str, help='Root path to search for audio directories from', default='Y:\\bigasr_dataset\\tedlium')
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parser.add_argument('--root_path', type=str, help='Root path to search for audio directories from', default='Y:\\filtered\\big_podcast')
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parser.add_argument('--clip_size', type=int, help='Amount of audio samples to pull from each file', default=22050)
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args = parser.parse_args()
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