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from resemblyzer import preprocess_wav, VoiceEncoder |
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import numpy as np |
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import torch |
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def get_spk_emb(audio_file_dir, segment_len=960000): |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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resemblyzer_encoder = VoiceEncoder(device=device) |
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wav = preprocess_wav(audio_file_dir) |
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l = len(wav) // segment_len |
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l = np.max([1, l]) |
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all_embeds = [] |
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for i in range(l): |
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mean_embeds, cont_embeds, wav_splits = resemblyzer_encoder.embed_utterance( |
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wav[segment_len * i:segment_len* (i + 1)], return_partials=True, rate=2) |
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all_embeds.append(mean_embeds) |
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all_embeds = np.array(all_embeds) |
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mean_embed = np.mean(all_embeds, axis=0) |
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return mean_embed, all_embeds |
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if __name__ == '__main__': |
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m, a = get_spk_emb(r'E:\Dataset\TalkingToon\Obama\test_wav_files\obama_example.wav') |
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print('Speaker embedding:', m) |
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print(m.shape) |
