forked from mrq/tortoise-tts
Updates
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2
api.py
2
api.py
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@ -138,7 +138,7 @@ class TextToSpeech:
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heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False,
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train_solo_embeddings=False,
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average_conditioning_embeddings=True).cpu().eval()
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self.autoregressive.load_state_dict(torch.load('.models/autoregressive.pth'))
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self.autoregressive.load_state_dict(torch.load('.models/autoregressive_diverse.pth'))
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self.clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=12,
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text_seq_len=350, text_heads=8,
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@ -7,7 +7,7 @@ from utils.audio import load_audio
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if __name__ == '__main__':
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fname = 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv'
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outpath = 'D:\\tmp\\tortoise-tts-eval\\eval_new_autoregressive'
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outpath = 'D:\\tmp\\tortoise-tts-eval\\diverse_auto_256_samp_100_di_4'
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outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real'
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os.makedirs(outpath, exist_ok=True)
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@ -24,9 +24,9 @@ if __name__ == '__main__':
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path = os.path.join(os.path.dirname(fname), line[1])
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cond_audio = load_audio(path, 22050)
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torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050)
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sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=512, k=1,
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sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=256, k=1,
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repetition_penalty=2.0, length_penalty=2, temperature=.5, top_p=.5,
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diffusion_temperature=.7, cond_free_k=2, diffusion_iterations=200)
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diffusion_temperature=.7, cond_free_k=2, diffusion_iterations=100)
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down = torchaudio.functional.resample(sample, 24000, 22050)
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fout_path = os.path.join(outpath, os.path.basename(line[1]))
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