forked from mrq/ai-voice-cloning
rude
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@ -172,7 +172,7 @@ def generate(
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progress(0, desc="Loading voice...")
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voice_samples, conditioning_latents = load_voice(voice)
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if voice_samples is not None and len(voice_samples) > 0:
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if voice_samples and len(voice_samples) > 0:
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sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
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conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
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@ -185,7 +185,8 @@ def generate(
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else:
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if conditioning_latents is not None:
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sample_voice, _ = load_voice(voice, load_latents=False)
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sample_voice = torch.cat(sample_voice, dim=-1).squeeze().cpu()
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if sample_voice and len(sample_voice) > 0:
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sample_voice = torch.cat(sample_voice, dim=-1).squeeze().cpu()
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else:
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sample_voice = None
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@ -392,7 +393,7 @@ def generate(
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fixed_output_voices.append(fixed)
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output_voices = fixed_output_voices
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if voice is not None and conditioning_latents is not None:
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if voice and voice != "random" and conditioning_latents is not None:
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with open(f'{get_voice_dir()}/{voice}/cond_latents.pth', 'rb') as f:
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info['latents'] = base64.b64encode(f.read()).decode("ascii")
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