71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
import tempfile
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import os
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import torch
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import torchaudio
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class OmniVoiceGenerate:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("OMNIVOICE_MODEL",),
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"text": ("STRING", {"multiline": True, "default": ""}),
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"mode": (
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["voice_cloning", "voice_design", "auto_voice"],
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{"default": "voice_cloning"},
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),
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},
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"optional": {
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"ref_audio": ("AUDIO",),
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"ref_text": ("STRING", {"default": ""}),
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"instruct": ("STRING", {"default": ""}),
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"speed": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 3.0, "step": 0.1}),
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"num_step": ("INT", {"default": 32, "min": 1, "max": 100}),
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},
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("audio",)
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FUNCTION = "generate"
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CATEGORY = "OmniVoice"
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def generate(self, model, text, mode, ref_audio=None, ref_text="", instruct="", speed=1.0, num_step=32):
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kwargs = {"text": text, "speed": speed, "num_step": num_step}
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if mode == "voice_cloning" and ref_audio is None:
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raise ValueError("voice_cloning mode requires ref_audio to be connected")
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if mode == "voice_design" and not instruct:
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raise ValueError("voice_design mode requires an instruct string (e.g. 'female, low pitch')")
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if mode == "voice_cloning" and ref_audio is not None:
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tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
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tmp_path = tmp.name
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tmp.close()
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try:
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waveform = ref_audio["waveform"].squeeze(0).cpu() # (channels, samples)
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torchaudio.save(tmp_path, waveform, int(ref_audio["sample_rate"]))
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kwargs["ref_audio"] = tmp_path
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if ref_text:
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kwargs["ref_text"] = ref_text
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audio_tensors = model.generate(**kwargs)
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finally:
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try:
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os.unlink(tmp_path)
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except OSError:
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pass
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elif mode == "voice_design" and instruct:
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kwargs["instruct"] = instruct
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audio_tensors = model.generate(**kwargs)
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else: # auto_voice or fallback
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audio_tensors = model.generate(**kwargs)
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# Concatenate chunks: each tensor is (1, T) → concat along T → (1, T_total)
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combined = torch.cat(audio_tensors, dim=1).cpu() # (1, T_total) on CPU
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# ComfyUI AUDIO format: (batch, channels, samples)
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waveform = combined.unsqueeze(0) # (1, 1, T_total)
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return ({"waveform": waveform, "sample_rate": 24000},)
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