136 lines
6.0 KiB
Python
136 lines
6.0 KiB
Python
import tempfile
|
||
import os
|
||
import torch
|
||
import torchaudio
|
||
|
||
|
||
class OmniVoiceGenerate:
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"model": ("OMNIVOICE_MODEL", {
|
||
"tooltip": "OmniVoice model loaded by the OmniVoice Model Loader node.",
|
||
}),
|
||
"text": ("STRING", {
|
||
"multiline": True,
|
||
"default": "",
|
||
"tooltip": (
|
||
"Text to synthesize. Supports inline tags for expression and pronunciation:\n"
|
||
"\n"
|
||
"NON-VERBAL SOUNDS:\n"
|
||
" [laughter] – insert a laugh\n"
|
||
" [sigh] – insert a sigh\n"
|
||
"\n"
|
||
"QUESTION / CONFIRMATION:\n"
|
||
" [question-en] – rising English question intonation\n"
|
||
" [confirmation-en] – confirmation sound\n"
|
||
"\n"
|
||
"SURPRISE:\n"
|
||
" [surprise-ah] [surprise-oh] [surprise-wa] [surprise-yo]\n"
|
||
"\n"
|
||
"DISSATISFACTION:\n"
|
||
" [dissatisfaction-hnn]\n"
|
||
"\n"
|
||
"ENGLISH PRONUNCIATION (CMU phoneme override):\n"
|
||
" You could probably still make [IH1 T] look good.\n"
|
||
"\n"
|
||
"CHINESE PRONUNCIATION (pinyin + tone number):\n"
|
||
" 严重SHE2本了\n"
|
||
"\n"
|
||
"EXAMPLE:\n"
|
||
" [laughter] You really got me. I didn't see that coming at all."
|
||
),
|
||
}),
|
||
"mode": (
|
||
["voice_cloning", "voice_design", "auto_voice"],
|
||
{
|
||
"default": "voice_cloning",
|
||
"tooltip": (
|
||
"voice_cloning – clone the voice from ref_audio (requires ref_audio)\n"
|
||
"voice_design – describe a voice with the instruct field (requires instruct)\n"
|
||
"auto_voice – model picks a voice automatically"
|
||
),
|
||
},
|
||
),
|
||
},
|
||
"optional": {
|
||
"ref_audio": ("AUDIO", {
|
||
"tooltip": "Reference audio clip to clone the voice from. Used in voice_cloning mode.",
|
||
}),
|
||
"ref_text": ("STRING", {
|
||
"default": "",
|
||
"tooltip": "Transcription of ref_audio. Leave blank to auto-transcribe with Whisper.",
|
||
}),
|
||
"instruct": ("STRING", {
|
||
"default": "",
|
||
"tooltip": (
|
||
"Voice description for voice_design mode. Combine attributes freely.\n"
|
||
"\n"
|
||
"GENDER: male, female\n"
|
||
"AGE: child, teenager, young adult, middle-aged, elderly\n"
|
||
"PITCH: very low, low, moderate, high, very high\n"
|
||
"STYLE: whisper\n"
|
||
"\n"
|
||
"ENGLISH ACCENTS (text must be English):\n"
|
||
" american, british, australian, canadian,\n"
|
||
" indian, chinese, korean, japanese, portuguese, russian\n"
|
||
"\n"
|
||
"EXAMPLE: female, high pitch, british accent"
|
||
),
|
||
}),
|
||
"speed": ("FLOAT", {
|
||
"default": 1.0, "min": 0.1, "max": 3.0, "step": 0.1,
|
||
"tooltip": "Playback speed multiplier. 1.0 = normal, >1.0 = faster, <1.0 = slower.",
|
||
}),
|
||
"num_step": ("INT", {
|
||
"default": 32, "min": 1, "max": 100,
|
||
"tooltip": "Diffusion steps. 32 = default quality. 16 = faster, slightly lower quality.",
|
||
}),
|
||
},
|
||
}
|
||
|
||
RETURN_TYPES = ("AUDIO",)
|
||
RETURN_NAMES = ("audio",)
|
||
FUNCTION = "generate"
|
||
CATEGORY = "OmniVoice"
|
||
|
||
def generate(self, model, text, mode, ref_audio=None, ref_text="", instruct="", speed=1.0, num_step=32):
|
||
kwargs = {"text": text, "speed": speed, "num_step": num_step}
|
||
|
||
if mode == "voice_cloning" and ref_audio is None:
|
||
raise ValueError("voice_cloning mode requires ref_audio to be connected")
|
||
if mode == "voice_design" and not instruct:
|
||
raise ValueError("voice_design mode requires an instruct string (e.g. 'female, low pitch')")
|
||
|
||
if mode == "voice_cloning":
|
||
tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
|
||
tmp_path = tmp.name
|
||
tmp.close()
|
||
try:
|
||
ref_waveform = ref_audio["waveform"].squeeze(0).cpu() # (channels, samples)
|
||
torchaudio.save(tmp_path, ref_waveform, int(ref_audio["sample_rate"]))
|
||
kwargs["ref_audio"] = tmp_path
|
||
if ref_text:
|
||
kwargs["ref_text"] = ref_text
|
||
audio_tensors = model.generate(**kwargs)
|
||
finally:
|
||
try:
|
||
os.unlink(tmp_path)
|
||
except OSError:
|
||
pass
|
||
|
||
elif mode == "voice_design" and instruct:
|
||
kwargs["instruct"] = instruct
|
||
audio_tensors = model.generate(**kwargs)
|
||
|
||
else: # auto_voice or fallback
|
||
audio_tensors = model.generate(**kwargs)
|
||
|
||
# Concatenate chunks: each tensor is (1, T) → concat along T → (1, T_total)
|
||
combined = torch.cat(audio_tensors, dim=1).cpu() # (1, T_total) on CPU
|
||
# ComfyUI AUDIO format: (batch, channels, samples)
|
||
waveform = combined.unsqueeze(0) # (1, 1, T_total)
|
||
|
||
return ({"waveform": waveform, "sample_rate": 24000},)
|