Ethanfel
d2cb5c4249
Language: ~170 world languages with type-to-filter dropdown Accent: 50+ regional varieties grouped by area Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
236 lines
11 KiB
Python
236 lines
11 KiB
Python
import tempfile
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import os
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import torch
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import soundfile as sf
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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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"tooltip": "OmniVoice model loaded by the OmniVoice Model Loader node.",
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}),
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"text": ("STRING", {
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"multiline": True,
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"default": "",
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"tooltip": (
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"Text to synthesize. Supports inline tags for expression and pronunciation:\n"
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"\n"
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"NON-VERBAL SOUNDS:\n"
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" [laughter] – insert a laugh\n"
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" [sigh] – insert a sigh\n"
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"\n"
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"QUESTION / CONFIRMATION:\n"
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" [question-en] – rising English question intonation\n"
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" [confirmation-en] – confirmation sound\n"
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"\n"
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"SURPRISE:\n"
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" [surprise-ah] [surprise-oh] [surprise-wa] [surprise-yo]\n"
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"\n"
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"DISSATISFACTION:\n"
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" [dissatisfaction-hnn]\n"
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"\n"
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"ENGLISH PRONUNCIATION (CMU phoneme override):\n"
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" You could probably still make [IH1 T] look good.\n"
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"\n"
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"CHINESE PRONUNCIATION (pinyin + tone number):\n"
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" 严重SHE2本了\n"
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"\n"
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"EXAMPLE:\n"
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" [laughter] You really got me. I didn't see that coming at all."
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),
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}),
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"mode": (
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["voice_cloning", "voice_design", "auto_voice"],
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{
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"default": "voice_cloning",
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"tooltip": (
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"voice_cloning – clone the voice from ref_audio (requires ref_audio)\n"
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"voice_design – describe a voice with the instruct field (requires instruct)\n"
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"auto_voice – model picks a voice automatically"
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),
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},
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),
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},
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"optional": {
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"ref_audio": ("AUDIO", {
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"tooltip": "Reference audio clip to clone the voice from. Used in voice_cloning mode.",
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}),
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"ref_text": ("STRING", {
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"default": "",
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"tooltip": "Transcription of ref_audio. Connect a Whisper (or other STT) node for best results.",
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}),
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"language": (
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[
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"auto",
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# A
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"Abkhazian", "Afar", "Afrikaans", "Akan", "Albanian", "Amharic",
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"Arabic", "Aragonese", "Armenian", "Assamese", "Avaric", "Avestan",
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"Aymara", "Azerbaijani",
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# B
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"Bambara", "Bashkir", "Basque", "Belarusian", "Bengali", "Bihari",
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"Bislama", "Bosnian", "Breton", "Bulgarian", "Burmese",
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# C
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"Catalan", "Chamorro", "Chechen", "Chichewa", "Chinese (Mandarin)",
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"Chinese (Cantonese)", "Chuvash", "Cornish", "Corsican", "Cree",
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"Croatian", "Czech",
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# D
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"Danish", "Divehi", "Dutch", "Dzongkha",
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# E
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"English", "Esperanto", "Estonian", "Ewe",
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# F
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"Faroese", "Fijian", "Finnish", "French", "Fula",
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# G
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"Galician", "Georgian", "German", "Greek", "Guaraní", "Gujarati",
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# H
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"Haitian Creole", "Hausa", "Hebrew", "Herero", "Hindi", "Hiri Motu",
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"Hungarian",
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# I
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"Interlingua", "Indonesian", "Igbo", "Inuktitut", "Irish",
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"Italian",
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# J
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"Japanese", "Javanese",
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# K
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"Kannada", "Kanuri", "Kashmiri", "Kazakh", "Khmer", "Kikuyu",
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"Kinyarwanda", "Komi", "Kongo", "Korean", "Kurdish", "Kyrgyz",
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# L
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"Lao", "Latin", "Latvian", "Limburgish", "Lingala", "Lithuanian",
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"Luganda", "Luxembourgish",
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# M
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"Macedonian", "Malagasy", "Malay", "Malayalam", "Maltese", "Manx",
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"Maori", "Marathi", "Marshallese", "Mongolian",
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# N
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"Nauruan", "Navajo", "Nepali", "Northern Sami", "Norwegian",
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"Norwegian Bokmål", "Norwegian Nynorsk",
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# O
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"Occitan", "Ojibwe", "Odia", "Oromo", "Ossetian",
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# P
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"Pali", "Pashto", "Persian", "Polish", "Portuguese",
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"Punjabi",
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# Q
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"Quechua",
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# R
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"Romanian", "Romansh", "Russian",
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# S
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"Samoan", "Sango", "Sanskrit", "Serbian", "Shona", "Sindhi",
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"Sinhala", "Slovak", "Slovenian", "Somali", "Southern Sotho",
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"Spanish", "Sundanese", "Swahili", "Swati", "Swedish",
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# T
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"Tagalog", "Tahitian", "Tajik", "Tamil", "Tatar", "Telugu",
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"Thai", "Tibetan", "Tigrinya", "Tonga", "Tsonga", "Tswana",
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"Turkish", "Turkmen", "Twi",
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# U
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"Ukrainian", "Urdu", "Uyghur", "Uzbek",
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# V
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"Vietnamese", "Volapük",
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# W
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"Walloon", "Welsh", "Western Frisian", "Wolof",
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# X
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"Xhosa",
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# Y
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"Yiddish", "Yoruba",
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# Z
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"Zhuang", "Zulu",
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],
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{
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"default": "auto",
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"tooltip": "Language of the text. Type to filter. OmniVoice supports 600+ languages — use 'auto' when unsure.",
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},
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),
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"instruct": ("STRING", {
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"default": "",
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"tooltip": (
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"Voice description for voice_design mode. Combine attributes freely.\n"
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"\n"
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"GENDER: male, female\n"
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"AGE: child, teenager, young adult, middle-aged, elderly\n"
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"PITCH: very low, low, moderate, high, very high\n"
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"STYLE: whisper\n"
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"\n"
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"ENGLISH ACCENTS (text must be English):\n"
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" american, british, australian, canadian,\n"
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" indian, chinese, korean, japanese, portuguese, russian\n"
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"\n"
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"EXAMPLE: female, high pitch, british accent"
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),
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}),
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"guidance_scale": ("FLOAT", {
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"default": 2.0, "min": 0.0, "max": 20.0, "step": 0.1,
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"tooltip": (
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"Classifier-free guidance scale. Higher = more faithful to the reference/instruct, "
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"but can over-saturate. 2.0 is a good default."
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),
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}),
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"speed": ("FLOAT", {
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"default": 1.0, "min": 0.1, "max": 3.0, "step": 0.1,
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"tooltip": "Playback speed multiplier. 1.0 = normal, >1.0 = faster, <1.0 = slower.",
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}),
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"num_step": ("INT", {
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"default": 32, "min": 1, "max": 100,
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"tooltip": "Diffusion steps. 32 = default quality. 16 = faster, slightly lower quality.",
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}),
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"seed": ("INT", {
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"default": 0, "min": 0, "max": 2**32 - 1,
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"tooltip": (
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"Random seed for the diffusion sampler. "
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"Set the same value across all Generate nodes in an audiobook pipeline "
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"to keep the voice consistent between paragraphs/chapters. "
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"0 = random (different each run)."
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),
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}),
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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="", language="auto",
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instruct="", guidance_scale=2.0, speed=1.0, num_step=32, seed=0):
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if seed != 0:
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torch.manual_seed(seed)
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kwargs = {"text": text, "speed": speed, "num_step": num_step, "guidance_scale": guidance_scale}
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if language and language != "auto":
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kwargs["language"] = language
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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":
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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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ref_waveform = ref_audio["waveform"].squeeze(0).cpu() # (channels, samples)
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audio_np = ref_waveform.numpy()
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# soundfile expects (samples,) for mono or (samples, channels) for multi-channel
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sf.write(tmp_path, audio_np[0] if audio_np.shape[0] == 1 else audio_np.T, 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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