OmniVoice Voice Clone TTS

This is the node people actually come to this pack for. OmniVoiceVoiceCloneTTS takes a few seconds of someone talking and makes that voice say whatever you type - no fine-tuning, no training, no API. The KB essay on audio nails the context: local tools closed the cloning gap in 2025, and by 2026 OmniVoice is one of the go-to answers in the ComfyUI corner, especially when you need languages beyond English. People in the community recommend it as a daily driver for cloning ("that's my current go-to" on r/comfyui), and it's the pick when you're cloning voices that whisper models and English-first TTS ignore.

The mechanism is zero-shot voice cloning on a diffusion language model. Feed it a reference clip, and instead of training on it, the model encodes the clip's voice tokens at inference time and conditions generation on them - which is why a good reference matters more than anything else in this node. The community's hard-won lesson: a 3-second flat sample gives you a flat clone. If you want range - shouting, whispering, emotion - your reference audio needs to contain that range. People report 10 seconds being enough for a basic clone and 30–60 seconds of emotionally varied speech making a real difference for expressiveness. OmniVoice's emotional range is still narrow next to something like Chatterbox's explicit emotion dial, so don't expect a miracle from a monotone sample.

Wire-up is about as simple as cloning gets. ref_audio (required) is your sample - 3–15 seconds of clear speech, and the node resamples to 24kHz for you. ref_text is the transcript; leave it empty and Whisper transcribes it automatically, but providing it yourself measurably improves quality. That auto-transcription is exactly why the pack ships a Whisper Loader - connect its whisper_model output here to avoid re-downloading a Whisper model on every run. text is what the clone says, and like the other nodes it accepts inline non-verbal tags ([laughter], [sigh]…) with the same caveat that they're hit-or-miss. instruct optionally pushes the style of delivery - whisper, male, british accent, etc. - from the model's fixed list of values; free-text will be rejected. Then model picks OmniVoice (fp32, ~4GB) or OmniVoice-bf16 (~2GB), both auto-downloaded to ComfyUI/models/omnivoice/.

The generation knobs are shared across the pack: steps (32 default; 16 faster, 64 better), guidance_scale (2.0), speed, and the temperature/penalty trio you can ignore until you're chasing a specific artifact. keep_model_loaded (on by default) offloads to CPU between runs. The single output is audio (AUDIO) - save it with a Vorbis/WAV node, or feed it into a talking-head pipeline as the voice track.

Install is the pack-wide story: search "OmniVoice" in ComfyUI Manager, or clone and run python install.py. The notable traps are the same ones that bite every node here: the omnivoice pip package pins torch==2.8.* and can downgrade your PyTorch to CPU-only, so let install.py do its --no-deps dance and don't pip-install omnivoice yourself. If the TTS nodes are missing but the Whisper Loader shows, that's an import failure - usually a too-old transformers (needs 5.3+) or missing soxr. And one honest caveat from the launch thread: cloning is quality-dependent on the source, so if your first clone doesn't sound like the target, it's often the reference, not the node. Expect ~5GB VRAM with bf16.