Whisper Large V3 Repack - Transcribe Audio

This is the node you plug in the middle of a workflow, not the one you point at a file. It takes a standard ComfyUI AUDIO input - say, from the built-in Load Audio node - and returns the full text of whatever's being said. No API key, no cloud, nothing to sign up for. It's a thin wrapper around faster-whisper running Whisper Large V3 locally, and it's MIT licensed, which is the whole reason this "repack" exists in the first place.

The backstory matters here. The Whisper node most people know from r/comfyui (yuvraj108c's ComfyUI-Whisper) carries a non-commercial license, which quietly disqualifies it for anything you might sell. This pack is a clean-room, MIT reimplementation that just calls faster-whisper directly - the README says so explicitly, and the code agrees. If "can I use this commercially?" is a question you have to answer with yes, reach for this one.

Why you'd reach for it: transcription is a surprisingly common middle step in this ecosystem. The classic chain is transcribe a reference clip, then feed the text to an F5-TTS or voice-cloning node so the clone says the right thing. Or you generate a talking-head video, load its audio track back in, and want the captions. Because this node takes an AUDIO input from the graph, the audio doesn't have to be a file on disk - it can be the output of another node.

How it works: the node writes the incoming waveform to a temporary WAV file (via soundfile, PCM 16-bit), then hands that path to faster-whisper's WhisperModel. The model itself downloads on first use into ComfyUI/models/whisper-large-v3-repack and gets cached there. A nice touch hidden in the source: if you leave device on auto and the CUDA run dies with a cuBLAS/cuDNN "could not load library" error, it automatically re-loads the model on CPU at int8 and re-runs. That's a real save on misconfigured installs.

The inputs you'll actually touch:

• model_name - large-v3 (best accuracy, ~3GB download) or large-v3-turbo (much faster, slightly worse). Turbo is a genuinely good default if you're iterating.
• device / compute_type - leave both on auto unless you know better. Auto picks float16 on CUDA and int8 on CPU, which are sane choices.
• language - empty means auto-detect. Fill in an ISO code like en or es to skip detection and speed things up.
• task - transcribe (keep the language) or translate (output English, Whisper-style).
• initial_prompt - freeform context that biases the model: proper nouns, domain vocabulary, "this is a cooking tutorial."
• word_timestamps - off by default. Flip it on if you need word-level timing rather than segment timing.

Everything else has a sensible default. beam_size 5 is standard, vad_filter on skips silence, temperature 0 is greedy decoding.

Outputs: transcript (full text), language (detected or forced code), duration_seconds, and segments_json - an array of {id, start, end, text} segments, each with a words list once word timestamps are on. Because the node is marked as an output node, the transcript also previews right on the canvas. Wire the transcript string into anything that takes text - a subtitle writer, an LLM node, or a text encoder for prompt conditioning.

Install - it's a two-step because the README expects you to install the Python deps yourself:

cd ComfyUI/custom_nodes
git clone https://github.com/endman100/ComfyUI-WhisperLargeV3-Repack.git
pip install -r ComfyUI-WhisperLargeV3-Repack/requirements.txt

or just search "Whisper Large V3" in ComfyUI Manager and let it handle the dependencies. Either way, restart ComfyUI. The deps are just faster-whisper and soundfile.

Where people get burned: the most common failure is forgetting to install requirements.txt - you'll get an ImportError that literally tells you to do it. First run is slow because Large V3's weights are big; that's the download, not a hang. And if you're on a 6GB card, large-v3 in float16 will OOM - switch to int8_float16 or the turbo model rather than fighting it. If the transcript comes back with no speech, that's the VAD filter being aggressive on quiet audio; turn vad_filter off and try again.