# Requirements

## Hardware

| | Minimum | Notes |
|---|---|---|
| GPU | NVIDIA, 12 GB VRAM | Developed on an RTX 4070 Ti SUPER (16 GB) |
| Driver | 525+ | Needed by the CUDA 12.8 wheels |
| Disk | ~35 GB free | ~22 GB installed, plus working room for output |
| RAM | 16 GB | The final mux briefly peaks around 2.6 GB |

The CUDA **toolkit** is not required. The PyTorch cu128 wheels bundle their own
CUDA runtime, so the system `nvcc` version is irrelevant — this machine has CUDA
11.5 installed system-wide while PyTorch runs cu128 perfectly well. Only the
driver version matters.

CPU-only inference technically works but is slow enough to be impractical for a
feature-length film.

### Disk footprint

```
~/miniforge3/envs/comfy-tts    9.7 GB   conda env (PyTorch and ~233 packages)
~/comfy-tts-app/ComfyUI        3.2 GB   application code and dependencies
  └── models/TTS               8.8 GB   Higgs Audio V3 weights
                              ------
                              21.7 GB
```

Output is roughly the size of the source video, since video is stream-copied. A
60-minute 10 Mbit/s episode produces about 5 GB. Intermediate FLAC clips add a
few hundred MB per film and are kept deliberately, so individual clips can be
regenerated without redoing a whole run (see
[usage.md](usage.md#working-modes)).

## Software

| Component | Version here | Notes |
|---|---|---|
| OS | Linux (Ubuntu 22.04) | Only Linux is supported by the installer |
| ComfyUI | 0.26.0 (`7cb784e0`) | Pinned |
| TTS-Audio-Suite | v5.3.0 (`09df1681`) | The only custom node installed |
| Higgs Audio V3 | `bosonai/higgs-audio-v3-tts-4b` | 8.7 GB, auto-downloaded |
| Python (ComfyUI) | 3.12.13 | conda-forge, in the `comfy-tts` env |
| PyTorch | 2.11.0+cu128 | With torchvision 0.26.0, torchaudio 2.11.0 |
| Python (script) | 3.8+ | System Python; 3.10.12 here |
| ffmpeg / ffprobe | 4.4+ | System install; 4.4.2 here |

## Dependency surface

This is the point most worth internalising, because it makes debugging much
simpler: **the two halves have almost nothing in common.**

### `lektor_ai_comfy.py` — standard library only

```python
argparse  json  random  re  shutil  subprocess  sys  threading  time  uuid
pathlib.Path            urllib.request
```

No third-party packages. No conda environment. No virtualenv. It runs on the
system Python:

```bash
/usr/bin/python3 lektor_ai_comfy.py --help    # works, nothing installed
```

Its only external requirements are **ffmpeg** and **ffprobe** on `PATH`, and a
reachable ComfyUI instance. It talks to ComfyUI over plain HTTP with
`urllib.request` — there is no ComfyUI client library involved.

> **`requirements.txt` in the project root does not apply here.** It belongs to
> the older `lektor_ai.py`, which used the hosted Boson AI API and needed
> `openai>=1.0.0`. `lektor_ai_comfy.py` requires nothing from it.

### ComfyUI — a large, isolated environment

233 pip packages in the `comfy-tts` conda env. The important ones:

```
torch 2.11.0+cu128      torchaudio 2.11.0+cu128     torchvision 0.26.0+cu128
transformers 5.12.1     tokenizers 0.22.2           accelerate 1.14.0
numpy 2.2.6             scipy 1.18.0                librosa 0.11.0
soundfile 0.14.0        safetensors 0.8.0           einops 0.8.2
```

The conda environment itself holds only the 27 base conda-forge packages that
make up the Python runtime. Everything else arrives via pip. In particular,
`portaudio`, `libsamplerate`, `ffmpeg` and `espeak-ng` are **not** installed in
the environment despite appearing in the original planning notes — the system
ffmpeg is used and espeak-ng is absent entirely. Everything works without them.

TTS-Audio-Suite installs its dependencies through its own `install.py`, not
through its `requirements.txt`. That file deliberately omits packages needing
special handling — `librosa`, `descript-audio-codec`, `onnxruntime`,
`opencv-python` and others are installed with `--no-deps` because pip would
otherwise drag numpy backwards and break the rest of the environment. Never
substitute `pip install -r requirements.txt` for `python install.py`.

## Input requirements

**Video** — anything ffmpeg can demux. MKV and MP4 are the usual cases. The
container must support what gets muxed back in; output is always MKV, which
accepts every codec involved.

**Subtitles** — either an external `.srt`, or a text-based track inside the
video (`subrip`, `ass`, `ssa`, `webvtt`, `mov_text`). Image-based tracks
(`hdmv_pgs_subtitle` from Blu-ray, `dvd_subtitle`) contain bitmaps rather than
text and cannot drive TTS without OCR; the `--embedded-subs` picker marks them
as unusable. They are still copied to the output untouched.

**Voice reference** — a `.wav` plus a `.txt` holding its exact transcript, both
in `voices/`. Three are bundled:

| Voice | Length | Format |
|---|---|---|
| `gudowski` | 2:10 | 44.1 kHz stereo |
| `gudowski_short` (default) | 0:52 | 44.1 kHz stereo |
| `gudowski_short_2` | 0:28 | 44.1 kHz stereo |

The transcript matters as much as the audio — voice cloning quality drops
noticeably without an accurate one. The script warns if the `.txt` is missing.

## Network

Needed only at install time, for the ComfyUI and TTS-Audio-Suite clones, the pip
downloads, and the 8.7 GB model. Generation afterwards is entirely local; no
API keys, no accounts, no per-use cost.

The model is published under a research / non-commercial licence and may require
accepting its terms on huggingface.co, then `hf auth login`, before it will
download.