Files
lektor-ai/docs/requirements.md
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claudeandClaude Opus 5 3dde6e58e4 Settings file, per-job clip folders, faster polling, voices/ rename
Poll interval
  wait_for_completion() polled every 3 s, which quantised every clip to a 3 s
  boundary — across 2075 existing clips, 91% of the gaps landed exactly on a
  multiple of 3 s. It now starts at 0.25 s and backs off to 2 s after 10 s, so
  short clips are picked up immediately without hammering the server on long
  ones. Measured over 34 clips: 4.75 s actual vs 6.35 s under the old poll,
  a 25% cut in TTS wait (~18 min on a 693-clip episode). Runs now print real
  generation time, e.g. "done (4.2s)".

  A WebSocket would remove polling entirely but needs a third-party client,
  which would cost this script its dependency-free property.

settings.ini
  voice, speed, temperature, max_tokens, strip_leading_ms, trailing_fade_ms,
  orig_volume, tts_volume, output_dir and comfy_url moved to an INI file next
  to the script. Precedence is CLI flag > settings.ini > built-in default,
  wired by feeding loaded values in as argparse defaults. --settings FILE
  selects an alternative profile.

  INI rather than TOML (tomllib is 3.11+, this runs on system Python 3.10) or
  JSON (no comments). A missing, partial, malformed or corrupt file degrades to
  built-in defaults with a warning instead of killing a long run.

  Run-specific flags are deliberately excluded: --start, --end, --seed,
  --regen, --rebuild, --resume-from, --embedded-subs, --skip-tts.

Per-job folders
  All state for one video now lives in <output_dir>/clips/<video name>/ —
  clips, tts_input.srt, tts_assembled.flac, embedded_sub.srt and
  video_fragment.mkv. Only <stem>_lektor.mkv stays at the top level.

  tts_input.srt moved with the clips because they are a matched pair: block N
  in the SRT is clip_NNNN.flac. Previously both sat at the top level and each
  new job overwrote the last one's, so rebuilding an older film silently
  paired its clips with the newest film's text. Any past job can now be
  rebuilt. A run against the old flat layout gets an explanatory message.

gudowski/ -> voices/
  VOICE_PROMPTS_DIR is now resolved relative to the script rather than
  hardcoded, so moving the project no longer breaks it. Voice filenames are
  unchanged, so --voice gudowski_short still works.

Docs updated throughout for the new layout, settings file and poll behaviour.

Known issue, not addressed here: with a non-zero --start the voiceover plays
early. extract_video_fragment() stream-copies, so it can only cut on a
keyframe and seeks backwards to it, while TTS timestamps are relative to the
requested start. On S01_E08 at --start 07:40 the nearest earlier keyframe is
458.375 s, putting the voiceover 1.625 s ahead of the picture. Runs with
--start 00:00 are unaffected, since 0 is always a keyframe.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-09 21:56:42 +02:00

133 lines
5.4 KiB
Markdown

# 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.