yt-dbl 1.8.0

CLI tool for automatic YouTube video dubbing with voice cloning (Apple Silicon)

yt-dbl

Dub any YouTube video into another language — with the original speaker's voice

yt-dbl dub "https://www.youtube.com/watch?v=VIDEO_ID" -t ru

[!WARNING] Apple Silicon ONLY (M1–M4), tested on M4 Pro (48 GB)

One command: download, transcribe, translate (Claude), clone each speaker's voice (Qwen3-TTS), mix with the original background — done. All ML inference runs locally on your Mac's GPU via MLX

Why yt-dbl

• Human-quality voice cloning
  Qwen3-TTS per speaker, not a generic synth. Multiple speakers are diarized and voiced separately
• LLM translation
  Claude handles idioms, context, and produces TTS-friendly text — not word-for-word machine translation
• Background preserved
  BS-RoFormer separates vocals from music/sfx. Sidechain ducking mixes them back naturally
• Production audio chain
  Loudnorm (-16 LUFS), de-essing, pitch-preserving speed-up, equal-power crossfade
• Checkpoint & resume
  Every step saves state. Interrupted? yt-dbl resume continues where it stopped
• Private
  Everything local except the Claude API call

Supported languages

TTS (synthesis): Russian, English, German, French, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, Hindi, Turkish, Dutch, Polish, Ukrainian

ASR (recognition): auto-detected via Unicode scripts (Latin, Cyrillic, Arabic, Devanagari, CJK, etc.)

Requirements

• macOS with Apple Silicon (M1–M4) — MLX needs Metal
• Python >= 3.12
• FFmpeg — audio extraction, postprocessing, final assembly
• yt-dlp — video download
• Anthropic API key — translation via Claude

Installation

1. Install system dependencies

brew install ffmpeg yt-dlp

Optional: brew install ffmpeg-full for pitch-preserving speed-up via rubberband Without it, falls back to ffmpeg's atempo filter (works fine, just no pitch correction)

2. Install yt-dbl

# From PyPI
uv tool install --prerelease=allow yt-dbl

# Or with pipx
pipx install yt-dbl

--prerelease=allow is needed because mlx-audio depends on a pre-release transformers

If yt-dbl is not found, run uv tool update-shell && source ~/.zshrc

From source

git clone [e-mail]:brolnickij/yt-dbl.git && cd yt-dbl
uv sync

Use uv run yt-dbl instead of yt-dbl when running from source

3. Set up the API key

echo 'export YT_DBL_ANTHROPIC_API_KEY="sk-ant-..."' >> ~/.zshrc
source ~/.zshrc

Or use a .env file:

YT_DBL_ANTHROPIC_API_KEY=sk-ant-...

4. Pre-download models (optional)

Models (~8.2 GB) download automatically on first run, or fetch them ahead of time:

yt-dbl models download

Configuration

Priority: CLI args > env vars (YT_DBL_ prefix) > .env file > defaults

cp .env.example .env

Env variable	Default	Description
YT_DBL_ANTHROPIC_API_KEY	—	Required — Anthropic API key
YT_DBL_TARGET_LANGUAGE	ru	Target language (ISO 639-1)
YT_DBL_OUTPUT_FORMAT	mp4	mp4 / mkv
YT_DBL_SUBTITLE_MODE	softsub	softsub / hardsub / none
YT_DBL_BACKGROUND_VOLUME	0.15	Background volume during speech (0.0–1.0)
YT_DBL_MAX_SPEED_FACTOR	1.4	Max TTS speed-up to fit timing (1.0–2.0)
YT_DBL_MAX_LOADED_MODELS	0 (auto)	Max models in memory (0 = auto by RAM)
YT_DBL_WORK_DIR	dubbed	Output directory

See .env.example for all 33 parameters

Quick start

yt-dbl dub "https://www.youtube.com/watch?v=VIDEO_ID"           # dub to Russian (default)
yt-dbl dub "https://youtu.be/VIDEO_ID" -t es                    # dub to Spanish
yt-dbl dub "https://youtu.be/VIDEO_ID" -o ./out                 # custom output dir
yt-dbl dub "https://youtu.be/VIDEO_ID" --from-step translate    # re-run from a specific step
yt-dbl resume VIDEO_ID                                          # resume after interrupt
yt-dbl status VIDEO_ID                                          # check job progress

Commands

dub — dub a video

yt-dbl dub <URL> [options]

Option	Description	Default
-t, --target-language	Target language	ru
-o, --output-dir	Output directory	./dubbed
--bg-volume	Background volume (0.0–1.0)	0.15
--max-speed	Max TTS speed-up (1.0–2.0)	1.4
--max-models	Max models in memory	auto
--from-step	Start from: download / separate / transcribe / translate / synthesize / assemble	—
--no-subs	Disable subtitles	false
--sub-mode	softsub / hardsub / none	softsub
--format	mp4 / mkv	mp4

resume — pick up where it stopped

yt-dbl resume <video_id> [--max-models N] [-o DIR]

status — check job progress

yt-dbl status <video_id>

models list / models download

yt-dbl models list        # show models, download status, size
yt-dbl models download    # pre-download all models

How it works

┌─────────────────────────────────────────────────────────────────────────────────┐
│                                YouTube URL                                      │
└─────────────────────────────────────┬───────────────────────────────────────────┘
                                      │
                                      ▼
┌─────────────────────────────────────────────────────────────────────────────────┐
│  1. DOWNLOAD                                                                    │
│                                                                                 │
│  yt-dlp downloads the video, ffmpeg extracts the audio track                    │
│  Output: video.mp4, audio.wav (48 kHz, mono)                                    │
└─────────────────────────────────────┬───────────────────────────────────────────┘
                                      │
                                      ▼
┌─────────────────────────────────────────────────────────────────────────────────┐
│  2. SEPARATE                                                                    │
│                                                                                 │
│  BS-RoFormer splits audio into vocals and background (PyTorch MPS)              │
│  Output: vocals.wav, background.wav                                             │
└───────────────────────────┬────────────────────────────────────────────┬────────┘
                            │                                            │
                       vocals.wav                                  background.wav
                            │                                            │
                            ▼                                            │
┌──────────────────────────────────────────────────────┐                 │
│  3. TRANSCRIBE                                       │                 │
│                                                      │                 │
│  VibeVoice-ASR (MLX, ~5.7 GB)                        │                 │
│    → speech segments + speaker diarization           │                 │
│  Qwen3-ForcedAligner (MLX, ~600 MB)                  │                 │
│    → word-level timestamps                           │                 │
│  + language auto-detection via Unicode scripts       │                 │
│                                                      │                 │
│  Output: segments.json                               │                 │
└──────────────────────────┬───────────────────────────┘                 │
                           │                                             │
                           ▼                                             │
┌──────────────────────────────────────────────────────┐                 │
│  4. TRANSLATE                                        │                 │
│                                                      │                 │
│  Claude API (auto-batched by token budget)           │                 │
│  TTS-friendly output: short phrases, spelled-out     │                 │
│  numbers, no special characters                      │                 │
│                                                      │                 │
│  Output: translations.json, subtitles.srt            │                 │
└──────────────────────────┬───────────────────────────┘                 │
                           │                                             │
                           ▼                                             │
┌──────────────────────────────────────────────────────┐                 │
│  5. SYNTHESIZE                                       │                 │
│                                                      │                 │
│  Qwen3-TTS (MLX, ~1.7 GB) — voice cloning            │                 │
│  using a voice reference for each speaker            │                 │
│  Postprocessing (parallel, ThreadPool):              │                 │
│    • speed-up (rubberband or atempo)                 │                 │
│    • loudnorm (-16 LUFS, 2-pass)                     │                 │
│    • de-essing                                       │                 │
│                                                      │                 │
│  Output: segment_0000.wav, segment_0001.wav ...      │                 │
└──────────────────────────┬───────────────────────────┘                 │
                           │                                             │
                           ▼                                             ▼
┌─────────────────────────────────────────────────────────────────────────────────┐
│  6. ASSEMBLE                                                                    │
│                                                                                 │
│  Speech track (crossfade 50 ms, equal-power) + background (sidechain ducking)   │
│  + video (copy) + subtitles (softsub / hardsub / none)                          │
│  All in a single ffmpeg call                                                    │
│                                                                                 │
│  Output: result.mp4                                                             │
└──────────────────────────────────────────┬──────────────────────────────────────┘
                                           │
                                           ▼
                                 ┌───────────────────┐
                                 │    result.mp4     │
                                 └───────────────────┘

Memory management

LRU model manager — auto-selects how many models to keep loaded based on RAM:

RAM              Models     Batch (separation)
─────────────    ───────    ──────────────────
<= 16 GB         1          1
17–31 GB         2          2
32–47 GB         3          4
48+ GB           3          8

ASR (~5.7 GB) is unloaded before loading the Aligner to avoid holding both in memory

Output directory structure

dubbed/
└── <video_id>/
    ├── state.json                  ← pipeline checkpoint (JSON)
    ├── 01_download/
    │   ├── video.mp4               ← original video
    │   └── audio.wav               ← extracted audio track (48 kHz, mono)
    ├── 02_separate/
    │   ├── vocals.wav              ← isolated vocals
    │   └── background.wav          ← background music/noise
    ├── 03_transcribe/
    │   └── segments.json           ← segments, speakers, words with timestamps
    ├── 04_translate/
    │   ├── translations.json       ← translated texts
    │   └── subtitles.srt           ← subtitles (SRT)
    ├── 05_synthesize/
    │   ├── ref_SPEAKER_00.wav      ← speaker voice reference
    │   ├── segment_0000.wav        ← final segments (after postprocessing)
    │   ├── segment_0001.wav
    │   └── synth_meta.json         ← synthesis metadata
    ├── 06_assemble/
    │   └── speech.wav              ← assembled speech track
    └── result.mp4                  ← final output (in job dir root)

Models

Model	Size	Task
VibeVoice-ASR	~5.7 GB	ASR + speaker diarization
Qwen3-ForcedAligner	~600 MB	Word-level alignment
Qwen3-TTS	~1.7 GB	TTS + voice cloning
MelBand-RoFormer (BS-RoFormer)	~200 MB	Vocal/background separation
Claude Sonnet 4.5	—	Translation (API)

All local models run on MLX (Metal GPU), total ~8.2 GB

Development

just check    # lint + format + typecheck + tests
just test     # fast tests (parallel, coverage)
just test-e2e # E2E (needs ffmpeg + network)
just fix      # auto-fix lint
just format   # auto-format

License

MIT