#speech-to-text #audio #subtitles #unified

Super-Transcribe — Unified Speech-to-Text

Unified speech-to-text skill. Use when the user asks to transcribe audio or video, generate subtitles, identify speakers, translate speech, search transcripts, diarize meetings, or perform any speech-to-text task. Also use when a voice message or audio file appears in chat and the user's intent to transcribe it is very clear.

Sarah Mak@theplasmak

Install

openclaw skills install @theplasmak/super-transcribe

🎙️ Super-Transcribe — Unified Speech-to-Text

A self-contained transcription skill with two bundled backends that intelligently routes based on task requirements:

🦜 Parakeet (NVIDIA NeMo) — best accuracy (6.34% WER), ~3380× realtime, auto-punctuation, 25 European languages, NeMo diarization
🗣️ faster-whisper (CTranslate2) — translation, 99+ languages, initial prompting, advanced inference tuning

Lazy loading: each backend sets up its own venv on first use. No pre-configuration needed — just transcribe and the right backend installs itself.

⚡ First-Time Setup

Follow these 3 steps on first use. Goal: install only what this user's hardware needs — nothing extra.

Step 1 — Check readiness (instant, no downloads)

bash

./scripts/transcribe --check --json

`action` result	What to do
`"ready"`	Skip setup — already installed. Go straight to usage.
`"install_python"`	Tell user to install Python 3.10+ first, then re-run
`"run_quickstart"`	Continue to Step 2

Key fields: gpu (name + VRAM), ffmpeg, backends status, estimated_install (download size for recommended backend), missing_optional (non-blocking items — do NOT eagerly install these).

Step 2 — Preview the install (no downloads)

bash

./scripts/transcribe --quickstart --dry-run --json

Tell the user what will be downloaded before proceeding. Relay from the estimated field:

Which backend and why it was chosen
Total download size (setup + model on first use)

Example message: "Your system has an NVIDIA GPU, so I'd install the Parakeet backend for best accuracy. That's ~5GB for setup plus ~1.2GB for the model on first use (~6.2GB total). OK to proceed?"

For GPU systems where the user wants a smaller install, see Lean Install Option below.

Step 3 — Install (after user confirms)

bash

./scripts/transcribe --quickstart --json

ok: true → backend installed. First transcription downloads model weights (~1-2 GB, cached permanently). ok: false → check errors array for what failed.

What Gets Installed

Quickstart installs exactly ONE backend — the best for detected hardware:

Hardware	Backend installed	Setup download	Model (first use)	Total
NVIDIA GPU	Parakeet (NeMo)	~5 GB (PyTorch + CUDA + NeMo)	~1.2 GB	~6.2 GB
CPU / macOS	faster-whisper	~300 MB (CTranslate2, no PyTorch)	~756 MB	~1 GB

NOT installed by quickstart (deferred until actually needed):

Second backend — auto-installs only when the user triggers a feature that needs it (--translate or non-EU language → faster-whisper; --fast/--multitalker → Parakeet)
System deps — install separately only if the user needs them:
- ffmpeg — only for non-WAV input (mp3/m4a/mp4/ogg). Install: sudo apt install ffmpeg
- yt-dlp — only for YouTube/URL downloads. Install: pipx install yt-dlp
- HuggingFace token — only for --diarize with faster-whisper. Setup: huggingface-cli login
PyTorch for faster-whisper — deferred until --diarize is first used (saves ~2.8 GB on initial install)

Do NOT pre-install optional deps. Wait until the user actually needs a feature — the script auto-installs or clearly reports what's missing at that point.

Lean Install Option

If a GPU user wants a smaller download (~1 GB instead of ~6.2 GB):

bash

./scripts/transcribe --setup faster-whisper

Trade-off: ~20× realtime speed (vs ~3380× with Parakeet), no built-in punctuation. Only offer this if the user explicitly asks for a minimal install or has limited bandwidth/disk.

Supplementary info: see Setup Details further down.

✅ Setup done. Everything below is usage reference — read on demand.

When to Use

Use this skill for any speech-to-text task. It replaces both the faster-whisper and parakeet skills as your single entry point.

Trigger phrases: "transcribe this", "speech to text", "what did they say", "make a transcript", "subtitle this video", "who's speaking", "translate this audio", "transcribe this podcast", "transcribe with parakeet", "transcribe with whisper", "best accuracy transcription", "transcribe in French", "diarize this meeting", "find where X is mentioned", "audio to text", "translate to English", "search transcript for", "when did they say", "at what timestamp", "add chapters", "detect chapters", "find breaks in the audio", "table of contents for this recording", "TTML subtitles", "DFXP subtitles", "broadcast format subtitles", "Netflix format", "ASS subtitles", "aegisub format", "LRC subtitles", "timed lyrics", "karaoke subtitles", "HTML transcript", "confidence-colored transcript", "separate audio per speaker", "export speaker audio", "transcript as CSV", "spreadsheet output", "podcast RSS feed", "per-file language", "remove filler words", "clean up ums and uhs", "transcribe left channel", "wrap subtitle lines", "character limit per line", "detect paragraphs"

Auto-Routing Logic

The router picks the backend automatically:

text

┌─────────────────────────────────────────────┐
│             --backend specified?             │
│         YES → use that backend              │
│         NO  ↓                               │
├─────────────────────────────────────────────┤
│     --fast or --multitalker?                │
│         YES → parakeet                      │
│         NO  ↓                               │
├─────────────────────────────────────────────┤
│     Needs faster-whisper-only feature?      │
│  (translate, initial-prompt, hotwords,      │
│   multilingual, non-EU language, advanced   │
│   inference tuning params)                  │
│         YES → faster-whisper                │
│         NO  ↓                               │
├─────────────────────────────────────────────┤
│      Needs parakeet-only feature?           │
│  (--long-form, --streaming, --no-align)     │
│         YES → parakeet                      │
│         NO  ↓                               │
├─────────────────────────────────────────────┤
│  Default: prefer Parakeet (best accuracy    │
│  + speed + auto-punctuation)                │
│  Fall back to faster-whisper if Parakeet    │
│  not installed                              │
└─────────────────────────────────────────────┘

Quick Reference

Basic (Auto-Routes to Best Available)

Task	Command
Basic transcription	`./scripts/transcribe audio.mp3`
SRT subtitles	`./scripts/transcribe audio.mp3 --format srt -o subs.srt`
VTT subtitles	`./scripts/transcribe audio.mp3 --format vtt -o subs.vtt`
ASS subtitles	`./scripts/transcribe audio.mp3 --format ass -o subs.ass`
LRC lyrics	`./scripts/transcribe audio.mp3 --format lrc -o lyrics.lrc`
TTML broadcast	`./scripts/transcribe audio.mp3 --format ttml -o subs.ttml`
CSV spreadsheet	`./scripts/transcribe audio.mp3 --format csv -o out.csv`
JSON output	`./scripts/transcribe audio.mp3 --format json -o out.json`
YouTube/URL	`./scripts/transcribe https://youtube.com/watch?v=...`
Batch process	`./scripts/transcribe *.mp3 -o ./transcripts/`
Force backend	`./scripts/transcribe --backend parakeet audio.mp3`
List backends	`./scripts/transcribe --backends`
Show version	`./scripts/transcribe --version`
Probe metadata	`./scripts/transcribe --probe audio.mp3`
Fast mode (110M)	`./scripts/transcribe --fast audio.mp3`
Multitalker	`./scripts/transcribe --multitalker meeting.wav`
Resume batch	`./scripts/transcribe *.mp3 --resume progress.json -o ./out/`
Exact model	`./scripts/transcribe -m nvidia/parakeet-tdt-1.1b audio.wav`
Model alias	`./scripts/transcribe --backend pk -m 1.1b audio.wav`
Speaker diarization	`./scripts/transcribe meeting.wav --diarize`
Search transcript	`./scripts/transcribe audio.mp3 --search "keyword"`
Detect chapters	`./scripts/transcribe audio.mp3 --detect-chapters`
Clean filler words	`./scripts/transcribe audio.mp3 --clean-filler`
Denoise audio	`./scripts/transcribe audio.mp3 --denoise`
Podcast RSS feed	`./scripts/transcribe --rss https://feed.url`
Burn subtitles	`./scripts/transcribe video.mp4 --burn-in out.mp4`
Name speakers	`./scripts/transcribe audio.mp3 --diarize --speaker-names "Alice,Bob"`
Export speaker audio	`./scripts/transcribe audio.mp3 --diarize --export-speakers ./spk/`

Routes to Faster-Whisper Automatically

Task	Command	Why
Translate → English	`./scripts/transcribe audio.mp3 --translate`	Whisper-specific feature
Canary translation	`./scripts/transcribe audio.mp3 --backend parakeet --translate --source-lang fr --target-lang de`	NeMo Canary (EN/FR/DE/ES bidirectional)
Domain jargon	`./scripts/transcribe audio.mp3 --initial-prompt "Kubernetes"`	Whisper-specific prompting
Non-European language	`./scripts/transcribe audio.mp3 -l ja`	Parakeet: 25 EU langs only
Multilingual mode	`./scripts/transcribe audio.mp3 --multilingual`	Whisper-specific feature
Hotwords boost	`./scripts/transcribe audio.mp3 --hotwords 'JIRA Kubernetes'`	Whisper-specific feature
Prefix conditioning	`./scripts/transcribe audio.mp3 --prefix 'Good morning,'`	Whisper-specific feature
Clip time range	`./scripts/transcribe audio.mp3 --clip-timestamps "30,60"`	Whisper-specific feature
Detect language only	`./scripts/transcribe audio.mp3 --detect-language-only`	Routes to fw by default; use `--backend parakeet` for Parakeet detection
Parallel batch	`./scripts/transcribe *.mp3 --parallel 4 -o ./out/`	Whisper-specific feature

Routes to Parakeet Automatically

Task	Command	Why
Long audio (>24 min)	`./scripts/transcribe lecture.wav --long-form`	Parakeet-only: local attention mode
Streaming output	`./scripts/transcribe audio.wav --streaming`	Parakeet-only: chunked inference
Fast / low VRAM (110M)	`./scripts/transcribe --fast audio.mp3`	Parakeet 110M model (~2GB VRAM)
Multitalker / overlap	`./scripts/transcribe --multitalker meeting.wav`	Sortformer + multitalker pipeline
Skip alignment	`./scripts/transcribe audio.wav --no-align`	Parakeet-only: faster, less precise
Canary translation	`./scripts/transcribe --backend parakeet --translate --source-lang fr --target-lang de audio.wav`	Parakeet-only: Canary bidirectional EU translation
Detect language (Parakeet)	`./scripts/transcribe --backend parakeet --detect-language-only audio.wav`	Parakeet language detection (requires `--backend parakeet`)
Danish transcription	`./scripts/transcribe -l da audio.wav`	Auto-selects dedicated Danish model

⚠️ Agent Guidance — Keep Invocations Minimal

CORE RULE: The default command (./scripts/transcribe audio.mp3) is the fastest path. Add flags only when the user explicitly asks for that capability. The router handles backend selection automatically.

⚠️ Model preference: When the router selects faster-whisper, it uses distil-large-v3.5 by default — this is the preferred model (fastest, better accuracy than large-v3-turbo). Don't override unless the user asks.

Probe Mode (`--probe`)

Use --probe to check audio metadata before deciding whether to transcribe:

bash

./scripts/transcribe --probe audio.mp3
# → {"file": "audio.mp3", "path": "/tmp/audio.mp3", "duration": 2714.5, "duration_human": "45m 14s", "format": "mp3", "channels": 2, "sample_rate": 44100, "bitrate": 128000, "size_mb": 43.2}

Returns compact JSON with duration, format, channels, sample rate, bitrate, and file size
No transcription — runs in milliseconds using ffprobe
Use to implement the "ask before transcribing long files" flow:
- Probe → duration > 10 min → ask user "This is 45 minutes, want me to transcribe?"
- Probe → duration < 2 min → transcribe immediately
Works with multiple files: ./scripts/transcribe --probe *.mp3

Agent Output Mode (`--agent`)

Use --agent for structured output the agent can parse directly:

bash

./scripts/transcribe --agent audio.ogg
# → {"text": "Hello world", "duration": 4.2, "language": "en", "language_probability": 0.98, "processing_time": 0.8, "backend": "faster-whisper", "segments": 1, "speakers": null, "word_count": 2, "avg_confidence": 0.94, "file_path": "/tmp/audio.ogg"}

Emits a single compact JSON line to stdout — no stderr noise (implies --quiet)
Core fields: text, duration, language, language_probability, processing_time, backend, segments (count), speakers (list or null), word_count
Agent-specific fields:
- avg_confidence (0.0–1.0) — average word/segment confidence; use to gauge transcript reliability. Present for faster-whisper (from avg_logprob); absent for Parakeet (NeMo doesn't expose confidence scores)
- file_path — absolute path of input file (for multi-file tracking)
- output_path — absolute path of written output file (when -o is used)
- summary_hint — {"first": "...", "last": "..."} for long transcripts (>400 chars); gives the agent a quick preview without reading the full text
Combine with -o to also save full transcript to file: ./scripts/transcribe --agent -o /tmp/transcript.txt audio.mp3
Use for quick voice message transcription where the agent just needs the text and metadata

Exit Codes

Standardized exit codes for agent error handling:

Code	Meaning	Agent response
0	Success	Normal reply
1	General error	"Transcription failed"
2	Missing dependency	"Backend not set up yet — need to run setup"
3	Bad input (file not found, invalid args)	"Couldn't process that audio file"
4	GPU/VRAM error (OOM)	"GPU ran out of memory — try a smaller model or CPU"

Chat Media Handling (OpenClaw Agents)

OpenClaw downloads media and provides local file paths in the message context (e.g. <media:voice path="/path/to/audio.ogg"> or <media:audio path="/path/to/file.mp3">).

⚠️ Do NOT auto-transcribe every voice message. Determine intent first:

When to Transcribe Immediately (No Confirmation Needed)

User explicitly asks for transcription: "transcribe this", "what does this say", "make subtitles for this"
User sends an audio/video file (not a voice note) with a transcription request
User sends media in an ongoing transcription conversation (context makes intent clear)

When to Ask First

User sends a voice message with no context — they're probably talking to you, not asking for a transcript. Respond to the content of their message instead, or ask: "Want me to transcribe this, or were you telling me something?"
User sends a long audio/video file (>10 min) without explicit instructions — confirm before committing to a long transcription
User forwards audio from someone else without comment — unclear if they want transcription or something else

Technical Notes

Voice messages (.ogg opus) are auto-converted by ffmpeg — no special handling needed
Video files work too — ffmpeg extracts the audio track automatically
Use -q (quiet) for short voice messages to suppress stderr progress noise

Example Flows

User wants transcription:

text

User: Can you transcribe this? [sends audio file]
Agent: Transcribing now...
Agent: [runs ./scripts/transcribe -q /path/to/audio.mp3]
Agent: Here's the transcript: "..."

User is talking to the agent via voice:

text

User: [sends voice message saying "Hey, what's on my calendar today?"]
Agent: [transcribes silently to understand the request]
Agent: [responds to the calendar question, does NOT parrot back the transcript]

Ambiguous intent (use --probe to check duration first):

text

User: [sends 45-minute meeting recording, no comment]
Agent: [runs ./scripts/transcribe --probe /path/to/recording.mp3]
Agent: Got a 45-minute recording. Want me to transcribe it? I can also do speaker identification or subtitles if you need those.

Output Length Management for Chat

Transcripts can be very long. Follow these rules to avoid flooding the chat:

Short audio (<2 min, <~2000 chars): Reply directly with the full transcript text
Medium audio (2-10 min): Reply with full text; if it exceeds ~3000 chars, consider saving to file and offering a summary
Long audio (>10 min): Always save to a file (-o /tmp/transcript.txt), share the file path, and provide a brief summary of the content
Subtitle/data formats (SRT, VTT, ASS, CSV, JSON, etc.): Always save to file with -o, tell the user the path
Search results (--search): Show directly — they're already concise
Language detection (--detect-language-only): Show directly — single line output

When in doubt, save to file and summarize. Users can always ask for the full text.

Do NOT add flags unless explicitly requested:

--backend — unless the user specifically requests a backend
--diarize — unless the user asks "who said what" / "identify speakers"
--translate — unless the user wants audio translated to English
--format srt/vtt/ass/etc. — unless the user asks for subtitles in that format
--long-form — unless the audio is confirmed >24 minutes
--denoise/--normalize — unless the user mentions bad audio quality
--initial-prompt — unless there's domain-specific jargon to prime
--search — unless the user asks to find/locate a word in audio
--detect-chapters — unless the user asks for chapters/sections
--clean-filler — unless the user asks to remove filler words
--word-timestamps — unless the user needs word-level timing
--stream / --streaming — unless the user wants live/progressive output
--clip-timestamps — unless the user wants a specific time range
--temperature 0.0 — unless the model is hallucinating on music/silence
--vad-threshold — unless VAD is aggressively cutting speech or including noise
--min-speakers/--max-speakers — unless you know the speaker count
--hf-token — unless the token is not cached at ~/.cache/huggingface/token
--max-words-per-line / --max-chars-per-line — unless the user asks for subtitle wrapping
--filter-hallucinations — unless the transcript contains obvious artifacts
--merge-sentences — unless the user asks for sentence-level subtitle cues
--channel left|right — unless the user mentions stereo tracks
--detect-paragraphs — unless the user asks for paragraph breaks
--speaker-names — unless the user provides real names (always requires --diarize)
--hotwords — unless the user names specific rare terms
--prefix — unless the user knows the exact words the audio starts with
--detect-language-only — unless the user only wants language ID
--stats-file — unless the user asks for performance stats
--parallel N — only for large CPU batch jobs
--retries N — only for unreliable inputs (URLs, network files)
--burn-in — only when user explicitly asks to embed subtitles into video
--keep-temp — only when the user may re-process the same URL
--output-template — only when user specifies a custom naming pattern
--timestamps (parakeet) — unless the user asks for word-level timestamps
--fast — unless the user explicitly wants speed over accuracy or mentions low VRAM
--multitalker — unless the user mentions overlapping speech or asks for better multi-speaker handling
--resume <path> — unless the user is resuming a batch job that crashed/was interrupted
--no-align — unless the user wants faster processing and doesn't need precise word timestamps
--multitalker-diar-model / --multitalker-asr-model — only when user provides specific model names
Multi-format (--format srt,text) — only when user explicitly wants multiple formats; always pair with -o <dir>
--agent — use when you need structured JSON for parsing (e.g. silent transcription of voice messages to understand user intent); don't use when the user explicitly asked for a transcript (just show them the text directly)
--probe — use to check duration before transcribing ambiguous long files; don't probe short voice messages (just transcribe them directly)

Overhead notes:

Any word-level feature (faster-whisper) auto-runs wav2vec2 alignment (~5-10s overhead)
--diarize adds ~20-30s on top

Search guidance:

--search replaces the normal transcript output — it prints only matching segments with timestamps
Add --search-fuzzy only when the user mentions approximate/partial matching
To save search results to a file, use -o results.txt

Chapter detection guidance:

Default --chapter-gap 8 (8-second silence = new chapter) works for most content; tune down for dense content
--chapter-format youtube (default) outputs YouTube-ready timestamps; use json for programmatic use
Always use --chapters-file PATH when combining chapters with a transcript output
Batch mode limitation: --chapters-file takes a single path — in batch mode, each file's chapters overwrite the previous

Speaker audio export guidance:

Always pair --export-speakers with --diarize
Requires ffmpeg; outputs SPEAKER_1.wav, SPEAKER_2.wav, etc. (or real names if --speaker-names is set)

Language map guidance:

Only use --language-map in batch mode when the user has confirmed different languages across files
Inline format: "interview*.mp3=en,lecture*.mp3=fr" — fnmatch globs on filename
JSON file format: @/path/to/map.json

RSS / Podcast guidance:

Default fetches 5 newest episodes; --rss-latest 0 for all; --skip-existing to resume safely
Always use -o <dir> with --rss — without it, all episode transcripts print to stdout concatenated

Output format for agent relay:

Text transcript → show directly to user (summarise long ones)
Subtitle formats (SRT, VTT, ASS, LRC, TTML) → write to -o file, tell user the path
Data formats (CSV, TSV, JSON, HTML) → write to -o file, tell user the path
Search results → show directly (human-readable)
Chapter output → show directly or write to --chapters-file
Stats output (--stats-file) → summarise key fields for the user
Language detection (--detect-language-only) → print directly; it's a single line
Multi-format (--format srt,text) → requires -o <dir>; tell user all paths written

Don't use this skill when:

User wants text-to-speech (use the tts tool instead)
User wants audio editing or music generation
User wants to summarize a YouTube video without needing a raw transcript (use summarize skill)

Backend Comparison

Feature	🦜 Parakeet	🗣️ faster-whisper
Accuracy	✅ Best (6.34% avg WER)	Good (distil: 7.08% WER)
Speed	✅ ~3380× realtime	~20× realtime
Auto punctuation	✅ Built-in	❌ Requires post-processing
Languages	25 European	✅ 99+ worldwide
Diarization	✅ NeMo ClusteringDiarizer	✅ pyannote speaker ID
Translation	✅ Canary: EN/FR/DE/ES	✅ Any → English
Language detection	✅ Auto-detect + output	✅ --detect-language-only
Chapters/search	✅ Shared	✅ Shared
Output formats	✅ All 10 formats	✅ All 10 formats
Audio preprocessing	✅ --denoise, --normalize	✅ --denoise, --normalize
Filler removal	✅ --clean-filler	✅ --clean-filler
Long audio	✅ Up to 3 hours	Limited by VRAM
Streaming	✅ Chunked inference	✅ Segment streaming
RSS/podcast	✅ --rss	✅ --rss
VRAM usage	~2GB	~1.5GB (distil)
Burn-in subtitles	✅ --burn-in	✅ --burn-in
Initial prompt	❌	✅ Domain jargon priming
Word timestamps	✅ NeMo + wav2vec2 aligned	✅ wav2vec2 aligned (~10ms)
Custom models	✅ Any NeMo / HuggingFace	✅ CTranslate2 / HuggingFace
Multitalker	✅ Sortformer + per-speaker	❌
Fast/small model	✅ 110M (--fast)	✅ distil-small.en (166M)
Batch resume	✅ --resume	✅ --resume
Noise handling	Good (built-in robustness)	✅ --denoise, --normalize

Setup Details

For first-time setup, see ⚡ First-Time Setup at the top. This section is supplementary reference.

What Gets Installed (and What Doesn't)

Quickstart installs one backend — the best for the user's hardware. The second backend auto-installs later only if a feature needs it. Heavy optional deps are deferred until first use.

Hardware	Backend	Setup download	Model (first use)	What triggers more downloads
NVIDIA GPU	Parakeet	~5 GB (PyTorch+CUDA+NeMo)	~1.2 GB	`--translate` or non-EU language → faster-whisper
CPU / macOS	faster-whisper	~300 MB (CTranslate2)	~756 MB	`--diarize` → PyTorch (~2.8 GB); `--fast`/`--multitalker` → Parakeet

Why the size difference? Parakeet needs PyTorch+CUDA upfront (~5 GB, NeMo depends on it). faster-whisper uses CTranslate2 directly — PyTorch is only pulled in for diarization/alignment features.

Parakeet uses nemo_toolkit[asr-only] — this skips ~19 training-only packages (wandb, transformers, datasets, lightning, pandas, peft, etc.) saving ~400 MB vs [asr].

Optional Extras (Not Needed for Basic Transcription)

Feature	Install	Auto-installs?
Non-WAV audio (mp3/m4a/mp4)	`sudo apt install ffmpeg` (Linux) · `brew install ffmpeg` (macOS)	No — quickstart reports if missing
YouTube/URL downloads	`pipx install yt-dlp`	No
Speaker diarization (faster-whisper)	`huggingface-cli login` + accept model	Partial (PyTorch auto-installs; token + model agreement manual)
Both backends	`--quickstart --all --json`	Yes (second backend auto-installs on first use of a feature that needs it)

Manual Backend Setup

bash

./scripts/transcribe --check              # Status check (human-readable)
./scripts/transcribe --check --json       # Status check (agent-parseable)
./scripts/transcribe --setup parakeet     # Best accuracy/speed (needs GPU)
./scripts/transcribe --setup faster-whisper  # Translation, 99+ languages, CPU-compatible
./scripts/transcribe --setup all --diarize   # Full install with diarization

Hard Requirements & Install Strategy

Hard requirements: Python 3.10+ only. Everything else is installed by --quickstart or auto-installs on first use.

Lean install strategy:

faster-whisper: ~300 MB base (CTranslate2, no PyTorch). PyTorch (~2.8 GB) deferred until --diarize.
Parakeet: ~5 GB base (NeMo + PyTorch CUDA). Uses nemo_toolkit[asr-only] — skips training-only packages.
System deps: --quickstart does NOT auto-install system packages (ffmpeg, yt-dlp). It reports them as missing; install them separately if needed.

Auto-install behavior: Backends lazy-load on first use. Optional deps (like pyannote for diarization) auto-install when you use the feature that needs them. In --agent mode, setup emits structured JSON status to stderr.

Platform Support

Platform	Acceleration	Speed
Linux + NVIDIA GPU	CUDA	Parakeet ~3380× RT, Whisper ~20× RT
WSL2 + NVIDIA GPU	CUDA	Parakeet ~3380× RT, Whisper ~20× RT
macOS Apple Silicon	CPU	Whisper ~3-5× RT (Parakeet limited)
macOS Intel	CPU	Whisper ~1-2× RT
Linux (no GPU)	CPU	~1× RT

GPU Troubleshooting

If setup didn't detect your GPU, manually install PyTorch with CUDA:

bash

# For CUDA 12.x (in the backend's venv)
uv pip install --python .venv/bin/python torch --index-url https://download.pytorch.org/whl/cu121

WSL2 users: Ensure you have the NVIDIA CUDA drivers for WSL installed on the Windows side.

Options

Super-Transcribe Routing Options

text

--quickstart         One-command setup: install best backend for hardware (lean)
--quickstart --all   Install both backends (Parakeet + faster-whisper)
--quickstart --json  Same, structured JSON output for agent parsing
--quickstart --dry-run --json  Preview what would be installed with estimated sizes
--check              Check prerequisites + backend status (exit 0=ready, 2=not ready)
--check --json       Same check, compact JSON with estimated install sizes
--backend <name>     Force a backend: faster-whisper | parakeet | fw | pk
--backends           List available/installed backends and exit
--probe              Quick audio metadata (duration, format, channels) — no transcription
--setup <backend>    Pre-install a backend: faster-whisper | parakeet | all
--help-routing       Show detailed auto-routing logic
--fast               Use small 110M Parakeet model (quick, lower accuracy)
--multitalker        Multi-speaker pipeline with overlapped speech handling
--resume <path>      Resume batch processing from checkpoint file
--version            Show super-transcribe + backend versions

Shared Options (Work with Both Backends)

text

AUDIO                 Audio file(s), directory, glob, or URL
-f, --format FMT      text | json | srt | vtt | ass | lrc | ttml | csv | tsv | html
-o, --output PATH     Output file or directory
-m, --model NAME      Model name (backend-specific)
-l, --language CODE   Language code
--max-words-per-line  Subtitle word wrapping
--max-chars-per-line  Subtitle character wrapping
--batch-size N        Inference batch size
--skip-existing       Skip already-transcribed files
--resume PATH         Resume batch from checkpoint file (skips completed files)
--device DEV          auto | cpu | cuda
-q, --quiet           Suppress progress messages
--version             Print version info
--diarize             Speaker diarization
--min-speakers N      Min speakers hint for diarization
--max-speakers N      Max speakers hint for diarization
--speaker-names LIST  Replace SPEAKER_1, SPEAKER_2 with names
--export-speakers DIR Export each speaker's audio to WAV files
--search TERM         Search transcript for TERM
--search-fuzzy        Fuzzy/approximate search matching
--detect-chapters     Detect chapter breaks from silence gaps
--chapter-gap SEC     Min silence gap for chapter break (default: 8.0)
--chapters-file PATH  Write chapter markers to file
--chapter-format FMT  youtube | text | json (default: youtube)
--clean-filler        Remove hesitation fillers (um, uh, etc.)
--filter-hallucinations  Filter common hallucination patterns
--merge-sentences     Merge segments into sentence chunks
--detect-paragraphs   Insert paragraph breaks based on gaps
--paragraph-gap SEC   Min gap for paragraph break (default: 3.0)
--normalize           EBU R128 volume normalization
--denoise             High-pass + FFT noise reduction
--channel CH          left | right | mix (default: mix)
--burn-in OUTPUT      Burn subtitles into video file
--rss URL             Podcast RSS feed to transcribe
--rss-latest N        Latest N episodes from RSS (default: 5)
--stats-file PATH     Write performance stats JSON sidecar

Faster-Whisper Only Options

text

Model & Language:
  --revision REV        Model revision (git branch/tag/commit) to pin a specific version
  --initial-prompt TEXT  Prompt to condition the model (terminology, formatting style)
  --prefix TEXT         Prefix to condition the first segment (e.g. known starting words)
  --hotwords WORDS      Space-separated hotwords to boost recognition
  --translate           Translate any language to English (instead of transcribing)
  --multilingual        Enable multilingual/code-switching mode
  --hf-token TOKEN      HuggingFace token for private/gated models and diarization
  --model-dir PATH      Custom model cache directory (default: ~/.cache/huggingface/)

Output:
  --word-timestamps     Include word-level timestamps (wav2vec2 aligned automatically)
  --stream              Output segments as they are transcribed (disables diarize/alignment)
  --output-template TPL Batch output filename template ({stem}, {lang}, {ext}, {model})

Inference Tuning:
  --beam-size N         Beam search size; higher = more accurate but slower (default: 5)
  --temperature T       Sampling temperature or comma-separated fallback list
  --no-speech-threshold PROB  Mark segments as silence (default: 0.6)
  --no-vad              Disable voice activity detection
  --vad-threshold T     VAD speech probability threshold (default: 0.5)
  --vad-neg-threshold T VAD negative threshold for ending speech
  --min-speech-duration MS  Minimum speech segment duration in ms
  --max-speech-duration SEC Maximum speech segment duration
  --min-silence-duration MS Minimum silence before splitting (default: 2000)
  --speech-pad MS       Padding around speech segments (default: 400)
  --no-batch            Disable batched inference
  --hallucination-silence-threshold SEC  Skip silent sections where model hallucinates
  --no-condition-on-previous-text  Don't condition on previous text (auto-enabled for distil models)
  --condition-on-previous-text  Override auto-disable for distil models
  --compression-ratio-threshold RATIO  Filter segments above this ratio (default: 2.4)
  --log-prob-threshold PROB  Filter segments below avg log probability (default: -1.0)
  --max-new-tokens N    Maximum tokens per segment
  --clip-timestamps RANGE  Transcribe specific time ranges: '30,60' or '0,30;60,90'
  --progress            Show transcription progress bar
  --best-of N           Candidates when sampling with non-zero temperature (default: 5)
  --patience F          Beam search patience factor (default: 1.0)
  --repetition-penalty F  Penalty for repeated tokens (default: 1.0)
  --no-repeat-ngram-size N  Prevent n-gram repetitions (default: 0 = off)

Advanced Inference:
  --no-timestamps       Output text without timing info
  --chunk-length N      Audio chunk length for batched inference
  --language-detection-threshold T  Confidence threshold for language detection (default: 0.5)
  --language-detection-segments N  Segments to sample for detection (default: 1)
  --length-penalty F    Beam search length penalty (default: 1.0)
  --prompt-reset-on-temperature T  Reset prompt on temperature fallback (default: 0.5)
  --no-suppress-blank   Disable blank token suppression
  --suppress-tokens IDS Token IDs to suppress
  --max-initial-timestamp T  Maximum timestamp for first segment (default: 1.0)
  --prepend-punctuations CHARS  Punctuation merged into preceding word
  --append-punctuations CHARS  Punctuation merged into following word

Advanced:
  --min-confidence PROB Filter segments below avg word confidence (0.0–1.0)
  --detect-language-only  Detect language and exit (no transcription)
  --keep-temp           Keep temp files from URL downloads
  --parallel N          Parallel workers for batch processing
  --retries N           Retry failed files with exponential backoff
  --language-map MAP    Per-file language overrides for batch mode

Device:
  --compute-type TYPE   auto | int8 | int8_float16 | float16 | float32
  --threads N           CPU thread count for CTranslate2
  --log-level LEVEL     debug | info | warning | error

Parakeet Only Options

text

--long-form           Local attention for audio >24 min (up to ~3 hours)
--streaming           Print segments as they're transcribed (chunked inference)
--timestamps          Enable word/segment/char timestamps (auto-enabled for timed formats)
--fast                Use small 110M model for quick transcription (~2GB VRAM)
--no-align            Skip wav2vec2 alignment (faster, less precise timestamps)
--multitalker         Multi-speaker pipeline (Sortformer diar + per-speaker ASR)
--multitalker-diar-model MODEL   Override diarizer model for multitalker
--multitalker-asr-model MODEL    Override ASR model for multitalker

Parakeet Model Aliases

Short names for -m (Parakeet backend only):

Alias	Resolves to	Notes
`v3`, `tdt-v3`	`nvidia/parakeet-tdt-0.6b-v3`	Default multilingual
`v2`, `tdt-v2`	`nvidia/parakeet-tdt-0.6b-v2`	English only
`1.1b`, `tdt-1.1b`	`nvidia/parakeet-tdt-1.1b`	1.1B English
`110m`, `fast`, `small`	`nvidia/parakeet-tdt_ctc-110m`	Tiny/fast model
`ja`, `japanese`	`nvidia/parakeet-tdt_ctc-0.6b-ja`	Japanese
`vi`, `vietnamese`	`nvidia/parakeet-ctc-0.6b-Vietnamese`	Vietnamese
`da`, `danish`	`nvidia/parakeet-rnnt-110m-da-dk`	Danish
`canary`, `canary-v2`	`nvidia/canary-1b-v2`	Translation (25 EU)
`canary-flash`	`nvidia/canary-1b-flash`	Fast translation (4 langs)
`multitalker`	`nvidia/multitalker-parakeet-streaming-0.6b-v1`	Multi-speaker

Full HuggingFace model paths always work too (e.g. -m nvidia/parakeet-tdt-1.1b). Both backends accept any valid model name via -m.

🗣️ faster-whisper Backend

Local speech-to-text using faster-whisper — a CTranslate2 reimplementation of OpenAI's Whisper that runs 4-6x faster with identical accuracy. With GPU acceleration, expect ~20x realtime transcription (a 10-minute audio file in ~30 seconds).

Faster-Whisper Model Selection

Choose the right model for your needs:

dot

digraph model_selection {
    rankdir=LR;
    node [shape=box, style=rounded];

    start [label="Start", shape=doublecircle];
    need_accuracy [label="Need maximum\naccuracy?", shape=diamond];
    multilingual [label="Multilingual\ncontent?", shape=diamond];
    resource_constrained [label="Resource\nconstraints?", shape=diamond];

    large_v3 [label="large-v3\nor\nlarge-v3-turbo", style="rounded,filled", fillcolor=lightblue];
    large_turbo [label="large-v3-turbo", style="rounded,filled", fillcolor=lightblue];
    distil_large [label="distil-large-v3.5\n(default)", style="rounded,filled", fillcolor=lightgreen];
    distil_medium [label="distil-medium.en", style="rounded,filled", fillcolor=lightyellow];
    distil_small [label="distil-small.en", style="rounded,filled", fillcolor=lightyellow];

    start -> need_accuracy;
    need_accuracy -> large_v3 [label="yes"];
    need_accuracy -> multilingual [label="no"];
    multilingual -> large_turbo [label="yes"];
    multilingual -> resource_constrained [label="no (English)"];
    resource_constrained -> distil_small [label="mobile/edge"];
    resource_constrained -> distil_medium [label="some limits"];
    resource_constrained -> distil_large [label="no"];
}

Standard Models (Full Whisper)

Model	Size	Speed	Accuracy	Use Case
`tiny` / `tiny.en`	39M	Fastest	Basic	Quick drafts
`base` / `base.en`	74M	Very fast	Good	General use
`small` / `small.en`	244M	Fast	Better	Most tasks
`medium` / `medium.en`	769M	Moderate	High	Quality transcription
`large-v1/v2/v3`	1.5GB	Slower	Best	Maximum accuracy
`large-v3-turbo`	809M	Fast	Excellent	High accuracy (slower than distil)

Distilled Models (~6x Faster, ~1% WER Difference)

Model	Size	Speed vs Standard	Accuracy	Use Case
`distil-large-v3.5`	756M	~6.3x faster	7.08% WER	Default, best balance
`distil-large-v3`	756M	~6.3x faster	7.53% WER	Previous default
`distil-large-v2`	756M	~5.8x faster	10.1% WER	Fallback
`distil-medium.en`	394M	~6.8x faster	11.1% WER	English-only, resource-constrained
`distil-small.en`	166M	~5.6x faster	12.1% WER	Mobile/edge devices

.en models are English-only and slightly faster/better for English content.

Note for distil models: HuggingFace recommends disabling condition_on_previous_text for all distil models to prevent repetition loops. The script auto-applies --no-condition-on-previous-text whenever a distil-* model is detected. Pass --condition-on-previous-text to override if needed.

Custom & Fine-Tuned Models

WhisperModel accepts local CTranslate2 model directories and HuggingFace repo names — no code changes needed.

bash

# Local CTranslate2 model
./scripts/transcribe audio.mp3 --model /path/to/my-model-ct2

# HuggingFace repo name (auto-downloads)
./scripts/transcribe audio.mp3 --model username/whisper-large-v3-ct2

# Custom model cache directory
./scripts/transcribe audio.mp3 --model-dir ~/my-models

# Convert a HuggingFace model to CTranslate2
pip install ctranslate2
ct2-transformers-converter \
  --model openai/whisper-large-v3 \
  --output_dir whisper-large-v3-ct2 \
  --copy_files tokenizer.json preprocessor_config.json \
  --quantization float16

Faster-Whisper Quick Reference

Essential commands (faster-whisper-specific features only — shared features like diarization, search, chapters, formats are in the shared options above):

Task	Command	Notes
Translate → English	`./scripts/transcribe audio.mp3 --translate`	Any language → English
Domain terms	`./scripts/transcribe audio.mp3 --initial-prompt 'Kubernetes gRPC'`	Boost rare terminology
Hotwords boost	`./scripts/transcribe audio.mp3 --hotwords 'JIRA Kubernetes'`	Bias decoder toward specific words
Clip time range	`./scripts/transcribe audio.mp3 --clip-timestamps "30,60"`	Only 30s–60s
Stream output	`./scripts/transcribe audio.mp3 --stream`	Live segments as transcribed
Fix hallucinations	`./scripts/transcribe audio.mp3 --temperature 0.0 --no-speech-threshold 0.8`	Lock temperature + skip silence
Tune VAD sensitivity	`./scripts/transcribe audio.mp3 --vad-threshold 0.6 --min-silence-duration 500`	Tighter speech detection
Hybrid quantization	`./scripts/transcribe audio.mp3 --compute-type int8_float16`	Save ~1GB VRAM, minimal quality loss
Parallel batch	`./scripts/transcribe *.mp3 --parallel 4 -o ./out/`	CPU multi-file processing
Language map (batch)	`./scripts/transcribe .mp3 --language-map "int.mp3=en,lec*.mp3=fr"`	Per-file language in batch
Detect language only	`./scripts/transcribe audio.mp3 --detect-language-only`	Fast language ID, no transcription
Custom model	`./scripts/transcribe audio.mp3 -m ./my-model-ct2`	Local CTranslate2 model dir
Output filename template	`./scripts/transcribe *.mp3 -o ./out/ --output-template "{stem}_{lang}.{ext}"`	Custom batch output naming

Output Formats (Both Backends)

Both backends support all 10 output formats via --format:

Format	Extension	Use Case
`text`	`.txt`	Default. Plain transcript
`json`	`.json`	Full metadata: segments, timestamps, language, stats
`srt`	`.srt`	Standard subtitle format (video players)
`vtt`	`.vtt`	WebVTT (web video players)
`ass`	`.ass`	Advanced SubStation Alpha (Aegisub, mpv, VLC)
`lrc`	`.lrc`	Timed lyrics (music players)
`ttml`	`.ttml`	Broadcast standard (Netflix, Amazon, BBC)
`csv`	`.csv`	Spreadsheet-ready (with speaker column if diarized)
`tsv`	`.tsv`	OpenAI Whisper–compatible TSV
`html`	`.html`	Confidence-colored transcript

All formats support diarization labels. Multi-format output: --format srt,text -o ./out/

Faster-Whisper Speaker Diarization

Identifies who spoke when using pyannote.audio.

Setup:

bash

./setup.sh --diarize

Requirements:

HuggingFace token at ~/.cache/huggingface/token (huggingface-cli login)
Accepted model agreements:
- https://hf.co/pyannote/speaker-diarization-3.1
- https://hf.co/pyannote/segmentation-3.0

Usage:

bash

./scripts/transcribe meeting.wav --diarize
./scripts/transcribe meeting.wav --diarize --format srt -o meeting.srt
./scripts/transcribe meeting.wav --diarize --format json

Speakers are labeled SPEAKER_1, SPEAKER_2, etc. in order of first appearance. Runs on GPU automatically if CUDA is available.

Faster-Whisper Precise Word Timestamps

Whenever word-level timestamps are computed (--word-timestamps, --diarize, or --min-confidence), a wav2vec2 forced alignment pass automatically refines them from Whisper's ~100-200ms accuracy to ~10ms. Uses the MMS (Massively Multilingual Speech) model — supports 1000+ languages, cached after first load.

Faster-Whisper Performance Notes

First run: Downloads model to ~/.cache/huggingface/ (one-time)
Batched inference: Enabled by default — ~3x faster than standard mode; VAD on by default
GPU: Automatically uses CUDA if available
Quantization: INT8 used on CPU for ~4x speedup
Benchmark (RTX 3070, 21-min file): ~24s with batched inference vs ~69s without
--precise overhead: ~5-10s for wav2vec2 model load + alignment (cached for batch)
Diarization overhead: ~10-30s depending on audio length
Memory: distil-large-v3: ~2GB RAM / ~1GB VRAM; large-v3-turbo: ~4GB RAM / ~2GB VRAM; Diarization: +~1-2GB VRAM
OOM: Lower --batch-size (try 4) if you hit out-of-memory errors
Silero VAD V6: faster-whisper 1.2.1+ has improved speech detection

Faster-Whisper Common Mistakes

Mistake	Problem	Solution
Using CPU when GPU available	10-20x slower	Check `nvidia-smi`; verify CUDA
Not specifying language	Wastes time auto-detecting	Use `--language en` when known
Using wrong model	Unnecessary slowness	Default `distil-large-v3.5` is excellent
Ignoring distilled models	Missing 6x speedup	Try `distil-large-v3.5` first
Out of memory	Model too large	Use smaller model, `--compute-type int8`, or `--batch-size 4`
--diarize without pyannote	Import error	Run `setup.sh --diarize` first
--diarize without HF token	Download fails	Run `huggingface-cli login`
URL without yt-dlp	Download fails	Install: `pipx install yt-dlp`
--min-confidence too high	Drops good segments	Start at 0.5, adjust up
Batch without -o directory	All output mixed in stdout	Use `-o ./transcripts/`
Hallucinations on silence/music	Bad output	Try `--temperature 0.0 --no-speech-threshold 0.8`

Faster-Whisper Troubleshooting

"CUDA not available — using CPU": Install PyTorch with CUDA (see GPU Support above)
Setup fails: Make sure Python 3.10+ is installed
Out of memory: Use smaller model, --compute-type int8, or --batch-size 4
Model download fails: Check ~/.cache/huggingface/ permissions
Diarization model fails: Ensure HuggingFace token exists and model agreements accepted
URL download fails: Check yt-dlp is installed (pipx install yt-dlp)
Check installed version: ./scripts/transcribe --version
Upgrade: ./scripts/backends/faster-whisper/setup.sh --update
VAD splits speech incorrectly: Tune with --vad-threshold 0.3 or --min-silence-duration 300

Faster-Whisper References

🦜 Parakeet Backend

Local speech-to-text using NVIDIA's Parakeet TDT models via the NeMo toolkit. The default parakeet-tdt-0.6b-v3 is a 600-million-parameter multilingual ASR model that delivers state-of-the-art accuracy with automatic punctuation and capitalization, word-level timestamps, and an insane ~3380× realtime inference speed on GPU.

Only needs ~2GB VRAM to run — plenty of room on an RTX 3070 (8GB).

Parakeet Model Selection

Available Models

Model	Alias	Params	Languages	VRAM	Speed	Use Case
`nvidia/parakeet-tdt-0.6b-v3`	`v3`	600M	25 EU languages	~2GB	~3380× RT	Default, best multilingual
`nvidia/parakeet-tdt-0.6b-v2`	`v2`	600M	English only	~2GB	~3380× RT	English-only, slightly better English WER
`nvidia/parakeet-tdt-1.1b`	`1.1b`	1.1B	English only	~3GB	Slower	Maximum English accuracy
`nvidia/parakeet-tdt_ctc-1.1b`	`tdt-ctc-1.1b`	1.1B	English only	~3GB	Fast	Hybrid TDT+CTC; 11hrs in one pass
`nvidia/parakeet-tdt_ctc-110m`	`110m`/`fast`	110M	English only	~1GB	Fastest	Low-VRAM / quick drafts
`nvidia/parakeet-tdt_ctc-0.6b-ja`	`ja`	600M	Japanese	~2GB	~3380× RT	Japanese (auto-selected with `-l ja`)
`nvidia/parakeet-ctc-0.6b-Vietnamese`	`vi`	600M	Vietnamese	~2GB	~3380× RT	Vietnamese (auto-selected with `-l vi`)
`nvidia/parakeet-rnnt-110m-da-dk`	`da`	110M	Danish	~1GB	Fast	Danish (auto-selected with `-l da`)
`nvidia/canary-1b-v2`	`canary`	1B	25 EU languages	~3GB	~200× RT	ASR + translation (bidirectional)
`nvidia/canary-1b-flash`	`canary-flash`	883M	EN/DE/FR/ES	~2.5GB	>1000× RT	Fast Canary variant (4 languages only)
`nvidia/multitalker-parakeet-streaming-0.6b-v1`	—	600M	English	~4GB*	~3380× RT	Multi-speaker with overlapped speech

*Multitalker loads diarizer (~2GB) + ASR model (~2GB). Both are lazy-loaded and cached.

Language-Specific Model Auto-Selection

When using the default model (parakeet-tdt-0.6b-v3) and specifying a language with -l, dedicated models are auto-selected:

-l da → nvidia/parakeet-rnnt-110m-da-dk
-l ja → nvidia/parakeet-tdt_ctc-0.6b-ja
-l vi → nvidia/parakeet-ctc-0.6b-Vietnamese

All models are lazy-loaded — downloaded from HuggingFace on first use and cached locally.

Supported Languages (v3)

Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), German (de), Greek (el), Hungarian (hu), Italian (it), Latvian (lv), Lithuanian (lt), Maltese (mt), Polish (pl), Portuguese (pt), Romanian (ro), Slovak (sk), Slovenian (sl), Spanish (es), Swedish (sv), Russian (ru), Ukrainian (uk)

Language is auto-detected — no prompting or configuration needed.

Accuracy Benchmarks (v3)

Benchmark	WER
Open ASR Leaderboard (avg)	6.34%
LibriSpeech test-clean	1.93%
LibriSpeech test-other	3.59%
AMI	11.31%
GigaSpeech	9.59%
SPGI Speech	3.97%
TED-LIUM v3	2.75%
VoxPopuli	6.14%

Parakeet Quick Reference

Essential commands (Parakeet-specific features only — shared features are in the shared options above):

Task	Command	Notes
Long audio (>24 min)	`./scripts/transcribe lecture.wav --long-form`	Local attention, up to ~3 hours
Streaming output	`./scripts/transcribe audio.wav --streaming`	Print segments as transcribed
Fast / 110M model	`./scripts/transcribe --fast audio.wav`	Quick, lower accuracy (~1GB VRAM)
Skip alignment	`./scripts/transcribe audio.wav --no-align`	Faster, less precise timestamps
Multitalker mode	`./scripts/transcribe --multitalker meeting.wav`	Overlapped speech, per-speaker ASR
Japanese	`./scripts/transcribe audio.wav -l ja`	Auto-selects Japanese model
Vietnamese	`./scripts/transcribe audio.wav -l vi`	Auto-selects Vietnamese model
Danish	`./scripts/transcribe audio.wav -l da`	Auto-selects Danish model
Canary translation	`./scripts/transcribe audio.wav --backend parakeet --translate --source-lang fr --target-lang de`	Bidirectional EU translation
Fast Canary	`./scripts/transcribe audio.wav -m canary-flash --translate`	>1000× RT, EN/DE/FR/ES only
Upgrade NeMo	`./scripts/backends/parakeet/setup.sh --update`	Upgrade + verify torch >= 2.6.0

Parakeet Output

Same 10 formats as listed in "Output Formats" above. Parakeet text output is automatically punctuated and capitalized. JSON output includes word/segment/char timestamps and performance stats.

Parakeet Long-Form Audio

Parakeet v3 supports two attention modes:

Mode	Max Duration	Flag	Notes
Full attention (default)	~24 min	(none)	Best accuracy, requires more VRAM
Local attention	~3 hours	`--long-form`	Slightly reduced accuracy, much lower VRAM

For audio over 24 minutes:

bash

./scripts/transcribe long-lecture.wav --long-form

This changes the attention model to rel_pos_local_attn with a context window of [256, 256].

Parakeet Audio Format Support

Format	Support	Notes
`.wav`	✅ Native	Preferred; 16kHz mono
`.flac`	✅ Native	Lossless; works directly
`.mp3`	🔄 Converts	Requires ffmpeg
`.m4a`	🔄 Converts	Requires ffmpeg
`.mp4`	🔄 Converts	Requires ffmpeg
`.ogg`	🔄 Converts	Requires ffmpeg
`.webm`	🔄 Converts	Requires ffmpeg
`.aac`	🔄 Converts	Requires ffmpeg
`.wma`	🔄 Converts	Requires ffmpeg
`.opus`	🔄 Converts	Requires ffmpeg
`.mkv`	🔄 Converts	Requires ffmpeg

Non-native formats are automatically converted to 16kHz mono WAV using ffmpeg before transcription.

Parakeet Multitalker Mode

For audio with overlapping speakers (meetings, debates, panel discussions), the --multitalker mode uses a dedicated pipeline:

Sortformer diarizer (nvidia/diar_streaming_sortformer_4spk-v2.1) identifies speakers and their active regions
Per-speaker audio is extracted using ffmpeg
Multitalker ASR model (nvidia/multitalker-parakeet-streaming-0.6b-v1) transcribes each speaker separately
Results are merged and sorted by timestamp

bash

# Basic multitalker
./scripts/transcribe --multitalker meeting.wav

# With speaker names
./scripts/transcribe --multitalker meeting.wav --speaker-names "Alice,Bob,Charlie"

# With custom models
./scripts/transcribe --multitalker \
  --multitalker-diar-model nvidia/diar_streaming_sortformer_4spk-v2.1 \
  --multitalker-asr-model nvidia/multitalker-parakeet-streaming-0.6b-v1 \
  meeting.wav

Requirements: CUDA GPU with ~6GB VRAM (both models loaded simultaneously). English only. Both models are lazy-loaded on first use.

vs standard --diarize: Standard diarization transcribes the full audio first, then assigns speakers post-hoc — it can't handle overlapping speech. Multitalker transcribes each speaker independently, so overlapping regions are transcribed correctly per speaker.

Parakeet Timestamp Alignment

Parakeet includes automatic wav2vec2 forced alignment that refines word-level timestamps from NeMo's native output (~100ms) to ~10ms accuracy using the MMS model. This runs automatically after transcription.

To skip alignment (faster processing): use --no-align
Alignment is automatically skipped in --streaming mode
The MMS model is cached after first load for batch efficiency

Parakeet Troubleshooting

Problem	Solution
CUDA not available	Install PyTorch with CUDA: `pip install torch --index-url https://download.pytorch.org/whl/cu121`
NeMo 2.6+ crashes	Requires torch >= 2.6.0. Run `./scripts/backends/parakeet/setup.sh --update` to upgrade both torch and NeMo
NeMo import error	Run `./setup.sh` or `./scripts/backends/parakeet/setup.sh`
Out of memory	Use `--fast` (110M model, ~1GB VRAM) or `--device cpu`
Model download fails	Check HuggingFace connectivity and `~/.cache/huggingface/` permissions
ffmpeg not found	Install ffmpeg: `apt install ffmpeg` (needed for non-WAV input)
Multitalker fails	Requires NeMo 2.4+ and ~6GB VRAM. Check `nvidia-smi` for available memory
Diarization fails (NeMo)	Falls back to pyannote automatically. Install: `pip install pyannote.audio`
pyannote auth error	Run `huggingface-cli login` and accept model agreements
Streaming mode not real-time	`--streaming` uses chunked attention inference, not live audio streaming
Japanese/Vietnamese not detected	Explicitly pass `-l ja` or `-l vi` for auto-selection of dedicated models
Batch crashed mid-way	Use `--resume progress.json` to continue from where it stopped
Wrong model loaded	Check `--model` value. Use exact HuggingFace paths or aliases (see table above)

Architecture

Both backends share a common library to avoid code duplication:

text

scripts/
├── transcribe           # Unified entry point (router)
└── backends/
    ├── lib/             # Shared code (imported by both backends)
    │   ├── formatters.py    # All 10 output formats
    │   ├── postprocess.py   # Search, chapters, filler, paragraphs, merge, hallucination filter
    │   ├── audio.py         # Preprocessing, conversion, download, burn-in, resolve_inputs
    │   ├── alignment.py     # wav2vec2 forced alignment (MMS model, shared by both backends)
    │   ├── speakers.py      # Speaker name mapping, audio export
    │   └── rss.py           # RSS feed parsing
    ├── faster-whisper/  # CTranslate2 backend (imports from lib/)
    └── parakeet/        # NeMo backend (imports from lib/)

Super-Transcribe — Unified Speech-to-Text

Install

🎙️ Super-Transcribe — Unified Speech-to-Text

⚡ First-Time Setup

Step 1 — Check readiness (instant, no downloads)

Step 2 — Preview the install (no downloads)

Step 3 — Install (after user confirms)

What Gets Installed

Lean Install Option

When to Use

Auto-Routing Logic

Quick Reference

Basic (Auto-Routes to Best Available)

Routes to Faster-Whisper Automatically

Routes to Parakeet Automatically

⚠️ Agent Guidance — Keep Invocations Minimal

Probe Mode (--probe)

Agent Output Mode (--agent)

Exit Codes

Chat Media Handling (OpenClaw Agents)

When to Transcribe Immediately (No Confirmation Needed)

When to Ask First

Technical Notes

Example Flows

Output Length Management for Chat

Backend Comparison

Setup Details

What Gets Installed (and What Doesn't)

Optional Extras (Not Needed for Basic Transcription)

Manual Backend Setup

Hard Requirements & Install Strategy

Platform Support

GPU Troubleshooting

Options

Super-Transcribe Routing Options

Shared Options (Work with Both Backends)

Faster-Whisper Only Options

Parakeet Only Options

Parakeet Model Aliases

🗣️ faster-whisper Backend

Faster-Whisper Model Selection

Standard Models (Full Whisper)

Distilled Models (~6x Faster, ~1% WER Difference)

Custom & Fine-Tuned Models

Faster-Whisper Quick Reference

Output Formats (Both Backends)

Faster-Whisper Speaker Diarization

Faster-Whisper Precise Word Timestamps

Faster-Whisper Performance Notes

Faster-Whisper Common Mistakes

Faster-Whisper Troubleshooting

Faster-Whisper References

🦜 Parakeet Backend

Parakeet Model Selection

Available Models

Language-Specific Model Auto-Selection

Supported Languages (v3)

Accuracy Benchmarks (v3)

Parakeet Quick Reference

Parakeet Output

Parakeet Long-Form Audio

Parakeet Audio Format Support

Parakeet Multitalker Mode

Parakeet Timestamp Alignment

Parakeet Troubleshooting

Architecture

Related skills

Probe Mode (`--probe`)

Agent Output Mode (`--agent`)