trx

name	trx
description	Transcribe audio/video using trx CLI and post-process results with agent corrections. Use when: (1) user wants to transcribe a video or audio file, (2) user shares a YouTube/Twitter/Instagram URL for transcription, (3) user says "transcribe", "subtitles", "srt", "transcript", (4) user wants to fix/clean up a whisper transcription, (5) user asks to extract text from a video.
metadata	{"author":"Railly Hugo","version":"0.7.0"}

trx -- Agent-First Transcription CLI

Install: npx skills add crafter-station/trx -g

Prerequisites

Check setup: trx doctor --output json. If dependencies missing, run trx init.

Install (Bun recommended, works with npm):

bun add -g @crafter/trx
# or
npm i -g @crafter/trx

trx init
trx doctor --output json

trx init installs deps (whisper-cli, yt-dlp, ffmpeg), downloads a Whisper model, and installs the agent skill.

On Windows, IT or CI can run a non-interactive local setup in PowerShell:

trx init --yes --backend local --model small --language es
trx doctor --output json

The local backend does not upload the source. Whisper, ffmpeg, ffprobe, and yt-dlp are stored in %USERPROFILE%\.trx\bin. Setup prefers winget and falls back to checksum-verified portable downloads. Corporate software policies can still block the install.

Workflow

1. Dry-run first (always)

trx transcribe <input> --dry-run --output json

Validates input, checks dependencies, shows execution plan without running.

2. Transcribe

For URLs (YouTube, Twitter, Instagram, etc.):

trx transcribe "https://youtube.com/watch?v=..." --output json

For Instagram or private URLs that need login:

trx transcribe "https://www.instagram.com/reel/..." --cookies-from-browser chrome --output json

For local files:

trx transcribe ./recording.mp4 --output json

Agent-optimized (text only, saves tokens):

trx transcribe <input> --fields text --output json

Raw JSON payload (preferred for agents, avoids shell quoting issues):

trx transcribe video.mp4 --json '{"input":"video.mp4","language":"es","backend":"local"}' --output json

Backends

trx v0.7.0 supports three backends: local Whisper (default), OpenAI API, and Vercel AI Gateway.

Discover available transcription models with trx models, or filter with trx models --backend <name>.

# Local Whisper (default, offline, free)
trx transcribe <input> --backend local

# OpenAI API (faster, SOTA accuracy, requires OPENAI_API_KEY)
export OPENAI_API_KEY=sk-...
trx transcribe <input> --backend openai

# Vercel AI Gateway (requires AI_GATEWAY_API_KEY)
export AI_GATEWAY_API_KEY=...
trx transcribe <input> --backend vercel --model openai/whisper-1

# ElevenLabs Scribe (requires ELEVENLABS_API_KEY, separates speakers)
export ELEVENLABS_API_KEY=...
trx transcribe <input> --backend elevenlabs --diarize

OpenAI models:

• gpt-4o-transcribe — SOTA accuracy (default for openai backend)
• gpt-4o-mini-transcribe — cheapest
• whisper-1 — legacy, supports per-segment SRT timestamps

Vercel model IDs use creator/model-name format. The default is openai/whisper-1. One AI_GATEWAY_API_KEY covers every provider on the gateway. Use a transcription model returned by trx models --backend vercel; this is Vercel AI Gateway, not Cloudflare AI Gateway.

ElevenLabs models: scribe_v2 (default) and scribe_v1. This is the only backend that reports who is speaking. On macOS the key may live in the login Keychain under elevenlabs instead of the environment; trx doctor resolves it the same way a run does, so trust its verdict over echo $ELEVENLABS_API_KEY.

Speaker diarization

trx transcribe interview.m4a --backend elevenlabs --diarize --output json
trx transcribe interview.m4a --backend elevenlabs --speakers 2 --language spa --output json

--diarize prefixes every SRT cue with its speaker ([speaker_0]) and turns the .txt into a conversation, one paragraph per turn. --speakers <n> (1-32) passes the count when it is known and implies --diarize. A change of speaker always starts a new cue, so no cue attributes two people to one line.

Both flags are an error on local, openai and vercel rather than being ignored: no other backend returns speaker labels, and silently dropping them would hand back an undiarized transcript that looks like the request succeeded. --language accepts ISO 639-1 or ISO 639-3 here (es and spa both work); every other backend takes 639-1 only.

Local models: tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v3-turbo.

Set the backend persistently with trx init --backend vercel (or local/openai/elevenlabs) or in config.

Model discovery

trx models
trx models --backend local --output json
trx models --backend openai --output json
trx models --backend vercel --output json
trx models --backend elevenlabs --output json

Local, OpenAI and ElevenLabs model lists are static. Vercel models are fetched live from the gateway and filtered to transcription models, so do not hard-code that list. AI_GATEWAY_API_KEY is required when requesting only the Vercel backend. Without it, the all-backends view still returns local and OpenAI models plus a Vercel error.

Automatic cloud-file chunking

OpenAI uploads over 25 MB and Vercel uploads over 100 MB are chunked automatically with ffmpeg. The ElevenLabs limit is 5 GB, so that backend never chunks. trx transcribes chunks sequentially, joins their text in order, offsets and renumbers SRT timestamps, and removes intermediate chunk files. Use --no-chunk to disable this behavior and fail on an oversized cloud upload.

3. Post-process (fix whisper mistakes)

After transcription, read the .txt output and apply corrections. Read whisper-fixes.md for common patterns.

Correction checklist:

1. Punctuation: Whisper drops periods at paragraph boundaries and misplaces commas. Fix sentence boundaries.
2. Accents (Spanish): Whisper often drops diacritics. Restore: como -> cómo (how), esta -> está (is), mas -> más (more), si -> sí (yes), el -> él (he/him), que -> qué (what), cuando -> cuándo (when), numero -> número, tambien -> también, informacion -> información.
3. Technical terms: Whisper misspells domain-specific words. Ask user for a glossary or infer from context.
4. Repeated phrases: Whisper sometimes stutters on word boundaries. Remove exact consecutive duplicates.
5. Speaker attribution: If user provides speaker names, insert [Speaker Name]: markers.
6. Filler words: Remove "um", "uh", "este", "o sea" if user wants clean output. Ask first when the transcript feeds an edit rather than a reader: a tool that cuts a recording finds those spans by reading them, so removing them here deletes the evidence it needs. --preset verbatim exists to keep them in the transcript for exactly that case.
7. Timestamp alignment: If editing .srt, preserve the timestamp structure. Only modify text between timestamps.

4. Schema introspection

trx schema transcribe
trx schema init
trx schema models

These are the three schemas available in v0.7.0. Use trx <command> --help for the runtime CLI flags.

Commands

Command	Example
init	trx init --model small
transcribe	trx transcribe <url-or-file> --output json
doctor	trx doctor --output json
models	trx models --output json
schema	trx schema transcribe
skills	trx skills get trx --full

Loading this skill

This file ships with the CLI, so it can be read straight from an install instead of being copied around:

trx skills list              # what is bundled
trx skills get trx           # this file, raw markdown on stdout
trx skills get trx --full    # plus references/
trx skills path trx          # where it lives on disk

--output json works on each. TRX_SKILLS_DIR overrides where trx looks for the directory.

Shorthand

trx <input> is equivalent to trx transcribe <input>.

Output format

• --output json: Machine-readable (default when piped)
• --output table: Human-readable with progress (default when TTY)
• --fields text: Only return transcript text (saves tokens)
• --fields srt,metadata,files: Select specific fields
• --dry-run: Validate without executing

Example JSON response (filtered with --fields text):

{
  "success": true,
  "input": "recording.mp4",
  "backend": "local",
  "text": "Hola, cómo estás. Este es un ejemplo de transcripción...",
  "files": {
    "wav": "./recording.wav",
    "srt": "./recording.srt",
    "txt": "./recording.txt"
  },
  "metadata": {
    "language": "es",
    "model": "small",
    "inputDurationMs": 90538,
    "transcribedDurationMs": 90539,
    "lastCueEndMs": 89120
  }

Full response includes text, files, metadata, input, backend.

Read the three durations before trusting a short transcript. They are numbers, not a verdict, and the gaps between them say different things:

• inputDurationMs against transcribedDurationMs is what the cleaning stage changed. They should now be within a millisecond of each other; a large gap means the timeline was rewritten and the timestamps do not describe the file you passed in.
• transcribedDurationMs against lastCueEndMs is audio that produced no words. A big gap is trailing silence, or a transcription that stopped early.

A five-cue transcript reads identically whether the recording is mostly silence, the model stopped early, or the file handed in was not the one intended. These separate those cases, and checking them is faster than filing a bug.

Flags reference

Flag	Description	Default
-b, --backend <name>	local, openai, or vercel	from config (local)
-l, --language <code>	ISO 639-1 language code	auto (from config)
-m, --model <size>	Override model: a local model, an OpenAI model, or creator/model-name for Vercel	from config
-w, --words	Word-level timestamps in SRT	false
--preset <name>	verbatim keeps fillers and false starts; needs --language	none
--prompt <text>	Initial prompt in the spoken language; beats --preset	none
--output-dir <dir>	Output directory, created if missing	. (cwd)
-o, --output <format>	json, table, or auto. Global flag, goes before the subcommand	auto (TTY=table, piped=json)
--fields <list>	Limit output: text,srt,metadata,files (comma-separated)	all
--no-download	Skip yt-dlp (input must be local)	false
--no-clean	Skip ffmpeg audio cleaning	false
--no-chunk	Disable automatic chunking for oversized OpenAI and Vercel uploads	false
--cookies-from-browser <browser>	Load yt-dlp cookies from browser (e.g. chrome, chrome:Default)	-
--json <payload>	Raw JSON input for agents: {"input","language","model","backend","cookiesFromBrowser"}	-
--dry-run

Config stored at ~/.trx/config.json after trx init:

{
  "modelPath": "~/.trx/models/ggml-small.bin",
  "modelSize": "small",
  "language": "auto",
  "backend": "local",
  "threads": 8,
  "openai": { "model": "gpt-4o-transcribe" },
  "vercel": { "model": "openai/whisper-1" }
}

Edge cases & troubleshooting

• yt-dlp extension mismatch: yt-dlp sometimes outputs .mp4.webm instead of .mp4. The CLI handles this by scanning for the downloaded file by prefix.
• Instagram empty media response: Retry with --cookies-from-browser chrome or --cookies-from-browser chrome:Default. If it still fails, update yt-dlp (brew upgrade yt-dlp) and confirm the reel opens in that browser profile.
• Oversized cloud uploads: Chunking is automatic above 25 MB for OpenAI and 100 MB for Vercel. Remove --no-chunk if you want trx to split the file. If trx cannot determine the audio duration for chunking, confirm ffprobe is available with command -v ffprobe; it is installed with ffmpeg.
• Slow local transcription: Use a smaller local model such as tiny or base, or switch to the OpenAI or Vercel backend.
• Multilingual content: Specify the primary language with --language es instead of relying on auto.
• OpenAI backend fails: Ensure OPENAI_API_KEY is set, then run trx doctor --output json. Do not print the key.
• Vercel backend fails: Ensure AI_GATEWAY_API_KEY is set, run trx doctor --output json, and confirm the model with trx models --backend vercel --output json. Model IDs must use creator/model-name format.
• trx models reports a missing gateway key: trx models without a backend still returns local and OpenAI lists plus a Vercel error. trx models --backend vercel requires AI_GATEWAY_API_KEY and exits with an error when it is absent.
• npm vs bun: Package requires bun >=1.0.0 runtime (engines field). npm i -g @crafter/trx works if bun is installed globally (curl -fsSL https://bun.sh/install | bash). bun add -g is recommended.