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Audio and video transcription using Whisper AI via the transcribe-anything package. Supports local files, YouTube URLs, and microphone input with multiple backends (faster-whisper, openai-whisper, Whisper API).
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/oimiragieo/agent-studio --skill transcriptionコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Agent frontmatter enhancements — disallowedTools, mcpServers scoping, fork_eligible field
Context management — pre-compact persistence, threshold alignment, microcompact/circuit breaker detection
Hook enhancements — updatedInput for bash safety prefixes, suppressOutput for verbose blocks, denial-based routing feedback
SKILL.md を表示中
SOC 職業分類に基づく
| name | transcription |
| description | Audio and video transcription using Whisper AI via the transcribe-anything package. Supports local files, YouTube URLs, and microphone input with multiple backends (faster-whisper, openai-whisper, Whisper API). |
| version | 1.1.0 |
| model | sonnet |
| invoked_by | both |
| user_invocable | true |
| tools | ["Bash","Read","Write"] |
| agents | ["developer","ai-ml-expert","researcher"] |
| lastVerifiedAt | "2026-03-15T00:00:00.000Z" |
| category | AI/ML |
| tags | ["transcription","audio","video","whisper","speech-to-text","faster-whisper","subtitles","srt"] |
| best_practices | ["Use faster-whisper backend by default — it is 4x faster than openai-whisper on the same hardware","Select model size based on quality/speed tradeoff (tiny→large-v3)","Always specify output directory to keep files organized","Use --lang to skip auto-detection when the source language is known"] |
| error_handling | strict |
| source | builtin |
| trust_score | 100 |
| provenance_sha | null |
Transcribe audio/video files (local or remote) using Whisper AI via transcribe-anything. Supports local files, YouTube URLs, and microphone input. Output formats: SRT, VTT, plain text, JSON.
pip install transcribe-anything
Backends install automatically in isolated virtual environments.
# Local file
transcribe-anything audio.mp3
# YouTube URL
transcribe-anything "https://www.youtube.com/watch?v=VIDEO_ID"
# With options
transcribe-anything audio.mp3 --model large-v3 --lang en --output_dir ./transcripts/
# GPU / device selection
transcribe-anything audio.mp3 --device cuda # NVIDIA GPU
transcribe-anything audio.mp3 --device mlx # Mac Apple Silicon (fastest on Mac)
transcribe-anything audio.mp3 --device groq # Cloud API (fastest overall)
# Speaker diarization (requires HuggingFace token)
transcribe-anything audio.mp3 --device insane --hf_token YOUR_HF_TOKEN
| Option | Description | Default |
|---|---|---|
--model | tiny, small, medium, large, large-v3 | large-v3 |
--lang | Language code (en, fr, de) or auto | auto-detect |
--device | cpu, cuda, mlx, insane, groq | auto-select |
--output_dir | Directory to write transcript files | ./ |
--task | transcribe or translate (→ English) |
transcribe |
--hf_token | HuggingFace token for speaker diarization | — |
--initial_prompt | Domain vocabulary hint for technical terms | — |
| Backend | Platform | Speed | Requires |
|---|---|---|---|
faster-whisper | Windows/Linux/Mac | Fast | No internet |
mlx | Mac Apple Silicon only | 4x faster | No internet |
insane | Windows/Linux GPU | Fastest local | No internet, optional HF token |
groq | Cloud API | 189–250x real-time | Internet + Groq API key |
cpu | Universal | Slowest | No internet |
| File | Format |
|---|---|
.srt | SubRip subtitles with timestamps |
.vtt | WebVTT subtitles |
.txt | Plain text transcript |
.json | Structured segments with timestamps and confidence |
speaker.json | Speaker-partitioned dialogue (insane backend only) |
tiny/small for speed, large-v3 for accuracycuda for GPU, mlx for Apple Silicontranscribe-anything <input> --model <model> --output_dir <dir>.jsonFor audio files >30 minutes or processing multiple files:
# Batch process all audio files in a directory
for f in audio/*.mp3; do
transcribe-anything "$f" \
--model large-v3 \
--output_dir "transcripts/$(basename "$f" .mp3)/" \
--device cuda
done
# Process large files with chunking (split at silence boundaries)
# Install: pip install pydub
python3 -c "
from pydub import AudioSegment
from pydub.silence import split_on_silence
import os
audio = AudioSegment.from_file('long_audio.mp3')
chunks = split_on_silence(audio, min_silence_len=1000, silence_thresh=-40)
for i, chunk in enumerate(chunks):
chunk_path = f'chunks/chunk_{i:04d}.mp3'
chunk.export(chunk_path, format='mp3')
os.system(f'transcribe-anything {chunk_path} --output_dir chunks/output/')
"
Performance targets:
| File Length | Backend | Expected Speed |
|---|---|---|
| <10 min | faster-whisper | 1-2 min |
| 10-60 min | mlx (Mac) / cuda | 2-8 min |
| >60 min | groq (cloud) | 1-3 min |
| Real-time | groq / insane | <1x duration |
WhisperX extends Whisper with word-level timestamps and speaker diarization:
# Install WhisperX (used by transcribe-anything --device insane)
pip install whisperx
# Direct WhisperX usage for advanced control
python3 -c "
import whisperx
import json
# Load model
device = 'cuda'
compute_type = 'float16'
model = whisperx.load_model('large-v3', device, compute_type=compute_type)
# Transcribe
audio = whisperx.load_audio('audio.mp3')
result = model.transcribe(audio, batch_size=16)
# Align timestamps (word-level)
model_a, metadata = whisperx.load_align_model(language_code=result['language'], device=device)
result = whisperx.align(result['segments'], model_a, metadata, audio, device)
# Speaker diarization (requires HuggingFace token)
diarize_model = whisperx.DiarizationPipeline(use_auth_token='YOUR_HF_TOKEN', device=device)
diarize_segments = diarize_model(audio)
result = whisperx.assign_word_speakers(diarize_segments, result)
print(json.dumps(result['segments'], indent=2))
"
Speaker diarization output format:
{
"segments": [
{
"start": 0.5,
"end": 4.2,
"text": "Hello, welcome to the meeting.",
"speaker": "SPEAKER_00",
"words": [{ "word": "Hello", "start": 0.5, "end": 0.9, "speaker": "SPEAKER_00" }]
}
]
}
Requirements for speaker diarization:
--hf_token)pyannote/speaker-diarization-3.1Input validated against schemas/input.schema.json. See hooks/pre-execute.cjs for validation logic.
Before starting: Read .claude/context/memory/learnings.md for prior transcription task context.
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.mdASSUME INTERRUPTION: If it's not in memory, it didn't happen.