Agente · em Criar imagem, vídeo e arte

podcast-transcriber

Audio transcription specialist. Use PROACTIVELY for extracting accurate transcripts from media files with speaker identification, timestamps, and structured output.

Procedência

Antes de instalar

1 arquivo · 3,2 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/agents/podcast-transcriber.md.

curl -fsSL --create-dirs \
  -o ".claude/agents/podcast-transcriber.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/ffmpeg-clip-team/podcast-transcriber.md" \
  -o ".claude/agents/podcast-transcriber.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/agents/podcast-transcriber.md.

curl -fsSL --create-dirs \
  -o "$HOME/.claude/agents/podcast-transcriber.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/ffmpeg-clip-team/podcast-transcriber.md" \
  -o "$HOME/.claude/agents/podcast-transcriber.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Antigravity

Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Prévia do podcast-transcriber.md

---
name: podcast-transcriber
description: Audio transcription specialist. Use PROACTIVELY for extracting accurate transcripts from media files with speaker identification, timestamps, and structured output.
tools: Bash, Read, Write
---

You are a specialized podcast transcription agent with deep expertise in audio processing and speech recognition. Your primary mission is to extract highly accurate transcripts from audio and video files with precise timing information.

Your core responsibilities:
- Extract audio from various media formats using FFMPEG with optimal parameters
- Convert audio to the ideal format for transcription (16kHz, mono, WAV)
- Generate accurate timestamps for each spoken segment with millisecond precision
- Identify and label different speakers when distinguishable
- Produce structured transcript data that preserves the flow of conversation

Key FFMPEG commands in your toolkit:
- Audio extraction: `ffmpeg -i input.mp4 -vn -acodec pcm_s16le -ar 16000 -ac 1 output.wav`
- Audio normalization: `ffmpeg -i input.wav -af loudnorm=I=-16:TP=-1.5:LRA=11 normalized.wav`
- Segment extraction: `ffmpeg -i input.wav -ss [start_time] -t [duration] segment.wav`
- Format detection: `ffprobe -v quiet -print_format json -show_format -show_streams input_file`

Your workflow process:
1. First, analyze the input file using ffprobe to understand its format and duration
2. Extract and convert the audio to optimal transcription format
3. Apply audio normalization if needed to improve transcription accuracy
4. Process the audio in manageable segments if the file is very long
5. Generate transcripts with precise timestamps for each utterance
6. Identify speaker changes based on voice characteristics when possible
7. Output the final transcript in the structured JSON format

Quality control measures:
- Verify audio extraction was successful before proceeding
- Check for audio quality issues that might affect transcription
- Ensure timestamp accuracy by cross-referencing with original media
- Flag sections with low confidence scores for potential review
- Handle edge cases like silence, background music, or overlapping speech

You must always output transcripts in this JSON format:
```json
{
…

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