/interview-transcription

name: interview-transcription
description: Transcription, recording management, and quote extraction. Use when processing audio/video or generating timestamped transcripts.

Interview transcription and management

Practical workflows for journalists managing interviews from preparation through publication.

When to activate

• Preparing questions for an interview
• Processing audio/video recordings
• Creating or managing transcripts
• Organizing notes from multiple sources
• Building a source relationship database
• Generating timestamped quotes for fact-checking
• Converting recordings to publishable quotes

Recording setup for transcription

For pre-interview research, question design, attribution agreements, and consent scripts, use the **interview-prep** skill. The notes here cover only the recording configuration that affects transcription quality.

# Standard recording configuration for clean transcription
RECORDING_SETTINGS = {
    'format': 'wav',           # Lossless for transcription
    'sample_rate': 16000,      # Whisper resamples to 16k anyway; 16k saves disk
    'channels': 1,             # Mono is fine for speech; stereo only if mics are positionally distinct
    'backup': True,            # Always run a backup recorder
}

# File naming convention
# YYYY-MM-DD_source-lastname_topic.wav
# Example: 2026-05-08_smith_budget-hearing.wav

**Two-device rule.** Always record on two devices. Phone as backup minimum. If using a wireless lav mic, the recorder built into the lav unit is one device; the phone running a backup app is the second.

**Mono is preferred** unless each speaker has their own dedicated microphone routed to a distinct channel. Stereo with both speakers bleeding into both channels is worse for diarization than clean mono.

Transcription workflows

Automated transcription pipeline

Vanilla OpenAI Whisper transcribes audio to text but does **not** assign speaker labels. To get diarized output ("Speaker 1:" / "Speaker 2:" / etc.) you need a tool that combines Whisper with a diarization model, typically **WhisperX** (`m-bain/whisperX`), which wraps faster-whisper transcription with pyannote.audio diarization and produces word-level timestamps with speaker IDs in one pass.

from pathlib import Path
import subprocess
import json

def transcribe_interview(
    audio_path: str,
    output_dir: str = "./transcripts",
    diarize: bool = True,
    hf_token: str | None = None,
    min_speakers: int = 2,
    max_speakers: int = 2,
) -> dict:
    """
    Transcribe an interview using WhisperX (Whisper + pyannote diarization).
    Returns a transcript with word-level timestamps and speaker labels.

    Diarization needs a Hugging Face token with access to the pyannote
    speaker-diarization-3.1 model. Accept the model EULA at
    huggingface.co/pyannote/speaker-diarization-3.1 once, then pass the token.
    """
    Path(output_dir).mkdir(exist_ok=True)

    cmd = [
        'whisperx', audio_path,
        '--model', 'large-v3',
        '--output_format', 'json',
        '--output_dir', output_dir,
        '--language', 'en',
        '--compute_type', 'int8',     # CPU-friendly; use 'float16' on GPU
        '--min_speakers', str(min_speakers),
        '--max_speakers', str(max_speakers),
    ]

    if diarize:
        cmd.append('--diarize')
        if hf_token:
            cmd += ['--hf_token', hf_token]

    subprocess.run(cmd, check=True, capture_output=True)

    json_path = Path(output_dir) / f"{Path(audio_path).stem}.json"
    with open(json_path) as f:
        return json.load(f)

def format_for_editing(transcript: dict) -> str:
    """Convert to journalist-friendly format with timestamps."""
    lines = []
    for segment in transcript.get('segments', []):
        timestamp = format_timestamp(segment['start'])
        text = segment['text'].strip()
        lines.append(f"[{timestamp}] {text}")
    return '\n\n'.join(lines)

def format_timestamp(seconds: float) -> str:
    """Convert seconds to HH:MM:SS format."""
    h = int(seconds // 3600)
    m = int((seconds % 3600) // 60)
    s = int(seconds % 60)
    return f"{h:02d}:{m:02d}:{s:02d}"

**Falling back to plain Whisper.** If diarization is overkill or you can't get a Hugging Face token, drop the `--diarize` flag, the model still produces accurate timestamped transcription and you label speakers manually based on context. `faster-whisper` (CTranslate2 backend) is the speed-optimized variant and works the same way at the CLI. `whisper.cpp` is the C++ port for resource-constrained machines (Raspberry Pi, older laptops); it doesn't include diarization but runs the small/medium models on CPU comfortably.

Manual transcription template

For sensitive interviews or when AI transcription fails:

## Transcript: [Source] - [Date]

**Recording file**: [filename]
**Duration**: [XX:XX]
**Transcribed by**: [name]
**Verified against recording**: [ ] Yes / [ ] No

---

[00:00:15] **Q**: [Your question]

[00:00:45] **A**: [Source response - verbatim, including ums, pauses noted as (...)]

[00:01:30] **Q**: [Follow-up]

[00:01:42] **A**: [Response]

---

## Notes
- [Anything not captured in audio: gestures, documents shown, etc.]

## Potential quotes
- [00:01:42] "Quote that stands out" - context: [why it matters]

Quote extraction and verification

Pull quotes workflow

from dataclasses import dataclass
from typing import Optional
import re

@dataclass
class Quote:
    text: str
    timestamp: str
    speaker: str
    context: str
    verified: bool = False
    used_in: Optional[str] = None

class QuoteBank:
    """Manage quotes from interview transcripts."""

    def __init__(self):
        self.quotes = []

    def extract_quote(self, transcript: str, start_time: str,
                      end_time: str, speaker: str, context: str) -> Quote:
        """Extract and store a quote with metadata."""
        # Pull text between timestamps
        pa