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

Pre-interview preparation

Research checklist

Before recording starts, you should already know:

## Source prep for: [Name]

### Background
- Role/title:
- Organization:
- Why they're relevant to this story:
- Previous media appearances (note inconsistencies):

### Key questions (prioritized)
1. [Must-ask question]
2. [Must-ask question]
3. [If time permits]

### Documents to reference
- [ ] Bring/share [specific document]
- [ ] Ask about [specific claim/data point]

### Red lines
- Topics they'll likely avoid:
- Sensitive areas to approach carefully:

Recording setup

# Standard recording configuration
RECORDING_SETTINGS = {
    'format': 'wav',           # Lossless for transcription
    'sample_rate': 44100,      # Standard quality
    'channels': 1,             # Mono is fine for speech
    'backup': True,            # Always run backup recorder
}

# File naming convention
# YYYY-MM-DD_source-lastname_topic.wav
# Example: 2024-03-15_smith_budget-hearing.wav

Two-device rule: Always record on two devices. Phone as backup minimum.

Transcription workflows

Automated transcription pipeline

from pathlib import Path
import subprocess
import json

def transcribe_interview(audio_path: str, output_dir: str = "./transcripts") -> dict:
    """
    Transcribe using Whisper with speaker diarization.
    Returns transcript with timestamps.
    """
    Path(output_dir).mkdir(exist_ok=True)

    # Use whisper.cpp or OpenAI Whisper
    result = subprocess.run([
        'whisper',
        audio_path,
        '--model', 'medium',
        '--output_format', 'json',
        '--output_dir', output_dir,
        '--language', 'en',
        '--word_timestamps', 'True'
    ], capture_output=True)

    # Load and return structured transcript
    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}"

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
        pattern = rf'\[{re.escape(start_time)}\](.+?)(?=\[\d|$)'
        match = re.search(pattern, transcript, re.DOTALL)

        if match:
            text = match.group(1).strip()
            quote = Quote(
                text=text,
                timestamp=start_time,
                speaker=speaker,
                context=context
            )
            self.quotes.append(quote)
            return quote
        return None

    def verify_quote(self, quote: Quote, audio_path: str) -> bool:
        """Mark quote as verified against original recording."""
        # In practice: listen to audio at timestamp, confirm accuracy
        quote.verified = True
        return True

    def export_for_story(self) -> str:
        """Export verified quotes ready for publication."""
        output = []
        for q in self.quotes:
            if q.verified:
                output.append(f'"{q.text}"\n— {q.speaker}\n[Timestamp: {q.timestamp}]')
        return '\n\n'.join(output)

Quote accuracy checklist

Before publishing any quote:

- [ ] Listened to original recording at timestamp
- [ ] Quote is verbatim (or clearly marked as paraphrased)
- [ ] Context preserved (not cherry-picked to change meaning)
- [ ] Speaker identified correctly
- [ ] Timestamp documented for fact-checker
- [ ] Source approved quote (if agreement made)

Source management database

Interview tracking schema

from dataclasses import dataclass, field
from datetime import datetime
from typing import List, Optional
from enum import Enum

class SourceStatus(Enum):
    ACTIVE = "active"           # Currently engaged
    DORMANT = "dormant"         # Not recently contacted
    DECLINED = "declined"       # Refused to participate
    OFF_RECORD = "off_record"   # Background only

class InterviewType(Enum):
    ON_RECORD = "on_record"
    BACKGROUND = "background"
    DEEP_BACKGROUND = "deep_background"
    OFF_RECORD = "off_record"

@dataclass
class Source:
    name: str
    organization: str
    contact_info: dict  # email, phone, signal, etc.
    beat: str
    status: SourceStatus = SourceStatus.ACTIVE
    interviews: List['Interview'] = field(default_factory=list)
    notes: str = ""

    # Relationship tracking
    first_contact: Optional[datetime] = None
    trust_level: int = 1  # 1-5 scale

@dataclass
class Interview:
    source: str
    date: datetime
    interview_type: InterviewType
    recording_path: Optional[str] = None
    transcript_path: Optional[str] = None
    story_slug: Optional[str] = None
    key_quotes: List[str] = field(default_factory=list)
    follow_up_needed: bool = False
    notes: str = ""

Quick source lookup

def find_sources_for_story(sources: List[Source], topic: str,
                           beat: str = None) -> List[Source]:
    """Find relevant sources for a new story."""
    matches = []
    for source in sources:
        # Filter by beat if specified
        if beat and source.beat != beat:
            continue
        # Only suggest active sources
        if source.status != SourceStatus.ACTIVE:
            continue
        # Check if they've spoken on similar topics
        for interview in source.interviews:
            if topic.lower() in interview.notes.lower():
                matches.append(source)
                break

    # Sort by trust level
    return sorted(matches, key=lambda s: s.trust_level, reverse=True)

Audio/video processing

Batch processing multiple recordings

from pathlib import Path
from concurrent.futures import ProcessPoolExecutor
import json

def batch_transcribe(recordings_dir: str, output_dir: str) -> dict:
    """Process all recordings in a directory."""
    recordings = list(Path(recordings_dir).glob('*.wav')) + \
                 list(Path(recordings_dir).glob('*.mp3')) + \
                 list(Path(recordings_dir).glob('*.m4a'))

    results = {}

    with ProcessPoolExecutor(max_workers=4) as executor:
        futures = {
            executor.submit(transcribe_interview, str(rec), output_dir): rec
            for rec in recordings
        }

        for future in futures:
            rec = futures[future]
            try:
                transcript = future.result()
                results[rec.name] = {
                    'status': 'success',
                    'transcript': transcript
                }
            except Exception as e:
                results[rec.name] = {
                    'status': 'error',
                    'error': str(e)
                }

    return results

Video interview extraction

import subprocess

def extract_audio_from_video(video_path: str, output_path: str = None) -> str:
    """Extract audio track from video for transcription."""
    if output_path is None:
        output_path = video_path.rsplit('.', 1)[0] + '.wav'

    subprocess.run([
        'ffmpeg', '-i', video_path,
        '-vn',  # No video
        '-acodec', 'pcm_s16le',  # WAV format
        '-ar', '44100',  # Sample rate
        '-ac', '1',  # Mono
        output_path
    ], check=True)

    return output_path

Legal and ethical considerations

Consent documentation

## Recording consent record

**Date**:
**Source name**:
**Recording type**: [ ] Audio [ ] Video
**Interview type**: [ ] On record [ ] Background [ ] Off record

### Consent obtained:
- [ ] Verbal consent recorded at start of interview
- [ ] Written consent form signed
- [ ] Email confirmation of consent

### Jurisdiction notes:
- Interview location state/country:
- One-party or two-party consent jurisdiction:
- Any specific restrictions agreed:

### Agreed terms:
- [ ] Full attribution allowed
- [ ] Organization attribution only
- [ ] Anonymous source
- [ ] Review quotes before publication
- [ ] Embargo until [date]:

Two-party consent states (US)

California, Connecticut, Florida, Illinois, Maryland, Massachusetts, Michigan, Montana, Nevada, New Hampshire, Pennsylvania, Washington require all-party consent.

Always get explicit consent on recording regardless of jurisdiction.

Tools and resources

Tool	Purpose	Notes
Whisper	Local transcription	Free, accurate, private
Otter.ai	Cloud transcription	Real-time, speaker ID
Descript	Edit audio like text	Good for pulling clips
Rev	Human transcription	For sensitive/legal
Trint	Journalist-focused	Collaboration features
oTranscribe	Free web player	Manual transcription aid

Related skills

• source-verification - Verify source credentials before interview
• foia-requests - Get documents to inform interview questions
• data-journalism - Analyze data sources mention in interviews

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Skill metadata

Field	Value
Version	1.0.0
Created	2025-12-26
Author	Claude Skills for Journalism
Domain	Journalism, Research
Complexity	Intermediate