jedireporter 0.1.0

JEDI - Journalistic Excellence through Domain-specific Intelligence

JEDI

Journalistic Excellence through Domain-specific Intelligence

AI-powered transcript processing that cleans up interview transcripts and converts them into polished article format.

Installation

pip install -U jedireporter

Quick Start

# Convert plain text interview to transcript JSON
jedireporter-impex import --format plain-text -i interview.txt -o transcript.jsonl

# Process transcript through the full pipeline
jedireporter -i transcript.jsonl -o article.jsonl --llm-profile default

# Export article to markdown
jedireporter-impex export --format markdown -i article.jsonl -o output.md

Configuration

Environment Variables

Copy the template file and fill in your credentials:

cp .env_template .env
# Edit .env with your API keys and credentials

The jedireporter command automatically loads .env from the current directory. Alternatively, set the environment variables directly in your shell.

LLM Profiles

Select an LLM profile using --llm-profile <name>. Available profiles:

Profile	Provider	Model	Environment Variable
default	OpenAI	gpt-5.1-2025-11-13	JEDI_OPENAI_API_KEY
gpt-4.1	OpenAI	gpt-4.1-2025-04-14	JEDI_OPENAI_API_KEY
gpt-5	OpenAI	gpt-5-2025-08-07	JEDI_OPENAI_API_KEY
gpt-5-mini	OpenAI	gpt-5-mini-2025-08-07	JEDI_OPENAI_API_KEY
gpt-5.1	OpenAI	gpt-5.1-2025-11-13	JEDI_OPENAI_API_KEY
gpt-5.2	OpenAI	gpt-5.2-2025-12-11	JEDI_OPENAI_API_KEY
claude	AWS Bedrock	Claude Sonnet 4.5	AWS credentials (see below)
mistral	Mistral	mistral-large-2512	JEDI_MISTRAL_API_KEY

AWS Bedrock Credentials (for claude profile)

export AWS_ACCESS_KEY_ID=<your-access-key>
export AWS_SECRET_ACCESS_KEY=<your-secret-key>
export AWS_DEFAULT_REGION=<aws-region>
export AWS_BEDROCK_ROLE=<bedrock-role-arn>

Langfuse Observability (Optional)

To enable Langfuse tracing for LLM calls, set both environment variables:

export LANGFUSE_PUBLIC_KEY=<your-public-key>
export LANGFUSE_SECRET_KEY=<your-secret-key>

When not configured, the pipeline runs without tracing and no warnings are shown.

Transcript Processing Workflow

The main jedireporter command processes transcripts through a three-stage pipeline:

1. Fix Transcript - Clean up ASR output, fix speaker boundaries, remove fillers
2. Create Article - Convert interview into structured article with topics, Q&A, and summary
3. Post-Process Article - Add styling, enrich with web search results

Usage

jedireporter -i transcript.jsonl -o article.jsonl --llm-profile <profile>

Options

Option	Description
-i, --input-file	Path to input transcript JSON file
-o, --output-file	Path to output article JSON file
--llm-profile	LLM profile name (default: default)
--include-transcript	Include transcript in the article output
--max-concurrency	Maximum parallel workflow invocations (default: 4)

Input Format

Transcript JSON structure (one JSON object per line in .jsonl format).

Minimal Example (mandatory fields only)

{
  "id": "interview-001",
  "language": "en",
  "segments": [
    {"id": "0", "speaker_id": "spk_0", "text": "Welcome to the show."},
    {"id": "1", "speaker_id": "spk_1", "text": "Thank you for having me."}
  ]
}

Full Example (with optional fields)

{
  "id": "interview-001",
  "language": "en",
  "segments": [
    {
      "id": "0",
      "speaker_id": "spk_0",
      "text": "Welcome to the show.",
      "timecodes": {"start_time": 0.0, "end_time": 2.5}
    },
    {
      "id": "1",
      "speaker_id": "spk_1",
      "text": "Thank you for having me.",
      "timecodes": {"start_time": 2.5, "end_time": 4.0}
    }
  ],
  "speakers": [
    {"speaker_id": "spk_0", "role": "host", "name": "John Smith", "description": "Host of the Morning Show"},
    {"speaker_id": "spk_1", "role": "guest", "name": "Jane Doe", "description": "CEO of TechCorp"}
  ],
  "url": "https://youtube.com/watch?v=abc123"
}

Mandatory Fields

Field	Description
id	Unique transcript identifier
language	Language code (e.g., "en", "cs")
segments	List of dialogue segments
segments[].id	Unique segment identifier
segments[].speaker_id	Speaker identifier
segments[].text	Spoken text content

Optional Fields

Field	Description
segments[].timecodes	Start and end times in seconds
speakers	Speaker metadata list
speakers[].speaker_id	Matches segment speaker_id (required if speakers provided)
speakers[].role	"host", "guest", or "other" (required if speakers provided)
speakers[].name	Human-readable name
speakers[].description	Title, affiliation, etc.
url	Source audio/video URL (enables timecode linking in exports)

Import/Export (impex)

Import: Plain Text to Transcript JSON

Convert plain text interviews to transcript JSON format:

jedireporter-impex import --format plain-text -i interview.txt -o transcript.jsonl

Plain text format:

Speaker Name: What they said in this segment...
Another Speaker: Their response goes here...
Speaker Name: And the conversation continues...

Each line follows the pattern Speaker Name: Text content. Empty lines are ignored.

Import: AWS Transcribe JSON to Transcript JSON

Convert AWS Transcribe output to transcript JSON format:

jedireporter-impex import --format aws-transcribe -i transcribe-output.json -o transcript.jsonl

Note: Speaker diarization must be enabled in AWS Transcribe for speaker labels to be included. Without diarization, all segments will be assigned to spk_0.

Options:

Option	Description
--format	Input format: plain-text or aws-transcribe
--language	Language code for the transcript
-i, --input-file	Input file path (supports glob patterns)
-o, --output-file	Output file path

Export: Article JSON to Markdown

jedireporter-impex export --format markdown -i article.jsonl -o output.md

If output is a directory, each article is saved as a separate file.

Export: Article JSON to Google Docs

jedireporter-impex export --format google-doc -i article.jsonl --credentials service-account.json

Options:

Option	Description
--format	Output format: markdown or google-doc
--credentials	Path to Google service account JSON file
--token	Path to OAuth access token file
--google-folder	Google Drive folder ID for output
-i, --input-file	Input article JSON file
-o, --output-file	Output file path (markdown only)

Command Reference

Command	Description
jedireporter	Main processing workflow (transcript to article)
jedireporter-impex import	Convert plain text or AWS Transcribe output to transcript JSON
jedireporter-impex export	Convert article JSON to markdown/Google Docs

Example End-to-End Workflow

# 1. Set up API key
export JEDI_OPENAI_API_KEY=sk-...

# 2. Prepare plain text interview file (interview.txt)
cat > interview.txt << 'EOF'
Host: Welcome to today's show. We have a special guest joining us.
Guest: Thank you for having me. I'm excited to be here.
Host: Let's dive right in. What inspired your latest project?
Guest: It all started when I noticed a gap in the market...
EOF

# 3. Import to transcript JSON
jedireporter-impex import --format plain-text --language en -i interview.txt -o transcript.jsonl

# 4. Process through the pipeline
jedireporter -i transcript.jsonl -o article.jsonl --llm-profile default

# 5. Export to markdown
jedireporter-impex export --format markdown -i article.jsonl -o article.md

# 6. View result
cat article.md

Funding

This project is supported by NextGenerationEU, the European Union's recovery and resilience initiative, through the Recovery and Resilience Facility.

License

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.