Earnings Call Processor

Transcribe earnings call audio via Whisper, analyze stock price data, and generate structured Feishu documents summarizing financial results.

End-to-end pipeline: audio → transcript → stock analysis → Feishu document.

Workflow

1. Transcribe — Run Whisper on earnings call audio (WAV/MP3/M4A)
2. Analyze — Load CSV stock history, compute key financial indicators
3. Generate — Build a structured Feishu document with transcript + financials
4. Publish — Create as draft first, then promote to shared doc after review

Usage

python3 src/process_earnings_call.py \
  --audio audio_transcripts/earnings_call_sample.wav \
  --stock-csv financial_reports/aapl_stock_history.csv \
  --symbol AAPL \
  --output-dir /tmp/earnings_output

Feishu document creation

After the local output is ready, use the feishu-doc skill to publish:

python3 /path/to/feishu-doc/scripts/doc_ctl.py create "AAPL Earnings Call Summary" --content "$(cat /tmp/earnings_output/feishu_content.md)"

Requirements

• whisper (OpenAI Whisper CLI)
• pandas
• Python 3.10+

Output

• {output_dir}/transcript.txt — Raw Whisper transcript
• {output_dir}/financial_indicators.json — Computed indicators
• {output_dir}/feishu_content.md — Structured content ready for Feishu doc