GSoC 2026 Candidate Submission: End-to-End Narrative Audio Pipeline

[meganho456]

This PR contains my GSoC 2026 test submission for a complete narrative-audio workflow, including all required tasks and a bonus storytelling analysis component.

What’s included
Task 1: Audio Processing Pipeline
Loads .wav recordings, normalizes audio, segments clips when needed, and extracts ML-ready features.
Features include MFCCs, pitch, spectral centroid, RMS energy, and duration.
Produces a structured feature dataset and normalized audio outputs.
Task 2: Narrative Tone Classification
Trains a neural-network classifier using labeled emotional-tone data.
Uses train/test split and reports evaluation metrics (accuracy, weighted F1, per-class report).
Task 3: AI-Based Transcription
Implements batch transcription with Whisper.
Exports transcripts to text format.
Measures transcription quality on a subset using WER.
Task 4: Narrative Audio Retrieval
Implements a retrieval prototype for narrative-style queries (e.g., calm narration, high-energy speech, dramatic dialogue).
Combines structured filtering and semantic ranking to return relevant recordings.
Bonus: Storytelling Audio Analysis
Analyzes storytelling-oriented cues: pacing/pauses, pitch variation, energy dynamics, and sentence-length characteristics.
Adds a heuristic storytelling score and ranks clips by storytelling-like expressiveness.
Deliverables in this submission
Full source code for Tasks 1–4 and bonus task, and run_pipeline that chains all the tasks together
Technical report PDF
README with setup and run instructions
Example output artifacts (feature CSVs, transcripts, analysis outputs)