evo-filler-word-detector

Filler Word Detector

Detects filler words and phrases in video using OpenAI Whisper for transcription with word-level timestamps, then classifies disfluencies and produces:

1. A JSON annotations file with detected fillers and timestamps
2. A stitched video of all filler word clips

Supported Filler Words

• Non-lexical: um, uh, hum, hmm, mhm
• Discourse markers: like, yeah, so, basically, well, okay
• Multi-word: you know, i mean, kind of, i guess

Scripts

• scripts/transcribe.py - Whisper transcription with word-level timestamps and caching
• scripts/detect_fillers.py - Filler word/phrase detection with overlap handling
• scripts/video_edit.py - FFmpeg-based clip extraction and concatenation
• scripts/pipeline.py - End-to-end orchestration

Quick Start

import sys
sys.path.insert(0, '/app/environment/skills/evo-filler-word-detector/scripts')
from pipeline import run_pipeline, validate_deliverables

# Run the full pipeline
run_pipeline(
    input_video="/root/input.mp4",
    annotations_path="/root/annotations.json",
    output_video="/root/output.mp4",
    model_name="base",
    cache_dir="/root/cache",
    padding=0.1
)

# Validate deliverables
validate_deliverables("/root/annotations.json", "/root/output.mp4")

Component Usage

Transcription

from transcribe import transcribe_video
words = transcribe_video("/root/input.mp4", model_name="base", cache_path="/root/cache/transcript.json")
# Returns: [{"word": "hello", "start": 0.0, "end": 0.5}, ...]

Filler Detection

from detect_fillers import detect_fillers, fillers_to_annotations
fillers = detect_fillers(words)
# Returns: [{"word": "um", "timestamp": 3.5, "start": 3.4, "end": 3.7}, ...]
annotations = fillers_to_annotations(fillers)
# Returns: [{"word": "um", "timestamp": 3.5}, ...]

Video Editing

from video_edit import extract_and_stitch_fillers, validate_output
extract_and_stitch_fillers("/root/input.mp4", fillers, "/root/output.mp4", padding=0.1)
validate_output("/root/output.mp4")