How timeline_view.py Composites Filmstrip, Waveform, and Word Labels for Visual Decision Making

The render_timeline function in helpers/timeline_view.py synthesizes a synchronized PNG composite by extracting evenly-spaced video frames, computing an RMS audio envelope, and overlaying transcript word labels with silence detection, enabling precise temporal correlation between visual and audio cues.

The video-use toolkit by browser-use includes a specialized visualization component that transforms raw video segments into analytical decision-making aids. The helpers/timeline_view.py module orchestrates this process by unifying three distinct data streams—visual frames, audio waveforms, and transcription metadata—into a single coherent image that editors can scan to judge cut points, retake boundaries, and pause durations.

The Three-Layer Composite Architecture

The compositing pipeline in helpers/timeline_view.py constructs a 1920-pixel-wide canvas that vertically stacks three synchronized layers. Each layer derives from a distinct helper function, unified through a common time-to-pixel coordinate system.

• Filmstrip Layer: Horizontal strip of thumbnail images extracted uniformly across the time range
• Waveform Layer: RMS audio envelope rendered as a translucent ribbon
• Annotation Layer: Word labels and silence shading drawn above the waveform

The entry point render_timeline (lines 84–130) coordinates these stages, delegating data preparation to specialized utilities before executing the final draw operations.

Stage 1: Frame Extraction and Filmstrip Assembly

The extract_frames function (lines 37–62) generates the visual backbone by executing a single ffmpeg command per target frame. It spaces n frames uniformly between the start and end timestamps, outputting JPEGs that are resized to a standardized height of frame_h = 180 pixels.

Inside render_timeline, these frames are pasted sequentially onto the canvas beginning at x = 50. If the cumulative width of all thumbnails exceeds available space, the code calculates a uniform scale factor to fit the strip while maintaining aspect ratio. The final horizontal span (strip_span) is stored for coordinate conversion.

Stage 2: Audio Envelope Computation

The compute_envelope function (lines 68–111) prepares the audio visualization by extracting the segment to a temporary 16 kHz PCM WAV file. It reads raw samples using Python’s built-in wave module and computes a windowed RMS array of length samples (default 2000). This array is normalized to the range [0, 1] before rendering.

During the draw phase, the envelope generates two polylines (points_top and points_bot) that trace the upper and lower halves of the wave. A translucent polygon fills the area between them, creating the characteristic "ribbon" appearance that floats above the silence shading.

Stage 3: Transcript Processing and Word Labeling

Word Selection

The words_in_range function (lines 118–132) loads a JSON transcript formatted as { "words": [...] } and filters for words whose timestamps intersect the [start, end] interval. This filtered list drives the annotation layer.

Silence Detection

The find_silences function (lines 135–148) scans the selected words to identify gaps ≥ threshold seconds (default 0.4 s). These intervals are rendered as semi-transparent blue rectangles (SILENCE color) directly beneath the waveform, immediately highlighting dead air for editorial review.

Font Handling

The load_font utility (lines 154–171) ensures consistent text rendering across platforms by attempting a list of common monospaced fonts (Monaco, Consolas, Courier) before falling back to Pillow’s default. This guarantees that the 12-point word labels and time ruler remain legible.

Spatial Synchronization and Rendering

Canvas Layout

The render_timeline function establishes a fixed 1920-pixel-wide canvas using Image.new. Layout constants define vertical positions for each layer:

• filmstrip_y: Vertical placement of the frame strip
• wave_y: Baseline for the waveform ribbon
• label_y: Position for word annotations

Time-to-Pixel Conversion

The time_to_x helper (lines 159–162) provides the critical mapping that synchronizes all layers:

x = (t - start) / (end - start) * strip_span

This formula converts any timestamp t within [start, end] to an X-pixel coordinate relative to the filmstrip. All subsequent graphics—silence bands, waveform vertices, word ticks, and ruler markings—reuse this conversion to maintain perfect temporal alignment.

Visual Assembly Details

Silence Shading

For each gap returned by find_silences, render_timeline draws a rectangle using draw.rectangle in semi-transparent blue, creating a visual underlay that distinguishes quiet sections from active speech.

Word Label Overlay

The word annotation logic filters out tokens shorter than 50 ms and implements crowding prevention (cx - last_label_x < 28). For each accepted word, it draws a tiny tick mark on the waveform centerline and renders the text above using the loaded small_font at 12 pt.

Time Ruler

A horizontal ruler with six major ticks (n_ticks = 6) spans the width of the composite, each labeled with absolute timestamps ({t:.2f}s`) to provide absolute temporal reference.

Legend and Output

If silences were detected, a legend line is added to the canvas before the final canvas.save call writes the PNG to disk.

Practical Usage Examples

Command-Line Interface

Generate a 10-second timeline with 12 frames and transcript overlay:

python helpers/timeline_view.py \
    path/to/video.mp4 30.0 40.0 \
    --n-frames 12 \
    --transcript path/to/video.json \
    -o /tmp/segment.png

Python API Integration

Programmatically render a segment from Python:

from pathlib import Path
from helpers.timeline_view import render_timeline

video = Path("movie.mp4")
output = Path("movie_75.5-85.0.png")
transcript = Path("movie.json")

render_timeline(
    video=video,
    start=75.5,
    end=85.0,
    out_path=output,
    n_frames=15,
    transcript=transcript,
)
print(f"Composite saved to {output}")

Standalone Audio Envelope Extraction

Extract only the RMS envelope for custom visualizations:

import numpy as np
from helpers.timeline_view import compute_envelope
from pathlib import Path

env = compute_envelope(
    Path("lecture.mp4"), 
    120.0, 
    150.0, 
    samples=1000
)
print(f"Peak amplitude: {env.max():.3f}")

Summary

• render_timeline (lines 84–130) in helpers/timeline_view.py serves as the central orchestrator, unifying video frames, audio envelopes, and transcript data into a single PNG composite.
• Frame extraction occurs via extract_frames (lines 37–62), which uses ffmpeg to generate uniformly spaced thumbnails resized to 180 pixels height.
• Audio processing through compute_envelope (lines 68–111) produces a 2000-sample RMS envelope from 16 kHz PCM audio, rendered as a translucent ribbon.
• Transcript integration relies on words_in_range (lines 118–132) and find_silences (lines 135–148) to identify words and gaps ≥ 0.4 seconds for labeling and shading.
• Temporal synchronization is achieved via the time_to_x coordinate converter, ensuring pixel-perfect alignment between filmstrip frames, waveform peaks, and word labels.

Frequently Asked Questions

How does timeline_view.py handle font availability across different operating systems?

The load_font function (lines 154–171) attempts to load Monaco, Consolas, and Courier in sequence. If none are available, it gracefully falls back to Pillow’s default bitmap font, ensuring that word labels and time rulers render consistently on macOS, Linux, and Windows without requiring specific font installation.

What prevents word labels from overlapping in the composite?

The rendering logic implements a crowding guard that tracks the X-coordinate of the last placed label (last_label_x). The code skips rendering if the current word’s position (cx) is less than 28 pixels from the previous label. Additionally, tokens shorter than 50 milliseconds are filtered out entirely to prevent clutter from micro-utterances.

Can I generate the timeline without a transcript file?

Yes. The transcript parameter in render_timeline is optional. When omitted, the function skips word label rendering and silence detection, producing a composite showing only the filmstrip and waveform ribbon. This is useful for quick visual inspection of video segments before transcription is complete.

How is the audio waveform normalized for display?

The compute_envelope function calculates RMS values across windows of the 16 kHz audio stream, then applies min-max normalization to scale the array to the range [0, 1]. This normalized envelope is then mapped to pixel coordinates relative to the waveform baseline (wave_y), ensuring consistent amplitude representation regardless of source audio volume.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}