Simple and fast functions for building WebVTT formatted transcripts.

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Project description

vtt_builder

High-performance WebVTT file generation with spec compliance, powered by Rust.

Features

  • Spec Compliant: Automatic character escaping (&, <, >) per WebVTT specification
  • Fast: Rust core with PyO3 bindings for optimal performance
  • Safe: Input validation prevents malformed output
  • Multilingual: Full Unicode support for Spanish, Portuguese, French, German, Italian, Polish, and more
  • Flexible: Build from JSON files or Python dictionaries
  • Robust: Comprehensive error handling with specific exception types
  • Versatile: Rich set of transformation utilities (merge, split, shift, filter)
  • Insightful: Built-in statistics and analysis functions
  • Podcast Ready: Specialized functions for podcast transcription processing (filler removal, speaker diarization, confidence filtering, chapter detection)

Installation

pip install vtt-builder

Or from source:

git clone https://github.com/hello-world-bfree/vtt-builder.git
cd vtt-builder
pip install -e .

Quick Start

from vtt_builder import build_vtt_from_records

segments = [
    {"id": 1, "start": 0.0, "end": 2.5, "text": "Hello world"},
    {"id": 2, "start": 2.5, "end": 5.0, "text": "This is a test"},
]

build_vtt_from_records(segments, "output.vtt")

Output:

WEBVTT

1
00:00:00.000 --> 00:00:02.500
Hello world

2
00:00:02.500 --> 00:00:05.000
This is a test

Why Use VTT Builder?

Automatic Character Escaping

WebVTT spec requires special characters to be escaped. VTT Builder handles this automatically:

segments = [
    {"start": 0.0, "end": 2.0, "text": "Tom & Jerry"},
    {"start": 2.0, "end": 4.0, "text": "Math: 1 < 2"},
    {"start": 4.0, "end": 6.0, "text": "Use <html> tags"},
]
build_vtt_from_records(segments, "output.vtt")

# Output:
# Tom &amp; Jerry
# Math: 1 &lt; 2
# Use &lt;html&gt; tags

Input Validation

Catch errors before they become problems:

from vtt_builder import validate_segments, VttTimestampError

segments = [
    {"start": 5.0, "end": 2.0, "text": "Invalid!"},  # end < start
]

try:
    validate_segments(segments)
except VttTimestampError as e:
    print(f"Error: {e}")
    # "Segment 1: end time (2) must be >= start time (5)"

Specific Error Types

from vtt_builder import (
    VttError,           # Base exception
    VttValidationError, # General validation errors
    VttTimestampError,  # Timestamp issues
    VttHeaderError,     # Header format errors
    VttCueError,        # Cue content errors
)

try:
    validate_vtt_file("bad.vtt")
except VttTimestampError:
    # Handle timestamp-specific errors
    pass
except VttValidationError:
    # Handle any validation error
    pass

Data Formats

Segment Dictionary

segment = {
    "id": 1,           # Optional (auto-generated if missing)
    "start": 0.0,      # Required: Start time in seconds
    "end": 2.5,        # Required: End time in seconds
    "text": "Hello"    # Required: Cue text content
}

JSON File Format

{
  "transcript": "Full text of the transcript",
  "segments": [
    {"id": 1, "start": 0.0, "end": 2.5, "text": "First segment"},
    {"id": 2, "start": 2.5, "end": 5.0, "text": "Second segment"}
  ]
}

Functions

build_vtt_from_records(segments_list, output_file, escape_text=True, validate_segments=True)

Build a VTT file from a list of Python dictionaries.

segments = [
    {"start": 0.0, "end": 2.0, "text": "First cue"},
    {"start": 2.0, "end": 4.0, "text": "Second cue"},
]
build_vtt_from_records(segments, "output.vtt")

Parameters:

  • segments_list (list[dict]): List of segment dictionaries
  • output_file (str): Output file path
  • escape_text (bool): Escape special characters (default: True)
  • validate_segments (bool): Validate input data (default: True)

build_vtt_from_json_files(file_paths, output_file, escape_text=True, validate_segments=True)

Build a VTT file from one or more JSON transcript files. Timestamps are automatically offset for continuous playback.

build_vtt_from_json_files(
    ["part1.json", "part2.json", "part3.json"],
    "combined.vtt"
)

build_transcript_from_json_files(file_paths, output_file)

Extract plain text transcripts from JSON files.

build_transcript_from_json_files(
    ["part1.json", "part2.json"],
    "transcript.txt"
)

validate_vtt_file(vtt_file)

Validate an existing WebVTT file for spec compliance.

from vtt_builder import validate_vtt_file

try:
    validate_vtt_file("captions.vtt")
    print("Valid!")
except Exception as e:
    print(f"Invalid: {e}")

Validates:

  • WEBVTT header (with BOM support)
  • Timestamp formats (HH:MM:SS.mmm and MM:SS.mmm)
  • Cue structure and content
  • NOTE, STYLE, REGION blocks
  • Cue settings (position, align, etc.)

validate_segments(segments_list)

Pre-validate segment data before building.

from vtt_builder import validate_segments

segments = load_from_database()
validate_segments(segments)  # Raises if invalid
build_vtt_from_records(segments, "output.vtt", validate_segments=False)

escape_vtt_text(text)

Escape special characters for WebVTT compliance.

from vtt_builder import escape_vtt_text

text = "Tom & Jerry say 1 < 2"
escaped = escape_vtt_text(text)
# "Tom &amp; Jerry say 1 &lt; 2"

unescape_vtt_text(text)

Convert WebVTT escape sequences back to characters.

from vtt_builder import unescape_vtt_text

text = "Tom &amp; Jerry"
original = unescape_vtt_text(text)
# "Tom & Jerry"

Supports: &amp;, &lt;, &gt;, &nbsp;, &lrm;, &rlm;

Multilingual Support

Full Unicode support for international transcripts:

segments = [
    {"start": 0.0, "end": 2.0, "text": "English: Hello!"},
    {"start": 2.0, "end": 4.0, "text": "Español: ¿Cómo estás?"},
    {"start": 4.0, "end": 6.0, "text": "Français: Ça va bien"},
    {"start": 6.0, "end": 8.0, "text": "Deutsch: Größe und Übung"},
    {"start": 8.0, "end": 10.0, "text": "Polski: Łódź i Kraków"},
    {"start": 10.0, "end": 12.0, "text": "Português: São Paulo"},
    {"start": 12.0, "end": 14.0, "text": "Italiano: Città"},
]
build_vtt_from_records(segments, "multilingual.vtt")

Error Handling

from vtt_builder import (
    build_vtt_from_records,
    VttTimestampError,
    VttCueError,
)

try:
    build_vtt_from_records(segments, "output.vtt")
except VttTimestampError as e:
    print(f"Timestamp error: {e}")
except VttCueError as e:
    print(f"Cue content error: {e}")
except IOError as e:
    print(f"File error: {e}")

Advanced Usage

Disable Validation (Performance)

# Skip validation for trusted data
build_vtt_from_records(segments, "output.vtt", validate_segments=False)

Disable Escaping (Raw Output)

# Warning: May produce non-compliant VTT files
build_vtt_from_records(segments, "output.vtt", escape_text=False)

Manual Escaping

from vtt_builder import escape_vtt_text

# Pre-process text
text = "HTML: <div> & <span>"
clean = escape_vtt_text(text)
# "HTML: &lt;div&gt; &amp; &lt;span&gt;"

Transformation Utilities

VTT Builder provides powerful utilities for transforming segment data:

Merge Adjacent Segments

from vtt_builder import merge_segments

segments = [
    {"start": 0.0, "end": 1.0, "text": "Hello"},
    {"start": 1.0, "end": 2.0, "text": "world"},
    {"start": 10.0, "end": 11.0, "text": "Separate"},
]

merged = merge_segments(segments, gap_threshold=0.5)
# [{"id": 1, "start": 0.0, "end": 2.0, "text": "Hello world"},
#  {"id": 2, "start": 10.0, "end": 11.0, "text": "Separate"}]

Split Long Segments

from vtt_builder import split_long_segments

segments = [
    {"start": 0.0, "end": 10.0, "text": "Very long text that exceeds character limits"}
]

split = split_long_segments(segments, max_chars=20)
# Multiple segments with proportional timestamps

Shift Timestamps

from vtt_builder import shift_timestamps

segments = [{"start": 0.0, "end": 2.0, "text": "Test"}]
shifted = shift_timestamps(segments, offset_seconds=10.0)
# [{"start": 10.0, "end": 12.0, "text": "Test"}]

Filter by Time Range

from vtt_builder import filter_segments_by_time

segments = [
    {"start": 0.0, "end": 5.0, "text": "Early"},
    {"start": 10.0, "end": 15.0, "text": "Middle"},
    {"start": 20.0, "end": 25.0, "text": "Late"},
]

filtered = filter_segments_by_time(segments, start_time=8.0, end_time=18.0)
# [{"start": 10.0, "end": 15.0, "text": "Middle"}]

Timestamp Conversion

from vtt_builder import seconds_to_timestamp, timestamp_to_seconds

timestamp = seconds_to_timestamp(3661.123)
# "01:01:01.123"

seconds = timestamp_to_seconds("01:01:01.123")
# 3661.123

Statistics

from vtt_builder import get_segments_stats

segments = [
    {"start": 0.0, "end": 2.0, "text": "Hello world"},
    {"start": 2.0, "end": 5.0, "text": "This is a test"},
]

stats = get_segments_stats(segments)
# {
#   "total_duration": 5.0,
#   "num_segments": 2,
#   "avg_duration": 2.5,
#   "total_words": 6,
#   "words_per_second": 1.2,
#   ...
# }

In-Memory VTT Building

from vtt_builder import build_vtt_string

segments = [{"start": 0.0, "end": 2.0, "text": "Hello"}]
vtt_content = build_vtt_string(segments)
# Returns WebVTT string without writing to disk

Podcast Transcription Processing

VTT Builder is optimized for processing podcast transcriptions from services like Deepgram, OpenAI Whisper, and AssemblyAI.

Remove Filler Words

Clean up transcriptions by removing verbal fillers:

from vtt_builder import remove_filler_words

segments = [
    {"start": 0.0, "end": 2.0, "text": "Um so basically I think"},
    {"start": 2.0, "end": 4.0, "text": "You know like it's actually good"},
]

cleaned = remove_filler_words(segments)
# [{"text": "so I think", ...},
#  {"text": "it's good", ...}]

# Custom fillers
custom = remove_filler_words(segments, fillers=["well", "right", "okay"])

Speaker Diarization

Group segments by speaker with WebVTT voice tags:

from vtt_builder import group_by_speaker

segments = [
    {"start": 0.0, "end": 1.0, "text": "Hello", "speaker": "Alice"},
    {"start": 1.0, "end": 2.0, "text": "world", "speaker": "Alice"},
    {"start": 2.0, "end": 3.0, "text": "Hi there", "speaker": "Bob"},
]

grouped = group_by_speaker(segments)
# [{"text": "<v Alice>Hello world", "speaker": "Alice", ...},
#  {"text": "<v Bob>Hi there", "speaker": "Bob", ...}]

Confidence Filtering

Filter out low-confidence transcription segments:

from vtt_builder import filter_by_confidence

segments = [
    {"start": 0.0, "end": 2.0, "text": "Clear speech", "confidence": 0.95},
    {"start": 2.0, "end": 4.0, "text": "Mumbled", "confidence": 0.4},
]

# Remove low confidence segments
filtered = filter_by_confidence(segments, min_confidence=0.8)
# Only keeps "Clear speech"

# Or mark them for review
marked = filter_by_confidence(segments, min_confidence=0.8, remove_or_mark="mark")
# Adds "low_confidence": True flag

Word-Level to Segment Aggregation

Convert word-level timestamps to sentence-like segments:

from vtt_builder import words_to_segments

# Output from Deepgram/Whisper word-level API
words = [
    {"word": "Hello", "start": 0.0, "end": 0.5},
    {"word": "world.", "start": 0.6, "end": 1.0},
    {"word": "How", "start": 1.5, "end": 1.8},
    {"word": "are", "start": 1.9, "end": 2.1},
    {"word": "you?", "start": 2.2, "end": 2.5},
]

segments = words_to_segments(words, max_segment_duration=10.0, pause_threshold=1.0)
# Groups words into segments based on punctuation and pauses

Remove Repeated Phrases

Clean up stuttering and repetitions:

from vtt_builder import remove_repeated_phrases

segments = [
    {"start": 0.0, "end": 2.0, "text": "I think I think I think it's good"},
]

cleaned = remove_repeated_phrases(segments)
# [{"text": "I think it's good", ...}]

Automatic Chapter Detection

Detect chapter breaks based on pauses:

from vtt_builder import detect_chapters

segments = [
    {"start": 0.0, "end": 60.0, "text": "Introduction..."},
    {"start": 61.0, "end": 180.0, "text": "Main topic..."},
    {"start": 190.0, "end": 300.0, "text": "Different topic..."},  # 10s gap
]

chapters = detect_chapters(segments, min_chapter_duration=60.0, silence_threshold=5.0)
# [{"chapter": 1, "start": 0.0, "timestamp": "00:00"},
#  {"chapter": 2, "start": 190.0, "timestamp": "03:10"}]

Complete Podcast Processing Pipeline

from vtt_builder import (
    words_to_segments,
    remove_filler_words,
    remove_repeated_phrases,
    filter_by_confidence,
    group_by_speaker,
    build_vtt_from_records,
)

# Raw transcription from API
raw_words = api_response["words"]

# Process pipeline
segments = words_to_segments(raw_words)
segments = remove_filler_words(segments)
segments = remove_repeated_phrases(segments)
segments = filter_by_confidence(segments, min_confidence=0.7)
segments = group_by_speaker(segments, format_speaker=True)

# Generate clean VTT
build_vtt_from_records(segments, "podcast_episode.vtt")

Documentation

Requirements

  • Python 3.8+
  • Rust toolchain (for building from source)

Testing

pytest tests/ -v

License

MIT

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Add tests for new functionality
  4. Ensure all tests pass
  5. Submit a pull request

Version History

  • 0.5.0 - Podcast processing: filler removal, speaker diarization, confidence filtering, word aggregation, chapter detection
  • 0.4.0 - Transformation utilities: merge, split, shift, filter, stats, in-memory building
  • 0.3.0 - WebVTT spec compliance: character escaping, input validation, custom exceptions
  • 0.2.1 - Handle multiple newlines, tabs, carriage returns
  • 0.2.0 - Support NOTE and STYLE blocks
  • 0.1.0 - Initial release

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