audioslop

A document-to-audiobook pipeline with a web UI. Upload documents, clean text for TTS, generate audio with voice cloning, and listen with a synced transcript player that highlights words as they're spoken.

What it does

1. Extract and clean text from .docx, .pdf, .srt, .txt, and .md files. Strips abbreviations, footnotes, URLs, and other content that trips up voice models.
2. Generate audio using F5-TTS with zero-shot voice cloning from a short reference recording. Includes per-segment QA verification via Whisper transcription.
3. Listen with synced transcript -- words highlight in real time as the narrator reads, like an immersive reader. Click any word to seek to that position.

Requirements

• Python 3.10+
• NVIDIA GPU with CUDA (tested on RTX 4080 Super, 16GB VRAM)
• ~2GB disk space for models (downloaded automatically on first run)

Setup

git clone https://github.com/jamditis/audioslop.git
cd audioslop

pip install flask f5-tts whisper python-docx pdfplumber

# Create directories for user data
mkdir ref uploads jobs content output audio

# Copy your voice reference clip (5-15 seconds of speech, .wav format)
cp /path/to/your/voice.wav ref/

# Configure
cp .env.example .env
# Edit .env with your password and secret

Usage

Web UI

python app.py

Open http://localhost:5000. Log in with the password from your .env file.

1. Upload a document on the home page
2. Review and edit the cleaned text
3. Click "Generate audio" to start synthesis
4. Listen in the player with synced word highlighting

Command line

The pipeline scripts work standalone without the web UI:

# Clean a document for TTS
python audioslop.py content/mybook/ -o output/mybook/

# Generate audio from cleaned text
python synthesize.py output/mybook/ --ref-audio ref/voice.wav -o audio/mybook/

# Verify audio quality
python qa.py audio/mybook/ --source output/mybook/

Architecture

Browser (HTML/JS/Tailwind)
    |
Flask API (app.py)
    |
Background worker (worker.py)
    |
Pipeline: audioslop.py -> synthesize.py -> qa.py
    |
F5-TTS (voice synthesis) + Whisper (verification + word timestamps)

The web UI wraps three standalone Python scripts:

• audioslop.py -- Multi-format text extraction, TTS-specific cleaning (abbreviation expansion, dash normalization, footnote removal), and size-based chunking
• synthesize.py -- F5-TTS synthesis with per-paragraph generation, structural pauses between segments, and a QA verification loop
• qa.py -- Whisper-based transcription verification with word-level timestamps, accuracy scoring, and flow analysis

The player uses Whisper's word-level timestamps to sync transcript highlighting to audio playback via binary search on a flat timeline array, updated every animation frame.

Voice cloning

F5-TTS clones any voice from a short reference recording. For best results:

• Record 5-15 seconds of natural speech in a quiet room
• Save as .wav format
• The model mirrors whatever speaking style it hears in the reference

Configuration

Variable	Default	Purpose
AUDIOSLOP_PASSWORD	(required)	Web UI login password
AUDIOSLOP_SECRET	dev-secret-change-me	Flask session secret (change in production)

Project structure

audioslop/
├── app.py            # Flask web app and API routes
├── worker.py         # Background job processing thread
├── audioslop.py      # Text extraction and TTS cleaning
├── synthesize.py     # F5-TTS synthesis with QA loop
├── qa.py             # Transcription verification and timing
├── db.py             # SQLite database layer
├── activity.py       # Per-job event logging
├── static/
│   ├── app.css       # Styles
│   └── player.js     # Audio player and transcript sync engine
├── templates/        # Jinja2 templates (upload, review, player)
├── tests/            # pytest test suite
└── docs/             # Design specs and implementation plans

License

MIT