Shipped voice contract

clone_any_voice.py is the production CLI. Studio and pro fail if the only engine is WORLD/DSP or gTTS. The headline score is speaker identity. The old pitch/timbre number is legacy_heuristic and is not accuracy. Install neural engines with ./install_voice_engines.sh (includes Resemblyzer for speaker verification). A real clone still needs an authorized rvc_model.pth or a configured Qwen/XTTS/ElevenLabs backend. Pacaveli has no trained checkpoint in this repo.

🎤 AI Vocals Studio - Modern Edition

A beautiful, dark-themed AI voice cloning and generation application with advanced features and modern UI.

✨ Features

🎨 Modern Dark Theme UI

• Sleek dark interface with green accent colors
• Hover effects and smooth transitions
• Tabbed interface for better organization
• Professional typography and spacing

🤖 Model Management

• Dropdown Model Selection: Easy selection from available models
• Pre-trained Models: Download popular voice models instantly
• Custom Model Import: Add your own .pth model files
• Batch Model Import: Import entire folders of models
• Real-time Model Loading: Load models with visual feedback

📚 Training Data Management

• Folder Import: Import entire folders of audio files
• All Audio Formats: Support for WAV, MP3, FLAC, OGG, M4A, AIFF, AAC
• Recursive Search: Automatically finds audio files in subfolders
• Duplicate Handling: Smart filename handling for duplicates
• Visual Counter: Real-time count of training clips

🎵 Voice Generation

• Text-to-Speech: Generate vocals from text input
• Audio-to-Audio: Transform existing audio files
• Format Options: Output in WAV or MP3
• Auto Flow Alignment: Enhanced rap flow generation
• Real-time Progress: Visual feedback during processing

🚀 Quick Start

Install the complete local stack once, then launch the desktop app:

CLI Usage

• Run the service wrapper: aivoc
• Test with a WAV file (self‑signed TLS):

  aivoc --insecure test /path/to/file.wav "hello world"
  

• HTTPS endpoint (self‑signed): https://voice-134-122-113-44.sslip.io/voice_on_song
• Health check: https://voice-134-122-113-44.sslip.io/health

---

1. Run the Application:

   python app_modern.py
   

./install.sh
./run.sh

For the fastest no-training workflow, create a saved profile from 15–20 seconds of clean authorized voice audio, select a song under Generate → Audio Input, and click Replace Song Vocals. Seed-VC performs zero-shot neural conversion after Demucs separation; no paid API or per-speaker training is required.

The equivalent one-command CLI workflow and stem-only commands are in QUICK_START.md. A trained RVC model can still be imported or trained for voice-specific conversion.

Vercel + Worker Deployment

The Vercel frontdoor lives in vercel_frontdoor/ and supports two storage backends:

• Supabase: production persistence for queued jobs, uploaded voice samples, target songs/audio, outputs, and reports.
• Local filesystem: fallback for local/self-hosted runs when Supabase env vars are not set.

Production setup:

scripts/setup_vercel_supabase.sh
cd vercel_frontdoor
npx vercel --prod --yes --scope stephens-projects-8fbc16d0

Worker setup:

scripts/run_supabase_worker.sh

The launcher uses the installer's .venv/bin/python, accepts MAIN_PY for a custom runtime, and retains venv/bin/python compatibility. For a queue stored entirely on the VPS, unset the two Supabase variables and set LOCAL_STORAGE_DIR to the same directory used by the self-hosted frontdoor. This starts a worker; real neural rendering still requires installed engines, cached weights, and an authorized reference or RVC model.

The experimental scripts/voice_swap.py package demo is separate from the production song pipeline. Its Coqui path resynthesizes speech and its Qwen3 adapter is a stub; neither establishes singing or beat-preservation quality. Backend errors now propagate rather than silently substituting Silero, which ignores the reference voice. Use clone_any_voice.py --target-type song for the Demucs → Seed-VC/RVC → measured-quality → remix workflow.

For the one-click RVC training task, install a compatible trainer on the worker and set its executable path before starting the worker:

export RVC_TRAINER=/absolute/path/to/rvc-train
export RVC_TRAIN_EPOCHS=300
scripts/run_supabase_worker.sh

The Vercel page queues this job; training runs on the local/GPU worker because Vercel functions are not suitable for long-running model training.

Free Kaggle GPU Worker

When no local GPU is available, open cloud/kaggle_rvc_worker.ipynb in Kaggle, enable a free NVIDIA GPU, add Supabase Secrets named SUPABASE_URL, SUPABASE_SERVICE_ROLE_KEY, and SUPABASE_STORAGE_BUCKET, then run all cells. The notebook claims one queued RVC training job, trains it with Applio, uploads the .pth and .index to Supabase, and the regular worker hydrates that model automatically.

Free GPU availability and quotas are controlled by Kaggle; a session can be queued or unavailable during busy periods. The notebook is resumable at the job level and should be stopped after the training job completes.

🎙️ Authorized Pacaveli Workflow

Pacaveli is an authorized project voice. Keep source recordings and trained model binaries out of Git; use local/private object storage for those artifacts.

For high-fidelity rap/singing conversion, use a real RVC model rather than the legacy DSP/persona transform. Prepare/train an authorized dataset with:

.venv/bin/python rvc_training_cli.py \\
  --voice-dir models/voices/pacaveli \\
  --dataset /path/to/authorized/pacaveli-clean-vocals \\
  --i-have-permission

Or register an already-trained RVC model:

This is the beginning of the README. Read the rest at the original. Read the full README ↗