README.md

metadata

title: PrecisionVoice
emoji: 🎙️
colorFrom: blue
colorTo: purple
sdk: docker
app_file: app/main.py
pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

PrecisionVoice - STT & Speaker Diarization

A production-ready Speech-to-Text and Speaker Diarization web application using FastAPI, faster-whisper, and pyannote.audio.

Features

• 🎙️ Speech-to-Text using kiendt/PhoWhisper-large-ct2 (optimized for Vietnamese)
• 👥 Speaker Diarization using pyannote/speaker-diarization-3.1
• 🧼 Advanced Denoising using Facebook's Denoiser (dns64)
• 🎤 Vocal Isolation using MDX-Net (UVR-MDX-NET-Voc_FT)
• 🔄 Automatic speaker-transcript alignment
• 📥 Download results in TXT or SRT format
• 🐳 Docker-ready with persistent model caching and GPU support

Quick Start

Prerequisites

1. Docker and Docker Compose
2. (Optional) NVIDIA GPU with CUDA support
3. HuggingFace account with access to pyannote models

Setup

1. Clone and configure:

   cp .env.example .env
   # Edit .env and add your HuggingFace token
   

2. Build and run:

   docker compose up --build
   

3. Open http://localhost:8000

Audio Processing Pipeline

The system uses a state-of-the-art multi-stage pipeline to ensure maximum accuracy:

1. Speech Enhancement: Background noise, hums, and interference are removed using Facebook's Denoiser (Deep Learning Wave-U-Net).
2. Vocal Isolation: Vocals are stripped from any remaining background music or non-speech sounds using MDX-Net.
3. Refinement: Subtle highpass filtering and EBU R128 loudness normalization for consistent volume.
4. Transcription: High-precision Vietnamese transcription using PhoWhisper.
5. Diarization: Segmenting audio by speaker.
6. Alignment: Merging transcripts with speaker segments.

Configuration

Variable	Default	Description
HF_TOKEN	-	Required for Pyannote models
ENABLE_DENOISER	True	Toggle Facebook speech enhancement
DENOISER_MODEL	dns64	Model for denoising
ENABLE_VOCAL_SEPARATION	True	Toggle MDX-Net vocal isolation
MDX_MODEL	UVR-MDX-NET-Voc_FT	Model for vocal separation
DEVICE	auto	cuda, cpu, or auto

Development

Local Setup (without Docker)

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload

API Endpoints

Endpoint	Method	Description
/	GET	Web UI
/api/transcribe	POST	Upload and transcribe audio
/api/download/{filename}	GET	Download result files

Supported Audio Formats

• MP3
• WAV
• M4A
• OGG

License

MIT