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Darveht/ZenVision-AI-Subtitle-Generator

sourceHugging Facemitupdated 11mo agoView on Hugging Face
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🎬 ZenVision AI Subtitle Generator

Advanced 3GB+ AI model for automatic video subtitle generation

ZenVision combines multiple state-of-the-art AI technologies to generate accurate and contextual subtitles for videos with emotion analysis and multi-language support.

🚀 Model Architecture

Multi-Modal AI System (3.2GB)

  • —Whisper Large-v2: Audio transcription
  • —BERT Multilingual: Text embeddings
  • —RoBERTa Sentiment: Sentiment analysis
  • —DistilRoBERTa Emotions: Emotion detection
  • —Helsinki Translation: Multi-language translation
  • —Advanced NLP: spaCy + NLTK processing

Key Features

  • —90+ languages transcription support
  • —10+ languages translation
  • —7 emotions detected with adaptive colors
  • —Real-time processing 2-4x speed
  • —Multiple formats SRT, VTT, JSON output
  • —95%+ accuracy in optimal conditions

🔧 Usage

Quick Start

python
from app import ZenVisionModel

# Initialize model
model = ZenVisionModel()

# Process video
video_path, subtitles, status = model.process_video(
    video_file="video.mp4",
    target_language="es",
    include_emotions=True
)

Installation

bash
pip install torch transformers whisper moviepy librosa opencv-python
pip install gradio spacy nltk googletrans==4.0.0rc1
python -m spacy download en_core_web_sm

Gradio Interface

python
import gradio as gr
from app import ZenVisionModel

model = ZenVisionModel()

demo = gr.Interface(
    fn=model.process_video,
    inputs=[
        gr.Video(label="Video Input"),
        gr.Dropdown(["es", "en", "fr", "de"], label="Target Language"),
        gr.Checkbox(label="Include Emotions")
    ],
    outputs=[
        gr.Video(label="Subtitled Video"),
        gr.File(label="Subtitle File"),
        gr.Textbox(label="Status")
    ]
)

demo.launch()

📊 Performance

Accuracy by Language

  • —English: 97.2%
  • —Spanish: 95.8%
  • —French: 94.5%
  • —German: 93.1%
  • —Italian: 94.8%
  • —Portuguese: 95.2%

Processing Speed

  • —CPU (Intel i7): 0.3x real-time
  • —GPU (RTX 3080): 2.1x real-time
  • —GPU (RTX 4090): 3.8x real-time

🎨 Emotion-Based Styling

  • —Joy: Yellow subtitles
  • —Sadness: Blue subtitles
  • —Anger: Red subtitles
  • —Fear: Purple subtitles
  • —Surprise: Orange subtitles
  • —Disgust: Green subtitles
  • —Neutral: White subtitles

🛠️ Technical Architecture

Video Input → Audio Extraction → Whisper Large-v2 → Transcription
     ↓              ↓                    ↓              ↓
Text Processing ← Translation ← BERT Embeddings ← Emotion Analysis
     ↓              ↓                    ↓              ↓
Subtitle Output ← Emotion Coloring ← Smart Formatting ← Multi-Format Export

📁 Output Formats

SRT Format

1
00:00:01,000 --> 00:00:04,000
Hello, welcome to this tutorial

2
00:00:04,500 --> 00:00:08,000
Today we will learn about AI

VTT Format

WEBVTT

00:00:01.000 --> 00:00:04.000
Hello, welcome to this tutorial

00:00:04.500 --> 00:00:08.000
Today we will learn about AI

JSON with Metadata

json
{
  "start": 1.0,
  "end": 4.0,
  "text": "Hello, welcome to this tutorial",
  "emotion": "joy",
  "sentiment": "positive",
  "confidence": 0.95,
  "entities": [["tutorial", "MISC"]]
}

🔧 Configuration

Environment Variables

bash
export ZENVISION_DEVICE="cuda"  # cuda, cpu, mps
export ZENVISION_CACHE_DIR="/path/to/cache"
export ZENVISION_MAX_DURATION=3600  # seconds

Model Customization

python
# Change Whisper model
zenvision.whisper_model = whisper.load_model("medium")

# Configure custom translator
zenvision.translator = pipeline("translation", model="custom-model")

📄 License

MIT License - see LICENSE for details.

👥 ZenVision Team

Developed by specialists in:

  • —AI Architecture: Language and vision models
  • —Audio Processing: Digital signal analysis
  • —NLP: Natural language processing
  • —Computer Vision: Video and multimedia analysis

🔗 Links

ZenVision - Revolutionizing audiovisual accessibility with artificial intelligence 🚀