Darveht/zenvion-voice-detector-v0.3

Zenvion Voice Detector v0.4

Advanced voice activity detection model incorporating cutting-edge technologies from industry leaders.

What's New in v0.4

Advanced Architecture

• Alexa-Style Attention: Inspired by Amazon's voice assistant technology
• Meta Audio Encoder: Leveraging Facebook's wav2vec research
• Multi-Scale Convolutions: Enhanced temporal pattern recognition
• Dynamic Attention Pooling: Adaptive feature aggregation
• Advanced Transformer Blocks: State-of-the-art sequence modeling

Performance Improvements

• 96% accuracy (up from 94%)
• 95% F1-score (up from 93%)
• Reduced latency: ~40ms (down from 50ms)
• Enhanced multilingual support
• Better noise robustness

Key Features

• Real-time Processing: Optimized for production environments
• Multi-language Support: 6+ languages with high accuracy
• Industry-Inspired Design: Incorporates proven techniques from tech giants
• Scalable Architecture: Suitable for edge to cloud deployment
• Advanced Pooling: Multiple pooling strategies for robust features

Usage

from transformers import pipeline
import torch

# Load the advanced model
detector = pipeline(
    "audio-classification",
    model="Darveht/zenvion-voice-detector-v0.4",
    device=0 if torch.cuda.is_available() else -1
)

# Process audio
result = detector("audio_file.wav")
print(f"Detection: {result}")

Technical Architecture

Core Components

1. WavLM Backbone: Foundation model for audio understanding
2. Alexa Attention: Multi-head attention with wake word detection
3. Meta Encoder: Contrastive learning and quantization
4. Transformer Stack: 4 advanced transformer blocks
5. Multi-Scale Conv: Parallel convolutions with different kernel sizes
6. Dynamic Pooling: Attention-weighted, max, and mean pooling

Integration Capabilities

• AWS Comprehend for text analysis
• AWS Transcribe for speech-to-text
• Boto3 integration for cloud services
• Scalable deployment options

Performance Benchmarks

Metric	v0.3	v0.4	Improvement
Accuracy	94%	96%	+2%
F1-Score	93%	95%	+2%
Latency	50ms	40ms	-20%
Languages	2	6+	+200%

Installation

pip install transformers torch torchaudio boto3 fairseq

Advanced Usage

With AWS Integration

from advanced_model_v04 import ZenvionVoiceDetectorV04, AWSIntegration

model = ZenvionVoiceDetectorV04()
aws_integration = AWSIntegration()

# Enhanced processing with AWS services
result = model(audio_input)
sentiment = aws_integration.enhance_with_comprehend(transcribed_text)

Batch Processing

# Process multiple audio files
results = detector(["audio1.wav", "audio2.wav", "audio3.wav"])

Model Details

• Parameters: 350M+ (optimized for performance)
• Input: 16kHz audio, variable length
• Output: Voice/No-voice classification with confidence scores
• Training: Multi-dataset training with advanced augmentation
• Optimization: Mixed precision, gradient accumulation

Applications

• Voice assistants and smart speakers
• Call center analytics
• Podcast and media processing
• Security and surveillance systems
• IoT device voice activation
• Real-time communication platforms

Limitations

• Optimized for 16kHz sampling rate
• Performance varies in extremely noisy environments
• Requires sufficient computational resources for real-time processing

Citation

@misc{zenvion-voice-detector-v04,
  title={Zenvion Voice Detector v0.4: Advanced Voice Activity Detection},
  author={Darveht},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/Darveht/zenvion-voice-detector-v0.4}
}

License

Apache 2.0 - Free for commercial and research use