README.md

---
license: apache-2.0
base_model: microsoft/wavlm-large
tags:
- audio-classification
- voice-detection
- speech-recognition
- pytorch
- transformers
- real-time
- production-ready
- alexa-inspired
- wav2vec
- multi-scale
- attention-pooling
datasets:
- speech_commands
- common_voice
- voxceleb
- librispeech
language:
- en
- es
- fr
- de
- it
- pt
metrics:
- accuracy
- f1
- precision
- recall
pipeline_tag: audio-classification
widget:
- example_title: "Voice Detection"
  src: https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac
model-index:
- name: zenvion-voice-detector-v0.4
  results:
  - task:
      type: audio-classification
      name: Audio Classification
    dataset:
      type: speech_commands
      name: Speech Commands
    metrics:
    - type: accuracy
      value: 0.96
    - type: f1
      value: 0.95
---

# 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

```python
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

```bash
pip install transformers torch torchaudio boto3 fairseq
```

## Advanced Usage

### With AWS Integration
```python
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
```python
# 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

```bibtex
@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