hubert-base-persian-speech-gender-recognition
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Author | m3hrdadfi |
| Framework | PyTorch, Transformers |
| Dataset | shemo |
What is hubert-base-persian-speech-gender-recognition?
This is a specialized speech processing model designed for gender recognition in Persian speech using the HuBERT architecture. The model demonstrates exceptional accuracy with 98% overall performance in distinguishing between male and female voices. It leverages advanced speech processing capabilities to provide reliable gender classification for Persian audio inputs.
Implementation Details
The model is implemented using PyTorch and the Transformers library, built on the HuBERT architecture. It requires specific audio preprocessing steps including resampling and feature extraction. The implementation supports both CPU and GPU inference, with a straightforward prediction pipeline that outputs probability scores for male and female classifications.
- Achieves 98% precision and recall for both male and female classifications
- Utilizes the Wav2Vec2FeatureExtractor for audio processing
- Supports various audio input formats through torchaudio
- Includes built-in resampling capabilities
Core Capabilities
- Binary gender classification (Male/Female) for Persian speech
- High-accuracy predictions with confidence scores
- Real-time audio processing and inference
- Robust performance across different audio qualities
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Persian speech gender recognition, achieving remarkably high accuracy (98%) using the HuBERT architecture. Its specialized training on the shemo dataset makes it particularly effective for Persian language applications.
Q: What are the recommended use cases?
The model is ideal for applications requiring automated gender recognition from Persian speech, such as voice-based user interfaces, demographic analysis, and audio content classification systems. It can be integrated into larger speech processing pipelines or used standalone for gender classification tasks.
