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README.md

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 license: mit
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+
+# Model Card for RVC-HuBERT
+
+Welcome to our comprehensive repository, a treasure trove of pretrained models, HuBERT models, and an assortment of other files and models, all tailored for use in the Retrieval-based Voice Conversion (RVC) neural network.
+
+## Overview
+
+This repository is designed to be a one-stop-shop for all your RVC needs. It hosts a wide array of pretrained models, meticulously crafted to provide a robust foundation for your voice conversion tasks. The repository also includes a diverse range of HuBERT models, known for their proficiency in self-supervised speech representation learning.
+
+## Key Features
+
+1. **Pretrained Models**: A vast collection of pretrained models, ready to be fine-tuned for your specific voice conversion tasks. These models have been trained on diverse datasets, ensuring a broad spectrum of voice characteristics.
+
+2. **HuBERT Models**: A selection of HuBERT models, recognized for their ability to learn high-quality speech representations from raw audio data. These models are ideal for tasks that require a deep understanding of speech nuances.
+
+3. **Additional Files and Models**: A miscellaneous collection of files and models that can be beneficial for various aspects of voice conversion, from data preprocessing to model evaluation.
+
+We invite you to explore this repository, leverage its resources, and contribute to the advancement of voice conversion technology. Whether you're a seasoned researcher or a budding enthusiast, we believe you'll find something of value here.
+
+Happy exploring, and let's shape the future of voice conversion together!
+
+```python
+def convert_voice(source_audio, target_audio, model):
+    """
+    A simple function to convert the voice of the source audio to match the target audio using a given model.
+
+    Args:
+        source_audio (str): Path to the source audio file.
+        target_audio (str): Path to the target audio file.
+        model (Object): Trained model for voice conversion.
+
+    Returns:
+        numpy.ndarray: Converted audio data.
+    """
+    # Load audio files
+    source_wav, _ = librosa.load(source_audio, sr=model.sample_rate)
+    target_wav, _ = librosa.load(target_audio, sr=model.sample_rate)
+
+    # Extract features
+    source_features = model.extract_features(source_wav)
+    target_features = model.extract_features(target_wav)
+
+    # Convert voice
+    converted_features = model.convert(source_features, target_features)
+
+    # Generate audio from converted features
+    converted_wav = model.generate_audio(converted_features)
+
+    return converted_wav