Update README.md

README.md

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----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+tags:
+- audio
+- vocoder
+- deepfake-enhancement
+- synthetic-audio
+- vocos
+datasets:
+- grotsoV/4TTSDeepfakeData
+language:
+- en
+base_model:
+- charactr/vocos-mel-24khz
+---
+
+# Vocos for Deepfake Audio Enhancement
+
+## Model Description
+This model is a custom-trained checkpoint of the [Vocos](https://github.com/charactr-platform/vocos) vocoder architecture, specifically fine-tuned to enhance the phase coherence and acoustic quality of synthetic speech (deepfakes). 
+
+It was trained on the [4TTSDeepfakeDataset](https://huggingface.co/datasets/grotsoV/4TTSDeepfakeData), which contains 1:1 parallel bona fide and zero-shot deepfake utterances generated by F5-TTS, MaskGCT, CosyVoice2, and Fish-S1mini.
+
+* **Architecture:** Vocos (Mel-spectrogram to 24kHz waveform)
+* **Sampling Rate:** 24,000 Hz
+* **Primary Task:** Audio enhancement / Deepfake artifact reduction
+
+## Intended Use & Limitations
+This model is released strictly for **academic research, educational, and personal purposes**. 
+It is intended to be used in audio forensics, deepfake detection research, and synthetic audio analysis pipelines. 
+
+**Prohibited Uses:** Due to the CC BY-NC-SA 4.0 license, you may **not** use these weights for any commercial purposes, productization, or malicious voice cloning applications.
+
+## How to Use
+You can load these weights using the original `vocos` Python library. Because this is a fine-tune of the original 24kHz mel-spectrogram model, you instantiate the base model first and then load your downloaded weights.
+
+```bash
+pip install vocos
+```
+
+```python
+import torch
+import torchaudio
+from vocos import Vocos
+
+# 1. Initialize the base Vocos model
+vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+
+# 2. Load the custom deepfake enhancement weights
+# (Replace 'model.pt' with the actual name of the file you upload here)
+state_dict = torch.load("model.pt", map_location="cpu")
+
+# Handle nested state dictionaries if necessary
+if 'vocos_state_dict' in state_dict:
+    vocos.load_state_dict(state_dict['vocos_state_dict'])
+else:
+    vocos.load_state_dict(state_dict)
+
+vocos.eval()
+
+# 3. Inference example
+wav, sr = torchaudio.load("path/to/your/deepfake.wav")
+if sr != 24000:
+    wav = torchaudio.transforms.Resample(sr, 24000)(wav)
+
+with torch.no_grad():
+    mel = vocos.feature_extractor(wav)
+    x = vocos.backbone(mel)
+    enhanced_wav = vocos.head(x)
+
+torchaudio.save("enhanced_output.wav", enhanced_wav.cpu(), 24000)
+```
+
+## Acknowledgements and Attribution
+* **Architecture:** The original Vocos model was created by [Charactr](https://charactr.com/) (Licensed under MIT).
+* **Training Data:** Trained on the [4TTSDeepfakeDataset](https://huggingface.co/datasets/grotsoV/4TTSDeepfakeData), which is derived from the Hi-Fi TTS Dataset (CC BY 4.0) and utilizes outputs from F5-TTS, MaskGCT, Fish-S1mini, and CosyVoice2.