Merge branch 'main' of https://huggingface.co/cloud19/NSFW_MMaudio
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README.md
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license: mit
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---
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license: mit
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language: en
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pipeline_tag: text-to-audio
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base_model: hkchengrex/MMAudio
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---
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# Fine-tuned MMAudio Model
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This repository contains a fine-tuned version of the **MMAudio** model, specializing in generating audio from video, image, and text prompts. The model was fine-tuned on a private dataset, resulting in the `cloud19/NSFW_MMaudio` checkpoint. Specializing in NSFW videos of various kinds.
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**Original Project:** [hkchengrex/MMAudio](https://github.com/hkchengrex/MMAudio)
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## Model Details
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* **Base Architecture:** `large_44k` (from the original MMAudio)
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* **Fine-tuning:** The model was fine-tuned on a private dataset of NSFW content to explore the model's adaptability to specific audio domains. Due to the nature of the training data, the generated audio may reflect mature themes.
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* **Capabilities:** Video-to-Audio, Image-to-Audio, Text-to-Audio.
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## How to Use
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This repository includes a fully functional Gradio (`app.py`). To run it locally, follow these steps. GPU 12-16 GB preferred.
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**Prerequisites:**
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* Python 3.10+
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* Git and Git LFS (`git-lfs` must be installed)
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### Step-by-Step Instructions
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1. **Clone the repository:**
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Open your terminal and clone the repository. `git-lfs` will automatically handle downloading the large model file.
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```bash
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git clone https://huggingface.co/cloud19/NSFW_MMaudio
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cd NSFW_MMaudio
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```
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2. **Set up a virtual environment (recommended):**
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```bash
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python -m venv venv
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source venv/bin/activate
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# On Windows, use: venv\Scripts\activate
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```
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3. **Install the required dependencies:**
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The `requirements.txt` file contains all necessary packages.
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```bash
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pip install -r requirements.txt
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```
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4. **Run the Gradio application:**
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This will launch a local web server.
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```bash
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python app.py
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```
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Now you can use the web interface to generate audio with your fine-tuned model.
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**Limitations:**
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* The model's output is heavily influenced by its training data.
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* **Content Warning:** Due to the NSFW nature of the fine-tuning dataset, the model may generate explicit or mature audio content. User discretion is advised.
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* Like all generative models, it may produce artifacts or unexpected results.
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## Original Model Citation
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All credit for the original architecture, code, and pre-trained models goes to the authors of MMAudio. Please refer to their repository for more details.
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* **Original GitHub:** [https://github.com/hkchengrex/MMAudio](https://github.com/hkchengrex/MMAudio)
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