https://github.com/bpavan16/harmony-diffusion
Prompt-to-Music using a Diffusion model, implemented in PyTorch, and trained on the MusicCaps dataset. This project likely takes natural language prompts and generates music/audio.
https://github.com/bpavan16/harmony-diffusion
Last synced: 7 months ago
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Prompt-to-Music using a Diffusion model, implemented in PyTorch, and trained on the MusicCaps dataset. This project likely takes natural language prompts and generates music/audio.
- Host: GitHub
- URL: https://github.com/bpavan16/harmony-diffusion
- Owner: bPavan16
- Created: 2025-04-15T15:59:34.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-05-08T01:21:36.000Z (8 months ago)
- Last Synced: 2025-05-18T14:10:49.507Z (8 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 6.03 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 🎶 Harmony-Diffusion : Text-to-Music Generation with Diffusion Models
text-to-music is a research-oriented NLP course project (6th Sem) that explores generating music directly from natural language prompts using a **Diffusion-based generative model**, trained on the **MusicCaps dataset**. It leverages the power of **PyTorch** and recent advancements in text-conditioned generative modeling to bridge the gap between natural language and audio generation.
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## ✨ Highlights
- 🧠 **Diffusion-based architecture** for music synthesis
- 📝 **Text-prompt conditioning** using captions from MusicCaps
- 🎧 Generates raw waveform or mel spectrogram outputs
- 🔬 Built in **PyTorch** for research flexibility
- 📚 Uses [Google's MusicCaps dataset](https://github.com/google-research/google-research/tree/master/musiccaps)
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