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.gitattributes

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README.md

@@ -0,0 +1,421 @@
+---
+license: mit
+library_name: pytorch
+pipeline_tag: feature-extraction
+language:
+  - en
+  - zh
+datasets:
+  - GD-Studio/embeat_45m_spotify_tracks
+tags:
+  - music
+  - music-recommendation
+  - contrastive-learning
+  - collaborative-filtering
+  - content-based-recommendation
+  - spotify
+---
+
+<p align="center">
+  <img src="assets/banner.png" alt="Embeat Banner" width="100%">
+</p>
+
+<p align="center">
+  <a href="https://github.com/gdstudio-org/Embeat">Homepage</a> •
+  <a href="https://www.bilibili.com/opus/1218087093501165591">Blog</a> •
+  <a href="https://huggingface.co/GD-Studio/embeat-track2vec">Model</a> •
+  <a href="https://huggingface.co/datasets/GD-Studio/embeat_45m_spotify_tracks">Dataset</a> •
+  <a href="https://pan.baidu.com/s/1CWFzgM75Z4YjP1tZnGCZKg?pwd=0616">Database</a>
+</p>
+
+<p align="center">
+  <a href="https://github.com/gdstudio-org/Embeat"><img src="https://img.shields.io/github/stars/gdstudio-org/Embeat?style=social" alt="Stars"></a>
+  <a href="https://github.com/gdstudio-org/Embeat/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-CC--BY--NC%204.0-blue" alt="License"></a>
+</p>
+
+---
+
+# Embeat: A Music Recommendation System Based on Acoustic Features
+
+**GitHub project**: [https://github.com/gdstudio-org/Embeat](https://github.com/gdstudio-org/Embeat)
+
+## Introduction
+
+Embeat is a music recommendation system built on Spotify acoustic feature data. It encodes audio features into vectors via a **contrastive learning model** and combines them with a **multi-channel recall** strategy to deliver high-quality music recommendations.
+
+**Key Features:**
+
+- **Acoustic Similarity**: The EmbeatMLP model, trained on Spotify Audio Features (key, tempo, energy, valence, etc.), encodes acoustic features into 64-dim vectors
+- **Genre Awareness**: Leverages 6,000+ micro-genre tags to precisely assign genres to 2M+ artists, preventing "acoustically similar but stylistically different" recommendations
+- **Multi-Channel Recall**: 5 parallel recall channels (Acoustic Similarity / Same-Genre Popular / Same Artist / Similar Artists / Playlist Collaborative Filtering), merged and scored for final output
+- **Playlist Collaborative Filtering**: Track2Vec (Word2Vec-inspired) learns track co-occurrence patterns from 1.88M Spotify playlists
+- **Millisecond-Level Response**: Powered by the Qdrant vector database, retrieval across 45M tracks completes in 30–100ms
+
+
+## Roadmap
+
+> If you find this project helpful, please give it a ⭐️. It means a lot to a personal project, thanks!
+
+- [x] **2026-06-26**: Open-source initial codebase + [EmbeatMLP model weights](checkpoints/EmbeatMLP/)
+- [x] **2026-06-26**: Open-source [45M tracks dataset](https://huggingface.co/datasets/GD-Studio/embeat_45m_spotify_tracks) + [Technical blog](https://www.bilibili.com/opus/1218087093501165591)
+- [x] **2026-07-02**: Open-source Qdrant database V1 [[Google Drive](https://drive.google.com/drive/folders/1dFdueTmcWgGZXhJXs7c7YOjeniZsSW9x?usp=sharing)] [[Baidu Netdisk](https://pan.baidu.com/s/1CWFzgM75Z4YjP1tZnGCZKg?pwd=0616)]
+- [x] **2026-09-06**: Open-source Qdrant database V2 (same link above, 2GB+ RAM can deploy)
+- [x] **2026-09-06**: Open-source [Track2Vec model weights](https://huggingface.co/GD-Studio/embeat-track2vec)
+
+
+## Demo
+
+> Below are example recommendation results from Embeat (please unmute before playing)
+
+<details open>
+<summary><b>Uptown Funk - Bruno Mars [dance pop, pop]</b></summary>
+<table>
+<tr>
+<th width="25%">Seed Track</th>
+<th width="25%">Embeat #1</th>
+<th width="25%">Embeat #2</th>
+<th width="25%">Embeat #3</th>
+</tr>
+<tr>
+<td>Uptown Funk - Bruno Mars</td>
+<td>CAN'T STOP THE FEELING! - Justin Timberlake</td>
+<td>Happy - Pharrell Williams</td>
+<td>I Like to Move It - will.i.am</td>
+</tr>
+<tr>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_1_seed_track.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_1_embeat_1.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_1_embeat_2.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_1_embeat_3.mp3" controls preload="none"></audio>
+</td>
+</tr>
+</table>
+</details>
+
+<details>
+<summary><b>杀死那个石家庄人 - 万能青年旅店 [chinese indie rock]</b></summary>
+<table>
+<tr>
+<th width="25%">Seed Track</th>
+<th width="25%">Embeat #1</th>
+<th width="25%">Embeat #2</th>
+<th width="25%">Embeat #3</th>
+</tr>
+<tr>
+<td>杀死那个石家庄人 - 万能青年旅店</td>
+<td>大石碎胸口 - 万能青年旅店</td>
+<td>凄美地 - 郭顶</td>
+<td>不要停止我的音乐 - 痛仰乐队</td>
+</tr>
+<tr>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_2_seed_track.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_2_embeat_1.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_2_embeat_2.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_2_embeat_3.mp3" controls preload="none"></audio>
+</td>
+</tr>
+</table>
+</details>
+
+<details>
+<summary><b>Sis puella magica! - 梶浦由記 [anime score, japanese vgm]</b></summary>
+<table>
+<tr>
+<th width="25%">Seed Track</th>
+<th width="25%">Embeat #1</th>
+<th width="25%">Embeat #2</th>
+<th width="25%">Embeat #3</th>
+</tr>
+<tr>
+<td>Sis puella magica! - 梶浦由記</td>
+<td>Decretum - 梶浦由記</td>
+<td>Zoltraak - Evan Call</td>
+<td>Arrietty's Song - Cécile Corbel</td>
+</tr>
+<tr>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_3_seed_track.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_3_embeat_1.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_3_embeat_2.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_3_embeat_3.mp3" controls preload="none"></audio>
+</td>
+</tr>
+</table>
+</details>
+
+<details>
+<summary><b>Gizeh - Oskar Schuster [compositional ambient]</b></summary>
+<table>
+<tr>
+<th width="25%">Seed Track</th>
+<th width="25%">Embeat #1</th>
+<th width="25%">Embeat #2</th>
+<th width="25%">Embeat #3</th>
+</tr>
+<tr>
+<td>Gizeh - Oskar Schuster</td>
+<td>Vleurgat - Oskar Schuster</td>
+<td>Sleeping Lotus - Joep Beving</td>
+<td>Travelling - James Spiteri</td>
+</tr>
+<tr>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_4_seed_track.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_4_embeat_1.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_4_embeat_2.mp3" controls preload="none"></audio>
+</td>
+<td>
+<audio src="https://huggingface.co/GD-Studio/embeat-track2vec/resolve/main/assets/demo_4_embeat_3.mp3" controls preload="none"></audio>
+</td>
+</tr>
+</table>
+</details>
+
+### LLM Blind Evaluation
+
+Using the LLM-as-a-Judge method (GPT-5.5 / Gemini Flash 3.5 / Claude Sonnet 4.6), Embeat was blindly evaluated against Netease Cloud Music in AB tests:
+
+| Judge Model | Embeat Wins | Netease Wins | Tie |
+|-------------|:-----------:|:------------:|:---:|
+| Claude Sonnet 4.6 | **8** | 2 | 0 |
+| Gemini Flash 3.5 | **9** | 1 | 0 |
+| GPT 5.5 | **6** | 4 | 0 |
+
+**Conclusions:**
+
+- Embeat's core strength lies in its balance between style precision and artist diversity, with a particularly notable advantage in niche-style scenarios that span across languages and cultures
+- Netease Cloud Music retains some reference value only in its deep mining of Mandarin-language local content
+- For detailed comparison, please refer to the [Technical blog](https://www.bilibili.com/opus/1218087093501165591)
+
+
+## System Architecture
+
+### Model Details
+
+**EmbeatMLP** - Acoustic Feature Encoding Model
+
+- Input: 64-dim discrete features (key, mode, tempo, time_signature) + 64-dim continuous features (energy, valence, danceability, etc., 7 dimensions)
+- Architecture: Dual-tower MLP (Discrete Tower + Acoustic Tower -> Backbone)
+- Output: 64-dim L2-normalized vectors
+- Training: Masked InfoNCE Loss, batch_size=4096, converges in ~330 steps
+- Extremely small parameter count, supports real-time CPU-only inference
+
+**Track2Vec** - Playlist Collaborative Filtering Model
+
+- Based on Word2Vec Skip-Gram, treating playlists as "sentences" and tracks as "words"
+- Training data: 1.88M Spotify playlists
+- Vocabulary: 1.09M tracks, 64-dim vectors
+- Supports real-time CPU-only inference, single query latency < 200ms
+
+### Multi-Channel Recall
+
+```
+Input seed track: track_id / track_name + artist_name
+  │
+  ├─ Channel 1 [similar]: Acoustic Similarity Recall (genre filtering + EmbeatMLP cosine similarity)
+  ├─ Channel 2 [popular]: Same-Genre Popular Recall (genre filtering + popularity ranking)
+  ├─ Channel 3 [same_artist]: Same Artist Recall (same artist + EmbeatMLP cosine similarity)
+  ├─ Channel 4 [related_artist]: Similar Artists Recall (similar artists + EmbeatMLP cosine similarity)
+  ├─ Channel 5 [related_track]: Playlist Collaborative Filtering (Track2Vec cosine similarity)
+  │
+  ├─ ISRC Deduplication / Re-ranking / Same-Artist Ratio Control
+  │
+  └─ Output: Top-K Recommendation List
+```
+
+### Project Structure
+
+```
+Embeat/
+├── assets/                 # Static assets folder
+├── checkpoints/            # Model weights folder
+│   ├── EmbeatMLP/          # EmbeatMLP model weights
+│   └── Track2Vec/          # Track2Vec model weights (requires separate download)
+├── data/                   # Data processing folder (not fully organized)
+├── infer/                  # Inference code folder
+│   ├── Embeat.py           # Embeat recommendation system core
+│   ├── EmbeatUtils.py      # Embeat extension utilities
+│   ├── infer.py            # EmbeatMLP inference entry point
+│   ├── eval_infer.py       # EmbeatMLP evaluation utilities
+│   └── hf_to_qdrant.py     # Convert HF Dataset to Qdrant database
+├── train/                  # Training code folder
+│   ├── model.py            # EmbeatMLP model definition
+│   ├── dataset.py          # HF Dataset processing
+│   ├── sampler.py          # Positive/negative sample sampler
+│   ├── loss.py             # Masked InfoNCE Loss
+│   ├── trainer.py          # EmbeatMLP trainer
+│   ├── train.py            # EmbeatMLP training entry point
+│   └── train_track2vec.py  # Track2Vec training entry point
+├── .env.example            # Environment variables example for .env
+├── requirements.txt
+└── LICENSE
+```
+
+
+## Getting Started
+
+### Requirements (recommended)
+
+- Python >= 3.10
+- PyTorch >= 2.6, < 2.7 (required for training)
+- CUDA >= 12.0 (required for training)
+- [Qdrant](https://github.com/qdrant/qdrant/releases) >= 1.18 (required for inference)
+
+### Installation
+
+```bash
+conda create -n embeat python=3.10
+conda activate embeat
+
+# Install PyTorch (CUDA 12.x), see https://pytorch.org/get-started/previous-versions/
+pip install "torch>=2.6,<2.7" --index-url https://download.pytorch.org/whl/cu126
+
+pip install -r requirements.txt
+```
+
+### Train EmbeatMLP
+
+```bash
+# 1. Download the HuggingFace tracks dataset to `data/datasets/`, then rename it to `spotify_45m_tracks_metadata`
+# 2. If you want to make more detailed adjustments to the training parameters, please review the code
+cd train
+python train.py
+```
+
+### Train Track2Vec
+
+```bash
+# 1. Prepare the playlist training data (txt format, one playlist per line, space-separated track_ids)
+# 2. Rename it to `spotify_playlists.txt`, and place it in the `train` folder
+# 3. If you want to make more detailed adjustments to the training parameters, please review the code
+cd train
+python train_track2vec.py
+```
+
+### Inference: Compute Acoustic Similarity Between Two Tracks
+
+```python
+# 1. Get the acoustic feature data from HuggingFace tracks dataset
+# 2. Or you can find some existed examples from infer/eval_infer.py
+from infer.infer import infer
+
+# 晴天 - Jay Chou (G major with fast tempo)
+song_a = {"key": 7, "mode": 1, "tempo": 137, "time_signature": 4,
+          "danceability": 0.54, "energy": 0.56, "speechiness": 0.02,
+          "instrumentalness": 0.0, "valence": 0.41, "acousticness": 0.23,
+          "liveness": 0.1}
+
+# 夜曲 - Jay Chou (F minor with slow tempo)
+song_b = {"key": 5, "mode": 0, "tempo": 87, "time_signature": 4,
+          "danceability": 0.67, "energy": 0.65, "speechiness": 0.05,
+          "instrumentalness": 0.03, "valence": 0.57, "acousticness": 0.27,
+          "liveness": 0.19}
+
+# Compute acoustic similarity via EmbeatMLP
+similarity = infer(sample_a=song_a, sample_b=song_b,
+                   checkpoint_path="checkpoints/EmbeatMLP/model.pt")
+
+# Similarity: 0.6944
+print(f"Similarity: {similarity:.4f}")
+```
+
+### Inference: Qdrant-Based Music Recommendation
+
+```bash
+# 1. Start the Qdrant service and import the database
+# 2. Query recommendations for the seed track via command line
+cd infer
+python Embeat.py -t 5pIcwtJYNJx93l420oR2Vm   # Query by Spotify Track ID
+python Embeat.py -t TWK970300503   # Query by ISRC
+python Embeat.py -s "晴天 - Jay Chou"   # Query by track name and artist
+python Embeat.py -a "Jay Chou"   # Query by artist name
+
+
+# Output result for "晴天 - Jay Chou":
+Query track_id: 5pIcwtJYNJx93l420oR2Vm
+Query track info: 晴天 - Jay Chou
+Query artist genres: ['mandopop', 'taiwan pop', 'c-pop', 'zhongguo feng']
+-> Find query record used time: 21ms
+-> Similar recall used time: 48ms
+-> Popular recall used time: 24ms
+-> Same artist recall used time: 4ms
+-> Related artist recall used time: 5ms
+-> Related track recall used time: 123ms
+-> Re-ranking used time: 2ms
+Result artist genres: ['taiwan indie', 'mandopop', 'chinese viral pop', 'cantopop']
+======= Top 20 items =======
+index   track_id                track_name      artist_name     album_name      sources         score
+1       3Qj9Fy8BPbWmICTiNkuqB7  珊瑚海  Jay Chou        11月的蕭邦      ['same_artist', 'related_track']        1.0
+2       10VuSw48iPN2UK2xX9Y6P0  青花瓷  Jay Chou        我很忙  ['same_artist', 'related_track']        1.0
+3       0IAgufC1FlOg1nZMmRZxRr  突然好想你      Mayday  後 青春期的詩   ['popular', 'related_artist']   1.0
+4       2zB7NKVnzRh7xSUSPLErFr  明明就  Jay Chou        十二新作        ['same_artist', 'related_track']        1.0
+5       5WtMlbTDNZlbN8xZ5zfXva  Our Singapore   JJ Lin  My August 9th - 50 Wonderful Years (2016 Edition)       ['similar', 'related_artist']   1.0
+6       5cU1O9P0EDA0rPkPDykhIm  怎麼了  Eric Chou       終於了解自由 (Deluxe)   ['popular', 'related_track']    1.0
+7       4daA20tBusVX29bUWgd8Dw  交換餘生        JJ Lin  交換餘生        ['popular', 'related_track']    1.0
+8       1EgGTmmFGtlWuqgXFLrp9x  溫柔    Mayday  愛情萬歲        ['related_artist']      0.87
+9       3ZuyyfGJqx9qhWTVtdMCWz  生命線 - 電視劇《院長爸爸》片頭曲       Bii     生命線 (電視劇《院長爸爸》片頭曲)       ['similar']     0.85
+10      3p4UTiSIIpP4LFn0KEyEOj  十面埋伏        Eason Chan      Live For Today  ['related_artist']      0.85
+11      4lhbajK3dvUcJ0UNEeCdMn  飞鸟和蝉        Ren Ran         Ren然   ['related_track']       0.84
+12      3e8uw7YMiKVcIakItBENqm  天天晴朗（蘇打綠版）    sodagreen       秋：故事（蘇打綠版）    ['similar']     0.83
+13      26O8PmJ32hwAbZnIhbJJwZ  天使    Mayday  為愛而生        ['related_artist']      0.82
+14      3LgoekU3dE5ZMLvuL3NIt9  清醒 (戲劇《淺情人不知》片尾曲)         Ariel Tsai      清醒 (戲劇《淺情人不知》片尾曲)         ['similar']     0.81
+15      1WnTw4Tzpc5q9dHMjs4aHu  陰天快樂        Eason Chan      rice & shine    ['related_artist']      0.8
+16      14GFYAUxkeXranhS2qrYIZ  我想要佔據你    告五人  帶你飛  ['related_track']       0.8
+17      1ylx8p71GKQy5g1t4gzuEz  抱歉    Sam Lee         原諒我沒有說    ['similar']     0.79
+18      7fAdinC2UTc0Y9GiKrkTtu  字字句句        卢卢快闭嘴      字字句句        ['related_track']       0.78
+19      1VG8o5rUZQZ0wjs7Bi4siU  最熟悉的陌生人  Elva Hsiao      蕭亞軒 (最熟悉的)       ['similar']     0.77
+20      1lM4cYuhJHSsDRfD0ZCRN7  你的背包        Eason Chan      陳奕迅 國語精選 (HQCDII)        ['related_artist']      0.77
+
+Query used time: 0.229s
+```
+
+
+## Related Links
+
+<p align="center">
+  <img src="assets/gdmusic_embeat.png" alt="GDMusic Embeat" width="100%">
+</p>
+
+- GD Music (Live Demo): [https://music.gdstudio.xyz](https://music.gdstudio.xyz)
+- Bilibili: [https://space.bilibili.com/13715770](https://space.bilibili.com/13715770)
+- Telegram: [https://t.me/gdstudio_music](https://t.me/gdstudio_music)
+
+
+## Acknowledgements
+
+- [Anna's Archive](https://annas-archive.org)
+- [Every Noise at Once](https://everynoise.com)
+
+
+## License
+
+| Scope | License |
+|-------|---------|
+| Code, Model Weights | MIT |
+| Datasets, Database | CC-BY-NC 4.0 |
+
+> Made with ❤️ by [GD Studio](https://github.com/gdstudio-org)

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