Upload Music VAE checkpoint (epoch 99)

README.md

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+---
+license: mit
+tags:
+  - music-generation
+  - variational-autoencoder
+  - piano-roll
+  - midi
+  - pytorch
+---
+
+# Music VAE — CNN pretrained on the Lakh MIDI Dataset
+
+## Overview
+
+This is a **Convolutional Variational Autoencoder (CNN VAE)** pretrained on the
+[Lakh MIDI Dataset](https://colinraffel.com/projects/lmd/) (lmd_full, ~175k MIDI files).
+
+It was trained as part of a machine learning course assignment at Purdue University
+to give students a meaningful starting point for music-generation tasks.
+
+## Input / Output Format
+
+| Property        | Value                          |
+|-----------------|--------------------------------|
+| Input shape     | `[batch, 1, 88, 32]` — float32 |
+| Output shape    | `[batch, 1, 88, 32]` — float32 |
+| Pitch range     | MIDI 21–108 (A0 – C8, 88 keys) |
+| Time resolution | 16th notes at 120 BPM          |
+| Segment length  | 2 bars = 32 timesteps          |
+| Value range     | [0, 1] (Sigmoid output)        |
+
+A tensor value of `1` at position `[pitch_idx, time_step]` means that pitch
+`21 + pitch_idx` is active at that 16th-note time step.
+
+## Architecture Summary
+
+```
+ENCODER
+  Conv2d(1 → 32,  k=4, s=2, p=1) + ReLU + BN   →  [B, 32,  44, 16]
+  Conv2d(32 → 64, k=4, s=2, p=1) + ReLU + BN   →  [B, 64,  22,  8]
+  Conv2d(64 → 128,k=4, s=2, p=1) + ReLU + BN   →  [B, 128, 11,  4]
+  Conv2d(128→ 256,k=4, s=2, p=1) + ReLU + BN   →  [B, 256,  5,  2]
+  Flatten → 2560
+  Linear → mu      [B, 256]
+  Linear → log_var [B, 256]
+
+REPARAMETERISATION
+  z = mu + eps * exp(0.5 * log_var),  eps ~ N(0, I)
+
+DECODER
+  Linear(256 → 2560) → Reshape [B, 256, 5, 2]
+  ConvTranspose2d(256→128, k=4, s=2, p=1, output_padding=(1,0)) → [B, 128, 11,  4]
+  ConvTranspose2d(128→ 64, k=4, s=2, p=1) → [B,  64, 22,  8]
+  ConvTranspose2d( 64→ 32, k=4, s=2, p=1) → [B,  32, 44, 16]
+  ConvTranspose2d( 32→  1, k=4, s=2, p=1) + Sigmoid → [B, 1, 88, 32]
+```
+
+- **Latent dimension**: 256
+- **Trainable parameters**: ~4.2M
+
+## Loading the Model (Course Assignment)
+
+```python
+import torch
+from model import MusicVAE   # copy src/model.py into your project
+
+# Load checkpoint
+ckpt = torch.load("best_model.pt", map_location="cpu")
+config = ckpt["config"]
+
+model = MusicVAE(latent_dim=config["latent_dim"])
+model.load_state_dict(ckpt["model_state"])
+model.eval()
+
+# Generate new piano rolls
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+samples = model.sample(n=4, device=device)   # [4, 1, 88, 32]
+
+# Encode a segment and reconstruct it
+x = ...   # your [1, 1, 88, 32] piano-roll tensor
+x_recon, mu, log_var = model(x.to(device))
+
+# Interpolate between two points in latent space
+z1 = mu[0:1]
+z2 = mu[1:2]   # second example
+interp = model.interpolate(z1, z2, steps=8)
+```
+
+Also see `src/utils.py` for `pianoroll_to_midi()` and `visualize_pianoroll()`.
+
+## Training Details
+
+- **Dataset**: Lakh MIDI Dataset (lmd_full)
+- **Piano roll**: 88-pitch binary, 16th-note resolution, 120 BPM normalised
+- **Segments**: 2 bars (32 frames), stride 1 bar (16 frames)
+- **Loss**: BCE reconstruction + β-annealed KL (β: 0 → 1 over 20 epochs)
+- **Optimizer**: Adam, lr=1e-3, ReduceLROnPlateau (patience=5, factor=0.5)
+- **Batch size**: 256 | **Epochs**: 100 | **Gradient clip**: 1.0
+
+## Citation
+
+If you use this model in your work, please cite the Lakh MIDI Dataset:
+
+```bibtex
+@inproceedings{Raffel2016,
+  author    = {Colin Raffel},
+  title     = {Learning-Based Methods for Comparing Sequences, with Applications
+               to Audio-to-{MIDI} Alignment and Matching},
+  booktitle = {PhD Thesis, Columbia University},
+  year      = {2016}
+}
+```

best_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6f62b170a51e317c2c930d612a878aed2331f6855cc1272a160df019564abcc1
+size 13414709

config.json

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+{
+  "latent_dim": 256,
+  "n_pitches": 88,
+  "seg_frames": 32,
+  "pitch_min": 21,
+  "pitch_max": 108,
+  "architecture": "CNN-VAE"
+}