Update README.md

README.md

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 ---
 license: mit
+language:
+- en
+tags:
+- music-generation
+- midi
+- lstm
+- attention
+- pytorch
+- piano
+- symbolic-music
+- codealpha
+datasets:
+- ktonal/maestro-v3
+- pictureinthenoise/music-generation-with-giantmidi-piano
 ---
+
+# 🎵 Music LSTM — Symbolic Piano Generation
+
+A **Stacked LSTM + Attention** model for symbolic piano music generation, trained on 46 million musical events from ~12,000 MIDI files.
+
+Built from scratch as part of the **CodeAlpha AI Internship (Task 3)** — no pre-trained model, no external API, just raw deep learning on MIDI data.
+
+---
+
+## Model Description
+
+The model treats music generation as a **next-token prediction** problem — the same principle behind language models like GPT, applied to piano music.
+
+Each musical event is encoded as a single token representing three attributes simultaneously:
+- **Pitch** — MIDI note value (0–127)
+- **Duration** — discretized into 10 musical buckets (thirty-second → whole note)
+- **Velocity** — discretized into 8 classical nuance buckets (ppp → fff)
+
+The model learns to predict the next token given a sequence of 64 past tokens, then generates music autoregressively.
+
+### Architecture
+
+```
+Token sequence (64 tokens)
+        │
+        ▼
+Embedding (vocab_size=6543, dim=256)
+        │
+        ▼
+LSTM Layer 1 (hidden=1024)   ← local patterns: intervals, rhythm
+        │
+        ▼
+LSTM Layer 2 (hidden=1024)   ← higher-level patterns: phrases, motifs
+        │
+        ▼
+Attention (additive, Bahdanau-style)
+        │
+        ▼
+Linear → Softmax (6543 classes)
+        │
+        ▼
+Next token prediction
+```
+
+| Parameter | Value |
+|---|---|
+| Total parameters | 22,030,480 |
+| Vocabulary size | 6,543 tokens |
+| Sequence length | 64 tokens |
+| Hidden size | 1,024 |
+| LSTM layers | 2 |
+| Embedding dim | 256 |
+| Dropout | 0.3 |
+
+---
+
+## Training Data
+
+| Dataset | Files | Events |
+|---|---|---|
+| [Maestro v3.0.0](https://magenta.tensorflow.org/datasets/maestro) | 1,276 | ~6M |
+| [GiantMIDI-Piano v1.21](https://github.com/bytedance/GiantMIDI-Piano) | 10,841 | ~41M |
+| **Total** | **12,109** | **~46M** |
+
+Both datasets consist of professional and semi-professional **solo piano** recordings in classical style, ensuring a consistent musical domain.
+
+Preprocessing used **symusic** (C++ MIDI parser) for fast extraction of pitch, duration, and velocity attributes from raw MIDI files.
+
+---
+
+## Training
+
+Trained on **Kaggle 2×T4 GPUs** (30GB VRAM total) using `torch.nn.DataParallel`.
+
+| Hyperparameter | Value |
+|---|---|
+| Optimizer | Adam (lr=0.001) |
+| Scheduler | ReduceLROnPlateau (factor=0.5, patience=2) |
+| Batch size | 256 |
+| Gradient clipping | 5.0 |
+| Early stopping patience | 5 |
+
+### Results
+
+| Epoch | Train Loss | Val Loss |
+|---|---|---|
+| 1 | 6.834 | 6.407 |
+| 4 | 5.906 | 5.900 |
+| **8** | **5.505** | **5.806** ✓ best |
+| 13 | 5.041 | 5.857 — early stop |
+
+**Best checkpoint: epoch 8, val_loss = 5.805**
+
+Random baseline: `ln(6543) ≈ 8.78` — the model significantly outperforms random prediction.
+
+---
+
+## Usage
+
+### Quick start
+
+```bash
+git clone https://github.com/Tahsine/CodeAlpha_Music_Generation
+cd CodeAlpha_Music_Generation
+pip install -r requirements.txt
+```
+
+The model weights are downloaded automatically from this repo on first run:
+
+```bash
+# Generate MIDI (256 notes, temperature=0.9)
+python generate.py
+
+# Full options
+python generate.py \
+    --n_tokens    512 \
+    --temperature 0.9 \
+    --bpm         120 \
+    --output      artifacts/my_music.mid \
+    --device      cpu
+```
+
+### Generate MIDI + WAV
+
+```bash
+# Install FluidSynth
+sudo apt-get install fluidsynth   # Linux
+brew install fluidsynth            # macOS
+
+# Generate audio — soundfont (~30MB) downloads automatically
+python generate.py --n_tokens 512 --temperature 0.9 --audio
+```
+
+### Temperature guide
+
+| Temperature | Effect |
+|---|---|
+| `0.7` | Conservative — coherent but repetitive |
+| `0.9` | Balanced — musical and varied *(recommended)* |
+| `1.1` | Creative — surprising but less coherent |
+
+### Load model directly in Python
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Download weights
+model_path = hf_hub_download(
+    repo_id="KalineZephyr/music-lstm-midi-codealpha",
+    filename="best_model.pt",
+)
+
+# Load checkpoint
+ckpt = torch.load(model_path, map_location="cpu")
+print(f"Best epoch : {ckpt['epoch']}")
+print(f"Val loss   : {ckpt['val_loss']:.4f}")
+print(f"Config     : {ckpt['config']}")
+```
+
+---
+
+## Limitations
+
+- **Short-range coherence only** — LSTM memory is limited to ~50–100 tokens. The model generates locally coherent phrases but lacks long-range structure (no recurring themes, no global form).
+- **Piano only** — trained exclusively on solo piano data. Other instruments will produce poor results.
+- **Classical/romantic style** — dataset bias toward Western classical music.
+- **No rhythm quantization** — generated durations are discretized into 10 buckets, which may sound mechanical compared to human performance.
+
+These limitations are inherent to LSTM-based sequence models. A Transformer architecture with full self-attention would address the long-range coherence issue.
+
+---
+
+## Repository
+
+**GitHub:** [Tahsine/CodeAlpha_Music_Generation](https://github.com/Tahsine/CodeAlpha_Music_Generation)
+
+---
+
+## License
+
+MIT