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suyashnpande/
cinematic-music-descriptor-module3

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Cinematic Music Descriptor — Module 3 – Music Descriptor Heads

Multi-head MLP that takes Module2 context vectors and predicts all music descriptors for a scene: tempo, tonality, orchestration, etc.

Label Schema

Regression

tempo_bpm: 45–170 BPM
musical_valence: -0.93 to 0.68

Classification

tonality: ['atonal', 'major', 'minor']
harmonic_style: ['atonal', 'chromatic', 'cluster', 'diatonic', 'modal', 'pentatonic', 'whole_tone']
dynamic_shape_m4: ['crescendo', 'diminuendo', 'flat', 'subito_forte', 'subito_piano', 'sustained', 'swell', 'terraced']
rhythm_style: ['drive', 'off', 'ostinato', 'pulse', 'rubato', 'sparse']
texture: ['ambient', 'chamber', 'full', 'hybrid', 'solo']

Multi-label

orchestration: ['ambient_pad', 'brass', 'choir', 'electronic', 'ethnic', 'guitar', 'harp', 'organ', 'percussion', 'piano', 'solo_voice', 'strings', 'synth', 'woodwinds']

Training Details

Base model: roberta-base
Dataset: ~11,000 scenes from 60–80 movies
Framework: PyTorch + HuggingFace Transformers
Logging: Weights & Biases

Usage

python

import torch
from huggingface_hub import hf_hub_download

# Download weights
path = hf_hub_download(repo_id="suyashnpande/cinematic-music-descriptor-module3",
                       filename="module3.pt")

Citation

If you use this model, please cite the project.