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Granular guitar effect for Raspberry Pedal Pi
Official Code for DragGAN (SIGGRAPH 2023)
Official Implementation of Paella https://arxiv.org/abs/2211.07292v2
This is a repo providing same stable diffusion experiments, regarding textual inversion task and captioning task
A Bach music generator with Artificial Intelligence. This model is made by a VQ-VAE + Transformer (decoder-only). Sequences of midi 1 quarter length are compressed into 16 codebooks via VQ-VAE and …
Modeling Harmonic Complexity using two models of Conditional Variational Autoencoders - MSc. Thesis
Demucs Lightning: A PyTorch lightning version of Demucs with Hydra and Tensorboard features
A live speech recognition using Facebooks wav2vec 2.0 model.
Papers, repository and other data about anime or manga research. Please let me know if you have information that the list does not include.
Efficient neural networks for analog audio effect modeling
Neural style transfer in PyTorch.
Muzic: Music Understanding and Generation with Artificial Intelligence
Meetups data and resources archive for the London Audio & Music AI Meetup
Code and slides for the "Generating Sound with Neural Network" series on The Sound of AI Youtube channel.
Learning to ground explanations of affect for visual art.
Machine Learning for Audio Signals in Python
Source code for "MusCaps: Generating Captions for Music Audio" (IJCNN 2021)
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation
A Chord Detection and Chromagram Estimation Algorithm
Minimal class to download shared files from Google Drive.
Summarization Task using Bart and T5 models.
Use your webcam to identify gestures and trigger any script
Code for "Groove2Groove: One-Shot Music Style Transfer with Supervision from Synthetic Data"
Python package built to ease deep learning on graph, on top of existing DL frameworks.
This repository contains EmoBank, a large-scale text corpus manually annotated with emotion according to the psychological Valence-Arousal-Dominance scheme.
Multi-class sentiment analysis lstm, finetuned bert
A library to detect emotions from text using supervised learning techniques
Detects and recognizes various types of feelings through the expression of texts, such as anger, disgust, fear, happiness, sadness, and surprise.