vector-quantized-timbre

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vector-quantized-timbre

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Implementation/Interpretation of Vector Quantized Timbre Representation (Bitton et al 2020)

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# Vector-Quantized Timbre Representation (Bitton et al. 2020)
Implementation/exploration of the paper [Vector-Quantized Timbre Representation](https://arxiv.org/pdf/2007.06349.pdf)

Developed with [Adán Benito](https://github.com/adanlbenito) early in 2021

* This repository contains a Pytorch Lightning framework to train a model similar to the one described in the paper. 
* The main difference is the use of an exponential moving-average (EMA) codebook
* A conventional VQ codebook caused the codebook to collapse `perplexity = 1`.

## Setup
```
pip install -r requirements.txt
pip install -e .
```

## Data preparation
Data splits and preprocessing are performed for the URMP western musical instrument dataset
To split the URMP dataset into 3 second numpy files for efficient loading:
`python scripts/urmp_numpy_segments.py <path_to_URMP> <numpy_out_dir>`

Then, set the `URMP_DATA_DIR` variable in `gin_configs/vq_timbre.gin` to `<numpy_out_dir>`

## Training

`python scripts/run_train.py`

* Most hyperparameters are configured with `gin-config`
* set `URMP.instr_ids= [<instrument_id>]` to train for a target musical instrument
* To log to wandb, enable `lightning_run.logger = True` in `gin_configs/vq_timbre.gin`

## Evaluation
* download model checkpoint for a model trained on Violin: `https://drive.google.com/file/d/1fJ9bkM5eAuCNz4DClfeTm6b-IKjkTs0y/view?usp=sharing`  
* put it in `/checkpoints`
* `jupyter notebook`
* see `notebooks/eval_model.ipynb` for simple timbre transfer and feature-based synthesis

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