HS-TasNet 0.1.38

HS TasNet

HS-TasNet

Implementation of HS-TasNet, "Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet", proposed by the research team at L-Acoustics

Install

$ pip install HS-TasNet

Usage

# model

from hs_tasnet import HSTasNet

model = HSTasNet()

# the musdb dataset

import musdb
mus = musdb.DB(download = True)

# trainer

from hs_tasnet import Trainer

trainer = Trainer(
    model,
    dataset = mus,
    batch_size = 2,
    max_steps = 2,
    cpu = True,
)

trainer()

# after much training
# inferencing

model.sounddevice_stream(
    duration_seconds = 2,
    return_reduced_sources = [0, 2]
)

# or from the exponentially smoothed model (in the trainer)

trainer.ema_model.sounddevice_stream(...)

# or you can load from a specific checkpoint

model.load('./checkpoints/path.to.desired.ckpt.pt')
model.sounddevice_stream(...)

Training script

First make sure dependencies are there by running

$ sh install.sh

Then make sure uv is installed

$ pip install uv

Finally, and make sure the loss goes down

$ uv run train.py

For distributed training, you just need to run accelerate config first, courtesy of accelerate from 🤗 but single machine is fine too

Experiment tracking

To enable online experiment monitoring / tracking, you need to have wandb installed and logged in

$ pip install wandb && wandb login

Then

$ uv run train.py --use-wandb

Test

$ uv pip install '.[test]' --system

Then

$ pytest tests

Sponsors

This open sourced work is sponsored by Sweet Spot

Citations

@misc{venkatesh2024realtimelowlatencymusicsource,
    title    = {Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet}, 
    author   = {Satvik Venkatesh and Arthur Benilov and Philip Coleman and Frederic Roskam},
    year     = {2024},
    eprint   = {2402.17701},
    archivePrefix = {arXiv},
    primaryClass = {eess.AS},
    url      = {https://arxiv.org/abs/2402.17701}, 
}