| Name | HS-TasNet JSON |
| Version |
0.2.29
JSON |
| download |
| home_page | None |
| Summary | HS TasNet |
| upload_time | 2025-09-08 23:39:02 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025 Phil Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
artificial intelligence
deep learning
music separation
real time
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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<img src="./fig1.png" width="350px"></img>
## HS-TasNet
Implementation of [HS-TasNet](https://arxiv.org/abs/2402.17701), "Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet", proposed by the research team at L-Acoustics
## Install
```bash
$ pip install HS-TasNet
```
## Usage
```python
import torch
from hs_tasnet import HSTasNet
model = HSTasNet()
audio = torch.randn(1, 2, 204800) # ~5 seconds of stereo
separated_audios, _ = model(audio)
assert separated_audios.shape == (1, 4, 2, 204800) # second dimension is the separated tracks
```
With the `Trainer`
```python
# model
from hs_tasnet import HSTasNet, Trainer
model = HSTasNet()
# trainer
trainer = Trainer(
model,
dataset = None, # add your in-house Dataset
concat_musdb_dataset = True, # concat the musdb dataset automatically
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(...)
# to load an HS-TasNet from any of the saved checkpoints, without having to save its hyperparameters, just run
model = HSTasNet.init_and_load_from('./checkpoints/path.to.desired.ckpt.pt')
```
## Training script
First make sure dependencies are there by running
```shell
$ sh scripts/install.sh
```
Then make sure `uv` is installed
```shell
$ pip install uv
```
Finally run the following to train a newly initialized model on a small subset of MusDB, and make sure the loss goes down
```shell
$ uv run train.py
```
For distributed training, you just need to run `accelerate config` first, courtesy of [`accelerate` from 🤗](https://huggingface.co/docs/accelerate/en/index) but single machine is fine too
## Experiment tracking
To enable online experiment monitoring / tracking, you need to have `wandb` installed and logged in
```shell
$ pip install wandb && wandb login
```
Then
```shell
$ uv run train.py --use-wandb
```
To wipe the previous checkpoints and evaluated results, append `--clear-folders`
## Test
```shell
$ uv pip install '.[test]' --system
```
Then
```shell
$ pytest tests
```
## Sponsors
This open sourced work is sponsored by [Sweet Spot](https://github.com/sweetspotsoundsystem)
## Citations
```bibtex
@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},
}
```
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"description": "<img src=\"./fig1.png\" width=\"350px\"></img>\n\n## HS-TasNet\n\nImplementation of [HS-TasNet](https://arxiv.org/abs/2402.17701), \"Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet\", proposed by the research team at L-Acoustics\n\n## Install\n\n```bash\n$ pip install HS-TasNet\n```\n\n## Usage\n\n```python\nimport torch\nfrom hs_tasnet import HSTasNet\n\nmodel = HSTasNet()\n\naudio = torch.randn(1, 2, 204800) # ~5 seconds of stereo\n\nseparated_audios, _ = model(audio)\n\nassert separated_audios.shape == (1, 4, 2, 204800) # second dimension is the separated tracks\n```\n\nWith the `Trainer`\n\n```python\n# model\n\nfrom hs_tasnet import HSTasNet, Trainer\n\nmodel = HSTasNet()\n\n# trainer\n\ntrainer = Trainer(\n model,\n dataset = None, # add your in-house Dataset\n concat_musdb_dataset = True, # concat the musdb dataset automatically\n batch_size = 2,\n max_steps = 2,\n cpu = True,\n)\n\ntrainer()\n\n# after much training\n# inferencing\n\nmodel.sounddevice_stream(\n duration_seconds = 2,\n return_reduced_sources = [0, 2]\n)\n\n# or from the exponentially smoothed model (in the trainer)\n\ntrainer.ema_model.sounddevice_stream(...)\n\n# or you can load from a specific checkpoint\n\nmodel.load('./checkpoints/path.to.desired.ckpt.pt')\nmodel.sounddevice_stream(...)\n\n# to load an HS-TasNet from any of the saved checkpoints, without having to save its hyperparameters, just run\n\nmodel = HSTasNet.init_and_load_from('./checkpoints/path.to.desired.ckpt.pt')\n\n```\n\n## Training script\n\nFirst make sure dependencies are there by running\n\n```shell\n$ sh scripts/install.sh\n```\n\nThen make sure `uv` is installed\n\n```shell\n$ pip install uv\n```\n\nFinally run the following to train a newly initialized model on a small subset of MusDB, and make sure the loss goes down\n\n```shell\n$ uv run train.py\n```\n\nFor distributed training, you just need to run `accelerate config` first, courtesy of [`accelerate` from \ud83e\udd17](https://huggingface.co/docs/accelerate/en/index) but single machine is fine too\n\n## Experiment tracking\n\nTo enable online experiment monitoring / tracking, you need to have `wandb` installed and logged in\n\n```shell\n$ pip install wandb && wandb login\n```\n\nThen\n\n```shell\n$ uv run train.py --use-wandb\n```\n\nTo wipe the previous checkpoints and evaluated results, append `--clear-folders`\n\n## Test\n\n```shell\n$ uv pip install '.[test]' --system\n```\n\nThen\n\n```shell\n$ pytest tests\n```\n\n## Sponsors\n\nThis open sourced work is sponsored by [Sweet Spot](https://github.com/sweetspotsoundsystem)\n\n## Citations\n\n```bibtex\n@misc{venkatesh2024realtimelowlatencymusicsource,\n title = {Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet}, \n author = {Satvik Venkatesh and Arthur Benilov and Philip Coleman and Frederic Roskam},\n year = {2024},\n eprint = {2402.17701},\n archivePrefix = {arXiv},\n primaryClass = {eess.AS},\n url = {https://arxiv.org/abs/2402.17701}, \n}\n```\n",
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