https://github.com/desh2608/cacgmm
GPU-based CACGMM trainer in CuPy
https://github.com/desh2608/cacgmm
blind-source-separation
Last synced: 2 months ago
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GPU-based CACGMM trainer in CuPy
- Host: GitHub
- URL: https://github.com/desh2608/cacgmm
- Owner: desh2608
- License: mit
- Created: 2022-03-28T23:09:29.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-04-11T17:08:02.000Z (about 3 years ago)
- Last Synced: 2025-02-25T00:30:28.619Z (3 months ago)
- Topics: blind-source-separation
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GPU-based CACGMM trainer in CuPy
This package adapts the complex angular GMM model and trainer from [pb_bss](https://github.com/fgnt/pb_bss)
using [CuPy](https://github.com/cupy/cupy) for accelerated inference on the GPU.
At the moment, it is meant to be used with the [GSS](https://github.com/desh2608/gss) toolkit, but
it can also be used as a general CACGMM trainer tool.
## Installation
```bash
> pip install cupy-cuda102 # modify according to your CUDA version (https://docs.cupy.dev/en/stable/install.html#installing-cupy)
> pip install cacgmm-gpu
```
## Usage
```python
from cacgmm.cacgmm_trainer import CACGMMTrainer
import cupy as cp
source_activity = cp.random.rand(2, 1000)
source_activity = source_activity / cp.sum(initialization, keepdims=True, axis=0)
initialization = cp.repeat(source_activity[None, ...], 513, axis=0) # F x K x T
source_active_mask = cp.repeat(source_activity[None, ...], 513, axis=0)
X = cp.random.rand(4, 1000, 513) # D x T x F
cacGMM = CACGMMTrainer()
cur = cacGMM.fit(
y=X.T,
initialization=initialization,
iterations=10,
source_activity_mask=source_active_mask,
)
affiliation = cur.predict(X.T, source_activity_mask=source_active_mask) #
posterior = affiliation.transpose(1, 2, 0) # K x T x F
```