semvec/pypi-packages huggingface dataset:
fw-gear-rtp2-pipeline 0.2.3
Tract and metrics generation for  Diffusion MRI
License: MIT
Requires Python: <4.0,>=3.8
Keywords: Flywheel, Gears

# rtp2-pipeline

rtp2-pipeline is a Flywheel Gear that contains a multi-step pipeline designed to run
MRtrix3, LiFE and SIFT, ET, and obtains tracts and metrics. Necessary ROIs are created
in the freesurferator gear and the DWI preprocessing is done in rtp2-preproc.
MRTrix3 + Ensemble Tractography + LiFE generate a connectome which is then run
through a MRtrix3 based tracking system. Then tract profiles of tissue properties
for major fiber tracts in the brain are generated. It is possible to create ROI2ROI
tracts as well. Required inputs are (1) DWI NIfTI image, (2) BVEC file, (3) BVAL file,
(4) Anatomical NIfTI file, (5) fs.zip with the ROIs in the individual space generated
by the freesurferator gear, and (6) tractparams.csv, a comma-separated file specifying
the details of all the fasciculus to be tracked. If no tractparams is passed, it
will provide DTI metrics per ROI. If a qMAP file is passed, it will provide metrics
at the ROI and/or tract level as well.

## Documentation

The documentation of the gear can be found
[here](https://flywheel-io.gitlab.io/scientific-solutions/gears/rtp2-pipeline/index.html).

## Contributing

Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file for information on how to
contribute to the gear.
bigvganinference 0.0.3
An unofficial minimal package for using BigVGAN at inference time
License: MIT
Requires Python: <4.0,>=3.10

# BigVGAN Inference
An unofficial minimal package for using BigVGAN at inference time

[![PyPI version](https://img.shields.io/pypi/v/bigvganinference)](https://pypi.org/project/bigvganinference/)
![License](https://img.shields.io/pypi/l/bigvganinference)
![Python versions](https://img.shields.io/pypi/pyversions/bigvganinference)

## Installation

```bash
pip install bigvganinference
```

or install from source:

```bash
git clone https://github.com/thunn/BigVGANInference.git
cd BigVGANInference
poetry install
```

## Usage

Loading model is as simple as:
```python
from bigvganinference import BigVGANInference

# -- model loading ---

# model is loaded, set to eval and weight norm is removed
model = BigVGANInference.from_pretrained(
    'nvidia/BigVGAN-V2-44KHZ-128BAND-512X', use_cuda_kernel=False
)

# also supports loading from local directory
model = BigVGANInference.from_pretrained(
    "path/to/local/model", use_cuda_kernel=False
)

# --- usage example ---

path_to_audio = "path/to/audio.wav"
wav, sr = librosa.load(path_to_audio, sr=model.h.sampling_rate, mono=True)

# get mel spectrogram using bigvgan's implementation
mel = model.get_mel_spectrogram(wav)

# generate waveform from mel
# note: torch.inference_mode() is used internally
output_audio = model(input_mel)
```

See the [example](https://github.com/thunn/BigVGANInference/blob/main/example/inference.py) for full usage example.

## Acknowledgements
This is an unofficial implementation based on [original BigVGAN repository](https://github.com/NVIDIA/BigVGAN).

## License
This project is licensed under the MIT License. See the [LICENSE](https://github.com/thunn/BigVGANInference/blob/main/LICENSE) file for details.