[](https://github.com/sensein/senselab/actions/workflows/main-branch-status.yaml)
[](https://codecov.io/gh/sensein/senselab)
[](https://github.com/astral-sh/ruff)
[](https://pypi.org/project/senselab/)
[](https://pypi.org/project/senselab)
[](https://opensource.org/licenses/Apache-2.0)
[](https://sensein.github.io/senselab)
Welcome to ```senselab```! This is a Python package for streamlining the processing and analysis of behavioral data, such as voice and speech patterns, with robust and reproducible methodologies.
**Caution:**: this package is still under development and may change rapidly over the next few weeks.
## Installation
Install this package via:
```sh
pip install senselab
```
Or get the newest development version via:
```sh
pip install git+https://github.com/sensein/senselab.git
```
## Quick start
```Python
from senselab.audio.data_structures import Audio
from senselab.audio.tasks.preprocessing import resample_audios
audio1 = Audio.from_filepath('path_to_audio_file.wav')
print("The original audio has a sampling rate of {} Hz.".format(audio1.sampling_rate))
[audio1] = resample_audios([audio1], resample_rate=16000)
print("The resampled audio has a sampling rate of {} Hz.".format(audio1.sampling_rate))
```
For more detailed information, check out our [**Getting Started Tutorial**](https://github.com/sensein/senselab/blob/main/tutorials/audio/getting_started.ipynb).
## Why should I use senselab?
- **Modular Design**: Easily integrate or use standalone transformations for flexible data manipulation.
- **Pre-built Pipelines**: Access pre-configured pipelines to reduce setup time and effort.
- **Reproducibility**: Ensure consistent and verifiable results with fixed seeds and version-controlled steps.
- **Easy Integration**: Seamlessly fit into existing workflows with minimal configuration.
- **Extensible**: Modify and contribute custom transformations and pipelines to meet specific research needs.
- **Comprehensive Documentation**: Detailed guides, examples, and documentation for all features and modules.
- **Performance Optimized**: Efficiently process large datasets with optimized code and algorithms.
- **Interactive Examples**: Jupyter notebooks provide practical examples for deriving insights from real-world datasets.
## Contributing
Please see [**CONTRIBUTING.md**](https://github.com/sensein/senselab/blob/main/CONTRIBUTING.md) before contributing.
To find out what's currently in progress, please check the [**Project Board**](https://github.com/orgs/sensein/projects/45).
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