semvec/pypi-packages huggingface dataset:
rtvamp 0.3.1
Vamp plugin host for real-time audio feature analysis
License: MIT
Requires Python: >=3.6
Keywords: Vamp,plugin,audio,sound,music,analysis,feature extraction,real-time

# rtvamp

Python bindings of Vamp plugin host for real-time audio feature analysis.

- [API documentation Python](https://lukasberbuer.github.io/rt-vamp-plugin-sdk/python)
- [API documentation C++](https://lukasberbuer.github.io/rt-vamp-plugin-sdk)
- [Examples](https://github.com/lukasberbuer/rt-vamp-plugin-sdk/tree/master/python/examples)

## Installation

Install the latest version from PyPI:

```shell
$ pip install rtvamp
```

## Simple example

Using [librosa](https://librosa.org) to read an audio file and the [spectral roll-off plugin](https://github.com/lukasberbuer/rt-vamp-plugin-sdk/tree/master/examples/plugin) for analysis:

```python
>>> import rtvamp
>>> import librosa

>>> rtvamp.list_plugins()
['example-plugin:rms', 'example-plugin:spectralrolloff']

>>> rtvamp.get_plugin_metadata("example-plugin:spectralrolloff")
PluginMetadata(identifier='spectralrolloff', name='Spectral roll-off', description='', maker='LB', copyright='MIT', plugin_version=1, input_domain='frequency', parameter_descriptors=[{'identifier': 'rolloff', 'name': 'Roll-off factor', 'description': 'Some random parameter', 'unit': '', 'default_value': 0.8999999761581421, 'min_value': 0.0, 'max_value': 1.0, 'quantize_step': None, 'value_names': []}], output_descriptors=[{'identifier': 'frequency', 'name': 'Roll-off frequency', 'description': 'Frequency below which n% of the total energy is concentrated', 'unit': 'Hz', 'bin_count': 1, 'bin_names': [], 'has_known_extents': False, 'min_value': 0.0, 'max_value': 0.0, 'quantize_step': None}])

>>> y, sr = librosa.load(librosa.ex("trumpet"))
>>> t_rolloff, rolloff = rtvamp.compute_features(
...     y, sr,
...     plugin="example-plugin:spectralrolloff",
...     blocksize=256,
...     parameter={"rolloff": 0.5},
... )
>>> rolloff
array([[1291.992188, 1291.992188, 1722.656250, ..., 5684.765625,
        5598.632812, 6459.960938]], dtype=float32)
```
autort-swarms 0.0.6
AutoRT - Pytorch
License: MIT
Requires Python: >=3.6,<4.0
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# AutoRT
![AutoRTImage](autort.png)
Implementation of AutoRT: "AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents". This repo will implement the multi agent system that transforms a scene into a list of ranked and priortized tasks for an robotic action model to execute. This is an very effective setup that I personally believe is the future for swarming robotic foundation models!

This project will be implemented using Swarms, for the various llms and use the official RT-1 as the robotic action model.

[PAPER LINK](https://auto-rt.github.io/static/pdf/AutoRT.pdf)

## Install
`$ pip3 install autort-swarms`


## Usage


### AutoRTAgent
A single AutoRT agent that: analyzes a scene using visual COT -> generate tasks -> filter tasks -> execute it with a robotic transformer.
```python
# Import necessary modules
import os
from autort import AutoRTSwarm, AutoRTAgent

# Set the OpenAI API key
openai_api_key = os.getenv("OPENAI_API_KEY")

# Define a list of AutoRTAgent instances
agents = [
    AutoRTAgent(openai_api_key, max_tokens=1000),
    AutoRTAgent(openai_api_key, max_tokens=1000),
]

# Create an instance of AutoRTSwarm with the agents and datastore
autort_swarm = AutoRTSwarm(agents)

# Run the AutoRTSwarm with the given inputs
autort_swarm.run(
    "There is a bottle on the table.",
    "https://i.imgur.com/2qY9f8U.png",
)
```


### AutoRTSwarm
A team of AutoRT agents where you can plug in and play any number of `AutoRTAgents` with customization. First, the task will be routed to each agent and then all of their outputs will be saved.
```python
# Import necessary modules
import os
from autort import AutoRTSwarm, AutoRTAgent

# Set the OpenAI API key
openai_api_key = os.getenv("OPENAI_API_KEY")

# Define a list of AutoRTAgent instances
agents = [
    AutoRTAgent(openai_api_key, max_tokens=1000),
    AutoRTAgent(openai_api_key, max_tokens=1000),
]

# Create an instance of AutoRTSwarm with the agents and datastore
autort_swarm = AutoRTSwarm(agents)

# Run the AutoRTSwarm with the given inputs
autort_swarm.run(
    "There is a bottle on the table.",
    "https://i.imgur.com/2qY9f8U.png",
)
```

## Citation
```bibtex
@inproceedings{
    anonymous2023autort,
    title={Auto{RT}: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents},
    author={Anonymous},
    booktitle={Submitted to The Twelfth International Conference on Learning Representations},
    year={2023},
    url={https://openreview.net/forum?id=xVlcbh0poD},
    note={under review}
}

```


# License
MIT



# Todo
- [ ] Implement a run method into `AutoRTSwarm` that runs all the agents with APIs.
- [ ] Make it able to send commands to a certain agent using the swarm network.
- [ ] Send a task to all agents in the swarm network
- [ ]