semvec/pypi-packages huggingface dataset:
gym-collision-avoidance 0.0.3
Simulation environment for collision avoidance
Keywords: robotics planning gym rl

# gym-collision-avoidance

Updates:
- **2023-04-28:** Updated to be compatible with Python 3.10 and tensorflow 2. Corresponding v0.0.3 available on pypi as well, if you do not intend to modify the source code (`python -m pip install gym-collision-avoidance`)

<img src="docs/_static/combo.gif" alt="Agents spelling ``CADRL''">

This is the code associated with the following publications:

**Journal Version:** M. Everett, Y. Chen, and J. P. How, "Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning", IEEE Access Vol. 9, 2021, pp. 10357-10377. [10.1109/ACCESS.2021.3050338](http://doi.org/10.1109/ACCESS.2021.3050338), [Arxiv PDF](https://arxiv.org/abs/1910.11689)

**Conference Version:** M. Everett, Y. Chen, and J. P. How, "Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. [Arxiv PDF](https://arxiv.org/abs/1805.01956), [Link to Video](https://www.youtube.com/watch?v=XHoXkWLhwYQ)

This repo also contains the trained policy for the SA-CADRL paper (referred to as CADRL here) from the proceeding paper: Y. Chen, M. Everett, M. Liu, and J. P. How. “Socially Aware Motion Planning with Deep Reinforcement Learning.” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vancouver, BC, Canada, Sept. 2017. [Arxiv PDF](https://arxiv.org/abs/1703.08862)

If you're looking to train our GA3C-CADRL policy, please see [this repo](https://github.com/mit-acl/rl_collision_avoidance) instead.

---

### About the Code

Please see [the documentation](https://gym-collision-avoidance.readthedocs.io/en/latest/)!

### If you find this code useful, please consider citing:

```
@inproceedings{Everett18_IROS,
  address = {Madrid, Spain},
  author = {Everett, Michael and Chen, Yu Fan and How, Jonathan P.},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  date-modified = {2018-10-03 06:18:08 -0400},
  month = sep,
  title = {Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning},
  year = {2018},
  url = {https://arxiv.org/pdf/1805.01956.pdf},
  bdsk-url-1 = {https://arxiv.org/pdf/1805.01956.pdf}
}
```

or

```
@article{everett2021collision,
  title={Collision avoidance in pedestrian-rich environments with deep reinforcement learning},
  author={Everett, Michael and Chen, Yu Fan and How, Jonathan P},
  journal={IEEE Access},
  volume={9},
  pages={10357--10377},
  year={2021},
  publisher={IEEE}
}
```
audioprocessor 0.1.3
A Python package for recording, transcribing, and converting audio

AudioProcessor
==============

`AudioProcessor` is a Python class that provides functionality for recording audio, transcribing audio to text, and converting text to audio.

Class Methods
-------------

### `__init__(self, frame_duration_ms=30, sample_rate=16000, chunk_size=1024, vad_mode=2)`

Initialize the `AudioProcessor` class.

**Parameters:**

*   `frame_duration_ms`: Frame duration in milliseconds (default: 30 ms)
*   `sample_rate`: Audio sample rate (default: 16000 Hz)
*   `chunk_size`: Audio chunk size for processing (default: 1024)
*   `vad_mode`: Voice Activity Detection (VAD) mode (default: 2)

### `record_audio(self, output_file_path)`

Record audio from the microphone and save it to a .wav file.

**Parameters:**

*   `output_file_path`: Output file path for the recorded audio

### `audio_to_text(self, audio_file_path, **kwargs)`

Transcribe an audio file.

**Parameters:**

*   `audio_file_path`: Input audio file path
*   `**kwargs`: Keyword arguments for preprocessing and other options

**Returns:**

*   Transcribed text as a string

### `text_to_audio(self, text, audio_file_path, lang='en', volume=1.0, sample_rate=44100, bit_depth=16)`

Convert text to an audio file and play it.

**Parameters:**

*   `text`: Input text to convert to audio
*   `audio_file_path`: Output audio file path
*   `lang`: Language of the text (default: 'en')
*   `volume`: Volume level of the output audio (default: 1.0)
*   `sample_rate`: Audio sample rate (default: 44100 Hz)
*   `bit_depth`: Audio bit depth (default: 16)

Private Methods
---------------

These methods are used internally by the class and should not be called directly by the user.

### `_save_audio_to_file(self, audio, file_path)`

Save the audio data to a .wav file.

### `_process_audio_text(self, audio_text, **kwargs)`

Process the audio\_text and return the transcribed text.

### `_parse_recognition_response(self, response, show_all)`

Parse the response from the speech recognizer.

### `_process_recognized_audio(self, audio)`

Process the recognized audio and return the transcribed text.

### `_play_audio_file(self, audio_file_path, volume=1.0, normalize=False)`

Play the audio file with the specified volume and optional normalization.

### `_generate_frames(self, audio, sample_rate, frame_duration_ms)`

Generate audio frames from raw audio data.

### `_collect_voiced_segments(self, frames, vad, sample_rate, ratio=2)`

Collect voiced segments from audio frames using the Voice Activity Detection (VAD) algorithm.
CLAFX 0.1
For developers
Keywords: LegendsLIB


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```
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