semvec/pypi-packages huggingface dataset:
sonorus 0.1.1
Named after a spell in the Harry Potter Universe, where it      amplies the sound of a speaker. In muggles' terminology, this is a repository      of modules for audio and speech processing for and on top of machine learning      based tasks such as speech-to-text.
License: MIT
Keywords: deep learning,speech recognition,speech to text,language modelling

# sonorus
Named after a spell in the Harry Potter Universe, where it amplifies the sound of a speaker. In muggles' terminology, this is a repository of modules for audio and speech processing for and on top of machine learning based tasks such as speech-to-text.

## Getting Started:

### Installation:
*Install dependencies*

The repository has dependencies such as `kenlm`, `pyflashlight`, `fairseq`, `portaudio` and `libsndfile1` which needs to be installed before pip-installable modules

To install `kenlm` with python bindings, refer to the `kenlm` [github repository](https://github.com/kpu/kenlm).

To install `pyflashlight` with python bindings, refer to the [installation instructions](https://github.com/flashlight/flashlight/tree/master/bindings/python#installation). NOTE that the C++ build itself is not necessarily required for building python bindings. FURTHERMORE, `pyflashlight` will soon be made `pip`-installable via `pypi`.

To install `fairseq`, refer to [requirements and installations](https://github.com/pytorch/fairseq) from the `fairseq` github repository. NOTE that the current `pip`-installable `pypi` module is of version < 1.0 and hence installation from source is currently required. Once the `pypi` index is updated with the latest `fairseq` package, the same can be installed using `pip`.

`pyaudio` and `librosa`/`soundfile` have dependencies on `portaudio` and `libsndfile1`. If not using conda, make sure these are installed. For Ubuntu, the same can be installed by executing:

`sudo apt install portaudio19-dev libsndfile1`

Finally, install requirements by executing:

`pip install -r requirements.txt`

or install using conda in a conda environment.

*Finally, install the package using:*

`pip install sonorus`

### Environment set up:

*Note:* Environment set up is required while using Google Cloud's speech to text api. For this, Google Application Credentials is to be set as an environment variable by exporting e.g.: 
```
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/google-cloud-credentials.json
```

### Sample running instructions:

- Receives speech input from microphone and prints it on console using on-device Facebook's Wav2Vec2 model made available by Hugging Face..

`python3 examples/streaming-stt.py`

To modify the execution parameters of the on-device model such as providing GPU device index in case of availability, the program can be run as:

`python3 examples/streaming-stt.py --gpu_idx 0`

- For using Google cloud's speech to text execute:

`python3 examples/google-streaming-stt.py`
aiocoingecko 1.0.0
Asynchronous Python wrapper for the CoinGecko API
License: MIT
Keywords: async,api

# Asynchronous CoinGecko API wrapper
[![PyPi Version](https://img.shields.io/pypi/v/aiocoingecko.svg)](https://pypi.python.org/pypi/aiocoingecko/)

Python3 wrapper around the [CoinGecko](https://www.coingecko.com/) API (V3)

Features 100% API implementation and full Pythonic documentation.

### Installation

PyPI
```bash
pip install aiocoingecko
```

### Usage

```python
import asyncio

from aiocoingecko import AsyncCoinGeckoAPISession


async def main():
    async with AsyncCoinGeckoAPISession() as cg:
        print(await cg.ping())

        
asyncio.run(main())
```