semvec/pypi-packages huggingface dataset:
dtcm 0.2.20
Declassed Tor Circuits Manager
Requires Python: >=3.6


==============================
Declassed Tor Circuits Manager
==============================

Maintain the specified number of circuits and attach streams to them in a round-robin manner.

How to use:

.. code:: python

    import dtcm

    config = {
        'tor': {
            'address': '127.0.0.1',
            'socks_port': 9100,
            'control_port': 9051,
            'password': '12345'
        },
        'circuits': {
        'hops': 2,  # Although this can be 1, exit-only circuits
                    # do not work, need a guard relay.
                    # So 2 is the minimum.
        'count': 100
            stable_only: False  # only use relays with Stable flag
            fast_only: False    # only use relays with Fast flag
        }
    }

    dtcm.init(config)
    # dtcm.logger.setLevel(logging.DEBUG)
    # your code here, a crawler, right?
    # or just a sleep call if this is a standalone process
    dtcm.fini()

:copyright: Copyright 2022 AXY axy@declassed.art
:license: BSD, see LICENSE for details.
interval-timer 1.0.0
interval-timer is a Python package that enables iterating over a sequence of regular time intervals with high precision.
Requires Python: >=3.10,<4.0

# interval-timer

**interval-timer** is a Python package that enables iterating over a sequence of regular time intervals with high precision.

## Installation

Install from [PyPI](https://pypi.org/project/interval-timer/) via:

```shell
pip install interval-timer
```

## Usage

Basic usage is as follows:

```python
from interval_timer import IntervalTimer

for interval in IntervalTimer(0.5):
    print(interval)
    
    # Execute code exactly every half second here
    ...
```

Output:

```
Interval(index=0, time=0.000, lag=0.000)
Interval(index=1, time=0.500, lag=0.000)
Interval(index=2, time=1.000, lag=0.000)
...
```

For more usage examples see [examples/](examples).

## Description

`IntervalTimer` is an iterator object that returns `Interval` objects at regular time intervals. Code can then be executed upon each time interval, and the intervals will stay synchronised even when the code execution time is non-zero.

`IntervalTimer` is a more precise replacement for a loop that contains a wait. The following code:
    
```python
from time import sleep

# Iterates approximately every half second
for i in range(5):
    print(i)
    sleep(0.5)
```

can be replaced with:

```python
from interval_timer import IntervalTimer

# Iterates exactly every half second
for interval in IntervalTimer(0.5, stop=5):
    print(interval)
```

**interval-timer** uses [perf_counter](https://docs.python.org/3/library/time.html#time.perf_counter) under the hood to obtain high precision timing. It will not suffer from drift over long time periods.

If an interval iteration is delayed due to slow code execution, then future intervals will still be synchronised to absolute time if they're given time to catch up. The caller can see if synchronisation has been temporarily lost by checking if the `Interval` object's `lag` attribute returns a non-zero value (see the [lag.py](examples/lag.py) example).

## Timing diagram

![Timing diagram](timing.svg)

The above timing diagram shows that each returned `Interval` object has the following attributes:
- `time`: the nominal start time of the interval. Always has equal value to the `end_time` value of the previous interval.
- `buffer`: the length of time before the interval start time that the interval was requested. The minimum buffer is zero.
- `lag`: The length of time after the interval start time that the interval was requested. The minimum lag is zero. If the lag is non-zero, then the code executed within the previous interval took longer than the interval period, which is generally undesirable.
wav-autoencoder 0.0.2
WavAutoencoder: A Self-Supervised Framework for Learning Audio Representations
Requires Python: >=3.6

## WavAutoEncoder 

This repository is an implementation of  autoencoder based on Transformer architecture for self supervised speech representation . 

## Install

```bash
$ pip install wav-autoencoder
```


## Usage

Simple example for using the model

```python
import torch
from wav_autoencoder import WavAutoEncoderConfig ,  WavAutoEncoderModel 

>> config = WavAutoEncoderConfig()
>> model = WavAutoEncoderModel(config)
>> wav_signal = torch.randn(2, 1, 16000)
>> outputs = model(wav_signal)
>> print(outputs.shape)
```

## Todo

## Citations

```bibtex
@misc{
  title  = {WavAutoencoder: A Self-Supervised Framework for Learning Audio Representations},
  author = {Abdou Aziz DIOP},
  year   = {2022}
}
```