semvec/pypi-packages huggingface dataset:
phonetisaurus 0.3.0
Python wrapper for phonetisaurus grapheme to phoneme tool
Requires Python: >=3.7

# Phonetisaurus for Python

Python wrapper for the excellent [phonetisaurus](https://github.com/AdolfVonKleist/Phonetisaurus) grapheme to phoneme tool ([license](https://github.com/AdolfVonKleist/Phonetisaurus/blob/master/LICENSE)).

Includes pre-built binaries for:

* `x86_64` - desktop/laptop/server (64-bit)
* `armv6l` - Raspberry Pi 0/1
* `armv7l` - Raspberry Pi 2/3/4 (32-bit)
* `aarch64` - Raspberry Pi 3/4 (64-bit)

## Requirements

* Python 3.7+
* Linux
    * Tested with Debian Buster

## Installing

For `x86_64` systems:

```sh
$ pip install phonetisaurus
```

For Raspberry Pi, see [Releases](https://github.com/rhasspy/phonetisaurus-pypi/releases) for compatible wheels:

* Raspberry Pi 0/1
    * `phonetisaurus-<VERSION>-py3-none-linux_armv6l.whl`
* Raspberry Pi 2/3/4 (32-bit)
    * `phonetisaurus-<VERSION>-py3-none-linux_armv7l.whl`
* Raspberry Pi 3/4 (64-bit)
    * `phonetisaurus-<VERSION>-py3-none-linux_aarch64.whl`

## Training

Assuming you have a lexicon formatted like [the CMU pronouncing dictionary](https://github.com/cmusphinx/cmudict):

```
word1 phoneme1 phoneme2 ...
word2 phoneme1 phoneme2 phoneme3 ...
```

saved to `lexicon.dict` run:

```sh
$ phonetisaurus train --model /path/to/write/g2p.fst /path/to/lexicon.dict
```

You may supply more than one lexicon.

See `phonetisaurus train --help` for more options.

## Predicting

```sh
$ phonetisaurus predict --model /path/to/g2p.fst word1 word2 ...
```

If no words are provided on the command line, they will be read line-by-line from standard in.

You may optionally supply one or more `--lexicon /path/to/lexicon.dict` arguments to avoid guessing pronunciations for known words.

See `phonetisaurus predict --help` for more options.
ruconceptnet 0.0.1
Russian part of ConceptNet5.7 graph, fast access to relations.
Requires Python: >=3.5
Keywords: nlp,knowledge graph,russian language

# RussianConceptNet
ConceptNet 5.7 (Russian part) extraction scripts + fast access to relations. Note: a simple modification of the 
preprocessing script allows to build a queryable graph of **any other subset of ConceptNet**.

### Preparations

Please see the `prepare_data.sh` script. We get the Russian-Russian pairs of nodes with simple `grep` and build
a 3-dimensional array (source, target, relation) stored as a single sparse SciPy matrix.

### Usage

```
>>> from conceptnet import ConceptNet
>>> cn = ConceptNet()
>>> cn.get_targets("алкоголь")
[('этиловый_спирт', {'Synonym'}), ('спиртной_напиток', {'Synonym'}), ('алкогольный', {'RelatedTo'}), ('алкоголик', {'RelatedTo'}), ('спирт', {'Synonym'}), ('алкоголизация', {
'RelatedTo'})]

>>> cn.get_sources("йога")
[('йоги', {'FormOf'}), ('йогу', {'FormOf'}), ('йогический', {'RelatedTo'}), ('йогою', {'FormOf'}), ('йогой', {'FormOf'}), ('йог', {'RelatedTo'}), ('йоге', {'FormOf'})]

>>> cn.check_pair("человек", "зверь")
(['DistinctFrom'], [])

>>> cn.check_pair("зверь", "человек")
([], ['DistinctFrom'])
```

## Citing

Please do not forget to cite the ConceptNet5 paper:
```
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." In proceedings of AAAI 31.
```

Citing the repository is greatly appreciated as well, if you use this work.

## License

The code is released under the MIT license (please see the `LICENSE` file).

This work includes a subset data from ConceptNet 5, which was compiled by the
Commonsense Computing Initiative. ConceptNet 5 is freely available under
the Creative Commons Attribution-ShareAlike license (CC BY SA 3.0) from
http://conceptnet.io.

The included data was created by contributors to Commonsense Computing
projects, contributors to Wikimedia projects, DBPedia, OpenCyc, Games
with a Purpose, Princeton University's WordNet, Francis Bond's Open
Multilingual WordNet, and Jim Breen's JMDict.

The complete data in ConceptNet is available under the Creative Commons Attribution-ShareAlike 4.0 license.

For more details, please see ["Copying and sharing ConceptNet"](https://github.com/commonsense/conceptnet5/wiki/Copying-and-sharing-ConceptNet).