Metadata-Version: 2.1
Name: pydrobert-speech
Version: 0.1.0
Summary: Speech processing with Python
Home-page: https://github.com/sdrobert/pydrobert-speech
Author: Sean Robertson
Author-email: sdrobert@cs.toronto.edu
License: Apache 2.0
Description: [![Build Status](https://travis-ci.com/sdrobert/pydrobert-speech.svg?branch=master)](https://travis-ci.com/sdrobert/pydrobert-speech)
        [![Documentation Status](https://readthedocs.org/projects/pydrobert-speech/badge/?version=latest)](https://pydrobert-speech.readthedocs.io/en/latest/?badge=latest)
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
        
        # pydrobert-speech
        
        This pure-python library allows for flexible computation of speech features.
        
        For example, given feature configuration called `fbanks.json`:
        
        ``` json
        {
          "name": "stft",
          "bank": "fbank",
          "frame_length_ms": 25,
          "include_energy": true,
          "pad_to_nearest_power_of_two": true,
          "window_function": "hanning",
          "use_power": true
        }
        ```
        
        You can compute triangular, overlapping filters like
        [Kaldi](http://kaldi-asr.org/) or [HTK](http://htk.eng.cam.ac.uk/) with the
        commands
        
        ``` python
        import json
        from pydrobert.speech import *
        # get the feature computer ready
        params = json.load(open('fbank.json'))
        computer = util.alias_factory_subclass_from_arg(compute.FrameComputer, params)
        # assume "signal" is a numpy float array
        feats = computer.compute_full(signal)
        ```
        
        If you plan on using a [PyTorch](https://pytorch.org) `DataLoader` or Kaldi
        tables in your ASR pipeline, you can compute all a corpus' features by
        using the commmands `signals-to-torch-feat-dir` (requires *pytorch* package)
        or `compute-feats-from-kaldi-tables` (requires *pydrobert-kaldi* package).
        
        This package can compute much more than f-banks, with many different
        permutations. Consult the documentation for a more in-depth discussion of how
        to use it.
        
        ## Documentation
        
        - [Latest](https://pydrobert-speech.readthedocs.io/en/latest/)
        - [Stable](https://pydrobert-speech.readthedocs.io/en/stable/)
        
        ## Installation
        
        _pydrobert-speech_ is available via both PyPI and Conda.
        
        ``` sh
        conda install -c sdrobert pydrobert-speech
        pip install pydrobert-speech
        pip install git+https://github.com/sdrobert/pydrobert-speech # bleeding edge
        ```
        
        ## Licensing and How to Cite
        
        Please see the [pydrobert page](https://github.com/sdrobert/pydrobert) for more
        details on how to cite this package.
        
        `util.read_signal` can read NIST SPHERE files. To do so, code was adapted from
        [NIST sph2pipe
        program](https://www.ldc.upenn.edu/language-resources/tools/sphere-conversion-tools)
        and put into `pydrobert.speech._sphere`. License information can be found in
        `LICENSE_sph2pipe`. Please note that the license only permits the use of their
        code to decode the "shorten" file type, not encode it.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Provides-Extra: vis
Provides-Extra: kaldi
Provides-Extra: pytorch
