Metadata-Version: 2.1
Name: disvoice-phonological
Version: 0.0.1
Summary: A pip installable version of the phonological function from  jcvazquezc's DisVoice library
Home-page: https://github.com/lurein/DisVoice
Author: Lurein Perera
Author-email: lureinperera@gmail.com
License: UNKNOWN
Description: ## Phonological features
        
        
        ### Phonological features
        
        ```sh
        phonological.py
        ```
        Compute phonological features from continuous speech files.
        
        18 descriptors are computed, bases on 18 different phonological classes from the phonet toolkit 
        https://phonet.readthedocs.io/en/latest/?badge=latest
        
        It computes the phonological log-likelihood ratio features from phonet
        
        Static or dynamic matrices can be computed:
        
        Static matrix is formed with 108 features formed with (18 descriptors) x (6 functionals: mean, std, skewness, kurtosis, max, min)
        
        Dynamic matrix is formed with the 18 descriptors computed for frames of 25 ms with a time-shift of 10 ms.
        
        
        #### Running
        
        Script is called as follows
        
        ```sh
        python phonological.py <file_or_folder_audio> <file_features> <static (true or false)> <plots (true or false)> <format (csv, txt, npy, kaldi, torch)>
        ```
        
        #### Examples:
        
        Extract features in the command line
        
        
        ```sh
        python phonological.py "../audios/001_ddk1_PCGITA.wav" "phonologicalfeaturesAst.txt" "true" "true" "txt"
        python phonological.py "../audios/001_ddk1_PCGITA.wav" "phonologicalfeaturesUst.csv" "true" "true" "csv"
        python phonological.py "../audios/001_ddk1_PCGITA.wav" "phonologicalfeaturesUdyn.pt" "false" "true" "torch"
        
        python phonological.py "../audios/" "phonologicalfeaturesst.txt" "true" "false" "txt"
        python phonological.py "../audios/" "phonologicalfeaturesst.csv" "true" "false" "csv"
        python phonological.py "../audios/" "phonologicalfeaturesdyn.pt" "false" "false" "torch"
        python phonological.py "../audios/" "phonologicalfeaturesdyn.csv" "false" "false" "csv"
        
        KALDI_ROOT=/home/camilo/Camilo/codes/kaldi-master2
        export PATH=$PATH:$KALDI_ROOT/src/featbin/
        python phonological.py "../audios/001_ddk1_PCGITA.wav" "phonologicalfeaturesddk1dyn" "false" "false" "kaldi"
        
        python phonological.py "../audios/" "phonologicalfeaturesdyn" "false" "false" "kaldi"
        ```
        
        Extract features directly in Python
        
        
        ```
        phonological=Phonological()
        file_audio="../audios/001_ddk1_PCGITA.wav"
        features1=phonological.extract_features_file(file_audio, static=True, plots=True, fmt="npy")
        features2=phonological.extract_features_file(file_audio, static=True, plots=True, fmt="dataframe")
        features3=phonological.extract_features_file(file_audio, static=False, plots=True, fmt="torch")
        phonological.extract_features_file(file_audio, static=False, plots=False, fmt="kaldi", kaldi_file="./test")
        
        path_audio="../audios/"
        features1=phonological.extract_features_path(path_audio, static=True, plots=False, fmt="npy")
        features2=phonological.extract_features_path(path_audio, static=True, plots=False, fmt="csv")
        features3=phonological.extract_features_path(path_audio, static=False, plots=True, fmt="torch")
        phonological.extract_features_path(path_audio, static=False, plots=False, fmt="kaldi", kaldi_file="./test.ark")
        ```
        
        [Jupyter notebook](https://github.com/jcvasquezc/DisVoice/blob/master/notebooks_examples/phonological_features.ipynb)
        
        #### Results:
        
        
        
        Phonological analysis
        !![Image](https://github.com/jcvasquezc/DisVoice/blob/master/images/phonological1.png?raw=true)
        !![Image](https://github.com/jcvasquezc/DisVoice/blob/master/images/phonological2.png?raw=true)
        !![Image](https://github.com/jcvasquezc/DisVoice/blob/master/images/phonological3.png?raw=true)
        
        
        #### References
        
        [[1]](https://gita.udea.edu.co/uploads/1405-Phonet.pdf) Vásquez-Correa, J. C., Klumpp, P., Orozco-Arroyave, J. R., & Nöth, E. (2019). Phonet: A Tool Based on Gated Recurrent Neural Networks to Extract Phonological Posteriors from Speech. In INTERSPEECH (pp. 549-553).
        
        [2] Diez, M., Varona, A., Penagarikano, M., Rodriguez-Fuentes, L. J., & Bordel, G. (2014). On the projection of PLLRs for unbounded feature distributions in spoken language recognition. IEEE Signal Processing Letters, 21(9), 1073-1077.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
