Metadata-Version: 2.1
Name: speaker_verification_toolkit
Version: 0.0.2
Summary: A package designed to compose speaker verification systems
Home-page: https://github.com/Alohomora-team/speaker-verification-toolkit
Author: Alohomora Team
Author-email: alohomorafga@gmail.com
License: UNKNOWN
Description: # speaker-verification-toolkit
        This module contains some tools to make a simple speaker verification.
        
        You can download it with PyPI:
        ```r
        $ pip install speaker-verification-toolkit
        ```
        
        To import and use in your own projects:
        ```python
        import speaker_verification_toolkit.tools as svt
        
        #   svt.some_function(...)
        ```
        
        ## Usage
        ---
        ```python
        find_nearest_voice_data(voice_data_list, voice_sample)
        ```
        Find the nearest voice data based on this voice sample. Could be used to make the naive Accept/Reject decision.
        
        > ***voice_data_list***: a list containing all voices data from the dataset.
        >
        > ***voice_sample***: the voice sample reference.
        
        > ***returns***: the index of the element from voice_data_list that represents the nearest voice data.
        ---
        
        ```python
        compute_distance(sample1, sample3)
        ```
        Compute the distance between sample1 and sample2 using O(n) DTW algorithm
        
        > ***sample1***: the mfcc data extracted from the audio signal 1.
        >
        > ***sample2***: the mfcc data extracted from the audio signal 2.
        
        > ***returns***: Float number representing the minimum distance between sample1 and sample2.
        ---
        
        ```python
        extract_mfcc(signal_data, samplerate=16000, winlen=0.025, winstep=0.01)
        ```
        Compute MFCC features from an audio signal
        
        > ***signal***: the audio signal from which to compute features. Should be an N*1 array.
        >
        > ***samplerate***: the sample rate of the signal we are working with, in Hz.
        >
        > ***winlen***: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds).
        >
        > ***winstep***: the step between successive windows in seconds. Default is 0.01s (10 milliseconds).
        
        > ***returns***: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.
        ---
        
        ```python
        extract_mfcc_from_wav_file(path, samplerate=16000, winlen=0.025, winstep=0.01)
        ```
        Compute MFCC features from a wav file
        
        > ***path***: the wav file path to be open.
        >
        > ***samplerate***: the wanted sample rate, in Hz. Default is 16000. If you want no resampling fill this argument with None.
        >
        > ***winlen***: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds).
        >
        > ***winstep***: the step between successive windows in seconds. Default is 0.01s (10 milliseconds).
        
        > ***returns***: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.
        ---
        
        ```python
        rms_silence_filter(data, samplerate=16000, segment_length=None, threshold=0.001135)
        ```
        Cut off silence parts from the signal audio data. **Doesn't work with signals data affected by environment noise**.
        You would consider apply a noise filter before using this silence filter or make sure that environment noise is small enough to be considered as silence.
        
        >***data***: the audio signal data
        >
        >***samplerate***: if no segment_length is given, segment_length will be equals samplerate/100 (around 0.01 secs per segment).
        >
        >***segment_length***: the number of frames per segment. I.e. for a sample rate SR, a segment length equals SR/100 will represent a chunk containing 0.01 seconds of audio.
        >
        >***threshold***: the threshold value. Values less than or equal values will be cut off. The default value was defined at [1] (see the references).
        
        >***returns***: the param "data" without silence parts.
        
        # References
        
        [1] - Muhammad Asadullah & Shibli Nisar, "A SILENCE REMOVAL AND ENDPOINT DETECTION APPROACH FOR SPEECH PROCESSING", National University of Computer and Emerging Sciences, Peshawar
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
