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        "author": "Alohomora Team",
        "author_email": "alohomorafga@gmail.com",
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            "Programming Language :: Python :: 3"
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        "description": "# speaker-verification-toolkit\nThis module contains some tools to make a simple speaker verification.\n\nYou can download it with PyPI:\n```r\n$ pip install speaker-verification-toolkit\n```\n\nTo import and use in your own projects:\n```python\nimport speaker_verification_toolkit.tools as svt\n\n#   svt.some_function(...)\n```\n\n## Usage\n---\n```python\nfind_nearest_voice_data(voice_data_list, voice_sample)\n```\nFind the nearest voice data based on this voice sample. Could be used to make the naive Accept/Reject decision.\n\n> ***voice_data_list***: a list containing all voices data from the dataset.\n>\n> ***voice_sample***: the voice sample reference.\n\n> ***returns***: the index of the element from voice_data_list that represents the nearest voice data.\n---\n\n```python\ncompute_distance(sample1, sample3)\n```\nCompute the distance between sample1 and sample2 using O(n) DTW algorithm\n\n> ***sample1***: the mfcc data extracted from the audio signal 1.\n>\n> ***sample2***: the mfcc data extracted from the audio signal 2.\n\n> ***returns***: Float number representing the minimum distance between sample1 and sample2.\n---\n\n```python\nextract_mfcc(signal_data, samplerate=16000, winlen=0.025, winstep=0.01)\n```\nCompute MFCC features from an audio signal\n\n> ***signal***: the audio signal from which to compute features. Should be an N*1 array.\n>\n> ***samplerate***: the sample rate of the signal we are working with, in Hz.\n>\n> ***winlen***: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds).\n>\n> ***winstep***: the step between successive windows in seconds. Default is 0.01s (10 milliseconds).\n\n> ***returns***: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.\n---\n\n```python\nextract_mfcc_from_wav_file(path, samplerate=16000, winlen=0.025, winstep=0.01)\n```\nCompute MFCC features from a wav file\n\n> ***path***: the wav file path to be open.\n>\n> ***samplerate***: the wanted sample rate, in Hz. Default is 16000. If you want no resampling fill this argument with None.\n>\n> ***winlen***: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds).\n>\n> ***winstep***: the step between successive windows in seconds. Default is 0.01s (10 milliseconds).\n\n> ***returns***: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.\n---\n\n```python\nrms_silence_filter(data, samplerate=16000, segment_length=None, threshold=0.001135)\n```\nCut off silence parts from the signal audio data. **Doesn't work with signals data affected by environment noise**.\nYou 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.\n\n>***data***: the audio signal data\n>\n>***samplerate***: if no segment_length is given, segment_length will be equals samplerate/100 (around 0.01 secs per segment).\n>\n>***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.\n>\n>***threshold***: the threshold value. Values less than or equal values will be cut off. The default value was defined at [1] (see the references).\n\n>***returns***: the param \"data\" without silence parts.\n\n# References\n\n[1] - Muhammad Asadullah & Shibli Nisar, \"A SILENCE REMOVAL AND ENDPOINT DETECTION APPROACH FOR SPEECH PROCESSING\", National University of Computer and Emerging Sciences, Peshawar\n\n",
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