Metadata-Version: 2.1
Name: speaker-verification-toolkit
Version: 0.0.1
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
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
Requires-Dist: librosa (==0.7.0)
Requires-Dist: python-speech-features (==0.6)
Requires-Dist: fastdtw (==0.3.2)

# speaker-verification-toolkit
This module contains some tools to make a simple speaker verification.

You can download it with PyPI:
```
$ pip install speaker-verification-toolkit
```

To use in your own projects:
```python
import speaker_verification_toolkit.tools as svt
```

## Basic 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.

