GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → GauravWaghmare → Speaker-Identification

GauravWaghmare / Speaker-Identification

Licence: other
A program for automatic speaker identification using deep learning techniques.

Programming Languages

python
139335 projects - #7 most used programming language

Labels

kerasmfccspeaker-recognitionspeaker-verification

Projects that are alternatives of or similar to Speaker-Identification

speaker-recognition-papers
Share some recent speaker recognition papers and their implementations.
Stars: ✭ 92 (+9.52%)
Mutual labels:  speaker-recognition, speaker-verification
meta-SR
Pytorch implementation of Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs (Interspeech, 2020)
Stars: ✭ 58 (-30.95%)
Mutual labels:  speaker-recognition, speaker-verification
dropclass speaker
DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020
Stars: ✭ 20 (-76.19%)
Mutual labels:  speaker-recognition, speaker-verification
Huawei-Challenge-Speaker-Identification
Trained speaker embedding deep learning models and evaluation pipelines in pytorch and tesorflow for speaker recognition.
Stars: ✭ 34 (-59.52%)
Mutual labels:  speaker-recognition, speaker-verification
KaldiBasedSpeakerVerification
Kaldi based speaker verification
Stars: ✭ 43 (-48.81%)
Mutual labels:  speaker-recognition, speaker-verification
bob
Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland. - Mirrored from https://gitlab.idiap.ch/bob/bob
Stars: ✭ 38 (-54.76%)
Mutual labels:  speaker-recognition, speaker-verification
wavenet-classifier
Keras Implementation of Deepmind's WaveNet for Supervised Learning Tasks
Stars: ✭ 54 (-35.71%)
Mutual labels:  speaker-recognition, speaker-verification
deepaudio-speaker
neural network based speaker embedder
Stars: ✭ 19 (-77.38%)
Mutual labels:  speaker-recognition, speaker-verification
D-TDNN
PyTorch implementation of Densely Connected Time Delay Neural Network
Stars: ✭ 60 (-28.57%)
Mutual labels:  speaker-recognition, speaker-verification
Speaker-Recognition
This repo contains my attempt to create a Speaker Recognition and Verification system using SideKit-1.3.1
Stars: ✭ 94 (+11.9%)
Mutual labels:  speaker-recognition, speaker-verification
GE2E-Loss
Pytorch implementation of Generalized End-to-End Loss for speaker verification
Stars: ✭ 72 (-14.29%)
Mutual labels:  speaker-recognition, speaker-verification
kaldi-timit-sre-ivector
Develop speaker recognition model based on i-vector using TIMIT database
Stars: ✭ 17 (-79.76%)
Mutual labels:  speaker-recognition, speaker-verification
pytorch-mfcc
A pytorch implementation of MFCC.
Stars: ✭ 30 (-64.29%)
Mutual labels:  mfcc
speaker-recognition-pytorch
Speaker recognition ,Voiceprint recognition
Stars: ✭ 49 (-41.67%)
Mutual labels:  speaker-recognition
meta-embeddings
Meta-embeddings are a probabilistic generalization of embeddings in machine learning.
Stars: ✭ 22 (-73.81%)
Mutual labels:  speaker-recognition
UniSpeech
UniSpeech - Large Scale Self-Supervised Learning for Speech
Stars: ✭ 224 (+166.67%)
Mutual labels:  speaker-verification
timit-preprocessor
Extract mfcc vectors and phones from TIMIT dataset
Stars: ✭ 14 (-83.33%)
Mutual labels:  mfcc
DTW Digital Voice Recognition
基于DTW与MFCC特征进行数字0-9的语音识别，DTW，MFCC，语音识别，中英数据，端点检测，Digital Voice Recognition。
Stars: ✭ 28 (-66.67%)
Mutual labels:  mfcc
Voiceprint-recognition-Speaker-recognition
It is a complete project of voiceprint recognition or speaker recognition.
Stars: ✭ 82 (-2.38%)
Mutual labels:  speaker-recognition
Deep-learning-And-Paper
【仅作为交流学习使用】机器智能--相关书目及经典论文包括AutoML、情感分类、语音识别、声纹识别、语音合成实验代码等
Stars: ✭ 62 (-26.19%)
Mutual labels:  speaker-verification
View All Similar Projects ➔

Speaker-Identification

This is a program to train a deep neural network for the task of speaker identification.

The program depends on the following installations :-

1) TensorFlow or Theano (backend for Keras)

2) Keras

3) Scipy

4) Numpy

5) Scikit-learn

6) Mlpy

7) Scikits.talkbox

There are two modes to run the program :-

1) Make changes in code itself for specifying training and testing data. 
	Change the string variables storing the pathnames of the train 'direc' and test directories 'testdirec' in the file runMode_1.py. 
	The train directory should contain folders named as numbers starting from 0 representing the Speaker No. or class label for classification. 
	Each such folder should contain voice samples (.wav files) of the corresponding user. 
	Same for the test directory. 

2) Use command line arguments for specifying training and testing data. Run the file runMode_2.py by using the following arguments.
	python runMode_2.py train /path to train directory  for training
	python runMode_2.py test phone_number /path to test file for test

The basic architecture of the program remains the same for both the files.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].