Identify the emotion of multiple speakers in an Audio Segment

Overview

PR MIT License made-with-python


Logo

MevonAI - Speech Emotion Recognition

Identify the emotion of multiple speakers in a Audio Segment
Report Bug · Request Feature

Try the Demo Here

Open In Colab

Table of Contents

About The Project

Logo

The main aim of the project is to Identify the emotion of multiple speakers in a call audio as a application for customer satisfaction feedback in call centres.

Built With

Getting Started

Follow the Below Instructions for setting the project up on your local Machine.

Installation

  1. Create a python virtual environment
sudo apt install python3-venv
mkdir mevonAI
cd mevonAI
python3 -m venv mevon-env
source mevon-env/bin/activate
  1. Clone the repo
git clone https://github.com/SuyashMore/MevonAI-Speech-Emotion-Recognition.git
  1. Install Dependencies
cd MevonAI-Speech-Emotion-Recognition/
cd src/
sudo chmod +x setup.sh
./setup.sh

Running the Application

  1. Add audio files in .wav format for analysis in src/input/ folder

  2. Run Speech Emotion Recognition using

python3 speechEmotionRecognition.py
  1. By Default , the application will use the Pretrained Model Available in "src/model/"

  2. Diarized files will be stored in "src/output/" folder

  3. Predicted Emotions will be stored in a separate .csv file in src/ folder

Here's how it works:

Speaker Diarization

  • Speaker diarisation (or diarization) is the process of partitioning an input audio stream into homogeneous segments according to the speaker identity. It can enhance the readability of an automatic speech transcription by structuring the audio stream into speaker turns and, when used together with speaker recognition systems, by providing the speaker’s true identity. It is used to answer the question "who spoke when?" Speaker diarisation is a combination of speaker segmentation and speaker clustering. The first aims at finding speaker change points in an audio stream. The second aims at grouping together speech segments on the basis of speaker characteristics.

Logo

Feature Extraction

  • When we do Speech Recognition tasks, MFCCs is the state-of-the-art feature since it was invented in the 1980s.This shape determines what sound comes out. If we can determine the shape accurately, this should give us an accurate representation of the phoneme being produced. The shape of the vocal tract manifests itself in the envelope of the short time power spectrum, and the job of MFCCs is to accurately represent this envelope.

Logo

The Above Image represents the audio Waveform , the below image shows the converted MFCC Output on which we will Run our CNN Model.

CNN Model

  • Use Convolutional Neural Network to recognize emotion on the MFCCs with the following Architecture
model = Sequential()

#Input Layer
model.add(Conv2D(32, 5,strides=2,padding='same',
                 input_shape=(13,216,1)))
model.add(Activation('relu'))
model.add(BatchNormalization())

#Hidden Layer 1
model.add(Conv2D(64, 5,strides=2,padding='same',))
model.add(Activation('relu'))
model.add(BatchNormalization())

#Hidden Layer 2
model.add(Conv2D(64, 5,strides=2,padding='same',))
model.add(Activation('relu'))
model.add(BatchNormalization())

#Flatten Conv Net
model.add(Flatten())

#Output Layer
model.add(Dense(7))
model.add(Activation('softmax'))

Training the Model

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

FAQ

  • How do I do specifically so and so?
    • Create an Issue to this repo , we will respond to the query
Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out) created with Python.

Hand Gesture Volume Controller Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out). Code Firstly I have created a

Tejas Prajapati 16 Sep 11, 2021
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".

Deep Exemplar-based Video Colorization (Pytorch Implementation) Paper | Pretrained Model | Youtube video 🔥 | Colab demo Deep Exemplar-based Video Col

Bo Zhang 253 Dec 27, 2022
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation Figure 1: We estimate the 6DoF rigid transformation of a 3D face (rendered in si

Vítor Albiero 519 Dec 29, 2022
Code for the paper "Next Generation Reservoir Computing"

Next Generation Reservoir Computing This is the code for the results and figures in our paper "Next Generation Reservoir Computing". They are written

OSU QuantInfo Lab 105 Dec 20, 2022
MTCNN face detection implementation for TensorFlow, as a PIP package.

MTCNN Implementation of the MTCNN face detector for Keras in Python3.4+. It is written from scratch, using as a reference the implementation of MTCNN

Iván de Paz Centeno 1.9k Dec 30, 2022
Semantic Edge Detection with Diverse Deep Supervision

Semantic Edge Detection with Diverse Deep Supervision This repository contains the code for our IJCV paper: "Semantic Edge Detection with Diverse Deep

Yun Liu 12 Dec 31, 2022
Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network)

Deep Daze mist over green hills shattered plates on the grass cosmic love and attention a time traveler in the crowd life during the plague meditative

Phil Wang 4.4k Jan 03, 2023
SciPy fixes and extensions

scipyx SciPy is large library used everywhere in scientific computing. That's why breaking backwards-compatibility comes as a significant cost and is

Nico Schlömer 16 Jul 17, 2022
某学校选课系统GIF验证码数据集 + Baseline模型 + 上下游相关工具

elective-dataset-2021spring 某学校2021春季选课系统GIF验证码数据集(29338张) + 准确率98.4%的Baseline模型 + 上下游相关工具。 数据集采用 知识共享署名-非商业性使用 4.0 国际许可协议 进行许可。 Baseline模型和上下游相关工具采用

xmcp 27 Sep 17, 2021
Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

GANInversion_with_ConsecutiveImgs Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images" https://a

QingyangXu 38 Dec 07, 2022
DECAF: Deep Extreme Classification with Label Features

DECAF DECAF: Deep Extreme Classification with Label Features @InProceedings{Mittal21, author = "Mittal, A. and Dahiya, K. and Agrawal, S. and Sain

46 Nov 06, 2022
Real-time analysis of intracranial neurophysiology recordings.

py_neuromodulation Click this button to run the "Tutorial ML with py_neuro" notebooks: The py_neuromodulation toolbox allows for real time capable pro

Interventional Cognitive Neuromodulation - Neumann Lab Berlin 15 Nov 03, 2022
A crash course in six episodes for software developers who want to become machine learning practitioners.

Featured code sample tensorflow-planespotting Code from the Google Cloud NEXT 2018 session "Tensorflow, deep learning and modern convnets, without a P

Google Cloud Platform 2.6k Jan 08, 2023
A big endian Gentoo port developed on a Pine64.org RockPro64

Gentoo-aarch64_be A big endian Gentoo port developed on a Pine64.org RockPro64 The endian wars are over... little endian won. As a result, it is incre

Rory Bolt 6 Dec 07, 2022
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"

Dataset Distillation by Matching Training Trajectories Project Page | Paper This repo contains code for training expert trajectories and distilling sy

George Cazenavette 256 Jan 05, 2023
P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

P-tuning v2 P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks An optimized prompt tuning strategy achievi

THUDM 540 Dec 30, 2022
Probabilistic Tensor Decomposition of Neural Population Spiking Activity

Probabilistic Tensor Decomposition of Neural Population Spiking Activity Matlab (recommended) and Python (in developement) implementations of Soulat e

Hugo Soulat 6 Nov 30, 2022
Self-describing JSON-RPC services made easy

ReflectRPC Self-describing JSON-RPC services made easy Contents What is ReflectRPC? Installation Features Datatypes Custom Datatypes Returning Errors

Andreas Heck 31 Jul 16, 2022
DLFlow is a deep learning framework.

DLFlow是一套深度学习pipeline,它结合了Spark的大规模特征处理能力和Tensorflow模型构建能力。利用DLFlow可以快速处理原始特征、训练模型并进行大规模分布式预测,十分适合离线环境下的生产任务。利用DLFlow,用户只需专注于模型开发,而无需关心原始特征处理、pipeline构建、生产部署等工作。

DiDi 152 Oct 27, 2022