Popular GitHub Repositories for Mel Frequency Cepstral Coefficients

[CVPR 2023] SadTalker：Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

List of awesome resources for machine learning-based algorithmic trading

GAN-based Mel-Spectrogram Inversion Network for Text-to-Speech Synthesis

[CVPR 2025?] Identity-Preserving Text-to-Video Generation by Frequency Decomposition

[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

Self-supervised contrastive learning for time series via time-frequency consistency

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

This repository has implementation for "Neural Voice Cloning With Few Samples"

Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

Tools for Speech Enhancement integrated with Kaldi

PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech

A Non-Autoregressive Transformer based Text-to-Speech, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS

HFTFramework utilized for research on " A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm "

Pitch Estimating Neural Networks (PENN)

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract)

End-2-end speech synthesis with recurrent neural networks

:sound: :boy: :girl:Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)

A Large Collection of Bengali Words & Translations (বাংলা-শব্দ-bengali-shobdo-bengal-word-list-bangla-translation-spell-check-checker-dictionary-translator-dataset-data-set-english-to-bd-bn-en-e2b-sobdo)

Implementation of "MelNet: A Generative Model for Audio in the Frequency Domain"