Search Results for "waveform"
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Showing 34 open source projects for "waveform"
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1
Color to Waveform
Convert colors to synth presets
The purpose of the program is to convert a color to a waveform you can use as a synthesizer oscillator inside a DAW such as FL Studio from Image Line. Many synths are provided with an option to load your own waveform, to replace the basic saw, square and sine waveforms commonly used to create synth sounds. The waveform generated by the program will correspond to the subliminal synesthetic sensation of the selected color. -
2
MLX-Audio
A text-to-speech, speech-to-text and speech-to-speech library
...It includes examples such as audiobook generation to demonstrate long-form synthesis and joined audio segments. On top of that, MLX-Audio offers a modern web interface powered by FastAPI, with real-time waveform and 3D visualizations, file upload, and audio management. -
3
TorchAudio
Data manipulation and transformation for audio signal processing
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch... -
4
pyAudioAnalysis
Python Audio Analysis Library: Feature Extraction, Classification
pyAudioAnalysis is an open-source Python library designed for audio signal analysis, machine learning, and music information retrieval tasks. The project provides a collection of tools that allow developers to extract meaningful features from audio files and use those features for classification, segmentation, and analysis. The library supports multiple audio processing workflows, including feature extraction from raw audio signals, training of machine learning models, and automatic audio... -
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5
WavTokenizer
SOTA discrete acoustic codec models with 40/75 tokens per second
...Its architecture incorporates a broader vector-quantization space, extended contextual windows, and improved attention networks, combined with multi-scale discriminators and inverse Fourier transform blocks to enhance waveform reconstruction. Extensive experiments show that WavTokenizer matches or surpasses previous neural codecs across speech, music, and general audio on both objective metrics and subjective listening tests. -
6
WhisperSpeech
An Open Source text-to-speech system built by inverting Whisper
...Its architecture follows a token-based, multi-stage pipeline inspired by AudioLM and SPEAR-TTS: Whisper is used to produce semantic tokens, EnCodec compresses the waveform into acoustic tokens, and Vocos reconstructs high-fidelity audio from those tokens. The repository includes notebooks and scripts for inference, long-form synthesis, and finetuning, as well as pre-trained models and converted datasets hosted on Hugging Face. Performance optimizations like torch.compile, KV-caching, and architectural tweaks allow the main model to reach up to 12× real-time speed on a consumer RTX 4090. -
7
Step-Audio-EditX
LLM-based Reinforcement Learning audio edit model
Step-Audio-EditX is an open-source, 3 billion-parameter audio model from StepFun AI designed to make expressive and precise editing of speech and audio as easy as text editing. Rather than treating audio editing as low-level waveform manipulation, this model converts speech into a sequence of discrete “audio tokens” (via a dual-codebook tokenizer) — combining a linguistic token stream and a semantic (prosody/emotion/style) token stream — thereby abstracting audio editing into high-level token operations. This allows users to modify not only what is said (the text) but also how it's said: emotion, tone, speaking style, prosody, accent, even paralinguistic cues. ... -
8
SzTextCompar
Text similarity and comparison analyzer for Windows
A desktop tool that compares two documents (or a whole folder of them) using nine independent similarity methods, highlights matching passages side by side, and visualizes the result as an ECG-style waveform, charts, and an interactive report. Nine independent analysis methods, each giving its own similarity score and a plain-language verdict Open two text/document files side by side, with synchronized scrolling and a line-number gutter. - Visual Match View - Detailed Visualization - Full-Screen Match Waveform ("ECG") - HTML Report - Folder Matrix (batch comparison) -
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PyAMS
PyAMS (Python for Analog and Mixed Signals) CAD approach
PyAMS (Python for Analog and Mixed Signals) is used to simplify modeling analog elements and simulate electronic circuit The objectives of PyAMS is: - Drawing circuit by schematic (CAD approach); - Simulating the circuit; - Creating new symbols for models; - Creating new models of electrical elements by using Python language with pyams_lib; - Presenting simulation results in waveform or in probe; - PyAMS is developed with Python 3+, ElectronJS and NodeJs - Licensed under: PyAMS is free to use. GPLv3 . - Open source. -
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10
garysfm
An advanced file manager with qss themes and iso and folder previews
garysfm which stands for Gary's File Manager is a file manager with some advanced features. Those features include bulk renaming and folder image previews. I has rather advanced search functions, tab browsing with persistence between launches. It remembers your folder sorting and view options in icon view. It also remembers your active tabs between sessions. It has progress dialog while doing large operations like copying large files, and folders with many files. python version works on... -
11
Text to Waveform
Create synth presets from words
Convert words to waveforms you can load into a synthesizer oscillator to create synth presets. Have fun turning your name, your friends' names, your city name, your pet's name, your team's name into synth presets you can use to produce a track. -
12
Parallel WaveGAN
Unofficial Parallel WaveGAN
Parallel WaveGAN is an unofficial PyTorch implementation of several state-of-the-art non-autoregressive neural vocoders, centered on Parallel WaveGAN but also including MelGAN, Multiband-MelGAN, HiFi-GAN, and StyleMelGAN. Its main goal is to provide a real-time neural vocoder that can turn mel spectrograms into high-quality speech audio efficiently. The repository is designed to work hand-in-hand with ESPnet-TTS and NVIDIA Tacotron2-style front ends, so you can build complete TTS or singing... -
13
eCxx
A C++ library for AVR and NodeMCU
NOTE: This project is marked with 'Status: Abandoned' on SourceForge because not enough time can be dedicated to this project. However it may still get sporadic commits to the repository. eCxx is a library for AVR and NodeMCU tailored for micro LED displays and lighting effects. eCxx is utilizing Makefile build system. Java and Python based applications/tools are also included to ease the development and debugging process using the host PC. On one side, eCxx supports the original... -
14
Demucs
Code for the paper Hybrid Spectrogram and Waveform Source Separation
Demucs (Deep Extractor for Music Sources) is a deep-learning framework for music source separation—extracting individual instrument or vocal tracks from a mixed audio file. The system is based on a U-Net-like convolutional architecture combined with recurrent and transformer elements to capture both short-term and long-term temporal structure. It processes raw waveforms directly rather than spectrograms, allowing for higher-quality reconstruction and fewer artifacts in separated tracks. The... -
15
VALL-E
PyTorch implementation of VALL-E (Zero-Shot Text-To-Speech)
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called VALL-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems.... -
16
audio-diffusion-pytorch
Audio generation using diffusion models, in PyTorch
A fully featured audio diffusion library, for PyTorch. Includes models for unconditional audio generation, text-conditional audio generation, diffusion autoencoding, upsampling, and vocoding. The provided models are waveform-based, however, the U-Net (built using a-unet), DiffusionModel, diffusion method, and diffusion samplers are both generic to any dimension and highly customizable to work on other formats. Note: no pre-trained models are provided here, this library is meant for research purposes. -
17
Security Log Generator
Generates logs of typical formats that would often be found in a SOC
Generates logs of typical formats that would often be found in a SOC. As of 31st January 2023, it supports IDS, Web Access and Endpoint log formats. Can generate a specific number of events in a linear fashion or use a waveform to add 'bumpiness' to your data. The code is modular and extensible, adding additional formats can be done with relative ease. -
18
EnCodec
State-of-the-art deep learning based audio codec
...Unlike traditional codecs (like MP3 or Opus), Encodec uses a learned quantizer and decoder to reconstruct complex waveforms with remarkable accuracy at bitrates as low as 1.5 kbps. It employs a convolutional encoder–decoder architecture trained with perceptual loss functions that optimize for human auditory quality rather than raw waveform distance. The model can operate in real time and supports variable bandwidths, bitrates, and multi-band audio. Encodec has applications in speech and music compression, generative modeling, and efficient data transmission for communication systems. The repository includes pretrained checkpoints, PyTorch inference code, and examples for integrating Encodec as a module in downstream generative or streaming systems. -
19
DiffSinger
Singing Voice Synthesis via Shallow Diffusion Mechanism
DiffSinger is an open-source PyTorch implementation of a diffusion-based acoustic model for singing-voice synthesis (SVS) and also text-to-speech (TTS) in a related variant. The core idea is to view generation of a sung voice (mel-spectrogram) as a diffusion process: starting from noise, the model iteratively “denoises” while being conditioned on a music score (lyrics, pitch, musical timing). This avoids some of the typical problems of prior SVS models — like over-smoothing or unstable GAN... -
20
WaveRNN
WaveRNN Vocoder + TTS
WaveRNN is a PyTorch implementation of DeepMind’s WaveRNN vocoder, bundled with a Tacotron-style TTS front end to form a complete text-to-speech stack. As a vocoder, WaveRNN models raw audio with a compact recurrent neural network that can generate high-quality waveforms more efficiently than many traditional autoregressive models. The repository includes scripts and code for preprocessing datasets such as LJSpeech, training Tacotron to produce mel spectrograms, training WaveRNN on those... -
21
VoiceFixer
General Speech Restoration
...The architecture works in two stages: first an analysis stage that tries to extract “clean” intermediate features from the noisy audio (e.g. removing noise, denoising, dereverberation, upsampling), and then a neural vocoder-based synthesis stage that reconstructs a high-quality waveform from those features. Unlike many single-purpose noise reduction tools, VoiceFixer targets a “general speech restoration” problem (GSR), capable of handling multiple types of distortions at once, which makes it suitable for old recordings, phone-call audio, amateur voice recordings, or archival media. Evaluations show that VoiceFixer significantly improves both objective and subjective audio quality compared to baseline speech-enhancement methods. -
22
TensorFlowTTS
Real-Time State-of-the-art Speech Synthesis for Tensorflow 2
TensorFlowTTS is a state-of-the-art, open-source speech synthesis library built on TensorFlow 2. It offers a variety of architectures for text-to-speech, including classic and modern models such as Tacotron‑2, FastSpeech / FastSpeech2, and neural vocoders like MelGAN and Multiband‑MelGAN. Because it’s based on TensorFlow 2, it can leverage optimizations such as fake-quantization aware training and pruning — which allow models to run faster than real time and to be deployable on mobile or... -
23
VITS
Conditional Variational Autoencoder with Adversarial Learning
VITS is a foundational research implementation of “VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech,” a well-known neural TTS architecture. Unlike traditional two-stage systems that separately train an acoustic model and a vocoder, VITS trains an end-to-end model that maps text directly to waveform using a conditional variational autoencoder combined with normalizing flows and adversarial training. This architecture enables parallel generation (fast inference) while achieving speech quality that rivals or surpasses many two-stage systems. The repository provides training and inference pipelines for common datasets such as LJ Speech (single-speaker) and VCTK (multi-speaker), including filelists, configs, and preprocessing scripts. ... -
24
Denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)
...It uses a causal encoder-decoder architecture with skip connections, optimized with losses defined both in the time domain and frequency domain to better suppress noise while preserving speech. Unlike models that operate on spectrograms alone, this design enables lower latency and coherent waveform output. The implementation includes data augmentation techniques applied to the raw waveforms (e.g. noise mixing, reverberation) to improve model robustness and generalization to diverse noise types. The project supports both offline denoising (batch inference) and live audio processing (e.g. via loopback audio interfaces), making it practical for real-time use in calls or recording. ... -
25
pyrpl
PyRPL turns your Red Pitaya into a powerful analog feedback device.
The Red Pitaya is a commercial, affordable FPGA board with fast analog inputs and outputs. This makes it useful for quantum optics experiments, in particular as a digital feedback controller for analog systems. Based on the open source software provided by the board manufacturer, PyRPL (Python RedPitaya Lockbox) implements many devices that are needed for optics experiments with the Red Pitaya. PyRPL implements various digital signal processing (DSP) modules (see features below). It allows...
