Open-source alternatives to Python Speech Features

Librosa is a Python audio analysis library and digital signal processing framework. It functions as a feature extraction suite and music information retrieval tool designed to analyze the structural and sonic characteristics of audio signals. The library provides specialized capabilities for music analysis, including dynamic tempo tracking to identify rhythmic pulses and spectral feature extraction to compute harmonic spectra, chroma variants, and onset points. It also serves as a time-series audio processor for synchronizing audio streams. The system covers a broad range of audio processing

Vosk is an offline speech-to-text engine and API that converts spoken audio into text locally on a device. It provides a cross-platform speech toolkit with language bindings for integrating voice recognition into server environments, Android, iOS, and Raspberry Pi. The project includes a speaker identification tool to distinguish between different voices and an acoustic model trainer for building custom neural network models. These training tools enable speech feature extraction and model accuracy evaluation to improve recognition for specialized domains. The system supports real-time audio

ThinkDSP is a Python-based audio signal processing framework and educational resource designed for studying the mathematical properties of digital audio and waveforms. It functions as a digital signal processing library that provides tools for performing frequency analysis and harmonic decomposition of sound waves. The project covers the fundamentals of audio frequency analysis and sound synthesis, enabling the decomposition of sound into harmonics to analyze or modify spectral content. It facilitates Python audio programming by providing the means to manipulate audio files and generate synth

This library provides a deep learning framework for training neural networks to perform speech recognition and audio classification. It utilizes sequence-to-sequence architectures to map variable-length audio inputs into text or numerical outputs, enabling the development of custom speech-to-text transcription models. The project distinguishes itself through integrated audio processing capabilities that transform raw waveforms into spectrograms and high-dimensional numerical vectors. These tools allow for the extraction of unique vocal characteristics to identify speakers, as well as the clas

This project is a music information retrieval library and research dataset designed for audio feature extraction and music genre classification. It provides a framework for training and evaluating machine learning models that categorize audio tracks into hierarchical genre structures, supported by a collection of open-licensed MP3 tracks and pre-computed features. The project includes a music metadata API client to fetch structured track, album, and artist information from external data sources. It utilizes these external integrations to map parent-child relationships between genres and organ

This project is an AI voice training framework and singing voice conversion tool. It uses VITS and SoftVC technologies to transform the timbre of singing and spoken audio recordings, allowing a user to change the vocal characteristics of a recording to match a specific target speaker. The system provides a web-based voice converter interface for managing model checkpoints and performing timbre transformation and pitch shifting. It supports exporting trained models to the ONNX format for use in external interfaces and lightweight runtimes. The framework covers the full production pipeline, in

Tortoise-tts is a neural text-to-speech engine and voice cloning toolkit designed for high-quality audio generation. It functions as a zero-shot synthesis system, meaning it can generate speech for unseen speakers without requiring additional training or fine-tuning for each new voice. The system specializes in replicating human vocal characteristics using small sets of reference audio clips. It allows for the extraction of voice latents to mimic specific speakers, the generation of random synthetic identities, and the blending of multiple voice profiles to create hybrid vocal identities. Th

This project is a Chinese automatic speech recognition framework and deep learning system designed to convert spoken Chinese audio into written text. It functions as a toolkit for training, evaluating, and deploying speech-to-text models, utilizing a specialized pinyin-to-text converter that transforms phonetic sequences into Chinese characters using a probability graph model. The system is distinguished by its deployment flexibility, offering a dockerized recognition server that provides transcription capabilities as a remote API. It supports high-performance streaming through a gRPC speech-

AudioKit is an audio framework for iOS, macOS, and tvOS that provides tools for digital audio synthesis, signal processing, and audio analysis. It functions as a synthesis engine for generating audio waveforms and textures, a processing library for modifying tonal characteristics, and a toolkit for extracting frequency and amplitude data from sonic signals. The framework utilizes a modular node architecture and graph-based signal routing to connect audio generators, processors, and outputs. It wraps low-level audio primitives in high-level classes to facilitate sound generation and modificati

This project is a singing voice conversion tool based on VITS generative modeling. It transforms the identity of a singing voice to a target speaker while preserving the original melody, lyrics, and intonation. The system distinguishes itself through hybrid voice synthesis, allowing for the blending of multiple speaker identities via linear model interpolation. It utilizes cluster-based feature retrieval to increase target voice similarity and employs a diffusion probabilistic model as a post-processor to remove electronic artifacts and improve vocal clarity. The software covers a broad rang

Whisper.cpp is a high-performance, local-first speech recognition engine designed to run large-scale machine learning models on consumer hardware. It functions as a portable library that converts audio into text, supporting both static file transcription and real-time stream processing. By utilizing a lightweight inference engine and weight quantization, the project minimizes memory and compute overhead, allowing for efficient execution without reliance on external cloud APIs or internet connectivity. The project distinguishes itself through a hardware-agnostic compute abstraction that offloa

Annyang is a speech recognition library and web speech API wrapper that enables the integration of voice command interfaces into websites. It functions as a browser-based voice controller, mapping spoken phrases and regular expressions to specific JavaScript functions to trigger application actions. The library provides mechanisms for voice command mapping and simulation, allowing developers to associate spoken text with executable callbacks. It includes tools for command variable extraction using regular expression capture groups, which allows specific words from a spoken phrase to be passed

Spek is an acoustic spectrum tool and audio frequency visualizer designed to decode audio streams and analyze their spectral density. It functions as an audio spectrogram analyzer that displays frequency distributions to help identify the sonic characteristics of audio files. The tool specifically includes capabilities as a lossy compression detector, allowing for the identification of encoding artifacts and frequency cut-offs caused by lossy transcoding. The software covers audio file inspection and spectral analysis, providing the ability to select individual audio streams and channels. Us

Dejavu is a Python audio fingerprinting library and recognition engine. It functions as a digital audio signature tool used to analyze sound waves and create unique identifiers for the purposes of audio search and retrieval. The project enables automatic music identification by matching live audio feeds or recorded clips against a database of fingerprints. It covers audio content matching and digital audio archiving to identify original source recordings from a stored collection. The system incorporates capabilities for generating audio fingerprints, identifying audio tracks, and recognizing

Rhubarb is an automated lip sync generator and phonetic speech analyzer that converts audio recordings into timed mouth-shape animation data. It identifies sounds and syllables within audio files to map them to specific visual mouth shapes, serving as an animation timing exporter for external character animation software. The tool utilizes a language-independent phonetic recognizer to process speech regardless of the spoken language. To increase accuracy, it supports dialogue-guided recognition by using external text files to guide the phonetic analysis of specific spoken scripts. The system

Tachyon is a suite of hardware-accelerated engines and modular infrastructure for generating succinct non-interactive arguments of knowledge and polynomial commitments. It provides a GPU-accelerated zero-knowledge proof backend and a prover system designed to construct proofs from high-level circuit descriptions. The project distinguishes itself through a GPU cryptographic accelerator that offloads elliptic curve operations, field arithmetic, and cryptographic hashes to graphics hardware. This infrastructure includes a specialized polynomial commitment engine and modular components that allow

DeepSpeech is an open-source speech-to-text framework and machine learning engine designed to convert spoken audio into written text locally on a device. It provides on-device speech recognition that operates without requiring an internet connection to external servers. The system supports real-time speech transcription across a variety of hardware platforms, ranging from single-board computers and edge devices to GPU servers. This allows for audio analysis and processing directly on the local hardware.

ArrayFire is a hardware-agnostic compute framework and JIT-compiled tensor engine designed for high-performance numerical computing. It serves as a GPU numerical computing library and parallel signal processing toolkit that abstracts hardware backends, allowing the same codebase to execute across various GPU architectures and CPUs. The project distinguishes itself through a JIT engine that uses expression compilation to fuse operations and minimize memory overhead. It employs a deferred execution graph to optimize computation chains and provides interoperability primitives to share data and e

CTranslate2 is a C++ inference engine and runtime for Transformer models, designed to execute models on both CPU and GPU with optimizations for speed and memory efficiency. It functions as a model format converter, quantization tool, and REST API server, enabling deployment of neural machine translation, automatic speech recognition, and text generation models. The engine distinguishes itself through a suite of runtime optimizations including layer fusion, weight-matrix quantization, batch-by-length grouping, and a caching allocator that reuses GPU memory. It supports tensor-parallel model di

This project is a multi-room music server and library aggregator that centralizes local audio files and various streaming services into a single unified database. It functions as a media controller for DLNA and UPnP devices, enabling the management of music, podcasts, and audiobooks across networked speakers. The system is distinguished by its ability to synchronize audio playback across multiple speakers and brands simultaneously. It features a digital signal processor for loudness normalization and frequency correction, alongside an acoustic fingerprinting engine used for metadata enrichmen

This project is a comprehensive toolkit for on-device speech recognition, synthesis, and audio processing, specifically engineered for Apple Silicon. It provides a framework for building real-time, full-duplex voice agents that operate entirely offline, leveraging native hardware acceleration to maintain performance and privacy. By utilizing optimized machine learning models, the library enables local execution of complex audio tasks without reliance on external cloud services. The library distinguishes itself through its specialized focus on local, high-performance voice interaction. It incl

Lyra is a voice compression framework and low-bitrate speech codec designed to transmit high-quality audio over bandwidth-constrained networks. It utilizes an adaptive bitrate audio codec to balance audio quality and network bandwidth during active sessions. The project employs generative audio compression, using neural networks to synthesize speech signals from minimal data and reconstruct missing audio details. This allows for high-quality voice audio reconstruction from highly compressed byte streams. The system covers bandwidth-optimized voice over IP and real-time voice communication, f

Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ

EnCodec is a neural audio codec and compression tool designed to transform raw audio waveforms into discrete codes and reconstruct them back into sound. It functions as a system for neural audio representation, converting continuous audio signals into sequences of integer indices for use in generative AI tasks. The project utilizes a residual vector quantizer, which employs multiple layers of codebooks to represent audio signals with high precision at low bitrates. This approach allows the system to compress audio to discrete codes and perform low bitrate audio coding for efficient transmissi

Pyannote.audio is a PyTorch toolkit for speaker diarization, speaker identification, and speech activity detection. Its primary purpose is to partition audio recordings into segments and assign each segment to a specific speaker identity to determine who spoke when. The project includes a framework for classifying speaker identities and a pipeline for distinguishing human speech from background noise. It provides specialized tools for handling symmetric-overlap speech, where multiple speakers talk simultaneously, and employs learnable band-pass filters for raw waveform feature extraction. Th

AgenticSeek is a multi-agent orchestration system designed to decompose complex user objectives into granular, actionable tasks. By coordinating a team of specialized autonomous workers, the platform manages end-to-end workflows, ensuring that each component of a project is assigned to the most capable agent for execution. The system operates as a local-first runtime, executing all artificial intelligence models directly on user hardware to maintain data sovereignty and privacy. It integrates a browser automation engine for autonomous web research and interaction, alongside a sandboxed enviro

This is a collection of standard data structures and algorithmic implementations written in Rust. It provides a suite of specialized libraries designed for competitive programming and systems engineering. The project is organized into distinct toolkits for graph theory, number theory, range queries, and string processing. It includes implementations for computing shortest paths and network flows, performing primality tests and modular arithmetic, and managing associative range queries. The library covers broad computational areas including signal processing via fast Fourier transforms, text