47 个仓库
Systems that generate artificial audio signals, including advanced neural vocoders for voice and sound synthesis.
Explore 47 awesome GitHub repositories matching graphics & multimedia · Audio Synthesis. Refine with filters or upvote what's useful.
This project is a neural text-to-speech engine and voice cloning toolkit designed to generate synthetic speech that mimics the vocal characteristics of a target speaker. It functions as a real-time audio synthesizer, utilizing a deep learning pipeline to convert written text into high-fidelity speech output with minimal latency. The system employs a transfer learning framework that leverages pre-trained speaker verification models to adapt synthesis to new, unseen vocal identities. By using an encoder-based speaker embedding process, the toolkit maps variable-length audio samples into a laten
Synthesizes high-fidelity audio waveforms from spectral representations using models optimized for rapid inference.
GPT-SoVITS is a text-to-speech synthesis engine and voice cloning toolkit designed for generating natural-sounding human speech. It functions as a neural audio processing pipeline that maps input text to high-fidelity audio waveforms, utilizing conditional variational autoencoders and flow-based decoders to ensure expressive output. The platform distinguishes itself through its ability to perform few-shot voice cloning and cross-lingual speech generation, allowing users to maintain a specific speaker's vocal identity and emotional delivery across multiple languages. By employing cross-modal l
Transforms generated spectral data into high-fidelity time-domain audio waveforms using specialized neural models.
VibeVoice is a generative artificial intelligence platform designed for text-to-speech synthesis. It functions as a neural audio generation framework that converts written text into natural-sounding spoken audio, specifically engineered to maintain consistent vocal characteristics and narrative prosody across extended passages of content. The system distinguishes itself through its ability to generate long-form conversational speech while preserving speaker identity and linguistic content. By utilizing latent space disentanglement, the model separates speaker traits from the input text, allow
Converts high-level acoustic features into raw waveform audio using deep generative neural vocoders.
这是一个深度学习文本转语音工具包,用于训练和部署神经语音合成模型。它提供了一个完整的框架,用于将书面文本转换为口语音频,利用神经声码器将合成的频谱图转换为高保真音频波形。 该工具包包括一个语音克隆系统,通过从短音频样本中提取说话人嵌入来复制特定的人声。它还支持多说话人音频合成,允许使用专门的模型架构生成不同声线身份的语音。 该系统涵盖了完整的语音合成流水线,包括语音数据集整理工具、带有性能跟踪的模型自定义训练,以及用于音频生成的命令行界面。对于网络访问,它提供了一个自托管的 HTTP 服务器,将语音合成模型部署为 API。
Includes neural vocoders that transform synthesized spectrograms into high-fidelity time-domain audio waveforms.
MockingBird is an AI voice cloning tool and text-to-speech system designed to generate synthetic speech. It functions as a voice synthesis trainer for building custom models from audio datasets, a command-line generator for producing audio files, and a text-to-speech server for remote application integration. The project specializes in real-time voice cloning, which extracts vocal characteristics from short audio samples to mimic a target speaker's unique timbre. It utilizes reference-driven audio synthesis to condition pre-trained models on specific audio samples, allowing for the generation
Generates arbitrary speech conditioned on a specific audio sample to maintain voice identity.
This project is a comprehensive software suite for voice synthesis and model management, providing a framework for training custom acoustic models and performing voice conversion. It utilizes deep-learning-based acoustic modeling to map source audio characteristics to target voice identities, enabling the transformation of input audio into specific vocal profiles. The system distinguishes itself through a feature-retrieval-based inference mechanism, which employs vector index files to perform nearest-neighbor searches on acoustic features for high-fidelity timbre matching. Users can manage th
Synthesizes vocal characteristics by applying learned voice models to input audio sources.
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
Blends multiple speaker models to create hybrid voice identities through linear interpolation.
Chatterbox is a comprehensive machine learning platform designed for multilingual speech synthesis and real-time audio generation. It functions as an engine that converts text into natural-sounding speech, capable of replicating specific human vocal characteristics and emotional expressions from short audio samples. The platform distinguishes itself through advanced control over the synthesis process, allowing for the manipulation of emotional intensity and the injection of non-verbal vocalizations such as laughter or coughing. It is engineered for low-latency performance, utilizing an optimi
Transforms generated spectral data into high-fidelity time-domain audio waveforms.
CosyVoice is a speech synthesis framework that utilizes large language models to generate expressive, multilingual audio. The system functions as an audio generation engine capable of producing natural-sounding speech across multiple languages while preserving regional dialects and specific emotional tones. The platform distinguishes itself through its zero-shot voice cloning capabilities, which allow for the creation of synthetic voice profiles from short audio samples without requiring additional model training. It provides fine-grained control over vocal attributes, enabling users to adjus
Converts raw acoustic tokens into high-fidelity waveforms using deep learning models.
Magenta is a comprehensive toolkit for training, synthesizing, and performing music through neural models and hardware-integrated engines. It functions as a machine learning framework that enables the generation, manipulation, and real-time performance of audio, providing the structural foundations for musical intelligence through hierarchical sequence modeling and symbolic processing. The project distinguishes itself by enabling real-time, low-latency neural audio synthesis that can be integrated directly into professional digital audio workstations. It supports interactive musical jamming a
Transforms learned feature representations into raw waveforms using deep learning models.
Index-tts is a neural audio generation engine designed to convert written text into high-fidelity human speech. By utilizing deep learning models and phoneme-based sequence modeling, the system transforms text into natural-sounding audio waveforms suitable for a variety of accessibility and media applications. The platform functions as a server-side inference pipeline that provides a programmatic interface for integrating voice generation into external applications. It distinguishes itself through asynchronous audio streaming, which buffers and delivers generated speech chunks in real time to
Transforms linguistic feature representations into high-fidelity raw audio waveforms using deep learning models.
Pyxel is a Python-based retro game engine designed for creating pixel-art applications with vintage aesthetics. It functions as a pixel art framework that emulates classic hardware limitations through fixed-resolution displays, constrained color palettes, and a 2D tilemap engine for rendering scaled sprites and grid-based game worlds. The engine features a dedicated MML audio synthesizer that uses Macro Language notation to compose and play back retro-style sound sequences. It also includes capabilities for seed-based background music generation and the playback of raw PCM audio data for cust
Includes an audio synthesis system that generates wave forms in real-time from MML notation.
VideoLingo is an automated video localization suite designed to transcribe, translate, and dub video content. It functions as a translation pipeline that utilizes large language models to convert spoken audio into precise text segments and translate them into multiple languages. The system differentiates itself through a multi-step translation refinement process and a specialized natural language processing utility that segments text into single-line captions meeting broadcast standards. It also integrates synthetic voiceover generation to replace or augment original audio tracks. The projec
Generates artificial voiceovers based on translated subtitles to replace original audio.
MuseScore is a professional music notation application designed for composing, arranging, and engraving musical scores. It provides a graphical interface that renders notation in real-time, allowing users to create and edit complex musical arrangements with immediate visual feedback. The software distinguishes itself through a robust document-object model that manages the relationships between notes, staves, and layout formatting. It supports the standard markup language for music interchange, ensuring that scores can be shared across different notation platforms. Additionally, the applicatio
Processes musical data through a low-latency pipeline to generate high-fidelity sound output based on loaded soundfont samples.
Hammerspoon is a programmable automation engine for macOS that enables deep system-level control through a Lua scripting environment. By bridging high-level scripts with native Objective-C APIs, it allows users to interact with the operating system's accessibility tree, intercept hardware input streams, and manage the lifecycle of running applications. The project distinguishes itself through an event-driven architecture that registers asynchronous hooks for system notifications and hardware events. This allows for real-time automation, such as remapping keyboard and mouse inputs, managing wi
Controls internal MIDI audio synthesis directly from automation scripts.
PaddleSpeech is a comprehensive toolkit of neural models for speech recognition, synthesis, and translation built on the PaddlePaddle deep learning framework. It provides a collection of frameworks and tools for converting spoken audio into written text, synthesizing natural audio from text, and performing direct speech translation. The toolkit includes specialized capabilities for keyword spotting to detect trigger words and speaker verification systems that extract unique voiceprints to identify and distinguish between individuals. It also features end-to-end translation tools that map audi
Transforms generated spectral data into high-fidelity time-domain audio waveforms using neural vocoders.
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
Generates artificial audio waveforms and original sounds from scratch using digital synthesis.
AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural audio pipelines. It functions as a multimodal audio generator and processing system, integrating a collection of pretrained models to handle speech synthesis, sound generation, and audio manipulation. The system is distinguished by its ability to generate audio from diverse inputs, including text and images, and its capacity to produce synchronized talking head videos. It also operates as a neural speech translator, converting spoken language between different tongues while pre
Translates natural language descriptions into structured control signals to parameterize audio generation models.
LMMS is a digital audio workstation and MIDI sequencer designed for composing, arranging, and mixing music. It functions as a comprehensive production environment that integrates a MIDI sequencer, a sample-based synthesizer, and an audio mixing console. The project distinguishes itself through a versatile synthesis engine that includes additive synthesis, wavetable generation, and emulations of vintage hardware such as NES audio and FM chips. It also serves as a VST plugin host, allowing for the integration of third-party virtual instruments and audio effects via a standardized interface. Be
Implements frequency modulation and additive synthesis to generate artificial audio signals and emulate hardware chips.
ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech recognition, synthesis, and translation models. It provides a structured framework for developing automatic speech recognition systems using transducer and encoder-decoder architectures, alongside engines for text-to-speech synthesis and speech translation pipelines. The project distinguishes itself through a recipe-based workflow execution system that ensures experimental reproducibility by running standardized sequences of scripts for data preparation and model training. It
Generates melodic singing audio using non-autoregressive models with multi-speaker and multilingual support.