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Awesome GitHub RepositoriesSpeech Synthesis Models

Generative neural network architectures designed to convert text input into realistic human speech and audio output.

Distinguishing note: None of the candidates were provided; this is a specific generative AI application for audio synthesis.

Explore 44 awesome GitHub repositories matching artificial intelligence & ml · Speech Synthesis Models. Refine with filters or upvote what's useful.

Awesome Speech Synthesis Models GitHub Repositories

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  • coqui-ai/ttscoqui-ai 的头像

    coqui-ai/TTS

    45,568在 GitHub 上查看↗

    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。

    Runs pre-trained synthesis models as an HTTP server to provide audio generation over a network.

    Pythondeep-learningglow-ttshifigan
    在 GitHub 上查看↗45,568
  • 2noise/chattts2noise 的头像

    2noise/ChatTTS

    39,464在 GitHub 上查看↗

    ChatTTS is a conversational text-to-speech generative model designed to convert written dialogue into natural sounding audio. It functions as a multilingual speech synthesis framework capable of producing human-like audio across different languages and speaker profiles. The system is distinguished by its ability to generate interactive dialogue with realistic vocal nuances. It utilizes a speech nuance controller to insert specific tokens that trigger non-verbal elements, such as laughter, pauses, and interjections, during the synthesis process. The project includes a streaming audio generato

    Provides a generative framework capable of producing human-like speech across multiple languages.

    Pythonagentchatchatgpt
    在 GitHub 上查看↗39,464
  • suno-ai/barksuno-ai 的头像

    suno-ai/bark

    39,159在 GitHub 上查看↗

    Bark is a generative audio engine and machine learning inference library designed to convert written text into high-fidelity speech and sound effects. It functions as a text-to-audio transformer, utilizing multi-stage neural network architectures to map semantic input tokens into detailed audio codebooks for synthesis. The system distinguishes itself through a hierarchical transformer stacking approach that separates semantic understanding from acoustic realization. By employing autoregressive token prediction and vector quantized codebook mapping, the engine bridges linguistic and sonic doma

    A generative model that converts written text into realistic speech and sound effects using multi-stage neural network architectures.

    Jupyter Notebook
    在 GitHub 上查看↗39,159
  • myshell-ai/openvoicemyshell-ai 的头像

    myshell-ai/OpenVoice

    36,720在 GitHub 上查看↗

    OpenVoice is a multilingual text-to-speech framework and voice cloning AI model designed for high-fidelity voice replication and low-latency audio generation. It functions as an instant speech synthesis engine that converts text to audio while replicating a specific speaker's tone and color. The system is distinguished by its ability to perform cross-lingual cloning, allowing the vocal characteristics of a reference speaker to be applied to speech in different languages regardless of the original training data. It utilizes a decoupled representation to separate the physical identity of a voic

    Synthesizes natural sounding speech across multiple languages while preserving a specific speaker's unique characteristics.

    Pythontext-to-speechttsvoice-clone
    在 GitHub 上查看↗36,720
  • sillytavern/sillytavernSillyTavern 的头像

    SillyTavern/SillyTavern

    29,463在 GitHub 上查看↗

    SillyTavern is a comprehensive interface and orchestration platform designed for immersive AI roleplay and interactive chat experiences. It functions as a unified gateway that connects users to a wide array of local and cloud-based large language models, providing a centralized environment to manage complex character personas, narrative context, and model-driven interactions. The platform distinguishes itself through its advanced prompt engineering and automation capabilities. It utilizes a sophisticated macro-based templating engine and vector-database retrieval to dynamically inject lore, c

    Converts text messages into spoken audio using various voice models and real-time voice cloning.

    JavaScriptaichatllm
    在 GitHub 上查看↗29,463
  • openbmb/minicpm-vOpenBMB 的头像

    OpenBMB/MiniCPM-V

    25,653在 GitHub 上查看↗

    MiniCPM-V is a multimodal large language model and vision-language system designed for complex visual and linguistic understanding. It functions as an on-device AI model, providing the capacity to process text, images, and video as a compact neural network. The project is specifically developed as an edge AI framework, utilizing quantization and weight sharding to run on memory-constrained mobile chipsets. This allows for the deployment of multimodal intelligence directly on mobile operating systems for local inference. Its capabilities cover multimodal content analysis of high-resolution im

    Generates natural speech waveforms by predicting discrete acoustic tokens using a generative neural network.

    Python
    在 GitHub 上查看↗25,653
  • openbmb/minicpm-oOpenBMB 的头像

    OpenBMB/MiniCPM-o

    23,850在 GitHub 上查看↗

    MiniCPM-o is a multimodal large language model designed to function as a real-time conversational assistant on edge devices. By mapping text, image, video, and audio inputs into a unified latent space, the system enables simultaneous cross-modal reasoning and full-duplex interaction. It is built as an edge-side inference engine, utilizing quantized model weights to maintain high-performance processing on consumer hardware. The system distinguishes itself through its integrated speech synthesis and voice cloning capabilities, which allow for the generation of expressive, personalized vocal out

    Generates expressive, personalized vocal output from reference audio samples without requiring model retraining.

    Pythonminicpmminicpm-vmulti-modal
    在 GitHub 上查看↗23,850
  • pytorch/examplespytorch 的头像

    pytorch/examples

    23,752在 GitHub 上查看↗

    This repository serves as a comprehensive collection of reference implementations for the PyTorch machine learning library. It provides practical examples for building, training, and deploying deep learning models, functioning as a toolkit for developers to explore neural network architectures and training workflows. The project distinguishes itself by offering concrete demonstrations of complex machine learning operations, ranging from computer vision tasks like object detection and depth estimation to the training of large-scale transformer models. These examples illustrate how to implement

    Implements generative models to synthesize high-quality speech waveforms from spectrogram data.

    Python
    在 GitHub 上查看↗23,752
  • aidc-ai/pixelle-videoAIDC-AI 的头像

    AIDC-AI/Pixelle-Video

    23,403在 GitHub 上查看↗

    Pixelle-Video is a text-to-video automation platform and generation engine that converts text topics into complete videos with synchronized narration, images, and music. It functions as a modular system for producing short-form content, utilizing large language models to automate script composition, visual asset generation, and voiceover production. The platform features a node-based workflow orchestrator that allows the composition of custom generation pipelines by linking different AI models. It includes a dynamic video layout designer that uses HTML templates to define aspect ratios and vi

    Creates synthetic voiceovers from text using reference audio samples for voice cloning.

    Pythonaigccomfyuiimage-generation
    在 GitHub 上查看↗23,403
  • resemble-ai/chatterboxresemble-ai 的头像

    resemble-ai/chatterbox

    22,751在 GitHub 上查看↗

    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

    Enables the injection of non-verbal vocalizations like laughter or coughing into the synthesis sequence.

    Python
    在 GitHub 上查看↗22,751
  • funaudiollm/cosyvoiceFunAudioLLM 的头像

    FunAudioLLM/CosyVoice

    21,673在 GitHub 上查看↗

    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

    Improves quality and naturalness of generated audio by applying additional training steps after initial generation.

    Pythonaudio-generationcantonesechatbot
    在 GitHub 上查看↗21,673
  • chrox/readestchrox 的头像

    chrox/readest

    21,527在 GitHub 上查看↗

    Readest is a cross-platform digital book reader and library management system designed to render multi-format ebook files across different devices. It provides a consistent interface for viewing digital content while coordinating reading progress, bookmarks, and notes through synchronization services. The application includes specialized tools for technical and academic reading, such as code syntax highlighting, a virtual split-screen viewport for comparing documents, and full-text search for rapid information retrieval. It further extends the reading experience with integrated dictionary loo

    Generates multilingual narration of written content using integrated voice synthesis technology.

    TypeScript
    在 GitHub 上查看↗21,527
  • titanwings/colleague-skilltitanwings 的头像

    titanwings/colleague-skill

    19,817在 GitHub 上查看↗

    This project is a large language model persona simulation framework designed to distill individuals into AI personas using professional data and interpersonal context. It functions as a personal knowledge base ingestor and agent configuration manager, allowing for the creation of digital twins that reproduce a specific person's mental models, speaking styles, and professional workflows. The system utilizes a dual-model persona architecture that separates professional work skills from interpersonal personality traits. It distinguishes itself through a multimodal persona generator capable of pr

    Replicates both visual appearance and vocal characteristics to complement text simulations.

    Python
    在 GitHub 上查看↗19,817
  • livekit/livekitlivekit 的头像

    livekit/livekit

    19,358在 GitHub 上查看↗

    LiveKit is a comprehensive framework for building and orchestrating real-time, multimodal AI agents that interact with users through voice, video, and text. It provides a centralized, event-driven architecture to manage the entire lifecycle of automated participants, from initialization and session state management to graceful shutdown. By utilizing a selective forwarding unit, the platform efficiently routes media streams between participants and agents, ensuring low-latency communication and secure, token-based authentication for all connections. The platform distinguishes itself through it

    Pairs real-time speech-understanding models with separate text-to-speech engines for balanced performance.

    Gogolangmedia-serversfu
    在 GitHub 上查看↗19,358
  • nari-labs/dianari-labs 的头像

    nari-labs/dia

    19,324在 GitHub 上查看↗

    Dia is a generative AI audio tool and text-to-speech synthesis engine designed for the production-ready deployment of machine learning models. It provides a framework for creating lifelike synthetic speech by conditioning generation on reference audio samples to replicate specific vocal characteristics, emotional tones, and delivery styles. The system distinguishes itself through its ability to perform custom voice cloning and precise control over audio output. Users can adjust generation parameters such as temperature and guidance scale to modify the pacing, creativity, and style of the synt

    Generates personalized vocal output from reference audio samples to mimic unique vocal characteristics.

    Pythonaiopen-weighttext-to-speech
    在 GitHub 上查看↗19,324
  • pytorch/visionpytorch 的头像

    pytorch/vision

    17,743在 GitHub 上查看↗

    This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection of neural network architectures, datasets, and high-performance transformation utilities. It serves as a foundational framework for building, training, and deploying deep learning models, offering a centralized model registry that allows developers to instantiate architectures with pre-trained weights for tasks such as image classification, object detection, and semantic segmentation. The library distinguishes itself through its modular approach to data and compute management

    Converts mel spectrograms into audible speech waveforms using generative model architectures.

    Pythoncomputer-visionmachine-learning
    在 GitHub 上查看↗17,743
  • swivid/f5-ttsSWivid 的头像

    SWivid/F5-TTS

    14,798在 GitHub 上查看↗

    F5-TTS is a text-to-speech system that utilizes a flow matching engine and diffusion transformers to generate fluent synthetic speech. It functions as a multilingual speech synthesizer and neural training framework, providing tools for voice cloning and high-performance inference serving. The project distinguishes itself through a voice cloning toolkit capable of mimicking specific speaker characteristics and tones from reference audio clips. It supports cross-lingual generation, allowing for the synthesis of audio across various global languages or the mixing of multiple languages within a s

    Generates synthetic speech that mimics the specific tone and characteristics of a target speaker from a reference audio clip.

    Python
    在 GitHub 上查看↗14,798
  • duixcom/duix-avatarduixcom 的头像

    duixcom/Duix-Avatar

    13,711在 GitHub 上查看↗

    Duix-Avatar is an AI digital human toolkit used to create, clone, and animate realistic virtual personas. It functions as a digital persona cloning tool and a text-to-speech animation API that converts written text or audio into synthetic voice and facial motion markers. The framework provides an offline video generation engine that renders digital human animations and lip-synced videos on local hardware. It includes a specialized lip sync engine to synchronize mouth movements with audio waveforms and a pipeline for extracting facial and vocal features from source media to create synthetic re

    Captures facial features and vocal characteristics from samples to create realistic digital models of people.

    Cai-avatarai-avatarscloning
    在 GitHub 上查看↗13,711
  • k2-fsa/sherpa-onnxk2-fsa 的头像

    k2-fsa/sherpa-onnx

    13,017在 GitHub 上查看↗

    Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access. The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web

    Converts mixed language text into audio using a multi-speaker model.

    C++aarch64androidarm32
    在 GitHub 上查看↗13,017
  • sparkaudio/spark-ttsSparkAudio 的头像

    SparkAudio/Spark-TTS

    10,930在 GitHub 上查看↗

    Spark-TTS is a deep learning text-to-speech synthesis engine designed to convert written text into high-fidelity audio. It utilizes a transformer-based architecture and autoregressive sequence modeling to generate coherent speech, transforming linguistic input into natural-sounding waveforms through neural speech codec synthesis. The platform distinguishes itself through zero-shot voice cloning, which allows users to mimic a target speaker’s unique vocal identity using only a short reference audio sample without requiring additional model training. It also features cross-lingual phonetic mapp

    Decodes compressed latent representations into high-fidelity audio waveforms using neural speech codec synthesis.

    Python
    在 GitHub 上查看↗10,930
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探索子标签

  • Expressive Speech Synthesis1 个子标签Neural speech generation that incorporates nonverbal cues and emotional markers for naturalness. **Distinct from Speech Synthesis Models:** Specifically adds nonverbal cues like laughing and sighing, going beyond basic text-to-speech neural architectures.
  • Hybrid Synthesis PipelinesArchitectures that combine real-time speech understanding with separate text-to-speech engines. **Distinct from Speech Synthesis Models:** Distinct from Speech Synthesis Models: focuses on the orchestration of understanding and synthesis components rather than the model architecture.
  • Multilingual SynthesisModels capable of synthesizing speech across multiple languages within a single system. **Distinct from Speech Synthesis Models:** Focuses on the multilingual capability of synthesis, whereas Speech Synthesis Models is the general architecture.
  • Paralinguistic MarkersSpecialized tokens used to trigger non-verbal vocalizations during speech synthesis. **Distinct from Speech Synthesis Models:** Distinct from general speech synthesis models: focuses on the injection of non-verbal markers like laughter or coughing.
  • Post-Training Refinement PipelinesAdditional training steps applied to synthesis models to polish generated audio quality. **Distinct from Speech Synthesis Models:** Distinct from Speech Synthesis Models: focuses on the post-generation refinement stage rather than the initial synthesis.
  • Synthesis API EndpointsNetwork-accessible endpoints that expose speech synthesis models for remote audio generation. **Distinct from Speech Synthesis Models:** Distinct from Speech Synthesis Models: focuses on the deployment and API layer rather than the model architecture
  • Training FrameworksToolkits for managing the iterative process of training and fine-tuning neural speech models. **Distinct from Speech Synthesis Models:** Focuses on the trainer framework rather than the resulting speech synthesis model architecture.
  • Voice Cloning Engines1 个子标签Systems that generate personalized vocal output from reference audio samples without requiring extensive retraining. **Distinct from Speech Synthesis Models:** Distinct from Speech Synthesis Models: focuses specifically on the cloning capability from reference samples rather than general text-to-speech.