Open-source alternatives to Piper

This project is a deep learning text-to-speech toolkit used for training and deploying neural speech synthesis models. It provides a comprehensive framework for converting written text into spoken audio, utilizing neural vocoders to transform synthesized spectrograms into high-fidelity audio waveforms. The toolkit includes a voice cloning system that replicates specific human voices by extracting speaker embeddings from short audio samples. It also supports multi-speaker audio synthesis, allowing the generation of speech across different vocal identities using specialized model architectures.

VoxCPM is a multilingual speech synthesis system and text-to-speech inference server. It functions as an AI voice cloning tool and a synthetic voice designer, capable of generating natural speech across global languages and regional dialects using a GPU-accelerated audio generator. The project features a speech model fine-tuning framework that supports both full parameter updates and low-rank adaptation for customizing voice characteristics. It enables high-fidelity voice cloning from reference audio, including cross-lingual voice transfer and acoustic environment mimicry, as well as the crea

This project is a comprehensive suite for neural speech synthesis, featuring a deep learning text-to-speech engine, a neural speech synthesis trainer, and a voice cloning toolkit. It provides a system for synthesizing human-like speech from text using neural network models and high-fidelity vocoders. The suite includes a speech model conversion utility to transform deep learning models between different formats for deployment across various hardware runtimes. It also provides a self-contained HTTP server to expose pre-trained text-to-speech models as a remote audio API. Capabilities include

Vocode-core is a framework for building real-time conversational AI voice agents. It serves as a conversational orchestrator and pipeline that integrates speech-to-text, large language models, and text-to-speech services to enable low-latency voice interactions. The project features a provider-agnostic interface that allows for swappable speech and language model providers, including support for both cloud APIs and local binaries. It distinguishes itself through a specialized telephony integration layer that enables agents to be deployed across phone lines, WebRTC, and virtual meeting platfor

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

This project is an on-device AI SDK providing a framework for running large language models, vision models, and speech models locally. It serves as an orchestration layer for local LLM execution, ensuring data privacy and offline availability by utilizing hardware acceleration on the device. The SDK is distinguished by its comprehensive voice and multimodal capabilities, including a coordinated voice pipeline for activity detection, speech-to-text, and text-to-speech synthesis. It also provides a dedicated implementation kit for local retrieval-augmented generation and tools for processing co

EmotiVoice is an emotional text-to-speech engine and bilingual speech synthesizer designed to generate synthetic audio in English and Chinese. It utilizes a deep learning architecture to produce high-fidelity speech with controllable emotional states and timbres. The project includes a voice cloning framework for replicating specific speaker identities by training custom acoustic models on personal audio datasets. It employs a jointly-trained acoustic-vocoder pipeline and style-embedding-based synthesis to manage expression and reduce audio artifacts. The system covers a broad range of speec

Kokoro-FastAPI is a text-to-speech API and LLM speech synthesis server that generates spoken audio from text via a REST interface. It functions as a Kubernetes-native deployment designed for orchestrated speech synthesis. The system includes a voice blending engine that creates unique vocal profiles by mixing multiple existing voices using custom weight ratios. The service provides real-time audio streaming to reduce latency and generates word-level timestamps for speech synchronization. It manages hardware efficiency through on-demand model loading to optimize VRAM usage and includes system

This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu

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

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

Neutts is a neural text-to-speech engine designed for real-time streaming output on edge devices such as phones and laptops. It supports voice cloning from short audio references, enabling zero-shot reproduction of a target speaker's voice, and can be fine-tuned or retrained from scratch for custom voices and styles. The system distinguishes itself through a decoder-only architecture that halves memory and accelerates generation on constrained hardware, combined with quantized model inference for reduced memory footprint. Its streaming decoder loop interleaves synthesis with playback, deliver

KittenTTS is a neural text-to-speech engine and text-to-audio synthesis tool that converts written text into spoken audio using lightweight neural network models. It functions as both a speech synthesizer and an audio file generator, producing spoken audio for offline playback. The system includes a text normalization processor that expands numbers and abbreviations into full spoken words to improve the naturalness of the synthesized speech. It supports diverse voice options and provides the ability to adjust playback speed.

Kokoro is a lightweight neural text-to-speech engine that converts written text into spoken audio using a compact model designed for fast inference. It supports multiple languages through language-specific grapheme-to-phoneme conversion pipelines, and offers voice profile selection to change the character of the generated speech. The engine provides GPU acceleration on Apple Silicon hardware by setting a single environment variable, enabling faster inference on Mac M-series machines. It also includes pattern-based text segmentation, allowing input text to be split at user-defined delimiters t

Voicebox is a local speech processing system that provides text-to-speech generation, speech-to-text transcription, and voice cloning. It utilizes local machine learning inference and GPU acceleration to process audio and text data without relying on external API calls. The project features a voice cloning toolkit for creating synthetic profiles from audio samples and a timeline-based voice editor for composing multi-character conversations. It also includes an AI voice management API that allows external applications and AI agents to programmatically manage voice profiles and generate speech

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

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

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

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

StyleTTS2 is an adversarial text-to-speech model that uses style diffusion and large speech language models to generate natural-sounding speech from text input. It combines adversarial training with large pre-trained speech models to improve speech quality and reduce artifacts, while employing a style diffusion process that extracts prosodic and timbral features from reference audio to guide speech generation. The model supports multi-speaker voice synthesis by conditioning the diffusion process on speaker-specific embeddings derived from reference utterances, enabling voice cloning and adapt

Parler-TTS is a library for generating high-quality speech from text, supporting both inference and model training. It combines a transformer-based text-to-speech generator with a mel-spectrogram decoder to convert written text into natural-sounding audio. The project distinguishes itself through text-conditioned voice control, which allows speaker attributes like gender, pitch, speaking rate, and style to be adjusted via a natural-language description. It also includes speaker embedding selection for maintaining voice identity across multiple generations, and a fine-tuning recipe system that

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

This project is a TensorFlow implementation of a neural network for raw audio waveform generation. It functions as a conditioned speech synthesis model that produces synthetic audio samples using a dilated convolutional neural network architecture. The system supports custom voice modeling by incorporating global conditioning and categorical identifiers during training and generation. This allows the model to mimic specific speakers or distinct audio characteristics for neural text-to-speech applications. The framework covers deep learning audio synthesis, including audio dataset processing,

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

This project is an expressive text-to-speech foundation model and voice cloning system designed to synthesize human-like speech with emotional nuance and high fidelity. It functions as a finetunable speech model that can generate audio mimicking a specific person using a reference voice sample. The system distinguishes itself through a high-performance inference engine that utilizes memory caching and hardware compilation to reduce latency during the audio generation process. It further allows for synthesis quality improvements by training the language model on custom datasets consisting of a

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