Search Results for "cpu-x"
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Showing 15 open source projects for "cpu-x"
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Kitten TTS
State-of-the-art TTS model under 25MB
KittenTTS is an open-source, ultra-lightweight, and high-quality text-to-speech model featuring just 15 million parameters and a binary size under 25 MB. It is designed for real-time CPU-based deployment across diverse platforms. Ultra-lightweight, model size less than 25MB. CPU-optimized, runs without GPU on any device. High-quality voices, several premium voice options available. Fast inference, optimized for real-time speech synthesis. -
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Pocket TTS
A TTS that fits in your CPU (and pocket)
...The project focuses on keeping the runtime footprint manageable while still producing natural-sounding speech, which makes it attractive for offline tools, prototypes, and privacy-sensitive workflows. Because it is CPU-oriented, it fits well in server environments where GPU access is limited, in desktop apps, or in edge deployments where simplicity matters more than maximum throughput. It also emphasizes developer ergonomics, providing a straightforward API surface that can be integrated into pipelines, assistants, accessibility tools, or batch generation scripts. -
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MOSS-TTS-Nano
MOSS-TTS-Nano is an open-source multilingual tiny speech generation
MOSS-TTS-Nano is a lightweight text-to-speech model designed for real-time voice generation in resource-constrained environments. It is part of the broader MOSS-TTS family and focuses on delivering high-quality speech synthesis with a compact architecture. The model operates efficiently on CPU-only systems, enabling deployment without specialized hardware. It supports multilingual voice cloning and produces high-fidelity audio with low latency. The system uses an autoregressive audio tokenization pipeline to generate natural-sounding speech. It is suitable for local applications, web services, and embedded systems. Overall, it brings advanced speech synthesis capabilities to lightweight and accessible environments. -
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FastKoko
Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
FastKoko is a self-hosted text-to-speech server built around the Kokoro-82M model and exposed through a FastAPI backend. It is designed to be easy to deploy via Docker, with separate CPU and GPU images so that users can choose between pure CPU inference and NVIDIA GPU acceleration. The project exposes an OpenAI-compatible speech endpoint, which means existing code that talks to the OpenAI audio API can often be pointed at a Kokoro-FastAPI instance with minimal changes. It supports multiple languages and voicepacks and allows phoneme based generation for more accurate pronunciation and prosody. ... -
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WhisperLive
A nearly-live implementation of OpenAI's Whisper
...It runs as a server–client system in which the server hosts a Whisper backend and clients stream audio to be transcribed with very low delay. The project supports multiple inference backends, including Faster-Whisper, NVIDIA TensorRT, and OpenVINO, allowing you to target GPUs and different CPU architectures efficiently. It can handle microphone input, pre-recorded audio files, and network streams such as RTSP and HLS, making it flexible for live events, monitoring, or accessibility workflows. Configuration options let you control the number of clients, maximum connection time, and threading behavior so the server can be tuned for different deployment environments. ... -
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Audiblez
Generate audiobooks from e-books
Audiblez is a tool for generating high-quality .m4b audiobooks directly from .epub e-books using the Kokoro-82M neural text-to-speech model. It focuses on making audiobook creation easy and fast: from a single command, the tool splits an e-book into chapters, synthesizes audio for each section, and then merges the results into a structured audiobook with chapter-based WAV files and a final .m4b container. The Kokoro-82M model it uses is compact (82M parameters) yet natural sounding, trained... -
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Sopro TTS
A lightweight text-to-speech model with zero-shot voice cloning
Sopro TTS is an open-source text-to-speech (TTS) project that implements a lightweight model capable of producing speech from text with zero-shot voice cloning, meaning it can mimic a speaker’s voice from only a few seconds of reference audio. Built with a 169 million-parameter architecture that uses dilated convolutions and cross-attention layers instead of large Transformer stacks, it achieves relatively fast real-time performance even on CPUs (about a 0.25 real-time factor measured on an... -
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Orpheus TTS
Towards Human-Sounding Speech
Orpheus TTS is a state-of-the-art open-source text-to-speech system built on a Llama-3B backbone, treating speech synthesis as a large language model problem instead of a traditional TTS pipeline. It is designed to produce human-like speech with natural intonation, emotion, and rhythm, targeting quality comparable to or better than many closed-source systems. The project ships both pretrained and finetuned English models, as well as a family of multilingual models released as a research... -
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OuteTTS
Interface for OuteTTS models
OuteTTS is an interface library for running OuteTTS text-to-speech models across a range of backends, making it easier to deploy the same model on different hardware and runtimes. It provides a high-level Interface API that wraps model configuration, speaker handling, and audio generation so you can focus on integrating speech into your application rather than wiring up low-level engines. The project supports multiple backends including llama.cpp (Python bindings and server), Hugging Face... -
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VibeVoice ComfyUI
ComfyUI integration for Microsoft's VibeVoice text-to-speech model
VibeVoice ComfyUI is a comprehensive wrapper that integrates Microsoft’s VibeVoice text-to-speech models directly into ComfyUI workflows. It exposes VibeVoice as a set of custom nodes so you can build single-speaker and multi-speaker voice generation pipelines visually, combining TTS with other audio or generative components. The integration supports high-quality single-speaker synthesis as well as scripted multi-speaker conversations, with optional voice cloning from audio samples for each... -
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fairseq2
FAIR Sequence Modeling Toolkit 2
...It supports multi-GPU and multi-node distributed training using DDP, FSDP, and tensor parallelism, capable of scaling up to 70B+ parameter models. The framework integrates seamlessly with PyTorch 2.x features such as torch.compile, Fully Sharded Data Parallel (FSDP), and modern configuration management. -
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Step-Audio-EditX
LLM-based Reinforcement Learning audio edit model
...Because the model is trained with a “large-margin learning” objective over many synthesized and natural speech samples, it gains robust control over expressive attributes, and can perform iterative editing: e.g. you could record a line, then ask the model to “make it sadder,” “speak slower,” or “change accent to X.” -
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TITTSE
Two Integrated Text To Speech Engines uses MMS & Silero
TITTSE is a Python Application that allows you to easily and quickly convert text to speech in 15 different languages (or add more easily) using Two TTS Engines. All you need is a text file ending in the tittse extension with 4 header lines including the TITTSE language code (see documentation), the 'base' file name for the audio files TITTSE creates, voice gender (girl or boy), offset (file numbers added to base file name start at this number). After those first four lines, every... -
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VALL-E X
Open source implementation of Microsoft's VALL-E X zero-shot TTS model
VALL-E-X is an open-source implementation of Microsoft’s VALL-E X zero-shot text-to-speech model, focused on multilingual, cross-lingual voice cloning. It is capable of synthesizing speech in English, Chinese, and Japanese from text while mimicking the voice characteristics of a speaker given only a short 3–10 second prompt. The model attempts to match not just timbre, but also tone, pitch, emotion, and prosody of the reference audio, resulting in highly personalized output. ... -
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Dia-1.6B
Dia-1.6B generates lifelike English dialogue and vocal expressions
...The model accepts speaker conditioning through audio prompts, allowing limited voice cloning and speaker consistency across generations. It is optimized for English and built for real-time performance on enterprise GPUs, though CPU and quantized versions are planned. The format supports [S1]/[S2] tags to differentiate speakers and integrates easily into Python workflows. While not tuned to a specific voice, user-provided audio can guide output style. Licensed under Apache 2.0, Dia is intended for research and educational use, with explicit restrictions on misuse like identity mimicry or deceptive content.
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