OmniVoice-GGUF is an open-source text-to-speech AI model distributed via Hugging Face. It allows developers and researchers to perform local voice synthesis and voice cloning for various applications. Below are 7 voice, tts & speech apps with similar functionality to OmniVoice, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
OmniVoice_k2 is an open-source text-to-speech model available on Hugging Face, designed for generating natural-sounding speech from text. It is suitable for developers and researchers building voice applications or experimenting with TTS technology. The model is pip-installable and offers open weights.
OmniVoice is a web application that converts user-input text into speech, supporting multiple languages and voice cloning from reference recordings. It is designed for content creators and developers who need customizable, high-quality AI-generated voices.
Qwen3-TTS-GGUF is an open-source text-to-speech AI model distributed via Hugging Face. It allows developers and researchers to perform local voice synthesis and create custom voices for various applications. The model is suitable for experimentation, prototyping, and integration into speech-enabled systems.
OmniCoder-9B-GGUF is an open-source AI model for code generation, distributed via Hugging Face for local inference and integration into developer workflows. It provides open weights and supports CLI-based usage for advanced programming tasks.
OmniVoice Studio is an open-source platform for AI-powered audio dubbing, voice cloning, and voice generation. It supports self-hosted deployment, API access, and multi-language synthesis, making it suitable for developers and enterprises building custom audio solutions.
gigaam-v3-ctc-gguf is an open-source Russian ASR model using CTC decoding, distributed in GGUF format for use with local inference tools. It supports quantized variants for efficient offline transcription.
Moss Tts is a text-to-speech model distributed in GGUF format and designed for use with the CrispASR moss-tts backend. The model is a GGUF conversion of OpenMOSS-Team/MOSS-TTS-v1.5 and incorporates a Qwen3-8B backbone that emits 32 RVQ audio codebooks under a delay pattern, along with the MOSS-Audio-Tokenizer 1.6B transformer codec. It is intended for generating speech from text inputs and is validated to produce intelligible, accurate speech end-to-end, as tested on CUDA (P100) hardware through a round-trip process of synthesis and automatic speech recognition. The model files are available in different configurations, including a Q4_K backbone version and an F16 backbone version, with the latter requiring more than 20 GB of VRAM or CPU resources. There is also an F16 transformer codec companion file. Usage instructions are provided for integrating the model with CrispASR, specifying how to synthesize speech from text and output audio files. The model is released under the Apache-2.0 license. Moss Tts is positioned as a resource for those seeking to implement or experiment with text-to-speech capabilities, particularly in conjunction with the CrispASR backend. The evidence does not specify particular user roles or application domains beyond this context.
