Home › Audio Tools › ClearerVoice-Studio

ClearerVoice-Studio - AI Audio Tools Tool

Overview

ClearerVoice-Studio is an open-source, AI-powered speech processing toolkit that provides state-of-the-art pretrained models and utilities for common speech tasks. According to the GitHub repository, the project offers components for speech enhancement (denoising), source separation, speech super-resolution (upsampling/quality restoration), and target-speaker extraction, packaged for research and applied use. The toolkit targets researchers and engineers who need ready-to-run models plus training/evaluation pipelines to reproduce or extend published results. The project is hosted under the modelscope organization and is released under the Apache-2.0 license, making it suitable for both academic and commercial experimentation. With hundreds of forks and thousands of stars, ClearerVoice-Studio supplies example scripts, model checkpoints, and inference utilities to accelerate prototyping (for example, end-to-end scripts for denoising audio and extracting a target speaker from mixtures). The repository's structure and pretrained checkpoints reduce the barrier to integrating high-quality speech enhancement and separation into voice-product prototypes and research baselines.

GitHub Statistics

  • Stars: 3,808
  • Forks: 310
  • Contributors: 7
  • License: Apache-2.0
  • Primary Language: Python
  • Last Updated: 2025-08-14T08:26:30Z

According to the GitHub repository, ClearerVoice-Studio has 3,808 stars, 310 forks, and 7 contributors, and is licensed under Apache-2.0. The project shows recent activity with the last commit recorded on 2025-08-14, indicating ongoing maintenance. The star and fork counts suggest healthy community interest; however, the relatively small number of contributors implies a compact core team. The permissive license and visible forks make it easy for others to adopt and extend the codebase.

Installation

Install via pip:

git clone https://github.com/modelscope/ClearerVoice-Studio.git
cd ClearerVoice-Studio
python -m pip install -r requirements.txt
python -m pip install -e .

Key Features

  • Pretrained models for speech enhancement (denoising) ready for inference.
  • Source separation models to isolate multiple concurrent speakers or audio sources.
  • Speech super-resolution models to upsample and restore low-quality audio.
  • Target speaker extraction to isolate one speaker from a multi-talker mixture.
  • Example scripts and checkpoints for training, evaluation, and inference pipelines.

Community

The repository shows solid interest—3,808 stars and 310 forks—while development is maintained by a small core team of 7 contributors. The Apache-2.0 license encourages reuse and commercial adoption. Community engagement appears focused on reproducibility and model use; contributors and forkers can open issues and pull requests on GitHub to propose enhancements or report bugs. (For detailed user feedback and issue volume, please consult the repository's Issues and Discussions pages.)

Last Refreshed: 2026-01-09

Key Information

  • Category: Audio Tools
  • Type: AI Audio Tools Tool
Visit Official Website

Related Tools

WhisperX

WhisperX is an Automatic Speech Recognition (ASR) tool that provides fast and accurate transcriptions with word-level timestamps and speaker diarization features, enhancing the capabilities of OpenAI's Whisper model.
Retrieval-based Voice Conversion WebUI

An open-source web UI that enables voice conversion using retrieval-based methods, offering configurable options and support for different models.
Replica

An AI tool capable of replicating human voice characteristics to generate expressive, high-quality speech from text.
GPT-SoVITS

A few-shot voice cloning and text-to-speech WebUI that can train a TTS model with just 1 minute of voice data. It supports zero-shot and few-shot TTS, cross-lingual inference, and includes integrated tools for voice separation, dataset segmentation, and ASR, making it easier to build and deploy custom TTS models.
Coqui TTS

A deep learning toolkit for advanced Text-to-Speech generation, providing pretrained models across 1100+ languages, tools for training and fine-tuning models, and utilities for dataset analysis. Battle-tested in both research and production environments.
Hugging Face Speech-to-Speech

An open-sourced, modular speech-to-speech pipeline developed by Hugging Face that integrates Voice Activity Detection, Speech-to-Text, Language Models, and Text-to-Speech. It leverages models from the Transformers library (e.g., Whisper, Parler-TTS) and supports various deployment approaches including server/client and local setups.