<strong>ClearerVoice-Studio</strong> is an open-source, AI-powered speech processing toolkit designed for researchers, developers, and end-users. It provides capabilities of speech enhancement, speech separation, speech super-resolution, target speaker extraction, and more. The toolkit provides state-of-the-art pre-trained models, along with training and inference scripts, all accessible from this repository.
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# 启动容器（必须带全以下参数）
docker run -it \
记得点击右上角的星星⭐来支持我们一下，您的支持是我们更新模型的最大动力！
--network=host \
--ipc=host \
## News :fire:
--shm-size=64G \
- Upcoming: More tasks will be added to ClearVoice.
--device=/dev/kfd \
-[2025.6] Add an interface for [ClearVoice](https://github.com/modelscope/ClearerVoice-Studio/tree/main/clearvoice) that allows passing a Numpy array into the model and receiving its output as a NumPy array. It allows a more flexible call of the models during a training or inference pipeline. Please check out [`demo_Numpy2Numpy.py`](https://github.com/modelscope/ClearerVoice-Studio/blob/main/clearvoice/demo_Numpy2Numpy.py).
--device=/dev/mkfd \
- [2025.5] Updated speechscore with more non-intrusive metrics: NISQA and DISTILL_MOS
--device=/dev/dri \
-[2025.4] Updated pip installation for [ClearVoice](https://github.com/modelscope/ClearerVoice-Studio/tree/main/clearvoice). Now you can simply type `pip install clearvoice` to use all the pretrained models in ClearVoice, see project description in PyPi [link](https://pypi.org/project/clearvoice/).
-v /opt/hyhal:/opt/hyhal \
-[2025.4] Added a training script for speech super-resolution, supporting both retraining and fine-tuning of models. For details, refer to the documentation [here](https://github.com/modelscope/ClearerVoice-Studio/tree/main/train/speech_super_resolution).
--group-add video \
-[2025.4] Added data generation scripts for training/finetuning speech enhancement models. The scripts generate either noisy speech or noisy-reverberant speech. Please check [here](https://github.com/modelscope/ClearerVoice-Studio/tree/main/train/data_generation/speech_enhancement).
--cap-add=SYS_PTRACE \
-[2025.1] ClearVoice demo is ready for try on both [HuggingFace](https://huggingface.co/spaces/alibabasglab/ClearVoice) and [ModelScope](https://modelscope.cn/studios/iic/ClearerVoice-Studio). However, HuggingFace has limited GPU usage, and ModelScope has more GPU usage quota.
--security-opt seccomp=unconfined \
-[2025.1] ClearVoice now offers **speech super-resolution**, also known as bandwidth extension. This feature improves the perceptual quality of speech by converting low-resolution audio (with an effective sampling rate of at least 16,000 Hz) into high-resolution audio with a sampling rate of 48,000 Hz. A full upscaled **LJSpeech-1.1-48kHz dataset** can be downloaded from [HuggingFace](https://huggingface.co/datasets/alibabasglab/LJSpeech-1.1-48kHz) and [ModelScope](https://modelscope.cn/datasets/iic/LJSpeech-1.1-48kHz).
-[2025.1] ClearVoice now supports more audio formats including **"wav", "aac", "ac3", "aiff", "flac", "m4a", "mp3", "ogg", "opus", "wma", "webm"**, etc. It also supports both mono and stereo channels with 16-bit or 32-bit precisions. A latest version of [ffmpeg](https://github.com/FFmpeg/FFmpeg) is required for audio codecs.
/bin/bash
-[2024.12] Upload pre-trained models on ModelScope. User now can download the models from either [ModelScope](https://www.modelscope.cn/models/iic/ClearerVoice-Studio/summary) or [Huggingface](https://huggingface.co/alibabasglab)
```
-[2024.11] Our FRCRN speech denoiser has been used over **3.0 million** times on [ModelScope](https://modelscope.cn/models/iic/speech_frcrn_ans_cirm_16k)
-[2024.11] Our MossFormer speech separator has been used over **2.5 million** times on [ModelScope](https://modelscope.cn/models/iic/speech_mossformer_separation_temporal_8k)
-**Pre-Trained Models:** Includes cutting-edge pre-trained models, fine-tuned on extensive, high-quality datasets. No need to start from scratch!
cd ClearerVoice-Studio
-**Ease of Use:** Designed for seamless integration with your projects, offering a simple yet flexible interface for inference and training.
pip install -r requirements.txt
-**Comprehensive Features:** Combines advanced algorithms for multiple speech processing tasks in one platform.
-**Community-Driven:** Built for researchers, developers, and enthusiasts to collaborate and innovate together.
cd clearvoice
pip install --editable .
## Contents of this repository
cd ..
This repository is organized into three main components: **[ClearVoice](https://github.com/modelscope/ClearerVoice-Studio/tree/main/clearvoice)**, **[Train](https://github.com/modelscope/ClearerVoice-Studio/tree/main/train)**, and **[SpeechScore](https://github.com/modelscope/ClearerVoice-Studio/tree/main/speechscore)**.
ClearVoice offers a user-friendly solution for speech processing tasks such as speech denoising, separation, super-resolution, audio-visual target speaker extraction, and more. It is designed as a unified inference platform leveraged pre-trained models (e.g., [FRCRN](https://arxiv.org/abs/2206.07293), [MossFormer](https://arxiv.org/abs/2302.11824)), all trained on extensive datasets. If you're looking for a tool to improve speech quality, ClearVoice is the perfect choice. Simply click on [`ClearVoice`](https://github.com/modelscope/ClearerVoice-Studio/tree/main/clearvoice) and follow our detailed instructions to get started.
```bash
### 2. **Train**
# 设置 HuggingFace 国内镜像（强烈建议）
For advanced researchers and developers, we provide model finetune and training scripts for all the tasks offerred in ClearVoice and more:
<ahref="https://github.com/modelscope/ClearerVoice-Studio/tree/main/speechscore">`SpeechScore`<a/> is a speech quality assessment toolkit. We include it here to evaluate different model performance. SpeechScore includes many popular speech metrics:
## 小贴士
- Signal-to-Noise Ratio (SNR)
- 模型下载慢请务必设置 `HF_ENDPOINT=https://hf-mirror.com`
- Perceptual Evaluation of Speech Quality (PESQ)
- Short-Time Objective Intelligibility (STOI)
- Deep Noise Suppression Mean Opinion Score (DNSMOS)
- Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)
- and many more quality benchmarks
## Contact
If you have any comments or questions about ClearerVoice-Studio, feel free to raise an issue in this repository or contact us directly at:
Alternatively, welcome to join our DingTalk group to share and discuss algorithms, technology, and user experience feedback. You may scan the following QR codes to join our official chat group.
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<ahref="./asset/QR.jpg"><imgalt="ClearVoice in DingTalk"src="https://img.shields.io/badge/ClearVoice-DingTalk-d9d9d9"></a>
