clearvoice 0.1.2
A unified inference platform for speech processing tasks
Requires Python: >=3.8

# ClearVoice

## 👉🏻[HuggingFace Space Demo](https://huggingface.co/spaces/alibabasglab/ClearVoice)👈🏻 |  👉🏻[ModelScope Space Demo](https://modelscope.cn/studios/iic/ClearerVoice-Studio)👈🏻 

## Table of Contents

- [1. Introduction](#1-introduction)
- [2. Usage](#2-usage)

## 1. Introduction

ClearVoice offers a unified inference platform for `speech enhancement`, `speech separation`, `speech super-resolution`, and `audio-visual target speaker extraction`. It is designed to simplify the adoption of our pre-trained models for your speech processing purpose or the integration into your projects. Currently, we provide the following pre-trained models:

| Tasks (Sampling rate) | Models (HuggingFace Links)|
|-------|--------------------------|
|Speech Enhancement (16kHz & 48kHz)| `MossFormer2_SE_48K` ([HuggingFace](https://huggingface.co/alibabasglab/MossFormer2_SE_48K)), `FRCRN_SE_16K` ([HuggingFace](https://huggingface.co/alibabasglab/FRCRN_SE_16K)), `MossFormerGAN_SE_16K` ([HuggingFace](https://huggingface.co/alibabasglab/MossFormerGAN_SE_16K)) | 
|Speech Separation (16kHz)|`MossFormer2_SS_16K` ([HuggingFace](https://huggingface.co/alibabasglab/MossFormer2_SS_16K))|
|Speech Super-Resolution (48kHz)|`MossFormer2_SR_48K`([HuggingFace](https://huggingface.co/alibabasglab/MossFormer2_SR_48K))|
|Audio-Visual Target Speaker Extraction (16kHz)|`AV_MossFormer2_TSE_16K` ([HuggingFace](https://huggingface.co/alibabasglab/AV_MossFormer2_TSE_16K))|

You don't need to manually download the pre-trained models—they are automatically fetched from HuggingFace during inference. If the models are not downloaded sucessfully to `./clearvoice/checkpoints`, you can manually download them from [ModelScope](https://modelscope.cn/models/iic/ClearerVoice-Studio/summary). 

## 2. Usage

### Install via PyPI

1. **Install ClearVoice via PyPI:**
    ``` sh
    pip install clearvoice
    ```

2. **In your Python code:**
    ``` python
    from clearvoice import ClearVoice
    ```
### Install FFmpeg (optional)

Clearvoice relies on FFmpeg for audio format conversion. If you're only working with `.wav` files, FFmpeg is not required. For all other formats, please follow the instructions below to install FFmpeg.

1. On **Ubuntu/Debian**:
   ``` sh
   sudo apt update
   sudo apt install ffmpeg
   ```
2. On **macOS** (with Homebrew):
   ``` sh
   brew install ffmpeg
   ```
3. On **Windows**:
    Download the static build from https://ffmpeg.org/download.html.
    Unzip and add the bin folder (which contains ffprobe.exe) to your System PATH.
   
### Install from GitHub

1. **Clone the GitHub repository and install the requirements:**

    ``` sh
    git clone https://github.com/modelscope/ClearerVoice-Studio.git
    cd ClearerVoice-Studio/clearvoice
    pip install --editable .
    ```
2. **In your Python code:**
    ``` python
    from clearvoice import ClearVoice
    ```

3. **Demo script**

    ``` sh
    cd ClearerVoice-Studio/clearvoice
    python demo.py
    ```

    or 

    ``` sh
    cd ClearerVoice-Studio/clearvoice
    python demo_with_more_comments.py
    ```

    or
    ``` sh
    cd ClearerVoice-Studio/clearvoice
    python demo_Numpy2Numpy.py
    ```
- You may activate each demo case by setting to True in `demo.py`, `demo_with_more_comments.py`, and `demo_Numpy2Numpy.py`.
- In `demo_Numpy2Numpy.py`, we added a new interface for ClearVoice that supports from numpy input to numpy output, instead of file I/O.
- Supported audio format: .flac .wav
- Supported video format: .avi .mp4 .mov .webm

### Sample Python Script

Use `MossFormer2_SE_48K` model for fullband (48kHz) speech enhancement task:

```python
from clearvoice import ClearVoice

myClearVoice = ClearVoice(task='speech_enhancement', model_names=['MossFormer2_SE_48K'])

#process single wave file
output_wav = myClearVoice(input_path='samples/input.wav', online_write=False)
myClearVoice.write(output_wav, output_path='samples/output_MossFormer2_SE_48K.wav')

#process wave directory
myClearVoice(input_path='samples/path_to_input_wavs', online_write=True, output_path='samples/path_to_output_wavs')

#process wave list file
myClearVoice(input_path='samples/scp/audio_samples.scp', online_write=True, output_path='samples/path_to_output_wavs_scp')
```

Parameter Description:
- `task`: Choose one of the three tasks `speech_enhancement`, `speech_separation`, and `target_speaker_extraction`
- `model_names`: List of model names, choose one or more models for the task
- `input_path`: Path to the input audio/video file, input audio/video directory, or a list file (.scp) 
- `online_write`: Set to `True` to enable saving the enhanced/separated audio/video directly to local files during processing, otherwise, the enhanced/separated audio is returned. (Only supports `False` for `speech_enhancement`, `speech_separation` when processing single wave file`)
- `output_path`: Path to a file or a directory to save the enhanced/separated audio/video file

这里给出了一个较详细的中文使用教程：https://stable-learn.com/zh/clearvoice-studio-tutorial 

## 3. Model Performance

**Speech enhancement models:**

We evaluated our released speech enhancement models on the popular benchmarks: [VoiceBank+DEMAND](https://paperswithcode.com/dataset/demand) testset (16kHz & 48kHz) and [DNS-Challenge-2020](https://paperswithcode.com/dataset/deep-noise-suppression-2020) (Interspeech) testset (non-reverb, 16kHz). Different from the most published papers that tailored each model for each test set, our evaluation here uses unified models on the two test sets. The evaluation metrics are generated by [SpeechScore](https://github.com/modelscope/ClearerVoice-Studio/tree/main/speechscore).

**VoiceBank+DEMAND testset (tested on 16kHz)**
|Model               |PESQ    |NB_PESQ |CBAK    |COVL    |CSIG    |STOI    |SISDR    |SNR      |SRMR    |SSNR    |P808_MOS|SIG     |BAK     |OVRL    |ISR      |SAR      |SDR      |FWSEGSNR |LLR     |LSD     |MCD|
|-----               |---     |------- |----    |----    |----    |----    |-----    |---      |----    |----    |------  |---     |---     |----    |---      |---      |---      |-------- |---     |---     |---|
|Noisy               |1.97    | 3.32   |2.79    |2.70    |3.32    |0.92    |8.44     |9.35     |7.81    |6.13    |3.05    |3.37    |3.32    |2.79    |28.11    |8.53     |8.44     |14.77    |0.78    |1.40    |4.15|
|FRCRN_SE_16K        |3.23    | 3.86   |3.47    |**3.83**|4.29    |0.95    |19.22    |19.16    |9.21    |7.60    |**3.59**|3.46    |**4.11**|3.20    |12.66    |21.16    |11.71    |**20.76**|0.37    |0.98    |**0.56**|
|MossFormerGAN_SE_16K|**3.47**|**3.96**|**3.50**|3.73    |**4.40**|**0.96**|**19.45**|**19.36**|9.07    |**9.09**|3.57    |**3.50**|4.09    |**3.23**|25.98    |21.18    |**19.42**|20.20    |**0.34**|**0.79**|0.70|
|MossFormer2_SE_48K  |3.16    | 3.77   |3.32    |3.58    |4.14    |0.95    |19.38    |19.22    |**9.61**|6.86    |3.53    |**3.50**|4.07    |3.22    |**12.05**|**21.84**|11.47    |16.69    |0.57    |1.72    |0.62|

**DNS-Challenge-2020 testset (tested on 16kHz)**
|Model               |PESQ    |NB_PESQ |CBAK    |COVL    |CSIG    |STOI    |SISDR    |SNR      |SRMR    |SSNR    |P808_MOS|SIG     |BAK     |OVRL    |ISR      |SAR      |SDR      |FWSEGSNR |LLR     |LSD     |MCD|
|-----               |---     |------- |----    |----    |----    |----    |-----    |---      |----    |----    |------  |---     |---     |----    |---      |---      |---      |-------- |---     |---     |---|
|Noisy               |1.58    | 2.16   |2.66    |2.06    |2.72    |0.91    |9.07     |9.95     |6.13    |9.35    |3.15    |3.39    |2.61    |2.48    |34.57    |9.09     |9.06     |15.87    |1.07    |1.88    |6.42|
|FRCRN_SE_16K        |3.24    | 3.66   |3.76    |3.63    |4.31    |**0.98**|19.99    |19.89    |8.77    |7.60    |4.03    |3.58    |4.15    |3.33    |**8.90** |20.14    |7.93     |**22.59**|0.50    |1.69    |0.97|
|MossFormerGAN_SE_16K|**3.57**|**3.88**|**3.93**|**3.92**|**4.56**|**0.98**|**20.60**|**20.44**|8.68    |**14.03**|**4.05**|**3.58**|**4.18**|**3.36**|8.88    |**20.81**|**7.98** |21.62    |**0.45**|**1.65**|**0.89**|
|MossFormer2_SE_48K  |2.94    | 3.45   |3.36    |2.94    |3.47    |0.97    |17.75    |17.65    |**9.26**|11.86    |3.92   |3.51    |4.13    |3.26    |8.55     |18.40    |7.48     |16.10    |0.98    |3.02    |1.15|

**VoiceBank+DEMAND testset (tested on 48kHz)** (We included our evaluations on other open-sourced models using SpeechScore)
|Model               |PESQ    |NB_PESQ |CBAK    |COVL    |CSIG    |STOI    |SISDR    |SNR      |SRMR    |SSNR    |P808_MOS|SIG     |BAK     |OVRL    |ISR      |SAR      |SDR      |FWSEGSNR |LLR     |LSD     |MCD|
|-----               |---     |------- |----    |----    |----    |----    |-----    |---      |----    |----    |------  |---     |---     |----    |---      |---      |---      |-------- |---     |---     |---|
|Noisy               |1.97    | 2.87   |2.79    |2.70    |3.32    |0.92    |8.39     |9.30     |7.81    |6.13    |3.07    |3.35    |3.12    |2.69    |33.75    |8.42     |8.39     |13.98    |0.75    |1.45    |5.41|
|MossFormer2_SE_48K  |**3.15**|**3.77**|**3.33**|**3.64**|**4.23**|**0.95**|**19.36**|**19.22**|9.61    |7.03    |**3.53**|  3.41  |**4.10**|**3.15**|**4.08**|**21.23** |4.06     |14.45    |NA      |1.86    |**0.53**|
|Resemble_enhance    |2.84    | 3.58   |3.14    |NA      |NA      |0.94    |12.42    |12.79    |9.08    |7.07    |**3.53**|**3.42**|  3.99  |3.12    |13.62    |12.66    |10.31    |14.56    |1.50    |1.66    |  1.54  |
|DeepFilterNet       |3.03    | 3.71   |3.29    |3.55    |4.20    |0.94    |15.71    |15.66    |**9.66**|**7.19**|3.47    |3.40    |4.00    |3.10    |28.01    |16.20    |**15.79**|**15.69**|**0.55**|**0.94**|  1.77  |

- Resemble_enhance ([Github](https://github.com/resemble-ai/resemble-enhance)) is an open-sourced 44.1kHz pure speech enhancement platform from Resemble-AI since 2023, we resampled to 48khz before making evaluation.
- DeepFilterNet ([Github](https://github.com/Rikorose/DeepFilterNet)) is a low complexity speech enhancement framework for Full-Band audio (48kHz) using on deep filtering. 
> **Note:** We observed anomalies in two speech metrics, LLR and LSD, after processing with the 48 kHz models. We will further investigate the issue to identify the cause.

**Speech separation models:**

We evaluated our speech separation model `MossFormer2_SS_16K` on the popular benchmark testset: LRS2_2Mix (16 kHz), WSJ0-2Mix (8 kHz), Libri2Mix (8 kHz), WHAM! (8 kHz). We compare our model with following state-of-the-art models: [Conv-TasNet](https://arxiv.org/abs/1809.07454), [DualPathRNN](https://arxiv.org/abs/1910.06379), [DPTNet](https://arxiv.org/abs/2007.13975), [SepFormer](https://arxiv.org/abs/2010.13154), [TDANet](https://openreview.net/pdf?id=fzberKYWKsI), [TF-GridNet](https://arxiv.org/abs/2209.03952), [SPMamba](https://arxiv.org/abs/2404.02063). The testing results are taken from [TDANet Github repo](https://github.com/JusperLee/TDANet) and [SPMamba GitHub repo](https://github.com/JusperLee/SPMamba). The performance metric of [SI-SNRi](https://arxiv.org/abs/1811.02508) (SI-SNR improvement) is used for the evaluations.

|Model |LRS2_2Mix (16 kHz)|WSJ0-2Mix (8 kHz)|Libri2Mix (8kHz)|WHAM! (8 kHz)|
|------|------------------|-----------------|----------------|-------------|
|Conv-TasNet |10.6|15.3|12.2|12.7|
|DualPathRNN|12.7|18.8|16.1|13.7|
|DPTNet     |13.3|20.2|16.7|14.9|     
|SepFormer  |13.5|20.4|17.0|14.4|
|TDANet Large|14.2|18.5|17.4|15.2|
|TF-GridNet   |-|**22.8**|19.8|16.9|
|SPMamba      |-|22.5|**19.9**|**17.4**|
|MossFormer2_SS_16K|**15.5**|22.0|16.7|**17.4**|

> **Note:** The MossFormer2_SS_16K results presented are from our unified model, evaluated without retraining on individual datasets.  This 16 kHz model was used for speech separation on the 16 kHz test set, with scores then calculated on the downsampled 8 kHz audio.  All comparison models were trained and tested separately on each dataset.

**Speech super-resolution model:**

We demonstrated the effectiveness of our speech super-resolution model, `MossFormer2_SR_48K`, using the VoiceBank+DEMAND 48 kHz test set.  For super-resolution evaluation, the test set was downsampled to 16 kHz, 24 kHz, and 32 kHz.  The Log Spectral Distance (LSD) and PESQ metrics was used for evaluation. Recognizing that speech quality is impacted by both lower sampling rates and background noise, we also incorporated our speech enhancement model, `MossFormer2_SE_48K`, to reduce noise prior to super-resolution processing.  Results are presented in the following table.

|Model | 16 kHz | 24 kHz | 32 kHz | 48 kHz |PESQ|
|------|--------|--------|--------|--------|-----|
|Origin|2.80    | 2.60   |  2.29  |1.46    |1.97|
|Enhanced|**1.93**  |**1.52**    |   **1.50** |**1.42**    |**3.15** |

For the 48 kHz case, speech super-resolution was not applied.  The final two columns show that` MossFormer2_SE_48K` significantly improves the 16 kHz PESQ score but only marginally improves LSD.  Therefore, LSD improvements at 16 kHz, 24 kHz, and 32 kHz are primarily attributed to `MossFormer2_SR_48K`.