MossFormer2_SE_48K

Introduction

The MossFormer2_SE_48K model weights for 48 kHz speech enhancement in ClearerVoice-Studio repo.

This model is trained on large scale datasets inclduing open-sourced and private data.

It enhances speech audios by removing background noise.

Install

Clone the Repository

git clone https://github.com/modelscope/ClearerVoice-Studio.git

Create Conda Environment

cd ClearerVoice-Studio
conda create -n clearvoice python=3.8
conda activate clearvoice
pip install -r requirements.txt

Run Script

Go to clearvoice/ and use the following examples. The MossFormer2_SE_48K model will be downloaded from huggingface automatically.

Sample example 1: use speech enhancement model MossFormer2_SE_48K to process one wave file of samples/input.wav and save the output wave file to samples/output_MossFormer2_SE_48K.wav

from clearvoice import ClearVoice

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

output_wav = myClearVoice(input_path='samples/input.wav', online_write=False)

myClearVoice.write(output_wav, output_path='samples/output_MossFormer2_SE_48K.wav')

Sample example 2: use speech enhancement model MossFormer2_SE_48K to process all input wave files in samples/path_to_input_wavs/ and save all output files to samples/path_to_output_wavs

from clearvoice import ClearVoice

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

myClearVoice(input_path='samples/path_to_input_wavs', online_write=True, output_path='samples/path_to_output_wavs')

Sample example 3: use speech enhancement model MossFormer2_SE_48K to process wave files listed in `samples/audio_samples.scp' file, and save all output files to 'samples/path_to_output_wavs_scp/'

from clearvoice import ClearVoice

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

myClearVoice(input_path='samples/scp/audio_samples.scp', online_write=True, output_path='samples/path_to_output_wavs_scp')