Update README.md

README.md

@@ -1,3 +1,67 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+# Introduction
+
+The MossFormer2_SR_48K model weights for 48 kHz speech super-resolution in [ClearerVoice-Studio](https://github.com/modelscope/ClearerVoice-Studio/tree/main) repo.
+
+This model is trained on large scale datasets inclduing open-sourced and private data.
+
+The purpose is to enhance the quality of speech signals by increasing their temporal and spectral resolution, typically by converting low-resolution (low sampling rate) 
+audio to high-resolution (high sampling rate) audio. This involves reconstructing the high-frequency components that are often missing in low-resolution signals.
+
+# Install
+
+**Clone the Repository**
+
+``` sh
+git clone https://github.com/modelscope/ClearerVoice-Studio.git
+```
+
+**Create Conda Environment**
+
+``` sh
+cd ClearerVoice-Studio
+conda create -n clearvoice python=3.12.1
+conda activate clearvoice
+pip install -r requirements.txt
+```
+
+**Run Script**
+
+Go to `clearvoice/` and use the following examples. The MossFormer2_SR_48K model will be downloaded from huggingface automatically.
+
+Sample example 1: use model `MossFormer2_SR_48K` to process one wave file of `samples/input.wav` and save the output wave file to `samples/output_MossFormer2_SR_48K.wav`
+
+```python
+from clearvoice import ClearVoice
+
+myClearVoice = ClearVoice(task='speech_super_resolution', model_names=['MossFormer2_SR_48K'])
+
+output_wav = myClearVoice(input_path='samples/input.wav', online_write=False)
+
+myClearVoice.write(output_wav, output_path='samples/output_MossFormer2_SR_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`
+
+```python
+from clearvoice import ClearVoice
+
+myClearVoice = ClearVoice(task='speech_super_resolution', model_names=['MossFormer2_SR_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/'
+
+```python
+from clearvoice import ClearVoice
+
+myClearVoice = ClearVoice(task='speech_super_resolution', model_names=['MossFormer2_SR_48K'])
+
+myClearVoice(input_path='samples/scp/audio_samples.scp', online_write=True, output_path='samples/path_to_output_wavs_scp')
+```
+
+Model Limitations: The current speech super-resolution model is trained on a clean speech dataset and is designed to work with clean speech inputs. For speech super-resolution on noisy speech audio, 
+we recommend using our 'MossFormer2_SE_48K' model for speech enhancement first, followed by 'MossFormer2_SR_48K' for speech super-resolution.