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Update README.md #26

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15 changes: 12 additions & 3 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -39,17 +39,21 @@ Download [model weights](https://huggingface.co/spaces/Audio-AGI/AudioSep/tree/m
from pipeline import build_audiosep, inference
import torch

# Check for GPU availability and set the device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Build the AudioSeparation (AudioSep) model
model = build_audiosep(
config_yaml='config/audiosep_base.yaml',
checkpoint_path='checkpoint/audiosep_base_4M_steps.ckpt',
device=device)

# Specify the input audio file, textual description, and output file
audio_file = 'path_to_audio_file'
text = 'textual_description'
output_file='separated_audio.wav'

# Perform audio source separation
# AudioSep processes the audio at 32 kHz sampling rate
inference(model, audio_file, text, output_file, device)
```
Expand All @@ -59,21 +63,26 @@ Download [model weights](https://huggingface.co/spaces/Audio-AGI/AudioSep/tree/m
To load directly from Hugging Face, you can do the following:

```python
import torch
from models.audiosep import AudioSep
from utils import get_ss_model
import torch
from pipeline import inference

# Check for GPU availability
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load the source separation model from the provided config and model_path
ss_model = get_ss_model('config/audiosep_base.yaml')

model = AudioSep.from_pretrained("nielsr/audiosep-demo", ss_model=ss_model)

# Specify input and output files
audio_file = 'path_to_audio_file'
text = 'textual_description'
output_file='separated_audio.wav'

# AudioSep processes the audio at 32 kHz sampling rate

# AudioSep processes the audio at 32 kHz sampling rate
# Run audio source separation
inference(model, audio_file, text, output_file, device)
```
<hr>
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