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import gradio as gr
import librosa
import torch
import soundfile as sf
from speechbrain.inference.separation import SepformerSeparation as separator
import torchaudio, torchmetrics, torch
# defineing model class
class SepformerFineTune(torch.nn.Module):
def __init__(self, model):
super(SepformerFineTune, self).__init__()
self.model = model
# disabling gradient computation
for parms in self.model.parameters():
parms.requires_grad = False
# enable gradient computation for the last layer
named_layers = dict(model.named_modules())
for name, layer in named_layers.items():
# print(f"Name: {name}, Layer: {layer}")
if name == "mods.masknet.output.0":
for param in layer.parameters():
param.requires_grad = True
if name == "mods.masknet.output_gate":
for param in layer.parameters():
param.requires_grad = True
# printing all tranble parameters
# for model_name, model_params in model.named_parameters():
# print(f"Model Layer Name: {model_name}, Model Params: {model_params.requires_grad}")
def forward(self, mix):
est_sources = self.model.separate_batch(mix)
return est_sources[:,:,0], est_sources[:,:,1] # NOTE: Working with 2 sources ONLY
class SourceSeparationApp:
def __init__(self, model_path,device="cpu"):
self.model = self.load_model(model_path)
self.device = device
def load_model(self, model_path):
model = separator.from_hparams(source="speechbrain/sepformer-wsj03mix", savedir='pretrained_models/sepformer-wsj03mix', run_opts={"device": device})
checkpoint = torch.load(model_path)
fine_tuned_model = SepformerFineTune(model)
fine_tuned_model.load_state_dict(checkpoint["model"])
return fine_tuned_model
def separate_sources(self, audio_file):
# Load input audio
# print(f"[LOG] Audio file: {audio_file}")
input_audio_tensor, sr = audio_file[1], audio_file[0]
if self.model is None:
return "Error: Model not loaded."
# sending input audio to PyTorch tensor
input_audio_tensor = torch.tensor(input_audio_tensor,dtype=torch.float).unsqueeze(0)
input_audio_tensor = input_audio_tensor.to(self.device)
# Source separation using the loaded model
self.model.to(self.device)
self.model.eval()
with torch.inference_mode():
# print(f"[LOG] mix shape: {mix.shape}, s1 shape: {s1.shape}, s2 shape: {s2.shape}, noise shape: {noise.shape}")
source1,source2 = self.model(input_audio_tensor)
# Save separated sources
sf.write("source1.wav", source1.squeeze().cpu().numpy(), sr)
sf.write("source2.wav", source2.squeeze().cpu().numpy(), sr)
return "Separation completed", "source1.wav", "source2.wav"
def run(self):
audio_input = gr.Audio(label="Upload or record audio")
output_text = gr.Label(label="Status:")
audio_output1 = gr.Audio(label="Source 1", type="filepath",)
audio_output2 = gr.Audio(label="Source 2", type="filepath",)
gr.Interface(
fn=self.separate_sources,
inputs=audio_input,
outputs=[output_text, audio_output1, audio_output2],
title="Audio Source Separation",
description="Separate sources from a mixed audio signal.",
allow_flagging=False
).launch()
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = "fine_tuned_sepformer-wsj03mix-7sec.ckpt" # Replace with your model path
app = SourceSeparationApp(model_path, device=device)
app.run()
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