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| import gradio as gr | |
| import torch | |
| import soundfile as sf | |
| from speechbrain.inference.separation import SepformerSeparation as separator | |
| # 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, map_location=torch.device("cpu") | |
| 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() | |