Add application file

.gitignore

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+*.ckpt
+*__

app.py

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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()

requirements.txt

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+soundfile>=0.10.3.post1
+tqdm>=4.46.1
+pysndfx>=0.3.6
+pandas>=1.0.1
+numpy>=1.18.1
+pyloudnorm>=0.1.0
+scipy>=1.4.1
+matplotlib>=3.1.3
+torch==2.2.1
+torchaudio==2.2.1
+speechbrain