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app.py

@@ -1,94 +1,20 @@
-import shlex
-import subprocess
-import spaces
-import torch
 import gradio as gr
-
-# install packages for mamba
-def install_mamba():
-    #subprocess.run(shlex.split("pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu118"))
-    #subprocess.run(shlex.split("pip install https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.4.0/causal_conv1d-1.4.0+cu122torch2.3cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"))
-    subprocess.run("pip install gradio --upgrade --force", shell=True)
-    subprocess.run(shlex.split("pip install https://github.com/state-spaces/mamba/releases/download/v2.2.2/mamba_ssm-2.2.2+cu122torch2.3cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"))
-    subprocess.run(shlex.split("pip install numpy==1.26.4"))
-
-install_mamba()
-
-ABOUT = """
-# SEMamba: Speech Enhancement
-A Mamba-based model that denoises real-world audio.
-Upload or record a noisy clip and click **Enhance** to hear + see its spectrogram.
-"""
-
-
-import torch
-import yaml
-import librosa
-import librosa.display
-import matplotlib
-from models.stfts    import mag_phase_stft, mag_phase_istft
-from models.generator import SEMamba
-from models.pcs400   import cal_pcs
-
-ckpt = "ckpts/SEMamba_advanced.pth"
-cfg_f = "recipes/SEMamba_advanced.yaml"
-
-# load config
-with open(cfg_f, 'r') as f:
-    cfg = yaml.safe_load(f)
-
-
-# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-device = "cuda"
-model  = SEMamba(cfg).to(device)
-sdict  = torch.load(ckpt, map_location=device)
-model.load_state_dict(sdict["generator"])
-model.eval()
-
+import spaces
+import numpy as np
+import soundfile as sf
 
 @spaces.GPU
-def enhance(filepath):
-    with torch.no_grad():
-        # load & (if needed) resample to model SR
-        wav, orig_sr = librosa.load(filepath, sr=None)
-        
-        if orig_sr != 16000:
-            wav = librosa.resample(wav, orig_sr, 16000)
-        # normalize → tensor
-        x = torch.from_numpy(wav).float().to(device)
-        norm = torch.sqrt(len(x)/torch.sum(x**2))
-        x = (x*norm).unsqueeze(0)
-        # STFT → model → ISTFT
-        amp ,pha , _ = mag_phase_stft(x, 400, 100, 400, 0.3)
-        with torch.no_grad():
-            amp2, pha2, comp = model(amp, pha)
-        out = mag_phase_istft(amp2, pha2, 400, 100, 400, 0.3)
-        out = (out/norm).squeeze().cpu().numpy()
-        # back to original rate
-        if orig_sr != 16000:
-            out = librosa.resample(out, 16000, orig_sr, 'PCM_16')
-        # write file
-        sf.write("enhanced.wav", out, orig_sr)
-        # build spectrogram
-
-        D = librosa.stft(out, n_fft=1024, hop_length=512)
-        S = librosa.amplitude_to_db(np.abs(D), ref=np.max)
-        fig, ax = plt.subplots(figsize=(6,3))
-        librosa.display.specshow(S, sr=orig_sr, hop_length=512, x_axis="time", y_axis="hz", ax=ax)
-        ax.set_title("Enhanced Spectrogram")
-        plt.colorbar(format="%+2.0f dB", ax=ax)
-
-        return "enhanced.wav"#, fig
-
+def dummy_enhance(audio_path):
+    print("Audio received:", audio_path)
+    # Return the same file as a dummy operation
+    return audio_path
 
 with gr.Blocks() as demo:
-    gr.Markdown(ABOUT)
-    input_audio = gr.Audio(label="Input Audio", type="filepath")
-    enhance_btn = gr.Button("Enhance")
-    output_audio = gr.Audio(label="Enhanced Audio", type="filepath")
-
-    #enhance_btn.click(fn=enhance, inputs=input_audio, outputs=output_audio)
-
-#demo.queue().launch()
-if __name__ == "__main__":
-    demo.launch()
+    gr.Markdown("# SEMamba: ZeroGPU Upload Test")
+    audio_input = gr.Audio(type="filepath", label="Upload Audio", interactive=True)
+    submit = gr.Button("Run Enhancement")
+    audio_output = gr.Audio(type="filepath", label="Enhanced Output")
+    
+    submit.click(dummy_enhance, inputs=[audio_input], outputs=[audio_output])
+
+demo.queue().launch()  # No ssr_mode=False