wip

app.py

@@ -1,7 +1,63 @@
-import gradio as gr
+import gradio
+import gradio.inputs
+import gradio.outputs
+import torch
+from df.enhance import enhance, init_df
 
-def greet(name):
-    return "Hello " + name + "!!"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+def mix_at_snr(clean, noise, snr, eps=1e-10):
+    """Mix clean and noise signal at a given SNR.
+
+    Args:
+        clean: 1D Tensor with the clean signal to mix.
+        noise: 1D Tensor of shape.
+        snr: Signal to noise ratio.
+
+    Returns:
+        clean: 1D Tensor with gain changed according to the snr.
+        noise: 1D Tensor with the combined noise channels.
+        mix: 1D Tensor with added clean and noise signals.
+
+    """
+    clean = torch.as_tensor(clean)
+    noise = torch.as_tensor(noise)
+    E_speech = torch.mean(clean.pow(2)) + eps
+    E_noise = torch.mean(noise.pow(2))
+    K = torch.sqrt((E_noise / E_speech) * 10 ** (snr / 10) + eps)
+    noise = noise / K
+    mixture = clean + noise
+    assert torch.isfinite(mixture)
+    return clean, noise, mixture
+
+
+def mix_and_denoise(speech, noise, snr):
+    model, df, _ = init_df()
+    speech, noise, noisy = mix_at_snr(speech, noise, snr)
+    enhanced = enhance(model.to(device=device).eval(), df, noisy)
+    return speech, noisy, enhanced
+
+
+inputs = [
+    gradio.inputs.Audio(
+        source="microphone", type="filepath", optional=True, label="Speech"
+    ),
+    gradio.inputs.Audio(
+        source="microphone", type="filepath", optional=True, label="Noise"
+    ),
+    gradio.inputs.Slider(minimum=-10, maximum=40, step=5, default=10),
+]
+examples = [
+    [],
+    ["samples/noise_freesound_2530.wav", "samples/noise_freesound_573577.wav"],
+]
+outputs = [
+    gradio.outputs.Audio(label="Clean"),
+    gradio.outputs.Audio(label="Noisy"),
+    gradio.outputs.Audio(label="Enhanced"),
+]
+iface = gradio.Interface(
+    fn=mix_and_denoise, inputs=inputs, outputs=outputs, examples=examples
+)
 iface.launch()

requirements.txt

@@ -0,0 +1,2 @@
+deepfilternet
+gradio