Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+import torch
+import numpy as np
+import librosa
+
+from torchmetrics.functional.audio.nisqa import non_intrusive_speech_quality_assessment as tm_nisqa
+
+SR = 16000
+
+def predict_nisqa(audio):
+    if isinstance(audio, tuple):
+        _sr, y = audio
+        y = librosa.resample(np.asarray(y).astype(np.float32), orig_sr=_sr, target_sr=SR)
+    else:
+        y, _ = librosa.load(audio, sr=SR, mono=True)
+
+    wav = torch.tensor(y, dtype=torch.float32)
+
+    out = tm_nisqa(wav, SR).detach().cpu().numpy().tolist()
+    mos, noisiness, discontinuity, coloration, loudness = out
+
+    table = {
+        "Metric": ["MOS (overall)", "Noisiness", "Discontinuity", "Coloration", "Loudness"],
+        "Score":  [round(mos, 3), round(noisiness, 3), round(discontinuity, 3),
+                   round(coloration, 3), round(loudness, 3)]
+    }
+    return table
+
+with gr.Blocks(title="NISQA Speech Quality (MOS) Demo") as demo:
+    gr.Markdown(
+        """
+        # 🎧 NISQA Speech Quality (MOS)  
+        Upload or record speech and get **MOS + quality dimensions**.  
+        Uses NISQA v2.0 via TorchMetrics.
+        """
+    )
+    with gr.Row():
+        audio = gr.Audio(sources=["mic", "upload"], type="filepath", label="Input audio (wav/mp3/m4a...)")
+    btn = gr.Button("Predict")
+    out = gr.Dataframe(headers=["Metric", "Score"], label="Results", interactive=False)
+
+    btn.click(fn=predict_nisqa, inputs=audio, outputs=out)
+
+if __name__ == "__main__":
+    demo.launch()