Create app.py

app.py

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+import gradio as gr
+from pyannote.audio import Pipeline
+import torch
+
+# Initialize the diarization pipeline
+pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", use_auth_token="YOUR_HF_TOKEN")
+pipeline.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
+
+def diarize(audio):
+    diarization = pipeline({"waveform": audio, "sample_rate": 16000})
+    speaker1_segments = []
+    speaker2_segments = []
+    for segment, _, speaker in diarization.itertracks(yield_label=True):
+        if speaker == 'SPEAKER_1':
+            speaker1_segments.append((segment.start, segment.end))
+        elif speaker == 'SPEAKER_2':
+            speaker2_segments.append((segment.start, segment.end))
+    return speaker1_segments, speaker2_segments
+
+interface = gr.Interface(
+    fn=diarize,
+    inputs=gr.Audio(source="upload", type="numpy"),
+    outputs=[
+        gr.Textbox(label="Speaker 1 Segments (start, end)"),
+        gr.Textbox(label="Speaker 2 Segments (start, end)")
+    ],
+    title="Speaker Diarization",
+    description="Upload an audio file and get the segments where each speaker talks."
+)
+
+interface.launch()