Create app.py

app.py

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+import torchaudio
+from torchaudio.functional import resample
+import os
+from pyannote.audio import Pipeline
+os.environ["PYANNOTE_SKIP_DEPENDENCY_CHECK"] = "1"
+
+def process(input_file):
+    pipeline = Pipeline.from_pretrained("shethjenil/speaker-diarization-community-1")
+    audio, sr = torchaudio.load(input_file)
+    target_sr = 16000
+    if sr != target_sr:
+        audio = resample(audio, sr, target_sr)
+    if audio.shape[0] > 1:
+        audio = audio.mean(dim=0, keepdim=True)
+    output = pipeline({"waveform":audio,"sample_rate":target_sr})
+    return {
+      "diarization":[[i['start'],i['end'],int(i['speaker'].lstrip("SPEAKER_"))] for i in output.serialize()['diarization']],
+      "exclusive_diarization":[[i['start'],i['end'],int(i['speaker'].lstrip("SPEAKER_"))] for i in output.serialize()['exclusive_diarization']],
+      "embedding":output.speaker_embeddings.tolist()
+    }
+
+import gradio as gr
+gr.Interface(process, inputs=gr.Audio(type="filepath"), outputs=gr.JSON()).launch()