Update app.py

app.py

@@ -1,23 +1,13 @@
+from pyannote_audio_fix import SpeakerDiarization
 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()
+def fn(audio,progress=gr.Progress(True)):
+  model = SpeakerDiarization()
+  wav , sr = torchaudio.load(audio)
+  if sr != 16000:
+    wav = torchaudio.functional.resample(wav,sr,16000)
+  if wav.shape[0] == 2:
+    wav = wav.mean(dim=0,keepdim=True)
+  return model(wav)
+
+gr.Interface(fn=fn, inputs=gr.Audio(type='filepath'), outputs="json").launch()