Update handler.py

handler.py

@@ -1,47 +1,2 @@
-from typing import Dict
-from pyannote.audio import Pipeline
-import torch 
-import base64
-import numpy as np
-
-SAMPLE_RATE = 16000
-
-class EndpointHandler():
-    def __init__(self, path=""):
-        # load the model
-        self.pipeline = Pipeline.from_pretrained("KIFF/pyannote-speaker-diarization-endpoint")
-
-    def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
-        """
-        Args:
-            data (:obj:):
-                includes the deserialized audio file as bytes
-        Return:
-            A :obj:`dict`:. base64 encoded image
-        """
-        # process input
-        inputs = data.pop("inputs", data)
-        parameters = data.pop("parameters", None) #  min_speakers=2, max_speakers=5
-
-        # decode the base64 audio data
-        audio_data = base64.b64decode(inputs)
-        audio_nparray = np.frombuffer(audio_data, dtype=np.int16)
-
-        # prepare pynannote input
-        audio_tensor= torch.from_numpy(audio_nparray).float().unsqueeze(0)
-        pyannote_input = {"waveform": audio_tensor, "sample_rate": SAMPLE_RATE}
-        
-        # apply pretrained pipeline
-        # pass inputs with all kwargs in data
-        if parameters is not None:
-            diarization = self.pipeline(pyannote_input, **parameters)
-        else:
-            diarization = self.pipeline(pyannote_input)
-
-        # postprocess the prediction
-        processed_diarization = [
-            {"label": str(label), "start": str(segment.start), "stop": str(segment.end)}
-            for segment, _, label in diarization.itertracks(yield_label=True)
-        ]
-        
-        return {"diarization": processed_diarization}
+torch==1.11.0
+git+https://github.com/philschmid/pyannote-audio.[email protected]