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from typing import Dict |
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from pyannote.audio import Pipeline |
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from io import BytesIO |
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import torch |
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import torchaudio |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.pipeline = Pipeline.from_pretrained("config.yaml") |
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def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]: |
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""" |
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Args: |
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data (:obj:): |
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includes the deserialized audio file as bytes |
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Return: |
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A :obj:`dict`:. base64 encoded image |
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""" |
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inputs = data.pop("inputs", data) |
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parameters = data.pop("parameters", None) |
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waveform, sample_rate = torchaudio.load(BytesIO(inputs)) |
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pyannote_input = {"waveform": waveform, "sample_rate": sample_rate} |
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if parameters is not None: |
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diarization = self.pipeline(pyannote_input, **parameters) |
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else: |
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diarization = self.pipeline(pyannote_input) |
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processed_diarization = [ |
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{"label": str(label), "start": str(segment.start), "stop": str(segment.end)} |
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for segment, _, label in diarization.itertracks(yield_label=True) |
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] |
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return {"diarization": processed_diarization} |
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