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from typing import Dict |
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from subprocess import run |
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run("pip install pyannote.audio==2.0.1", shell=True, check=True) |
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from pyannote.audio import Pipeline |
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from transformers.pipelines.audio_utils import ffmpeg_read |
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import torch |
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SAMPLE_RATE = 16000 |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization") |
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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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audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE) |
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audio_tensor= torch.from_numpy(audio_nparray).unsqueeze(0) |
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pyannote_input = {"waveform": audio_tensor, "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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