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phil-pyannote-speaker-diarization-endpoint

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phil-pyannote-speaker-diarization-endpoint / handler.py

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	from typing import Dict
	from pyannote.audio import Pipeline
	from transformers.pipelines.audio_utils import ffmpeg_read
	import torch

	SAMPLE_RATE = 16000



	class EndpointHandler():
	def __init__(self, path=""):
	# load the model
	self.pipeline = Pipeline.from_pretrained("philschmid/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


	# prepare pynannote input
	audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE)
	audio_tensor= torch.from_numpy(audio_nparray).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}