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from typing import Dict
from pyannote.audio import Pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import torch
from transformers.utils import logging
logger = logging.get_logger("MAXWELL")
SAMPLE_RATE = 16000
class EndpointHandler():
def __init__(self, path=""):
# load the model
self.pipeline = Pipeline.from_pretrained("pyannote/[email protected]")
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
logger.warning("MAXWELL")
logger.warning(data)
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}
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