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# MIT License
#
# Copyright (c) 2023 CNRS
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from pyannote.audio import Pipeline, Audio
import torch
class EndpointHandler:
def __init__(self, path=""):
# initialize pretrained pipeline
self._pipeline = Pipeline.from_pretrained("tensorlake/speaker-diarization-3.1")
# send pipeline to GPU if available
if torch.cuda.is_available():
self._pipeline.to(torch.device("cuda"))
# initialize audio reader
self._io = Audio()
def __call__(self, data):
inputs = data.pop("inputs", data)
waveform, sample_rate = self._io(inputs)
parameters = data.pop("parameters", dict())
diarization = self.pipeline(
{"waveform": waveform, "sample_rate": sample_rate}, **parameters
)
processed_diarization = [
{
"speaker": speaker,
"start": f"{turn.start:.3f}",
"end": f"{turn.end:.3f}",
}
for turn, _, speaker in diarization.itertracks(yield_label=True)
]
return {"diarization": processed_diarization}
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