handler.py

# 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
import os
import threading
import time

class EndpointHandler:
    def __init__(self, path=""):
        # initialize pretrained pipeline
        print("-----------------------------------")
        print(f"\nPATH: {path}\n")
        print(f"\nls {path}: {os.listdir(path)}")
        print("-----------------------------------")
        self._pipeline = Pipeline.from_pretrained("collinbarnwell/pyannote-speaker-diarization-31")
        HYPER_PARAMETERS = {
            "segmentation": {
                "min_duration_off": 3.0,
            }
        }
        self._pipeline.instantiate(HYPER_PARAMETERS)

        # 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 = torch.tensor(inputs["waveform"])
        sample_rate = inputs["sample_rate"]
        parameters = data.pop("parameters", dict())

        # Container for storing diarization result
        diarization_result = {}

        def diarize():
            nonlocal diarization_result
            diarization = self._pipeline(
                {"waveform": waveform, "sample_rate": sample_rate}, **parameters
            )
            diarization_result = [
                {
                    "speaker": speaker,
                    "start": f"{turn.start:.3f}",
                    "end": f"{turn.end:.3f}",
                }
                for turn, _, speaker in diarization.itertracks(yield_label=True)
            ]

        # Running diarization in a separate thread
        diarization_thread = threading.Thread(target=diarize)
        diarization_thread.start()

        # Wait for the diarization to complete or timeout
        diarization_thread.join(timeout=298)

        # Check if the thread is still alive (indicating a timeout occurred)
        if diarization_thread.is_alive():
            print("Diarization timed out")
            # Handle the timeout case, maybe by raising an error or a warning
            raise TimeoutError("Diarization process exceeded time limit.")

        return {"diarization": diarization_result}