Jaikinator
141 lines
4.3 KiB
Python
141 lines
4.3 KiB
Python
from pyannote.audio import Pipeline
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from torch import Tensor
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import os
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from typing import TypeVar, Union
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Annotation = TypeVar('Annotation')
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PYANNOTE_DEFAULT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)),
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"models", "pyannote",
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"speaker_diarization", "config.yaml")
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class Diariser:
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def __init__(self, model,*args,**kwargs) -> None:
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self.model = model
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def diarization(self, audiofile : Union[str, Tensor] ,
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*args, **kwargs) -> Annotation:
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"""
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Diarization of audio file
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:param audiofile: path to audio file or torch.Tensor
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:param args: args for diarization model
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:param kwargs: kwargs for diarization model
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:return: diarization
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"""
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diarization = self.model(audiofile,*args, **kwargs)
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out = self.format_diarization_output(diarization)
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return out
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@staticmethod
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def format_diarization_output(dia : Annotation) -> dict:
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"""
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Format diarization output to a list of tuples
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:param dia: diarization output
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:return: dict with speaker names as keys and list of tuples
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as values and list of different speakers
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"""
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dia_list = list(dia.itertracks(yield_label=True))
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diarization_output = {"speakers": [], "segments": []}
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normalized_output = []
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index_start_speaker = 0
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index_end_speaker = 0
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current_speaker = str()
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###
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# Sometimes two consecutive speakers are the same
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# This loop removes these duplicates
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###
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for i, (_, _, speaker) in enumerate(dia_list):
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if i == 0:
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current_speaker = speaker
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if speaker != current_speaker:
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index_end_speaker = i - 1
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normalized_output.append([index_start_speaker,
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index_end_speaker,
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current_speaker])
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index_start_speaker = i
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current_speaker = speaker
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if i == len(diarization_output["speakers"]) - 1:
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index_end_speaker = i
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normalized_output.append([index_start_speaker,
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index_end_speaker,
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current_speaker])
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for outp in normalized_output:
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start = dia_list[outp[0]][0].start
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end = dia_list[outp[1]][0].end
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diarization_output["segments"].append([start, end])
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diarization_output["speakers"].append(outp[2])
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return diarization_output
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@staticmethod
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def _get_token():
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# check ig .pyannotetoken.txt exists
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path = os.path.join(os.path.dirname(
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os.path.realpath(__file__)), '.pyannotetoken')
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if os.path.exists(path):
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with open(path, 'r') as f:
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token = f.read()
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else:
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raise ValueError('No token found.' \
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'Please create a token at https://huggingface.co/settings/token' \
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'and save it in a file called .pyannotetoken.txt')
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return token
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@classmethod
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def load_model(cls, model: str = PYANNOTE_DEFAULT_PATH,
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token: str = "",
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local : bool = True,
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*args, **kwargs) -> Pipeline:
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"""
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Load modules from pyannote
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Parameters
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----------
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model : str
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pyannote model
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default: /models/pyannote/speaker_diarization/config.yaml
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token : str
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HUGGINGFACE_TOKEN
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local : bool
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If true, load from local cache
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Returns
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-------
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Pipeline Object
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"""
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if local:
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diarization_model = Pipeline.from_pretrained(model,*args, **kwargs)
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else:
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if token == "":
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token = cls._get_token()
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diarization_model = Pipeline.from_pretrained(model, use_auth_token = token,
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*args, **kwargs)
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return cls(diarization_model)
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def __repr__(self):
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return f"Diarisation(model={self.model})"
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def __str__(self):
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return f"Diarisation(model={self.model})"
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