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This commit is contained in:
Jaikinator
2023-06-13 11:56:41 +02:00
parent 3cfdb894bf
commit 7ee784457a
1 changed files with 14 additions and 71 deletions
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autotranscript/diarisation.py
+14 -71
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@@ -1,7 +1,7 @@
from pyannote.audio import Pipeline
from time import time
from torch import Tensor
import os
from typing import TypeVar
from typing import TypeVar, Union
Annotation = TypeVar('Annotation')
@@ -9,15 +9,16 @@ PYANNOTE_DEFAULT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)),
"models", "pyannote",
"speaker_diarization", "config.yaml")
class Diarisation:
class Diariser:
def __init__(self, model,*args,**kwargs) -> None:
self.model = model
def diarization(self, audiofile : str , *args, **kwargs) -> Annotation:
def diarization(self, audiofile : Union[str, Tensor] ,
*args, **kwargs) -> Annotation:
"""
Diarization of audio file
:param audiofile: path to audio file
:param audiofile: path to audio file or torch.Tensor
:param args: args for diarization model
:param kwargs: kwargs for diarization model
:return: diarization
@@ -83,17 +84,21 @@ class Diarisation:
diarization_output["speakers"].append(outp[2])
return diarization_output
@staticmethod
def _get_token():
# check ig .pyannotetoken.txt exists
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.pyannotetoken')
path = os.path.join(os.path.dirname(
os.path.realpath(__file__)), '.pyannotetoken')
if os.path.exists(path):
with open(path, 'r') as f:
token = f.read()
else:
raise ValueError('No token found. Please create a token at https://huggingface.co/settings/token'
' and save it in a file called .pyannotetoken.txt')
raise ValueError('No token found.' \
'Please create a token at https://huggingface.co/settings/token' \
'and save it in a file called .pyannotetoken.txt')
return token
@classmethod
def load_model(cls, model: str = PYANNOTE_DEFAULT_PATH,
token: str = "",
@@ -129,69 +134,7 @@ class Diarisation:
def __repr__(self):
return f"Diarisation(model={self.model})"
def __str__(self):
return f"Diarisation(model={self.model})"
if __name__ == '__main__':
model = Diarisation.load_model()
print(model)
audiofile = "/home/jacob/PycharmProjects/autotranscript/tests/test.wav"
out = model.diarization(audiofile)
# # deprecated
# def create_temporary_wav(self, location_of_temp_folder : str = '.temp'):
# """
# Create temporary wav file for diarization
# :param location_of_temp_folder: folder to save the temporary wav file
# default: .temp
# :param savename: name of the temporary wav file prefix
# :param audiofile: audio file
# :return: temporary wav file
# """
# print("Linne 84 Diarisation.py create_temporary_wav :" /
# "location_of_temp_folder.split('/')[-1]",location_of_temp_folder.split('/')[-1])
# if location_of_temp_folder.split('/')[-1] != '.temp':
# folder =os.path.join(location_of_temp_folder, '.temp')
# else:
# folder = location_of_temp_folder
# if not os.path.exists(folder):
# os.makedirs(folder)
# folder = os.path.realpath(folder)
# if not hasattr(self, 'normalized_output') or not hasattr(self, 'diarization_output'):
# raise AttributeError("You need to run the diarization first")
# speaker = set(self.diarization_output["speakers"])
# num_speak_iter = [0 for _ in range(len(speaker))]
# for count, outp in enumerate(self.normalized_output):
# print(outp)
# print(self.diarization_output["segments"][outp[0]])
# print(self.diarization_output["segments"][outp[1]])
# start = self.diarization_output["segments"][outp[0]].start
# end = self.diarization_output["segments"][outp[1]].end
# print("start: ", start)
# print("end: ", end)
# start_milliseconds = start * 1000
# end_milliseconds = end * 1000
# print("start_milliseconds: ", start_milliseconds)
# print("end_milliseconds: ", end_milliseconds)
# print("cut audio")
# cut_audio = self.audio_file[start_milliseconds:end_milliseconds]
# print("save audio")
# print(f".temp/{count}_speaker_" + str(outp[2]) + ".wav")
# cut_audio.export(f".temp/{count}_speaker_" + str(outp[2]) + ".wav", format="wav")
# return os.path.realpath(folder)