unified docstrings
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@@ -1,36 +1,41 @@
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import os
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import yaml
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from pyannote.audio.core.model import CACHE_DIR as PYANNOTE_CACHE_DIR
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CACHE_DIR = os.getenv(
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"AUTOT_CACHE",
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os.path.expanduser("~/.cache/torch/models"),
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)
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if CACHE_DIR != PYANNOTE_CACHE_DIR:
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os.environ["PYANNOTE_CACHE"] = os.path.join(CACHE_DIR, "pyannote")
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WHISPER_DEFAULT_PATH = os.path.join(CACHE_DIR, "whisper")
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PYANNOTE_DEFAULT_PATH = os.path.join(CACHE_DIR, "pyannote")
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PYANNOTE_DEFAULT_CONFIG = os.path.join(PYANNOTE_DEFAULT_PATH, "config.yaml")
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def config_diarization_yaml(file, path_to_segmentation = None):
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def config_diarization_yaml(file_path: str, path_to_segmentation: str = None) -> None:
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"""Configure diarization pipeline from a YAML file.
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This function updates the YAML file to use the given segmentation model
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offline, and avoids manual file manipulation.
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Args:
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file_path (str): Path to the YAML file.
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path_to_segmentation (str, optional): Optional path to the segmentation model.
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Raises:
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FileNotFoundError: If the segmentation model file is not found.
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"""
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Configure diarization pipeline from yaml file to use the model offline
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and avoid manuel file manipulation.
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:param file: yaml file
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:type file: yaml
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"""
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with open(file, "r") as stream:
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yml = yaml.safe_load(stream)
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stream.close()
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if path_to_segmentation:
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yml["pipeline"]["params"]["segmentation"] = path_to_segmentation
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else:
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yml["pipeline"]["params"]["segmentation"] = os.path.join(PYANNOTE_DEFAULT_PATH, "pytorch_model.bin")
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if not os.path.exists(yml["pipeline"]["params"]["segmentation"]):
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raise FileNotFoundError(f"Segmentation model not found at {yml['pipeline']['params']['segmentation']}")
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with open(file, "w") as stream:
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with open(file_path, "r") as stream:
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yml = yaml.safe_load(stream)
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segmentation_path = path_to_segmentation or os.path.join(PYANNOTE_DEFAULT_PATH, "pytorch_model.bin")
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yml["pipeline"]["params"]["segmentation"] = segmentation_path
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if not os.path.exists(segmentation_path):
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raise FileNotFoundError(f"Segmentation model not found at {segmentation_path}")
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with open(file_path, "w") as stream:
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yaml.dump(yml, stream)
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stream.close()
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