# MIT License # # Copyright (c) 2021 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. import os import warnings from collections import OrderedDict from collections.abc import Iterator from functools import partial from pathlib import Path from typing import Callable, Dict, List, Optional, Text, Union import torch import torch.nn as nn import yaml from huggingface_hub import hf_hub_download from huggingface_hub.utils import RepositoryNotFoundError from pyannote.core.utils.helper import get_class_by_name from pyannote.database import FileFinder, ProtocolFile from pyannote.pipeline import Pipeline as _Pipeline from pyannote.audio import Audio, __version__ from pyannote.audio.core.inference import BaseInference from pyannote.audio.core.io import AudioFile from pyannote.audio.core.model import CACHE_DIR, Model from pyannote.audio.utils.reproducibility import fix_reproducibility from pyannote.audio.utils.version import check_version PIPELINE_PARAMS_NAME = "config.yaml" class Pipeline(_Pipeline): @classmethod def from_pretrained( cls, checkpoint_path: Union[Text, Path], hparams_file: Union[Text, Path] = None, use_auth_token: Union[Text, None] = None, cache_dir: Union[Path, Text] = CACHE_DIR, ) -> "Pipeline": """Load pretrained pipeline Parameters ---------- checkpoint_path : Path or str Path to pipeline checkpoint, or a remote URL, or a pipeline identifier from the huggingface.co model hub. hparams_file: Path or str, optional use_auth_token : str, optional When loading a private huggingface.co pipeline, set `use_auth_token` to True or to a string containing your hugginface.co authentication token that can be obtained by running `huggingface-cli login` cache_dir: Path or str, optional Path to model cache directory. Defaults to content of PYANNOTE_CACHE environment variable, or "~/.cache/torch/pyannote" when unset. """ checkpoint_path = str(checkpoint_path) if os.path.isfile(checkpoint_path): config_yml = checkpoint_path else: if "@" in checkpoint_path: model_id = checkpoint_path.split("@")[0] revision = checkpoint_path.split("@")[1] else: model_id = checkpoint_path revision = None try: config_yml = hf_hub_download( model_id, PIPELINE_PARAMS_NAME, repo_type="model", revision=revision, library_name="pyannote", library_version=__version__, cache_dir=cache_dir, # force_download=False, # proxies=None, # etag_timeout=10, # resume_download=False, use_auth_token=use_auth_token, # local_files_only=False, # legacy_cache_layout=False, ) except RepositoryNotFoundError: print( f""" Could not download '{model_id}' pipeline. It might be because the pipeline is private or gated so make sure to authenticate. Visit https://hf.co/settings/tokens to create your access token and retry with: >>> Pipeline.from_pretrained('{model_id}', ... use_auth_token=YOUR_AUTH_TOKEN) If this still does not work, it might be because the pipeline is gated: visit https://hf.co/{model_id} to accept the user conditions.""" ) return None with open(config_yml, "r") as fp: config = yaml.load(fp, Loader=yaml.SafeLoader) if "version" in config: check_version( "pyannote.audio", config["version"], __version__, what="Pipeline" ) # initialize pipeline pipeline_name = config["pipeline"]["name"] Klass = get_class_by_name( pipeline_name, default_module_name="pyannote.pipeline.blocks" ) params = config["pipeline"].get("params", {}) params.setdefault("use_auth_token", use_auth_token) pipeline = Klass(**params) # freeze parameters if "freeze" in config: params = config["freeze"] pipeline.freeze(params) if "params" in config: pipeline.instantiate(config["params"]) if hparams_file is not None: pipeline.load_params(hparams_file) if "preprocessors" in config: preprocessors = {} for key, preprocessor in config.get("preprocessors", {}).items(): # preprocessors: # key: # name: package.module.ClassName # params: # param1: value1 # param2: value2 if isinstance(preprocessor, dict): Klass = get_class_by_name( preprocessor["name"], default_module_name="pyannote.audio" ) params = preprocessor.get("params", {}) preprocessors[key] = Klass(**params) continue try: # preprocessors: # key: /path/to/database.yml preprocessors[key] = FileFinder(database_yml=preprocessor) except FileNotFoundError: # preprocessors: # key: /path/to/{uri}.wav template = preprocessor preprocessors[key] = template pipeline.preprocessors = preprocessors # send pipeline to specified device if "device" in config: device = torch.device(config["device"]) try: pipeline.to(device) except RuntimeError as e: print(e) return pipeline def __init__(self): super().__init__() self._models: Dict[str, Model] = OrderedDict() self._inferences: Dict[str, BaseInference] = OrderedDict() def __getattr__(self, name): """(Advanced) attribute getter Adds support for Model and Inference attributes, which are iterated over by Pipeline.to() method. See pyannote.pipeline.Pipeline.__getattr__. """ if "_models" in self.__dict__: _models = self.__dict__["_models"] if name in _models: return _models[name] if "_inferences" in self.__dict__: _inferences = self.__dict__["_inferences"] if name in _inferences: return _inferences[name] return super().__getattr__(name) def __setattr__(self, name, value): """(Advanced) attribute setter Adds support for Model and Inference attributes, which are iterated over by Pipeline.to() method. See pyannote.pipeline.Pipeline.__setattr__. """ def remove_from(*dicts): for d in dicts: if name in d: del d[name] _parameters = self.__dict__.get("_parameters") _instantiated = self.__dict__.get("_instantiated") _pipelines = self.__dict__.get("_pipelines") _models = self.__dict__.get("_models") _inferences = self.__dict__.get("_inferences") if isinstance(value, nn.Module): if _models is None: msg = "cannot assign models before Pipeline.__init__() call" raise AttributeError(msg) remove_from( self.__dict__, _inferences, _parameters, _instantiated, _pipelines ) _models[name] = value return if isinstance(value, BaseInference): if _inferences is None: msg = "cannot assign inferences before Pipeline.__init__() call" raise AttributeError(msg) remove_from(self.__dict__, _models, _parameters, _instantiated, _pipelines) _inferences[name] = value return super().__setattr__(name, value) def __delattr__(self, name): if name in self._models: del self._models[name] elif name in self._inferences: del self._inferences[name] else: super().__delattr__(name) @staticmethod def setup_hook(file: AudioFile, hook: Optional[Callable] = None) -> Callable: def noop(*args, **kwargs): return return partial(hook or noop, file=file) def default_parameters(self): raise NotImplementedError() def classes(self) -> Union[List, Iterator]: """Classes returned by the pipeline Returns ------- classes : list of string or string iterator Finite list of strings when classes are known in advance (e.g. ["MALE", "FEMALE"] for gender classification), or infinite string iterator when they depend on the file (e.g. "SPEAKER_00", "SPEAKER_01", ... for speaker diarization) Usage ----- >>> from collections.abc import Iterator >>> classes = pipeline.classes() >>> if isinstance(classes, Iterator): # classes depend on the input file >>> if isinstance(classes, list): # classes are known in advance """ raise NotImplementedError() def __call__(self, file: AudioFile, **kwargs): fix_reproducibility(getattr(self, "device", torch.device("cpu"))) if not self.instantiated: # instantiate with default parameters when available try: default_parameters = self.default_parameters() except NotImplementedError: raise RuntimeError( "A pipeline must be instantiated with `pipeline.instantiate(parameters)` before it can be applied." ) try: self.instantiate(default_parameters) except ValueError: raise RuntimeError( "A pipeline must be instantiated with `pipeline.instantiate(paramaters)` before it can be applied. " "Tried to use parameters provided by `pipeline.default_parameters()` but those are not compatible. " ) warnings.warn( f"The pipeline has been automatically instantiated with {default_parameters}." ) file = Audio.validate_file(file) if hasattr(self, "preprocessors"): file = ProtocolFile(file, lazy=self.preprocessors) return self.apply(file, **kwargs) def to(self, device: torch.device): """Send pipeline to `device`""" if not isinstance(device, torch.device): raise TypeError( f"`device` must be an instance of `torch.device`, got `{type(device).__name__}`" ) for _, pipeline in self._pipelines.items(): if hasattr(pipeline, "to"): _ = pipeline.to(device) for _, model in self._models.items(): _ = model.to(device) for _, inference in self._inferences.items(): _ = inference.to(device) self.device = device return self