# MIT License # # Copyright (c) 2021-2025 CNRS # Copyright (c) 2025- pyannoteAI # # 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 __future__ import annotations 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 import torch import torch.nn as nn import yaml from pyannote.audio import Audio from pyannote.audio.core.inference import BaseInference from pyannote.audio.core.io import AudioFile from pyannote.audio.core.model import Model from pyannote.audio.telemetry import track_pipeline_apply, track_pipeline_init from pyannote.audio.utils.dependencies import check_dependencies from pyannote.audio.utils.hf_hub import AssetFileName, download_from_hf_hub from pyannote.audio.utils.reproducibility import fix_reproducibility from pyannote.core.utils.helper import get_class_by_name from pyannote.database import FileFinder, ProtocolFile from pyannote.pipeline import Pipeline as _Pipeline def expand_subfolders( config, model_id: str | None = None, parent_revision: str | None = None, cache_dir: Path | str | None = None, token: str | None = None, ) -> None: """Expand $model subfolders in config Processes `config` dictionary recursively and replaces "$model/{subfolder}" values with {"checkpoint": model_id, "subfolder": {subfolder}, "token": token} Parameters ---------- config : dict model_id : str, optional Model identifier when loading from the huggingface.co model hub. parent_revision : str, optional Revision when loading from the huggingface.co model hub. token : str or bool, optional Huggingface token to be used for downloading from Huggingface hub. cache_dir: Path or str, optional Path to the folder where files downloaded from Huggingface hub are stored. """ if isinstance(config, dict): for key, value in config.items(): if isinstance(value, str) and value.startswith("$model/"): subfolder = "/".join(value.split("/")[1:]) # if subfolder contains '@', split it to get revision if "@" in subfolder: subfolder, revision = subfolder.split("@") # otherwise, use parent revision if any else: revision = parent_revision config[key] = { "checkpoint": model_id, "revision": revision, "subfolder": subfolder, "token": token, "cache_dir": cache_dir, } else: expand_subfolders( value, model_id, parent_revision=parent_revision, token=token, cache_dir=cache_dir, ) elif isinstance(config, list): for idx, value in enumerate(config): if isinstance(value, str) and value.startswith("$model/"): subfolder = "/".join(value.split("/")[1:]) # if subfolder contains '@', split it to get revision if "@" in subfolder: subfolder, revision = subfolder.split("@") # otherwise, use parent revision if any else: revision = parent_revision config[idx] = { "checkpoint": model_id, "revision": revision, "subfolder": subfolder, "token": token, "cache_dir": cache_dir, } else: expand_subfolders( value, model_id, parent_revision=parent_revision, token=token, cache_dir=cache_dir, ) class Pipeline(_Pipeline): @classmethod def from_pretrained( cls, checkpoint: str | Path | dict, revision: str | None = None, hparams_file: str | Path | None = None, token: str | bool | None = None, cache_dir: Path | str | None = None, ) -> Optional["Pipeline"]: """Load pretrained pipeline Parameters ---------- checkpoint : str Pipeline checkpoint, provided as one of the following: * path to a local `config.yaml` pipeline checkpoint * path to a local directory containing such a file * identifier (str) of a pipeline on huggingface.co model hub * dictionary containing the actual content of a config file revision : str, optional Revision when loading from the huggingface.co model hub. hparams_file: Path or str, optional token : str or bool, optional Token to be used for the download. cache_dir: Path or str, optional Path to the folder where cached files are stored. """ # if checkpoint is a dict, assume it is the actual content of # a config file if isinstance(checkpoint, dict): if revision is not None: raise ValueError("Revisions cannot be used with local checkpoints.") model_id = Path.cwd() config = checkpoint otel_origin: str = "local" # if checkpoint is a directory, look for the pipeline checkpoint # inside this directory elif os.path.isdir(checkpoint): if revision is not None: raise ValueError("Revisions cannot be used with local checkpoints.") model_id = Path(checkpoint) config_yml = model_id / AssetFileName.Pipeline.value otel_origin: str = "local" # if checkpoint is a file, assume it is the pipeline checkpoint elif os.path.isfile(checkpoint): if revision is not None: raise ValueError("Revisions cannot be used with local checkpoints.") model_id = Path(checkpoint).parent config_yml = checkpoint otel_origin: str = "local" # otherwise, assume that the checkpoint is hosted on HF model hub else: model_id = str(checkpoint) if "@" in model_id: raise ValueError( "Revisions must be passed with `revision` keyword argument." ) config_yml = download_from_hf_hub( model_id, AssetFileName.Pipeline, revision=revision, cache_dir=cache_dir, token=token, ) if config_yml is None: return None otel_origin: str = ( model_id if model_id.lower().startswith(("pyannote/", "pyannoteai/")) else "huggingface" ) if not isinstance(checkpoint, dict): with open(config_yml, "r") as fp: config = yaml.load(fp, Loader=yaml.SafeLoader) # expand $model/{subfolder}-like entries in config expand_subfolders( config, model_id, parent_revision=revision, token=token, cache_dir=cache_dir, ) # before 4.x, pyannote.audio pipeline was using "version" key to # specify the version of pyannote.audio used to train the pipeline if "version" in config: config["dependencies"] = {"pyannote.audio": config["version"]} del config["version"] # check that dependencies are available (in their required version) dependencies: dict[str, str] = config.get("dependencies", dict()) check_dependencies(dependencies, "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("token", token) params.setdefault("cache_dir", cache_dir) pipeline = Klass(**params) # save pipeline origin (HF, local, etc) and class name as attributes for telemetry purposes pipeline._otel_origin = otel_origin pipeline._otel_name = pipeline_name track_pipeline_init(pipeline) # 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: Callable | None = None) -> Callable: def noop(*args, **kwargs): return return partial(hook or noop, file=file) def default_parameters(self): raise NotImplementedError() def classes(self) -> 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, preload: bool = False, **kwargs): """Validate file, (optionally) load it in memory, then process it Parameters ---------- file : AudioFile File to process preload : bool, optional Whether to preload waveform before applying the pipeline. kwargs : keyword arguments, optional Additional keyword arguments passed to `self.apply(...)` Returns ------- output : Any Whatever `self.apply(...)` returns """ 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) # check if the instance has preprocessors and wrap the file if so if hasattr(self, "preprocessors"): file = ProtocolFile(file, lazy=self.preprocessors) # pre-load the audio in memory if requested if preload: # raise error if `waveform`` is already in memory (or will be via a preprocessor) if ( "waveform" in getattr(self, "preprocessors", dict()) or "waveform" in file ): raise ValueError( "Cannot preload audio: `waveform` key is already available or will be via a preprocessor." ) # load waveform in memory (and keep track of its original sample rate) file["waveform"], file["sample_rate"] = Audio()(file) # the above line already took care of channel selection, # therefore we remove the `channel` key from the file file.pop("channel", None) # send file duration to telemetry as well as # requested number of speakers in case of diarization track_pipeline_apply(self, file, **kwargs) return self.apply(file, **kwargs) def to(self, device: torch.device) -> Pipeline: """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 def cuda(self, device: torch.device | int | None = None) -> Pipeline: """Send pipeline to (optionally specified) cuda device""" if device is None: return self.to(torch.device("cuda")) elif isinstance(device, int): return self.to(torch.device("cuda", device)) else: if device.type != "cuda": raise ValueError("Expected CUDA device. Use `Pipeline.to(device)` for other devices.") return self.to(device)