U ,:%e@s|ddlmZddlmZddlmZmZmZmZeGdddZ e dddZ d e _ eGd d d Z e d ddZ d e _ dS)) dataclass)load_state_dict_from_url)squim_objective_basesquim_subjective_baseSquimObjectiveSquimSubjectivec@sJeZdZUdZeed<eed<ddZddedd d Z e d d Z dS) SquimObjectiveBundleu,Data class that bundles associated information to use pretrained :py:class:`~torchaudio.models.SquimObjective` model. This class provides interfaces for instantiating the pretrained model along with the information necessary to retrieve pretrained weights and additional data to be used with the model. Torchaudio library instantiates objects of this class, each of which represents a different pretrained model. Client code should access pretrained models via these instances. This bundle can estimate objective metric scores for speech enhancement, such as STOI, PESQ, Si-SDR. A typical use case would be a flow like `waveform -> list of scores`. Please see below for the code example. Example: Estimate the objective metric scores for the input waveform. >>> import torch >>> import torchaudio >>> from torchaudio.pipelines import SQUIM_OBJECTIVE as bundle >>> >>> # Load the SquimObjective bundle >>> model = bundle.get_model() Downloading: "https://download.pytorch.org/torchaudio/models/squim_objective_dns2020.pth" 100%|████████████| 28.2M/28.2M [00:03<00:00, 9.24MB/s] >>> >>> # Resample audio to the expected sampling rate >>> waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate) >>> >>> # Estimate objective metric scores >>> scores = model(waveform) >>> print(f"STOI: {scores[0].item()}, PESQ: {scores[1].item()}, SI-SDR: {scores[2].item()}.") _path _sample_ratecCs,d|j}|dkrin|}t|f|}|SNz/https://download.pytorch.org/torchaudio/models/r rself dl_kwargsurlZ state_dictrc/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torchaudio/pipelines/_squim_pipeline.py_get_state_dict-s  z$SquimObjectiveBundle._get_state_dictNrreturncCs"t}|||||S)aConstruct the SquimObjective model, and load the pretrained weight. The weight file is downloaded from the internet and cached with :func:`torch.hub.load_state_dict_from_url` Args: dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`. Returns: Variation of :py:class:`~torchaudio.models.SquimObjective`. )rload_state_dictrevalrrmodelrrr get_model3s zSquimObjectiveBundle.get_modelcCs|jSzUSample rate of the audio that the model is trained on. :type: float r rrrr sample_rateDsz SquimObjectiveBundle.sample_rate) __name__ __module__ __qualname____doc__str__annotations__floatrrrpropertyrrrrrrs  rzsquim_objective_dns2020.pthi>raSquimObjective pipeline trained using approach described in :cite:`kumar2023torchaudio` on the *DNS 2020 Dataset* :cite:`reddy2020interspeech`. The underlying model is constructed by :py:func:`torchaudio.models.squim_objective_base`. The weights are under `Creative Commons Attribution 4.0 International License `__. Please refer to :py:class:`SquimObjectiveBundle` for usage instructions. c@sJeZdZUdZeed<eed<ddZddedd d Z e d d Z dS) SquimSubjectiveBundleuData class that bundles associated information to use pretrained :py:class:`~torchaudio.models.SquimSubjective` model. This class provides interfaces for instantiating the pretrained model along with the information necessary to retrieve pretrained weights and additional data to be used with the model. Torchaudio library instantiates objects of this class, each of which represents a different pretrained model. Client code should access pretrained models via these instances. This bundle can estimate subjective metric scores for speech enhancement, such as MOS. A typical use case would be a flow like `waveform -> score`. Please see below for the code example. Example: Estimate the subjective metric scores for the input waveform. >>> import torch >>> import torchaudio >>> from torchaudio.pipelines import SQUIM_SUBJECTIVE as bundle >>> >>> # Load the SquimSubjective bundle >>> model = bundle.get_model() Downloading: "https://download.pytorch.org/torchaudio/models/squim_subjective_bvcc_daps.pth" 100%|████████████| 360M/360M [00:09<00:00, 41.1MB/s] >>> >>> # Resample audio to the expected sampling rate >>> waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate) >>> # Use a clean reference (doesn't need to be the reference for the waveform) as the second input >>> reference = torchaudio.functional.resample(reference, sample_rate, bundle.sample_rate) >>> >>> # Estimate subjective metric scores >>> score = model(waveform, reference) >>> print(f"MOS: {score}.") r r cCs,d|j}|dkrin|}t|f|}|Sr r r rrrrs  z%SquimSubjectiveBundle._get_state_dictNrrcCs"t}|||||S)aConstruct the SquimSubjective model, and load the pretrained weight. The weight file is downloaded from the internet and cached with :func:`torch.hub.load_state_dict_from_url` Args: dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`. Returns: Variation of :py:class:`~torchaudio.models.SquimObjective`. )rrrrrrrrrs zSquimSubjectiveBundle.get_modelcCs|jSrrrrrrrsz!SquimSubjectiveBundle.sample_rate) r r!r"r#r$r%r&rrrr'rrrrrr(\s "r(zsquim_subjective_bvcc_daps.ptha SquimSubjective pipeline trained as described in :cite:`manocha2022speech` and :cite:`kumar2023torchaudio` on the *BVCC* :cite:`cooper2021voices` and *DAPS* :cite:`mysore2014can` datasets. The underlying model is constructed by :py:func:`torchaudio.models.squim_subjective_base`. The weights are under `Creative Commons Attribution Non Commercial 4.0 International `__. Please refer to :py:class:`SquimSubjectiveBundle` for usage instructions. N) dataclassesrZtorchaudio._internalrZtorchaudio.modelsrrrrrZSQUIM_OBJECTIVEr#r(ZSQUIM_SUBJECTIVErrrrs  D F