a h@sddlmZddlZddlZddlmZmZmZmZeGdddZ e dddZ d e _ eGd d d Z e d ddZ d e _ dS)) dataclassN)squim_objective_basesquim_subjective_baseSquimObjectiveSquimSubjectivec@s<eZdZUdZeed<eed<edddZe ddZ d S) 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_ratereturncCs>t}tjd|j}tj|dd}||||S)zConstruct the SquimObjective model, and load the pretrained weight. Returns: Variation of :py:class:`~torchaudio.models.SquimObjective`. models/TZ weights_only) r torchaudioutilsdownload_assetrtorchloadload_state_dictevalselfmodelpathZ state_dictrR/var/www/auris/lib/python3.9/site-packages/torchaudio/pipelines/_squim_pipeline.py get_model.s  zSquimObjectiveBundle.get_modelcCs|jSzUSample rate of the audio that the model is trained on. :type: float r rrrr sample_rate;sz SquimObjectiveBundle.sample_rateN) __name__ __module__ __qualname____doc__str__annotations__floatrrpropertyrrrrrr s   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@s<eZdZUdZeed<eed<edddZe ddZ d S) 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}.") rr r cCs>t}tjd|j}tj|dd}||||S)zConstruct the SquimSubjective model, and load the pretrained weight. Returns: Variation of :py:class:`~torchaudio.models.SquimObjective`. r Tr ) rrrrrrrrrrrrrrzs  zSquimSubjectiveBundle.get_modelcCs|jSrrrrrrrsz!SquimSubjectiveBundle.sample_rateN) r r!r"r#r$r%r&rrr'rrrrrr(Ss " 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. )Z dataclassesrrrZtorchaudio.modelsrrrrrZSQUIM_OBJECTIVEr#r(ZSQUIM_SUBJECTIVErrrrs" : ;