@j&ddlmZddlZddlZddlmZmZmZmZeGddZ e ddZ d e _ eGd d Z e d dZ d e _ dS)) dataclassN)squim_objective_basesquim_subjective_baseSquimObjectiveSquimSubjectivecJeZdZUdZeed<eed<defdZe dZ 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_ratereturnct}tjd|j}t j|d}||||S)zConstruct the SquimObjective model, and load the pretrained weight. Returns: Variation of :py:class:`~torchaudio.models.SquimObjective`. models/T weights_only) r torchaudioutils_download_assetr torchloadload_state_dictevalselfmodelpath state_dicts UC:\Projects\Historical\venv\Lib\site-packages\torchaudio/pipelines/_squim_pipeline.py get_modelzSquimObjectiveBundle.get_model.sj %&&//0F$*0F0FGGZ4888  j)))  c|jSzUSample rate of the audio that the model is trained on. :type: float r rs r sample_ratez SquimObjectiveBundle.sample_rate;   rN) __name__ __module__ __qualname____doc__str__annotations__floatrrpropertyr$rrr r sn@ JJJ >    !!X!!!rr zsquim_objective_dns2020.pthi>r"aSquimObjective 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. cJeZdZUdZeed<eed<defdZe dZ 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 r ct}tjd|j}t j|d}||||S)zConstruct the SquimSubjective model, and load the pretrained weight. Returns: Variation of :py:class:`~torchaudio.models.SquimObjective`. rTr) rrrrr rrrrrs rrzSquimSubjectiveBundle.get_modelzsj &''//0F$*0F0FGGZ4888  j)))  rc|jSr!r"r#s rr$z!SquimSubjectiveBundle.sample_rater%rN) r&r'r(r)r*r+r,rrr-r$r.rrr0r0Ssn  D JJJ ?    !!X!!!rr0zsquim_subjective_bvcc_daps.ptha SquimSubjective pipeline trained as described in :cite:`manocha2022speech` and :cite:`kumar2023torchaudio` on the *BVCC* :cite:`cooper2021voices` and *DAPS* :cite:`mysore2014can` datasets. The underlying model is constructed by :py:func:`torchaudio.models.squim_subjective_base`. The weights are under `Creative Commons Attribution Non Commercial 4.0 International `__. Please refer to :py:class:`SquimSubjectiveBundle` for usage instructions. ) dataclassesrrrtorchaudio.modelsrrrrr SQUIM_OBJECTIVEr)r0SQUIM_SUBJECTIVEr.rrr7s!!!!!! jjjjjjjjjjjj 7!7!7!7!7!7!7! 7!t'&! 8!8!8!8!8!8!8! 8!v)($ r