o %ݫi@sdZddlZddlmZddlmZddlmZddlmZzddl m Z ddl m Z Wney=d Zed 7Zeewd Zgd ZeeZGd ddejZ dS)aThis lobe enables the integration of huggingface pretrained Vocos model. Vocos is a vocoder trained on top of EnCodec tokens. While EnCodec itself can be used for a lossy reconstruction of speech, a vocoder, such as Vocos, can be used to improve the quality. Repository: https://huggingface.co/charactr/vocos-encodec-24khz Paper: https://arxiv.org/pdf/2306.00814.pdf TODO: There is an open feature request to add this model to HuggingFace Transformers. If this is implemented, it will be possible to make this model inherit from HFTransformersInterface https://github.com/huggingface/transformers/issues/25123 Authors * Artem Ploujnikov 2023 N)hf_hub_download)nn)length_to_mask) get_logger)Vocos)EncodecFeaturesz,Please install vocos to use the Vocos model zE.G. run: pip install vocosi])?g@g@g(@cs8eZdZdZ   d fdd ZddZd d ZZS) ra]An wrapper for the HuggingFace Vocos model Arguments --------- source : str A HuggingFace repository identifier or a path save_path : str The location where the pretrained model will be saved revision : str The model revision bandwidth : float The bandwidth value Supported: 1.5, 3.0, 6.0, 12.0 freeze : bool Whether or not parameters should be frozen Example ------- >>> model_hub = "charactr/vocos-encodec-24khz" >>> save_path = "savedir" >>> model = Vocos(model_hub, save_path) >>> tokens = torch.randint(1024, (4, 10, 2)) >>> length = torch.tensor([1.0, 0.5, 0.75, 1.0]) >>> audio, out_length = model(tokens, length) >>> audio.shape torch.Size([4, 3200]) >>> out_length tensor([1.0000, 0.5000, 0.7500, 1.0000]) NrTcs|t||_||_||_||_||_||_t t | |_|jr:td|jD]}d|_q4dSdS)Nz$huggingface_Vocos - Vocos is frozen.F)super__init__source save_pathrevision _load_modelmodelfreeze bandwidthtorchtensor BANDWIDTHSabsargminitem bandwidth_idloggerwarning parameters requires_grad)selfr r r rrparam __class__k/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/lobes/models/huggingface_transformers/vocos.pyr Os   zVocos.__init__cCst|jd|j|jd}t|jd|j|jd}t|}tj|dd}t|j t r:dd|j j D}||||||S)zLoads the pretrained model. This is a customized implementation of Vocos.from_pretrained(), which has been customized to specify an alternate cache_dirz config.yaml)repo_idfilenamer cache_dirzpytorch_model.bincpu) map_locationcSsi|] \}}d||qS)zfeature_extractor.encodec.r!).0keyvaluer!r!r" ysz%Vocos._load_model..)rr r r VocosModel from_hparamsrload isinstancefeature_extractorrencodec state_dictitemsupdateload_state_dicteval)r config_path model_pathrr2encodec_parametersr!r!r"rfs,    zVocos._load_modelcCst|j 9|j|ddd}|jj|tj|jg|j dd}t || d| d|j d}|||fWdS1sCwYdS)aConverts EnCodec tokens to audio Arguments --------- inputs : torch.Tensor A tensor of EnCodec tokens length : torch.Tensor A 1-D tensor of relative lengths Returns ------- wavs : torch.Tensor A (Batch x Length) tensor of raw waveforms length : torch.Tensor Relative lengths r)device)r)max_lenr<N) rset_grad_enabledrrcodes_to_featurespermutedecoderrr<rsize)rinputslengthfeatureswavsmaskr!r!r"forwards  $z Vocos.forward)NrT)__name__ __module__ __qualname____doc__r rrH __classcell__r!r!rr"r.s$r)rLrhuggingface_hubrrspeechbrain.dataio.dataiorspeechbrain.utils.loggerrvocosrr,vocos.feature_extractorsr ImportErrorMSGDEFAULT_SAMPLE_RATErrIrModuler!r!r!r"s$