o @Tiq@sddlmZddlmZmZmZmZmZddlZddl Z ddl m Z ddlm Z ddl mZmZddlmZddlmZdd lmZdd lmZddlmZd d lmZd d lmZdddZGddde jZ dS)) annotations)AnyDictTupleUnionOptionalN)hf_hub_download)nn)FeatureExtractorEncodecFeatures) FourierHead)Backbone) ISTFTHead)autocast)crossover_merge_linkwitz_riley)UpSamplerBlockargsUnion[Any, Tuple[Any, ...]]initDict[str, Any]returnrcCsV|di}t|ts|f}|ddd\}}t||gd}t||}||i|S)zInstantiates a class with the given args and init. Args: args: Positional arguments required for instantiation. init: Dict of the form {"class_path":...,"init_args":...}. Returns: The instantiated class object. init_args class_path.r)fromlist)get isinstancetuplersplit __import__getattr)rrkwargs class_module class_namemodule args_classr'K/home/ubuntu/.local/lib/python3.10/site-packages/linacodec/vocoder/vocos.pyinstantiate_classs  r)csdeZdZdZd"fd d Zed#ddZed$d%ddZe d&ddZ e d'd d!Z Z S)(Vocosa# The Vocos class represents a Fourier-based neural vocoder for audio synthesis. This class is primarily designed for inference, with support for loading from pretrained model checkpoints. It consists of three main components: a feature extractor, a backbone, and a head. feature_extractorr backboner headr upsamplerrhead_48krcs8t||_||_||_||_||_d|_d|_dS)NiT) super__init__r+r,r-r.r/ freq_range return_48k)selfr+r,r-r.r/ __class__r'r(r1.s  zVocos.__init__ config_pathstrrc Cst|d }t|}Wdn1swYtd|dd}td|dd}td|dd}td|dd}td|d d}||||||d } | S) z} Class method to create a new Vocos model instance from hyperparameters stored in a yaml configuration file. rNr'r+)rrr,r-r.r/)r+r,r-r.r/)openyaml safe_loadr)) clsr7fconfigr+r,r-r.r/modelr'r'r( from_hparams:s  zVocos.from_hparamsNrepo_idrevision Optional[str]cCszt|d|d}t|d|d}||}tj|dd}t|jtr2dd|jj D}| || || |S)z Class method to create a new Vocos model instance from a pre-trained model stored in the Hugging Face model hub. z config.yaml)rBfilenamerCzpytorch_model.bincpu) map_locationcSsi|] \}}d||qS)zfeature_extractor.encodec.r').0keyvaluer'r'r( Ssz)Vocos.from_pretrained..) rrAtorchloadrr+r encodec state_dictitemsupdateload_state_dicteval)r=rBrCr7 model_pathr@rOencodec_parametersr'r'r(from_pretrainedIs    zVocos.from_pretrained audio_input torch.Tensorr"rcKs(|j|fi|}|j|fi|}|S)a' Method to run a copy-synthesis from audio waveform. The feature extractor first processes the audio input, which is then passed through the backbone and the head to reconstruct the audio output. Args: audio_input (Tensor): The input tensor representing the audio waveform of shape (B, T), where B is the batch size and L is the waveform length. Returns: Tensor: The output tensor representing the reconstructed audio waveform of shape (B, T). )r+decode)r4rWr"features audio_outputr'r'r(forward\sz Vocos.forwardfeatures_inputcKs|j|fi|dd}||dd}||}||dd}t|dd}|ddd|jdf}tddt | | |j d}Wdn1sTwY|j d kr`|S|S) a Method to decode audio waveform from already calculated features. The features input is passed through the backbone and the head to reconstruct the audio output. Args: features_input (Tensor): The input tensor of features of shape (B, C, L), where B is the batch size, C denotes the feature dimension, and L is the sequence length. Returns: Tensor: The output tensor representing the reconstructed audio waveform of shape (B, T). ri]iNF)enabled)cutoffT) r, transposer.r/r-AFresampleshaperrfloatr2r3)r4r]r"rZupsampled_features pred_audio pred_audio2 merged_audior'r'r(rYns   z Vocos.decode) r+r r,r r-r r.rr/r)r7r8rr*)N)rBr8rCrDrr*)rWrXr"rrrX)r]rXr"rrrX) __name__ __module__ __qualname____doc__r1 classmethodrArVrLinference_moder\rY __classcell__r'r'r5r(r*&s  r*)rrrrrr)! __future__rtypingrrrrrrLr;huggingface_hubrr vocos.feature_extractorsr r vocos.headsr vocos.modelsr rtorch.cuda.amprtorchaudio.functional functionalrblinkwitzrupsampler_blockrr)Moduler*r'r'r'r(s