gVdZddlZddlmZddlmZmZmZmZddl Z ddl Z ddl Z ddl m Z ddlmZddlmZdd lmZdd lmZdd lmZmZdd lmZdd lmZmZmZmZddl m!Z!ej"e#Z$dZ%eGddeZ&eGddeZ'e j(j)dZ* dBdZ+dZ,Gdde j j-Z.Gdde j-Z/Gdd e j-Z0Gd!d"e j-Z1Gd#d$e j-Z2Gd%d&e j-Z3Gd'd(e j-Z4Gd)d*e j-Z5Gd+d,e j-Z6Gd-d.e j-Z7Gd/d0e j-Z8Gd1d2e j-Z9Gd3d4e j-Z:Gd5d6e j-Z;Gd7d8e j-Z<Gd9d:e j-Z=Gd;dd=Z?d>Z@ed?e?Gd@dAe>ZAdS)CzPyTorch VITS model.N) dataclass)AnyOptionalTupleUnion)nn)ACT2FN)is_deepspeed_zero3_enabled)is_fsdp_managed_module)_prepare_4d_attention_mask)BaseModelOutput ModelOutput)PreTrainedModel)add_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) VitsConfigrceZdZUdZdZejed<dZejed<dZ e e ejed<dZ e e ejed<dZ e e ejed<dS)VitsModelOutputaC Describes the outputs for the VITS model, with potential hidden states and attentions. Args: waveform (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): The final audio waveform predicted by the model. sequence_lengths (`torch.FloatTensor` of shape `(batch_size,)`): The length in samples of each element in the `waveform` batch. spectrogram (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_bins)`): The log-mel spectrogram predicted at the output of the flow model. This spectrogram is passed to the Hi-Fi GAN decoder model to obtain the final audio waveform. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attention weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nwaveformsequence_lengths spectrogram hidden_states attentions)__name__ __module__ __qualname____doc__rtorch FloatTensor__annotations__rrrrrrb/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/vits/modeling_vits.pyrr.s0#'He&&&*.e'...6:K% 123:::8