fThzSrSSKrSSKJr SSKJrJrJrJr SSK r SSK r SSK r SSK J r SSKJr SSKJr SS KJr SS KJr SS KJrJr SS KJr SS KJrJr SSKJr \R@"\!5r"\"SS\55r#\"SS\55r$\ RJRLS5r'S=Sjr(Sr)"SS\ RRT5r+"SS\ RT5r,"SS\ RT5r-"SS\ RT5r."SS \ RT5r/"S!S"\ RT5r0"S#S$\ RT5r1"S%S&\ RT5r2"S'S(\ RT5r3"S)S*\ RT5r4"S+S,\ RT5r5"S-S.\ RT5r6"S/S0\ RT5r7"S1S2\ RT5r8"S3S4\ RT5r9"S5S6\ RT5r:\"S7S8\55r;\"S9S:9"S;S<\;55rzPyTorch VITS model.N) dataclass)AnyOptionalTupleUnion)nn)ACT2FN)is_deepspeed_zero3_enabled)is_fsdp_managed_module)_prepare_4d_attention_mask)BaseModelOutput ModelOutput)PreTrainedModel)auto_docstringlogging) VitsConfigc\rSrSr%SrSr\\R\ S'Sr \\R\ S'Sr \\ \R\ S'Sr \\ \R\ S'Sr\\ \R\ S'S rg) VitsModelOutput'a 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____firstlineno____doc__rrtorch FloatTensor__annotations__rrrrr__static_attributes__r^/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/vits/modeling_vits.pyrr's0-1Hhu(()048hu00186:K% 1 123:8