fTh[SrSSKrSSKrSSKJr SSKJr \R"\5r "SS\5r "SS \5r SS /r g) zSpeechT5 model configurationN)PretrainedConfig)loggingc^\rSrSrSrSrSSS.rS U4SjjrSrS r U=r $) SpeechT5Configa+ This is the configuration class to store the configuration of a [`SpeechT5Model`]. It is used to instantiate a SpeechT5 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SpeechT5 [microsoft/speecht5_asr](https://huggingface.co/microsoft/speecht5_asr) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 81): Vocabulary size of the SpeechT5 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed to the forward method of [`SpeechT5Model`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. encoder_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. encoder_ffn_dim (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. encoder_layerdrop (`float`, *optional*, defaults to 0.1): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer decoder. decoder_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer decoder. decoder_layerdrop (`float`, *optional*, defaults to 0.1): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. positional_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the text position encoding layers. hidden_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for activations inside the fully connected layer. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). feat_extract_norm (`str`, *optional*, defaults to `"group"`): The norm to be applied to 1D convolutional layers in the speech encoder pre-net. One of `"group"` for group normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D convolutional layers. feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the speech encoder pre-net. feat_extract_activation (`str, `optional`, defaults to `"gelu"`): The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`): A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the speech encoder pre-net. The length of *conv_dim* defines the number of 1D convolutional layers. conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`): A tuple of integers defining the stride of each 1D convolutional layer in the speech encoder pre-net. The length of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the speech encoder pre-net. The length of *conv_kernel* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_bias (`bool`, *optional*, defaults to `False`): Whether the 1D convolutional layers have a bias. num_conv_pos_embeddings (`int`, *optional*, defaults to 128): Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional embeddings layer. num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): Number of groups of 1D convolutional positional embeddings layer. apply_spec_augment (`bool`, *optional*, defaults to `True`): Whether to apply *SpecAugment* data augmentation to the outputs of the speech encoder pre-net. For reference see [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://arxiv.org/abs/1904.08779). mask_time_prob (`float`, *optional*, defaults to 0.05): Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking procedure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector span to be masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_time_length (`int`, *optional*, defaults to 10): Length of vector span along the time axis. mask_time_min_masks (`int`, *optional*, defaults to 2),: The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < mask_time_min_masks'' mask_feature_prob (`float`, *optional*, defaults to 0.0): Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The masking procedure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_feature_length (`int`, *optional*, defaults to 10): Length of vector span along the feature axis. mask_feature_min_masks (`int`, *optional*, defaults to 0),: The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' num_mel_bins (`int`, *optional*, defaults to 80): Number of mel features used per input features. Used by the speech decoder pre-net. Should correspond to the value used in the [`SpeechT5Processor`] class. speech_decoder_prenet_layers (`int`, *optional*, defaults to 2): Number of layers in the speech decoder pre-net. speech_decoder_prenet_units (`int`, *optional*, defaults to 256): Dimensionality of the layers in the speech decoder pre-net. speech_decoder_prenet_dropout (`float`, *optional*, defaults to 0.5): The dropout probability for the speech decoder pre-net layers. speaker_embedding_dim (`int`, *optional*, defaults to 512): Dimensionality of the *XVector* embedding vectors. speech_decoder_postnet_layers (`int`, *optional*, defaults to 5): Number of layers in the speech decoder post-net. speech_decoder_postnet_units (`int`, *optional*, defaults to 256): Dimensionality of the layers in the speech decoder post-net. speech_decoder_postnet_kernel (`int`, *optional*, defaults to 5): Number of convolutional filter channels in the speech decoder post-net. speech_decoder_postnet_dropout (`float`, *optional*, defaults to 0.5): The dropout probability for the speech decoder post-net layers. reduction_factor (`int`, *optional*, defaults to 2): Spectrogram length reduction factor for the speech decoder inputs. max_speech_positions (`int`, *optional*, defaults to 4000): The maximum sequence length of speech features that this model might ever be used with. max_text_positions (`int`, *optional*, defaults to 450): The maximum sequence length of text features that this model might ever be used with. encoder_max_relative_position (`int`, *optional*, defaults to 160): Maximum distance for relative position embedding in the encoder. use_guided_attention_loss (`bool`, *optional*, defaults to `True`): Whether to apply guided attention loss while training the TTS model. guided_attention_loss_num_heads (`int`, *optional*, defaults to 2): Number of attention heads the guided attention loss will be applied to. Use -1 to apply this loss to all attention heads. guided_attention_loss_sigma (`float`, *optional*, defaults to 0.4): Standard deviation for guided attention loss. guided_attention_loss_scale (`float`, *optional*, defaults to 10.0): Scaling coefficient for guided attention loss (also known as lambda). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Example: ```python >>> from transformers import SpeechT5Model, SpeechT5Config >>> # Initializing a "microsoft/speecht5_asr" style configuration >>> configuration = SpeechT5Config() >>> # Initializing a model (with random weights) from the "microsoft/speecht5_asr" style configuration >>> model = SpeechT5Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```speecht5encoder_attention_headsencoder_layers)num_attention_headsnum_hidden_layersc: >XlX lX0lXPlX@lX`lXplXlXlXl Xl Xl Xl Xl XlUUlUUlUUlUUlUUlUUl[+U5Ul[+U5Ul[+U5UlUUlUUlUUlR,5Ul[9UR.5UR::wdF[9UR05UR::wd#[9UR,5UR::waN[=S[9UR,5S[9UR.5S[9UR05S35eUUlUUl UUl!UUl"U Ul#U!Ul$U"Ul%U'Ul&U(Ul'U)Ul(U*Ul)U+Ul*U,Ul+U-Ul,U.Ul-U/Ul.U0Ul/U1Ul0U2Ul1U3Ul2U4Ul3U5Ul4U6Ul5U7Ul6U8Ul7U9Ul8[rT;U]"SU#U$U%U9U&S.U:D6 g)NzConfiguration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) = z`, `len(config.conv_stride) = z`, `len(config.conv_kernel) = z`.) pad_token_id bos_token_id eos_token_idis_encoder_decoderdecoder_start_token_id); vocab_size hidden_sizer encoder_ffn_dimr encoder_layerdropdecoder_layersdecoder_ffn_dimdecoder_attention_headsdecoder_layerdrop hidden_actpositional_dropouthidden_dropoutattention_dropoutactivation_dropoutinitializer_rangelayer_norm_epsscale_embeddingfeat_extract_normfeat_proj_dropoutfeat_extract_activationlistconv_dim conv_stride conv_kernel conv_biasnum_conv_pos_embeddingsnum_conv_pos_embedding_groupslennum_feat_extract_layers ValueErrorapply_spec_augmentmask_time_probmask_time_lengthmask_time_min_masksmask_feature_probmask_feature_lengthmask_feature_min_masks num_mel_binsspeech_decoder_prenet_layersspeech_decoder_prenet_unitsspeech_decoder_prenet_dropoutspeaker_embedding_dimspeech_decoder_postnet_layersspeech_decoder_postnet_unitsspeech_decoder_postnet_kernelspeech_decoder_postnet_dropoutreduction_factormax_speech_positionsmax_text_positionsencoder_max_relative_positionuse_guided_attention_lossguided_attention_loss_num_headsguided_attention_loss_sigmaguided_attention_loss_scale use_cachersuper__init__)<selfrrr r rrrrrrrrrr r!r"r#r$r%r&r'r)r*r+r,r-r.r2r3r4r5r6r7r8rrrrr9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrkwargs __class__s< k/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/speecht5/configuration_speecht5.pyrLSpeechT5Config.__init__suz%&,.'>$!2,.'>$!2$"4,!2"4!2,.!2!2'>$X  , ,"'>$-J*'*4=='9$!! "d&B&B BD$$%)E)EEDMM"d&B&BB &''Ec$JZJZF[E\]//243C3C/D.ERI #5, 0#6 !2#6 &<#(,H)+F(-J*%:"-J*,H)-J*.L+ 0$8!"4-J*)B&/N,+F(+F(""4  %%%1#9    cb[R"[RURS5$)N) functoolsreduceoperatormulr*)rMs rPinputs_to_logits_ratio%SpeechT5Config.inputs_to_logits_ratioPs! d.>.>BBrR)6r!r2r r,r)r+r*rrrrr rrr rEr'r%r&rGrIrHrrrr"rr#r7r8r6r4r5r3rCrDr.r-r0r9rrBr$r=rAr@r>r?r<r:r;rJrFr)9Qi r\ 皙?r]r\r^gelur^r^r^r^g{Gz?gh㈵>Fgroupr`)rcrcrcrcrcrc)rerererere) rrrrrereFTg?rfrerbrfrrTrrerePre?rcrdrjrdrkreiiTreg?g$@TT) __name__ __module__ __qualname____firstlineno____doc__ model_type attribute_maprLrY__static_attributes__ __classcell__rOs@rPrrs^ @J,E\lmM " "! &4)* #&(  %&$'&)!&'%(&''*!&)"&()$'$(uO bCCrRrc f^\rSrSrSrSrSSS/SQ/SQ/S Q/S Q/S Q/S Q/S S S 4 U4SjjrSrU=r$)SpeechT5HifiGanConfigiTaU This is the configuration class to store the configuration of a [`SpeechT5HifiGanModel`]. It is used to instantiate a SpeechT5 HiFi-GAN vocoder model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SpeechT5 [microsoft/speecht5_hifigan](https://huggingface.co/microsoft/speecht5_hifigan) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: model_in_dim (`int`, *optional*, defaults to 80): The number of frequency bins in the input log-mel spectrogram. sampling_rate (`int`, *optional*, defaults to 16000): The sampling rate at which the output audio will be generated, expressed in hertz (Hz). upsample_initial_channel (`int`, *optional*, defaults to 512): The number of input channels into the upsampling network. upsample_rates (`Tuple[int]` or `List[int]`, *optional*, defaults to `[4, 4, 4, 4]`): A tuple of integers defining the stride of each 1D convolutional layer in the upsampling network. The length of *upsample_rates* defines the number of convolutional layers and has to match the length of *upsample_kernel_sizes*. upsample_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[8, 8, 8, 8]`): A tuple of integers defining the kernel size of each 1D convolutional layer in the upsampling network. The length of *upsample_kernel_sizes* defines the number of convolutional layers and has to match the length of *upsample_rates*. resblock_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[3, 7, 11]`): A tuple of integers defining the kernel sizes of the 1D convolutional layers in the multi-receptive field fusion (MRF) module. resblock_dilation_sizes (`Tuple[Tuple[int]]` or `List[List[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`): A nested tuple of integers defining the dilation rates of the dilated 1D convolutional layers in the multi-receptive field fusion (MRF) module. initializer_range (`float`, *optional*, defaults to 0.01): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. leaky_relu_slope (`float`, *optional*, defaults to 0.1): The angle of the negative slope used by the leaky ReLU activation. normalize_before (`bool`, *optional*, defaults to `True`): Whether or not to normalize the spectrogram before vocoding using the vocoder's learned mean and variance. Example: ```python >>> from transformers import SpeechT5HifiGan, SpeechT5HifiGanConfig >>> # Initializing a "microsoft/speecht5_hifigan" style configuration >>> configuration = SpeechT5HifiGanConfig() >>> # Initializing a model (with random weights) from the "microsoft/speecht5_hifigan" style configuration >>> model = SpeechT5HifiGan(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```hifiganrii>rc)rzrzrz)r{r{r{)r )rTrrdg{Gz?r^Tc  >XlX lX0lX@lXPlX`lXplXlXlXl [T U],"S0U D6 g)Nr) model_in_dim sampling_rateupsample_initial_channelupsample_ratesupsample_kernel_sizesresblock_kernel_sizesresblock_dilation_sizesr"leaky_relu_slopenormalize_beforerKrL) rMrrrrrrrr"rrrNrOs rPrLSpeechT5HifiGanConfig.__init__sQ)*(@%,%:"%:"'>$!2 0 0 "6"rR) r"rrrrrrrrr) rmrnrorprqrrrLrtrurvs@rPrxrxTsA2 hJ!$#*(!*Iy A##rRrx) rqrUrWconfiguration_utilsrutilsr get_loggerrmloggerrrx__all__rrRrPrsW#3   H %vC%vCr O#,O#d 4 5rR