Zjp;SrSSKrSSKrSSKJr SSKJr SSKJr \"SS9\"S S \555r \"SS9\"S S \555r S S /r g) zSpeechT5 model configurationN)strict)PreTrainedConfig)auto_docstringzmicrosoft/speecht5_asr) checkpointc^\rSrSr%SrSrSSS.rSr\\ S'S r \\ S 'S r \\ S'S r \\ S'S r \\ S 'Sr\\-\ S'Sr\\ S'S r\\ S'S r\\ S'Sr\\-\ S'Sr\\ S'Sr\\-\ S'Sr\\-\ S'Sr\\-\ S'Sr\\-\ S'Sr\\ S'Sr\\ S'Sr\\ S 'S!r\\ S"'S#r\\-\ S$'Sr \\ S%'S&r!\"\\#\S'4-\ S('S)r$\"\\#\S'4-\ S*'S+r%\"\\#\S'4-\ S,'Sr&\\ S-'S.r'\\ S/'S0r(\\ S1'S2r)\\ S3'S4r*\\-\ S5'S6r+\\ S7'S8r,\\ S9'S#r-\\-\ S:'S6r.\\ S;'S\\ ST'SUr?\\ SV'SWr@\\ SX'S2rA\\ SY'S8rB\\ SZ'S[rC\\ S\'S]rD\\ S^'S2rE\\ S_'S2rF\\ S`'S2rG\\ Sa'U4SbjrHScrISdrJSerKU=rL$)fSpeechT5Configa positional_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the text position encoding layers. 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://huggingface.co/papers/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). 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_layersQ vocab_sizei hidden_size i encoder_ffn_dim皙?encoder_layerdropdecoder_layersdecoder_ffn_dimdecoder_attention_headsdecoder_layerdropgelu hidden_actpositional_dropouthidden_dropoutattention_dropoutactivation_dropoutg{Gz?initializer_rangegh㈵>layer_norm_epsFscale_embeddinggroupfeat_extract_normgfeat_proj_dropoutfeat_extract_activation)r)r)r)r)r)r).conv_dim)r,r,r,r,r, conv_stride) rrrrr,r, conv_kernel conv_biasnum_conv_pos_embeddingsnum_conv_pos_embedding_groupsTapply_spec_augmentg?mask_time_probr.mask_time_lengthr,mask_time_min_masksmask_feature_probmask_feature_lengthrmask_feature_min_masksN pad_token_id bos_token_id eos_token_iddecoder_start_token_idP num_mel_binsspeech_decoder_prenet_layersspeech_decoder_prenet_unitsg?speech_decoder_prenet_dropoutr)speaker_embedding_dimr+speech_decoder_postnet_layersspeech_decoder_postnet_unitsspeech_decoder_postnet_kernelspeech_decoder_postnet_dropoutreduction_factorimax_speech_positionsimax_text_positionsencoder_max_relative_positionuse_guided_attention_lossguided_attention_loss_num_headsg?guided_attention_loss_sigmag$@guided_attention_loss_scale use_cacheis_encoder_decodertie_word_embeddingsc Z>[UR5Ul[TU]"S0UD6 g)N)lenr*num_feat_extract_layerssuper __post_init__)selfkwargs __class__s ڄ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/speecht5/configuration_speecht5.pyr]SpeechT5Config.__post_init__s$'*4=='9$ ''c r[UR5UR:wdF[UR5UR:wd#[UR5UR:waN[ S[UR5S[UR5S[UR5S35eg)zOPart of `@strict`-powered validation. Validates the architecture of the config.zConfiguration 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`.N)rZr-r[r/r* ValueErrorr^s ravalidate_architecture$SpeechT5Config.validate_architectures!! "d&B&B BD$$%)E)EEDMM"d&B&BB &''Ec$JZJZF[E\]//243C3C/D.ERI Crccb[R"[RURS5$)Nr<) functoolsreduceoperatormulr-rfs rainputs_to_logits_ratio%SpeechT5Config.inputs_to_logits_ratios! d.>.>BBrc)r[)M__name__ __module__ __qualname____firstlineno____doc__ model_type attribute_maprint__annotations__rr r rrfloatrrrrrstrrrr r!r"r#r$boolr&r'r(r*listtupler-r/r0r2r4r5r6r7r8r9r:r;r=r>r?r@rBrCrErFrGrHrIrJrKrLrMrNrPrQrRrSrTrUrVrWr]rgrn__static_attributes__ __classcell__)r`s@rar r sm ^J,E\lmMJKNC#%S%OS%(us{(NCOS#%S%%(us{(J&) )"%NECK%%(us{(&) )#u# NE !OT!$s$%(us{(#)S),OHd3i%S/)O/DKcU38_,D/EKcU38_,EIt#&S&)+!3+##"&NECK&c  %(us{(!!"#C# L#*  L#* +,L#S /D(,)*C$J*L#() #)'**14!53;4!$3$)*!3*(+ #+)*!3*25"ECK5c $#$!!),!3,&*t*+,#S,),,)--It## $$( CCrcr c\rSrSr%SrSrSr\\S'Sr \\S'Sr \\S 'S r \ \\ \S 4-\S 'S r\ \\ \S 4-\S'Sr\ \\ \S 4-\S'Sr\ \ -\S'Sr\\S'Sr\\S'Sr\\S'Srg)SpeechT5HifiGanConfiga` model_in_dim (`int`, *optional*, defaults to 80): The number of frequency bins in the input log-mel spectrogram. 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. 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 ```speecht5_hifiganrA model_in_dimi> sampling_rater)upsample_initial_channel)rrr.upsample_rates)rrrupsample_kernel_sizes)r resblock_kernel_sizes)r<rr+rrresblock_dilation_sizesg{Gz?r"rleaky_relu_slopeTnormalize_beforerYN)rprqrrrsrtrurrwrxrrrr|r}rrrr"ryrrr{r~rYrcrarrs% N$JL#M3$'c'2>NDIc3h/>9E49uS#X6E9C49uS#X6C,MTE\M#u#!e!!d!rcr) rtrjrlhuggingface_hub.dataclassesrconfiguration_utilsrutilsrr r__all__rYrcrars#.3#34AC%AC5ACH343",3"53"l 4 5rc