U ,È-e[]ã@sndZddlZddlZddlmZddlmZe e¡Z dddd œZ d d iZ Gd d „d eƒZ Gdd„deƒZ dS)z SpeechT5 model configurationéNé)ÚPretrainedConfig)ÚloggingzFhttps://huggingface.co/microsoft/speecht5_asr/resolve/main/config.jsonzFhttps://huggingface.co/microsoft/speecht5_tts/resolve/main/config.jsonzEhttps://huggingface.co/microsoft/speecht5_vc/resolve/main/config.json)zmicrosoft/speecht5_asrzmicrosoft/speecht5_ttszmicrosoft/speecht5_vczmicrosoft/speecht5_hifiganzJhttps://huggingface.co/microsoft/speecht5_hifigan/resolve/main/config.jsonc9s8eZdZdZdZdddœZd+‡fd'd(„ Zd)d*„Z‡ZS),ÚSpeechT5Configa. 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 procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If reasoning from the propability 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 procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over the axis. If reasoning from the propability 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 ```Zspeecht5Úencoder_attention_headsÚencoder_layers)Znum_attention_headsZnum_hidden_layerséQéé é çš™™™™™¹?éÚgeluç{®Gáz”?çñh㈵øä>FÚgroupç©érrrrrr©éérrrrr©é rrrrrré€éTçš™™™™™©?rrrééPéçà?rré éÂé çš™™™™™Ù?ç$@c:; sØ||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_||_||_||_||_||_t|ƒ|_t|ƒ|_t|ƒ|_||_||_||_t|jƒ|_t|jƒ|jksìt|jƒ|jksìt|jƒ|jkrtdt|jƒ›dt|jƒ›dt|jƒ›dƒ‚||_||_ ||_!||_"| |_#|!|_$|"|_%|'|_&|(|_'|)|_(|*|_)|+|_*|,|_+|-|_,|.|_-|/|_.|0|_/|1|_0|2|_1|3|_2|4|_3|5|_4|6|_5|7|_6|8|_7|9|_8t9ƒj:f|#|$|%|9|&dœ|:—ŽdS)NzºConfiguration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) = z`, `len(config.conv_stride) = z`, `len(config.conv_kernel) = z`.)Ú pad_token_idÚ bos_token_idÚ eos_token_idÚis_encoder_decoderÚdecoder_start_token_id);Ú vocab_sizeÚ hidden_sizerÚencoder_ffn_dimrÚencoder_layerdropÚdecoder_layersÚdecoder_ffn_dimÚdecoder_attention_headsÚdecoder_layerdropÚ hidden_actÚpositional_dropoutÚhidden_dropoutÚattention_dropoutÚactivation_dropoutÚinitializer_rangeÚlayer_norm_epsÚscale_embeddingÚfeat_extract_normÚfeat_proj_dropoutÚfeat_extract_activationÚlistÚconv_dimÚ conv_strideÚ conv_kernelÚ conv_biasÚnum_conv_pos_embeddingsÚnum_conv_pos_embedding_groupsÚlenZnum_feat_extract_layersÚ ValueErrorÚapply_spec_augmentÚmask_time_probÚmask_time_lengthÚmask_time_min_masksÚmask_feature_probÚmask_feature_lengthÚmask_feature_min_masksÚ num_mel_binsÚspeech_decoder_prenet_layersÚspeech_decoder_prenet_unitsÚspeech_decoder_prenet_dropoutÚspeaker_embedding_dimÚspeech_decoder_postnet_layersÚspeech_decoder_postnet_unitsÚspeech_decoder_postnet_kernelÚspeech_decoder_postnet_dropoutÚreduction_factorÚmax_speech_positionsÚmax_text_positionsÚencoder_max_relative_positionÚuse_guided_attention_lossÚguided_attention_loss_num_headsÚguided_attention_loss_sigmaÚguided_attention_loss_scaleÚ use_cacher)ÚsuperÚ__init__);Úselfr+r,rrr-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r?r@rArBrCrDrGrHrIrJrKrLrMr&r'r(r*rNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^r_r)Úkwargs©Ú __class__©út/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/speecht5/configuration_speecht5.pyraÈs=    ÿþý(ÿûúzSpeechT5Config.__init__cCst tj|jd¡S)Nr)Ú functoolsÚreduceÚoperatorÚmulr@)rbrfrfrgÚinputs_to_logits_ratioYsz%SpeechT5Config.inputs_to_logits_ratio)9rr r r r r r r r r rr r r r rrFrrrrrrFrrTrrrrrrrrrrrrrr rrrrr rr!r"r#Trr$r%TT) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeZ attribute_maprarlÚ __classcell__rfrfrdrgr%s€  Ærc sheZdZdZdZdddddddgddddgdd d gd dd gd dd gd dd ggd ddf ‡fdd„ Z‡ZS)ÚSpeechT5HifiGanConfiga 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 ```Zhifiganri€>rééréé rrg{®Gáz„?r Tc  sN||_||_||_||_||_||_||_||_| |_| |_ t ƒj f| ŽdS)N) Ú model_in_dimÚ sampling_rateÚupsample_initial_channelÚupsample_ratesÚupsample_kernel_sizesÚresblock_kernel_sizesÚresblock_dilation_sizesr8Úleaky_relu_slopeÚnormalize_beforer`ra) rbrxryrzr{r|r}r~r8rr€rcrdrfrgra“szSpeechT5HifiGanConfig.__init__)rmrnrorprqrarrrfrfrdrgrs]s3  õrs)rprhrjZconfiguration_utilsrÚutilsrZ get_loggerrmÚloggerZ&SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAPZ.SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAPrrsrfrfrfrgÚs    ýÿ: