+ j< ,Rt^RIHt^RIHt^RIHt.^N^N^N^N^ N^ N^N^N^N^N^N^N^N^:N^;N^N^?N^ZN^[N^\N^]NRNRNRNRNRNR NR NR NR NR NRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNR NR!NR"NR#NR$NR%NR&NR'NR(NR)NR*NR+NR,NR-NR.NR/NR0NR1NR2NR3NR4NR5NR6NR7NR8NR9NR:NR;NRNR?NR@NRANRBNRCNRDNt.^N^N^N^N^ N^ N^N^N^N^N^N^N^N^:N^;N^N^?N^ZN^[N^\N^]NRENRFNRGNRHNRINRJNRKNRLNRMNRNNRONRPNRQNRRNRSNRTNRUNRVNRWNRXNRYNRZNR[NR\NR]NR^NR_NR`NRaNRbNRcNRdNReNRfNRgNRhNRiNRjNRkNRlNRmNRnNRoNRpNRqNRrNRsNRtNRuNRvNRwNRxNRyNRzNR{NR|NR}NR~NRNRNRCNRDNRNt]!RR7]!RR]444t R.t R#)zWhisper model configuration)strict)PreTrainedConfig)auto_docstringieiniiiiiii"iiiii?iaioiciiSiri9 i i iisiiiiii#i%i&iC)i"*i,i-i.ik3i5i5i9i;i@iAiHFiKi6LiPi!WiYiiiuiviii[i-ieiiQiiiigii iiii}iiiiiFi=ii iC i i i i iiyiWi;iiiiie#i$i(i*i.i/i+0i1i5iM7i+9i;i=i@i@iGi JikNiTiWifi1fiCgiwnisi{i.~i~iioiAiiNiRizopenai/whisper-tiny) checkpointca]tRt^0toRtRtR.tRRRRRRR R /tR t^Pt ^t ^t ^t ^t R tR tR tR tRtRtRtRtRtR tR tR tRtRtRtRtRtRtRt Rt!Rt"Rt#Rt$Rt%Rt&^ t'^t(R t)^ t*^t+^t,Rt-V3Rlt.Rt/Vt0R#) WhisperConfiga max_source_positions (`int`, *optional*, defaults to 1500): The maximum sequence length of log-mel filter-bank features that this model might ever be used with. max_target_positions (`int`, *optional*, defaults to 448): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). suppress_tokens (`list[int]`, *optional*): A list containing the non-speech tokens that will be used by the logit processor in the `generate` function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the `multilingual` model. begin_suppress_tokens (`list[int]`, *optional*, defaults to `[220,50256]`): A list containing tokens that will be suppressed at the beginning of the sampling process. Initialized as the token for `" "` (`blank_token_id`) and the `eos_token_id` use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an instance of [`WhisperForAudioClassification`]. classifier_proj_size (`int`, *optional*, defaults to 256): Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an instance of [`WhisperForAudioClassification`]. apply_spec_augment (`bool`, *optional*, defaults to `False`): Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. 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 == 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`. median_filter_width (`int`, *optional*, defaults to 7): Width of the median filter used to smoothen to cross-attention outputs when computing token timestamps. Should be an odd number. Example: ```python >>> from transformers import WhisperConfig, WhisperModel >>> # Initializing a Whisper tiny style configuration >>> configuration = WhisperConfig() >>> # Initializing a model (with random weights) from the tiny style configuration >>> model = WhisperModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```whisperpast_key_valuesnum_key_value_headsencoder_attention_headsnum_attention_heads hidden_sized_modelnum_hidden_layersencoder_layersiigrTgeluig{Gz?FiiPNg?c<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R &S[S[,;R &S[;R &S[;R &S[;R &S[;R&S[;R&S[S[,;R&S[S[,;R&S[S[,;R&S[;R&S[;R&S[;R&S[;R&S[R,;R&S[R,;R&S[S[S[,,R,;R&S[R,;R&S[S[,S[S[R3,,R,;R&S[;R&S[;R&S[;R &S[S[,;R!&S[;R"&S[;R#&S[S[,;R$&S[;R%&S[;R&&S[;R'&S[;R(&#)) vocab_size num_mel_binsrr decoder_layersdecoder_attention_headsdecoder_ffn_dimencoder_ffn_dimencoder_layerdropdecoder_layerdropdecoder_start_token_id use_cacheis_encoder_decoderactivation_functionrdropoutattention_dropoutactivation_dropoutinit_stdscale_embeddingmax_source_positionsmax_target_positionsN pad_token_id bos_token_id eos_token_idsuppress_tokens.begin_suppress_tokensuse_weighted_layer_sumclassifier_proj_sizeapply_spec_augmentmask_time_probmask_time_lengthmask_time_min_masksmask_feature_probmask_feature_lengthmask_feature_min_masksmedian_filter_widthtie_word_embeddings)intfloatboolstrlisttuple)format __classdict__s"r/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/whisper/configuration_whisper.py __annotate__WhisperConfig.__annotate__0s^_`abcd!$efgh!$ijklmns{(ops{(qr 'stuv#wx%yz{|S[}~s{(@ )ABCD!EF$GH#IJ*$KL*$MNS /D(0OPD['QR 9uS#X6=LST!(UV#WX$YZCK&[\]^ _`s{(ab!cd #ef gh$i)r)1__name__ __module__ __qualname____firstlineno____doc__ model_typekeys_to_ignore_at_inference attribute_maprrrr rrrrrrrr r!r"rr#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9__annotate_func____static_attributes____classdictcell__)rAs@rBrr0sA FJ#4"588y- MJLN#$N#$OO%(%("'I#%GG%(&)H!O $ #$L$L+0L#'O@L#( #$"&N %(!"#  $irErN) rLhuggingface_hub.dataclassesrconfiguration_utilsrutilsrNON_SPEECH_TOKENSNON_SPEECH_TOKENS_MULTIr__all__rFrErBrYs".3#                     " $& (*         ! #& (+ -0       $ &* ,0 26 8<          !  #'  )-  /3  59  ;?          !  #'  )-  /3  5: