Zj7SrSSKrSSKrSSKrSSKJr SSKJr SSK J r SSK J r J r SSKJr S S KJr \ R$"\5r\ "S S \55rS /rg) z Processor class for Bark N) BatchFeature)ProcessorMixin) BatchEncoding)auto_docstringlogging) cached_file) AutoTokenizerc^\rSrSrSSSS.rSU4Sjjr\SSj5rSS\4U4S jjjr SS \ S-4S jjr SS \ S-4S jjr \S \4Sj5rSS\4Sjjr\SS \4Sjj5rSrU=r$) BarkProcessor"r semantic_prompt coarse_prompt fine_promptNc0>[TU]U5 X lg)a speaker_embeddings (`dict[dict[str]]`, *optional*): Optional nested speaker embeddings dictionary. The first level contains voice preset names (e.g `"en_speaker_4"`). The second level contains `"semantic_prompt"`, `"coarse_prompt"` and `"fine_prompt"` embeddings. The values correspond to the path of the corresponding `np.ndarray`. See [here](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c) for a list of `voice_preset_names`. N)super__init__speaker_embeddings)self tokenizerr __class__s y/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/bark/processing_bark.pyrBarkProcessor.__init__*s #"4c ZURS5nUb[UUURSS5URSS5URSS5URSS5URSS5UURS S5SSSS 9 nUc9[R S [ R RX5S 35 SnO-[U5n[R"U5nSSS5 OSnWb S U;aXS '[R"U40UD6nU"XS9$!,(df  N8=f)ac Instantiate a Bark processor associated with a pretrained model. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained [`BarkProcessor`] hosted inside a model repo on huggingface.co. - a path to a *directory* containing a processor saved using the [`~BarkProcessor.save_pretrained`] method, e.g., `./my_model_directory/`. speaker_embeddings_dict_path (`str`, *optional*, defaults to `"speaker_embeddings_path.json"`): The name of the `.json` file containing the speaker_embeddings dictionary located in `pretrained_model_name_or_path`. If `None`, no speaker_embeddings is loaded. **kwargs Additional keyword arguments passed along to both [`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`]. tokenN subfolder cache_dirforce_downloadFproxieslocal_files_onlyrevision r r!r"r#r$rr% _raise_exceptions_for_gated_repo%_raise_exceptions_for_missing_entries'_raise_exceptions_for_connection_errors`z` does not exists , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json dictionary if wanted, otherwise set `speaker_embeddings_dict_path=None`. repo_or_path)rr) getr poploggerwarningospathjoinopenjsonloadr from_pretrained) cls!pretrained_processor_name_or_pathspeaker_embeddings_dict_pathkwargsrspeaker_embeddings_pathrspeaker_embeddings_jsonrs rr6BarkProcessor.from_pretrained7s8, 7# ' 3&11, **[$7 **[$7%zz*:EB 9d3!',>!FJ5166;8= ' #'."'',,'Hghi]` &*"126M)-3J)K&32"&   )!335V>2!112S^W]^ YNN32s D D* push_to_hubc >URGb&[R"[RR XS5SS9 0nXS'UR HnUR U5n0n URUHhn [R"[RR USX7SU 35XSS9 [RR X7SU S 35X'Mj XU'M [[RR X5S 5n [R"Xk5 SSS5 [T U]4"X40UD6 g!,(df  N =f) a Saves the attributes of this processor (tokenizer...) in the specified directory so that it can be reloaded using the [`~BarkProcessor.from_pretrained`] method. Args: save_directory (`str` or `os.PathLike`): Directory where the tokenizer files and the speaker embeddings will be saved (directory will be created if it does not exist). speaker_embeddings_dict_path (`str`, *optional*, defaults to `"speaker_embeddings_path.json"`): The name of the `.json` file that will contains the speaker_embeddings nested path dictionary, if it exists, and that will be located in `pretrained_model_name_or_path/speaker_embeddings_directory`. speaker_embeddings_directory (`str`, *optional*, defaults to `"speaker_embeddings/"`): The name of the folder in which the speaker_embeddings arrays will be saved. push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace). kwargs: Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. Nv2T)exist_okr+_F) allow_picklez.npyw)rr0makedirsr1r2available_voice_presets_load_voice_presetnpsaver3r4dumprsave_pretrained) rsave_directoryr9speaker_embeddings_directoryr>r:embeddings_dict prompt_key voice_presettmp_dictkeyfprs rrKBarkProcessor.save_pretrainedqs88  " " . KK ^SWXcg h O.CGG +N;=Y]iijknjo[p%)%* %'GGLL1MQ]]^_b^ccgOh$iHM?/7 +; bggll>PRUVZ\ /.W FvFWVs E ErPc |URUn0nURS5nSGHnXc;a[SUSUS35e[URRSS5X6UR SS5UR S S5UR S S 5UR S S5UR S S 5UUR SS5S S S S9 nUcL[S[ R RURRSS5X65SUS35e[R"U5XF'GM U$)Nrr#Voice preset unrecognized, missing z% as a key in self.speaker_embeddings[z].r+/r r!r"Fr#r$r%r&r*z{` does not exists , no preloaded voice preset will be used - Make sure to provide correct paths to the z embeddings.) rr, ValueErrorr r-r0r1r2rHr5)rrPr:voice_preset_pathsvoice_preset_dictrrRr1s rrG BarkProcessor._load_voice_presets[!44\B 7#FC, 9#>cdpcqqst''++NC@"' **[$7 **[$7%zz*:EB 9d3!',>!FJ5166;8= D| "'',,t'>'>'B'B>SV'WYkYpqrsjjviwx # &(WWT]  "7G:! rc lSHnX!;a[SUS35e[X[R5(d'[ US[ UR U5S35e[XR5UR U:wdM[US[ UR U5S35e g)NrrVz as a key.z voice preset must be a z D ndarray.) rX isinstancerHndarray TypeErrorstr preset_shapelenshape)rrPrRs r_validate_voice_preset_dict)BarkProcessor._validate_voice_preset_dictsFC& #Fse:!VWWl/<<3%'?DDUDUVYDZ@[?\\f ghh<$**+t/@/@/EE C5(@TEVEVWZE[A\@]]g!hiiGrreturncURc/$[URR55nSU;aURS5 U$)ze Returns a list of available voice presets. Returns: `list[str]`: A list of voice preset names. r+)rlistkeysremove)r voice_presetss rrF%BarkProcessor.available_voice_presetssJ  " " *IT4499;< ] *   0rremove_unavailablech/nURbURH&nURU5nUR U5 M( U(a%[ RS[U5SUS35 U(aUHnURU M ggg![a UR U5 Mf=f)NzThe following z' speaker embeddings are not available: zU If you would like to use them, please check the paths or try downloading them again.) rrFrGrXappendrdr.r/rb)rrmunavailable_keysrPrZs r_verify_speaker_embeddings(BarkProcessor._verify_speaker_embeddingss  " " . $ < < (,(?(? (M% 001BC!= $S)9%:$;;bcsbtukk "$4L// =%5"! /"$++L9sBB10B1c Ub[U[5(d[U[5(a.URb!X R;aUR U5nOF[U[5(aUR S5(dUS-n[ R"U5nUbUR"U40UD6 [X#S9nUR"U4USUUUUS.UD6n UbX)S'U $)a voice_preset (`str`, `dict[np.ndarray]`): The voice preset, i.e the speaker embeddings. It can either be a valid voice_preset name, e.g `"en_speaker_1"`, or directly a dictionary of `np.ndarray` embeddings for each submodel of `Bark`. Or it can be a valid file name of a local `.npz` single voice preset containing the keys `"semantic_prompt"`, `"coarse_prompt"` and `"fine_prompt"`. Returns: [`BatchEncoding`]: A [`BatchEncoding`] object containing the output of the `tokenizer`. If a voice preset is provided, the returned object will include a `"history_prompt"` key containing a [`BatchFeature`], i.e the voice preset with the right tensors type. z.npz)data tensor_type max_length)return_tensorspaddingrvreturn_attention_maskreturn_token_type_idsadd_special_tokenshistory_prompt) r]dictr`rrGendswithrHr5rdrr) rtextrPrwrvr{ryrzr: encoded_texts r__call__BarkProcessor.__call__s0  #J|T,J,J<--++7 $;$;;#66|D lC009N9Nv9V9V#/L!ww|4  #  , ,\ DV D'\VL~~   ) !"7"71      #-9) *r)r)N)speaker_embeddings_path.json)rrF)T)NNptFTF)__name__ __module__ __qualname____firstlineno__rar classmethodr6boolrKr`rGr}rdpropertyrhrFrqrrr__static_attributes__ __classcell__)rs@rr r "sL 5Mk7O7Ox&D%9! 6G  6G6Gp"!sTz"!H jt j   >T>. "#7 77rr )__doc__r4r0numpyrHfeature_extraction_utilsrprocessing_utilsrtokenization_utils_baserutilsrr utils.hubr autor get_loggerrr.r __all__rrrsb 4.4,$    H %TNTTn  r