j,SrSSKrSSKr"SS5rg)z>Tokenizer for semantic tokens. Author * Pooneh Mousavi 2024 Nc:\rSrSrSrSrSrS/S/S/4SjrS rg) DiscreteSSLTokenizer aThis class is tokenizer for DiscreteSSL models that apply post-processing on the semnatic tokens extracted from DiscreteSSL model. It makes the token ids of each layer to be unique by adding the token IDs of each layer by layer_num*sunmber_of _cluster. It applies deduplication for each layer independently if the field is set to true for the layer and padded all items with zero. It applies subwording for each layer independently if the sentence piece tokenizer is set to for the layer and padded all items with zero. If subwording is not applied, all token IDs are incremented by one to avoid conflict between pad_id(0) and cluster with centroid zero. Arguments --------- num_clusters: List[int] determine the number of clusters of the kmeans models. It could be varying for each layer. Example ------- >>> import torch >>> inputs = torch.randint(0, 1000, (3, 6, 2)) >>> ssl_layer_num = [7, 23] >>> deduplicate = [False, True] >>> bpe_tokenizers = [None, None] >>> num_clusters = [1000, 2000] >>> tokenizer = DiscreteSSLTokenizer(num_clusters=num_clusters) >>> tokens = tokenizer.encode( ... inputs, ... SSL_layers=ssl_layer_num, ... deduplicates=deduplicate, ... bpe_tokenizers=bpe_tokenizers, ... ) >>> print(tokens.shape) torch.Size([3, 6, 2]) cXlg)N num_clusters)selfrs ڻC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\speechbrain/tokenizers/discrete_SSL_tokenizer.py__init__DiscreteSSLTokenizer.__init__*s(c /nUH@nURSRUVs/sHn[US-5PM sn55 MB U$s snf)a Convert token ID to char to be used for training sentencepiece tokenizer. Arguments --------- tokens : torch.Tensor A (Batch x Seq ) tensor of audio tokens Returns ------- processed_tokens : list A (Batch x Seq) list of corresponding char for each token ID.  a)appendjoinchr)r tokens tokens_charrowtokens r textifyDiscreteSSLTokenizer.textify-sN C   sxx(Luer\):(LM N)MsA FNc URS[U5:Xd!SURS[U5S35e/n[U5GHyupg/nU(aUSS2SS2U4R5V s/sH<n U [R "U [R S9R[5PM> n n U V s/sH oR5R5PM" n n URU 5 OURUSS2SS2U45 XFbpURU5n URU V s/sH?n [R"XFRU 55X&UR U--PMA sn 5 GM?URUV s/sHn XUUR U--S-PM sn 5 GM| [R""[R$"[R&R(R*R-USS 9URS 5SS 9$s sn fs sn fs sn fs sn f) a8Takes an input tokenized wavform and return its corresponding processed tokens. Arguments --------- input : torch.Tensor A (Batch x Seq x num_SSL_layers) tensor of audio tokens. SSL_layers: List[int] (default: [7]): determine which layers of SSL should be used to extract information. deduplicates: List[boolean] (default: [False]): determine to apply deduplication(remove duplicate subsequent tokens) on the tokens extracted for the corresponding layer. bpe_tokenizers: List[int] (default: [None]): determine to apply subwording on the tokens extracted for the corresponding layer if the sentencePiece tokenizer is trained for that layer. Returns ------- processed_tokens : torch.Tensor A (Batch x Seq x num_SSL_layers) tensor of audio tokens after applying deduplication and subwording if necessary. z input shape:z7 has conflicts with the length of provided SSL_layers: zJ. The second dimension of input should be the same as number of layers!!!N)prependT) batch_firstr)dim)shapelen enumeratecpunpdiffnanastypeboolclonedetachextendrtorch LongTensor encode_as_idsrstacksplitnnutilsrnn pad_sequence) r input SSL_layers deduplicatesbpe_tokenizers token_idsi duplicaterrunique_token_idslayer_token_ids token_chars r encodeDiscreteSSLTokenizer.encode@s8*{{1~Z0 5;;-'^_bcm_n^opz { 0 %l3LAF %Q1W~113$3RVV4;;DAB3!$ 5E#4DSIIK&&(4D # o. eAq!Gn- ,!\\&1   $.$.C(():)H)H)MN$-$*;*;A*>>?#-  $*#)Cmd.?.?.BBBQF#)34@{{ KK""// t/L A    ;$#s6AH0?'H5:AH: $H? r) __name__ __module__ __qualname____firstlineno____doc__r rr@__static_attributes__r r rr s%<)("#5'4&? r r)rFnumpyr%r-rrHr r rJs  t t r