%`ivNddlmZddlZddlmZddlmZddlm Z m Z m Z m Z m Z mZddlZddlmZmZmZmZmZmZmZmZ m!Z"m#Z$ddl%m&Z'm(Z)m*Z+ddl,m-Z- dd l.m/Z0n #e1$r dd lm/Z0n #e1$rdZ0YnwxYwYnwxYwgd Z2ed gd Z3d Z4dZ5Gdde Z6GddeZ7GddeZ8GddZ9dddddddde:ddddddfd=d8Z;d>d;Zd<Z=dS)?) annotationsN)abstractmethod) namedtuple)DictList NamedTupleOptionalTupleUnion) CriterionTypeLexiconDecoderLexiconDecoderOptionsLexiconFreeDecoderLexiconFreeDecoderOptionsLMLMState SmearingModeTrieZeroLM)create_word_dict Dictionary load_words)_download_asset)KenLM) CTCHypothesis CTCDecoder CTCDecoderLMCTCDecoderLMState ctc_decoderdownload_pretrained_filesPretrainedFileslexicontokenslmc}t||}|d}|D]]\}} ||} ||| \} } | D]'} fd| D}||| | (^|tj |S)NFc:g|]}|S) get_index).0token tokens_dicts z/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/torchaudio/models/decoder/_ctc_decoder.py z#_construct_trie..;s'OOOUK11%88OOO) index_size_Triestartitemsr)scoreinsertsmear _SmearingModeMAX)r, word_dictr#r%silence vocab_sizetrie start_stateword spellingsword_idx_r4spelling spelling_idxs` r-_construct_trierD2s''))J W % %D((5//K"==??77i&&t,,88K225! 7 7HOOOOhOOOL KK h 6 6 6 6 7 JJ} !!! Kr/cd}|t|}|r|t|}n]|s[|Yt|turCfdt D}|gg||<t|}|S)Ncfi|]-}||gg.Sr() get_entry)r*ir,s r- z"_get_word_dict..Is= m m m[ " "1 % %)>)>q)A)A(B'C m m mr/) _Dictionary_create_word_dicttypestrranger0)r#r%lm_dictr,unk_wordr9ds ` r-_get_word_dictrRAsI(( )9$%g.. )*tBxx3 m m m mUS^SiSiSkSkMlMl m m m zl( %a(( r/cBeZdZUdZded< ded< ded< ded <d S) rzORepresents hypothesis generated by CTC beam search decoder :class:`CTCDecoder`.torch.LongTensorr$z List[str]wordsfloatr4torch.IntTensor timestepsN)__name__ __module__ __qualname____doc____annotations__r(r/r-rrPsRZZhLLL+nnr/rcHeZdZdZed fd Zd fd Zd d ZxZS) rzLanguage model state.returnDict[int, CTCDecoderLMState]c*tjS)zMap of indices to LM states)superchildren)self __class__s r-rczCTCDecoderLMState.childrenhswwr/ usr_indexintcFt|S)a!Returns child corresponding to usr_index, or creates and returns a new state if input index is not found. Args: usr_index (int): index corresponding to child state Returns: CTCDecoderLMState: child state corresponding to usr_index )rbchild)rdrfres r-rizCTCDecoderLMState.childmsww}}Y'''r/statecdS)zCompare two language model states. Args: state (CTCDecoderLMState): LM state to compare against Returns: int: 0 if the states are the same, -1 if self is less, +1 if self is greater. Nr(rdrjs r-comparezCTCDecoderLMState.compareys  r/)r_r`)rfrgr_r)rjrr_r) rYrZr[r\propertyrcrirm __classcell__)res@r-rres      X  ( ( ( ( ( (         r/rcZeZdZdZeddZedd Zedd Zd S)rzVLanguage model base class for creating custom language models to use with the decoder.start_with_nothingboolr_rct)zInitialize or reset the language model. Args: start_with_nothing (bool): whether or not to start sentence with sil token. Returns: CTCDecoderLMState: starting state NotImplementedError)rdrqs r-r2zCTCDecoderLM.starts "!r/rj usr_token_idxrgTuple[CTCDecoderLMState, float]ct)axEvaluate the language model based on the current LM state and new word. Args: state (CTCDecoderLMState): current LM state usr_token_idx (int): index of the word Returns: (CTCDecoderLMState, float) CTCDecoderLMState: new LM state float: score rt)rdrjrvs r-r4zCTCDecoderLM.scores "!r/ct)a8Evaluate end for language model based on current LM state. Args: state (CTCDecoderLMState): current LM state Returns: (CTCDecoderLMState, float) CTCDecoderLMState: new LM state float: score rtrls r-finishzCTCDecoderLM.finishs "!r/N)rqrrr_r)rjrrvrgr_rw)rjrr_rw)rYrZr[r\rr2r4rzr(r/r-rrsx`` " " "^ """"^"  " " "^ " " "r/rcbeZdZdZd(dZd)dZd*dZdZdZd+dZ d,dZ d,d Z d-d.d%Z d/d'Z d!S)0rzCTC beam search decoder from *Flashlight* :cite:`kahn2022flashlight`. .. devices:: CPU Note: To build the decoder, please use the factory function :func:`ctc_decoder`. nbestrgr#Optional[Dict]r9rJr,r%rdecoder_options9Union[_LexiconDecoderOptions, _LexiconFreeDecoderOptions] blank_tokenrM sil_tokenrPr_Nonec ||_||_||_|j||_|j|} g} |rKt ||||| } || } d} t || || |j| | | |_nt||| |j| |_||_ dS)a Args: nbest (int): number of best decodings to return lexicon (Dict or None): lexicon mapping of words to spellings, or None for lexicon-free decoder word_dict (_Dictionary): dictionary of words tokens_dict (_Dictionary): dictionary of tokens lm (CTCDecoderLM): language model. If using a lexicon, only word level LMs are currently supported decoder_options (_LexiconDecoderOptions or _LexiconFreeDecoderOptions): parameters used for beam search decoding blank_token (str): token corresopnding to blank sil_token (str): token corresponding to silence unk_word (str): word corresponding to unknown FN) r|r9r,r)blankrD_LexiconDecoderdecoder_LexiconFreeDecoderr%)rdr|r#r9r,r%r~rrrPr: transitionsr<token_lms r-__init__zCTCDecoder.__init__s4 "&%// << ",,Y77  f"; 7BPPD **844HH*   DLL/GTZYdeeDL r/idxsrWrTcdtj|D}tfd|}tjt |S)Nc3&K|] }|dV dS)rNr()r*gs r- z)CTCDecoder._get_tokens..s&//!//////r/c|jkSN)r)xrds r-z(CTCDecoder._get_tokens..s TZr/)itgroupbyfiltertorch LongTensorlistrdrs` r- _get_tokenszCTCDecoder._get_tokenssP//bj..///////66T +++r/cg}t|D];\}}||jkr|dks|||dz kr||>> decoder = torchaudio.models.decoder.ctc_decoder(...) >>> decoder.decode_begin() >>> decoder.decode_step(emission1) >>> decoder.decode_step(emission2) >>> decoder.decode_end() >>> result = decoder.get_final_hypothesis() emissions must be float32.emissions must be a CPU tensor.emissions must be contiguous.zemissions must be 2D. Found N) dtyperfloat32 ValueErroris_cpu RuntimeError is_contiguousndimshapesizer decode_stepdata_ptr)rdrTNs r-rzCTCDecoder.decode_step s( ?em + +9:: : B@AA A&&(( @>?? ? >Q  OioOOPP P~~1   !3!3!5!5q!<<<<..Gs/SSSqAQRFFt~//22FFFr/)r$rUr4rX)rrr$rUr4r)r*resultrds r-r.z'CTCDecoder._to_hypo..Ds~    '' 66SSSSFLSSSl--fm<<       r/r(rdresultss` r-_to_hypozCTCDecoder._to_hypoCs0    "    r/cx|j}||d|jS)a9Get the final hypothesis Returns: List[CTCHypothesis]: List of sorted best hypotheses. .. note:: This method is required only when performing online decoding. It is not necessary when performing batch decoding with :py:meth:`__call__`. N)rget_all_final_hypothesisrr|rs r-get_final_hypothesiszCTCDecoder.get_final_hypothesisNs4,7799}}W\tz\2333r/NlengthsOptional[torch.Tensor]List[List[CTCHypothesis]]c|jtjkrtd|jst d|st d|jdkrt d|j||jst d| \}}}|tj |f|}d}g}t|D]}| ||z| d zz} |j| |||} ||| d|j|S) a Performs batched offline decoding. .. note:: This method performs offline decoding in one go. To perform incremental decoding, please refer to :py:meth:`decode_step`. Args: emissions (torch.FloatTensor): CPU tensor of shape `(batch, frame, num_tokens)` storing sequences of probability distribution over labels; output of acoustic model. lengths (Tensor or None, optional): CPU tensor of shape `(batch, )` storing the valid length of in time axis of the output Tensor in each batch. Returns: List[List[CTCHypothesis]]: List of sorted best hypotheses for each audio sequence in the batch. rrrzemissions must be 3D. Found Nzlengths must be a CPU tensor.r)rrrrrrrrrrfullrNrstriderdecoderrr|) rdrrBrr float_byteshyposb emissions_ptrrs r-__call__zCTCDecoder.__call__]sk, ?em + +9:: : B@AA A&&(( @>?? ? >Q  OioOOPP P  w~ >?? ?..""1a ?j!q))G q ? ?A%..00;?YEUEUVWEXEX3XXMl))-QGGG LLw||'<== > > > > r/rc fd|DS)z Map raw token IDs into corresponding tokens Args: idxs (LongTensor): raw token IDs generated from decoder Returns: List: tokens corresponding to the input IDs chg|].}j|/Sr()r,rGitem)r*rrds r-r.z-CTCDecoder.idxs_to_tokens..s2GGG3 **388::66GGGr/r(rs` r-idxs_to_tokenszCTCDecoder.idxs_to_tokenss HGGG$GGGGr/)r|rgr#r}r9rJr,rJr%rr~rrrMrrMrPrMr_r)rrWr_rT)rrWr_rW)rr)r_rr)rrrrr_r)rrTr_r)rYrZr[r\rrrrrrrrrrr(r/r-rrs7777r,,,, * * * * $ $ $ " " "!=!=!=!=F      4 4 4 4 OS00000d H H H H H Hr/rr2rz-infF-|zr# Optional[str]r$Union[str, List[str]]r%Union[str, CTCDecoderLM]rOr|rg beam_sizebeam_size_token Optional[int]beam_thresholdrV lm_weight word_score unk_score sil_scorelog_addrrrrMrrPr_c V|%t|turtdt|}|rFt |}t ||p|||| | | | tj }n4t||p|||| | tj}t|||||}t|tur'ttdt||}n|t}t||||||| || S)aY Builds an instance of :class:`CTCDecoder`. Args: lexicon (str or None): lexicon file containing the possible words and corresponding spellings. Each line consists of a word and its space separated spelling. If `None`, uses lexicon-free decoding. tokens (str or List[str]): file or list containing valid tokens. If using a file, the expected format is for tokens mapping to the same index to be on the same line lm (str, CTCDecoderLM, or None, optional): either a path containing KenLM language model, custom language model of type `CTCDecoderLM`, or `None` if not using a language model lm_dict (str or None, optional): file consisting of the dictionary used for the LM, with a word per line sorted by LM index. If decoding with a lexicon, entries in lm_dict must also occur in the lexicon file. If `None`, dictionary for LM is constructed using the lexicon file. (Default: None) nbest (int, optional): number of best decodings to return (Default: 1) beam_size (int, optional): max number of hypos to hold after each decode step (Default: 50) beam_size_token (int, optional): max number of tokens to consider at each decode step. If `None`, it is set to the total number of tokens (Default: None) beam_threshold (float, optional): threshold for pruning hypothesis (Default: 50) lm_weight (float, optional): weight of language model (Default: 2) word_score (float, optional): word insertion score (Default: 0) unk_score (float, optional): unknown word insertion score (Default: -inf) sil_score (float, optional): silence insertion score (Default: 0) log_add (bool, optional): whether or not to use logadd when merging hypotheses (Default: False) blank_token (str, optional): token corresponding to blank (Default: "-") sil_token (str, optional): token corresponding to silence (Default: "|") unk_word (str, optional): word corresponding to unknown (Default: "") Returns: CTCDecoder: decoder Example >>> decoder = ctc_decoder( >>> lexicon="lexicon.txt", >>> tokens="tokens.txt", >>> lm="kenlm.bin", >>> ) >>> results = decoder(emissions) # List of shape (B, nbest) of Hypotheses Nz!lm_dict must be None or str type.) rrrrrrrrcriterion_type)rrrrrrrzflashlight-text is installed, but KenLM is not installed. Please refer to https://github.com/kpu/kenlm#python-module for how to install it.) r|r#r9r,r%r~rrrP)rLrMrrJ _load_words_LexiconDecoderOptionsr0_CriterionTypeCTC_LexiconFreeDecoderOptionsrR_KenLMr_ZeroLMr)r#r$r%rOr|rrrrrrrrrrrPr,r~r9s r-rrshrtG}}C77<===f%%K g&&0+G{/E/E/G/G)!)-    5+G{/E/E/G/G))-   wG[(KKI Bxx3 >d B " "  YY  '    r/model_PretrainedFilesc~|dvrt|dd|}t|d|d|dkr|dndS) N) librispeechzlibrispeech-3-gramzlibrispeech-4-gramzZ not supported. Must be one of ['librispeech-3-gram', 'librispeech-4-gram', 'librispeech']zdecoder-assets/z /lexicon.txtz /tokens.txtrz/lm.binr")rr)rprefixs r-_get_filenamesr s OOO p p p   'u & &F '''%%%!&-!7!7f    T   r/ct|}t|j}t|j}|jt|j}nd}t |||S)aM Retrieves pretrained data files used for :func:`ctc_decoder`. Args: model (str): pretrained language model to download. Valid values are: ``"librispeech-3-gram"``, ``"librispeech-4-gram"`` and ``"librispeech"``. Returns: Object with the following attributes * ``lm``: path corresponding to downloaded language model, or ``None`` if the model is not associated with an lm * ``lexicon``: path corresponding to downloaded lexicon file * ``tokens``: path corresponding to downloaded tokens file Nr")rrr#r$r%r)rfiles lexicon_file tokens_filelm_files r-r r sm" 5 ! !E"5=11L!%,//K x!%(++     r/)"r#rr$rr%rrOrr|rgrrgrrrrVrrVrrVrrVrrVrrrrrMrrMrPrMr_r)rrMr_r)> __future__r itertoolsrabcr collectionsrtypingrrrr r r rflashlight.lib.text.decoderr rr rrrrrrrr_LMr_LMStaterr7rr1rrflashlight.lib.text.dictionaryrrKrrJrrtorchaudio.utilsr!flashlight.lib.text.decoder.kenlmrr Exception__all__rrDrRrrrrrVrrr r(r/r-r sD""""""""""""AAAAAAAAAAAAAAAA                          -,,,,,AAAAAAA???????     :/1L1L1LMM      oooooJooo*        @."."."."."3."."."bcHcHcHcHcHcHcHcHR$(!%)uV}}!nnnnnb    s6AA8!A('A8(A2/A81A22A87A8