@jddlmZddlmZmZmZddlZddlmZddlm Z gZ e ddededeed eed e d eeeff d Z eGd dZ ddeded e d ee fdZdS)) dataclass)ListOptionalTupleN)Tensor)fail_if_no_align log_probstargets input_lengthstarget_lengthsblankreturnc||vrtd|dtj||jdkrtd|R|d|d}}tj|f|tj|j}|R|d|d}}tj|f|tj|j}|J|Jtjj |||||\}}||ddtj |jd|dffS) aAlign a CTC label sequence to an emission. .. devices:: CPU CUDA .. properties:: TorchScript Args: log_probs (Tensor): log probability of CTC emission output. Tensor of shape `(B, T, C)`. where `B` is the batch size, `T` is the input length, `C` is the number of characters in alphabet including blank. targets (Tensor): Target sequence. Tensor of shape `(B, L)`, where `L` is the target length. input_lengths (Tensor or None, optional): Lengths of the inputs (max value must each be <= `T`). 1-D Tensor of shape `(B,)`. target_lengths (Tensor or None, optional): Lengths of the targets. 1-D Tensor of shape `(B,)`. blank_id (int, optional): The index of blank symbol in CTC emission. (Default: 0) Returns: Tuple(Tensor, Tensor): Tensor: Label for each time step in the alignment path computed using forced alignment. Tensor: Log probability scores of the labels for each time step. Note: The sequence length of `log_probs` must satisfy: .. math:: L_{\text{log\_probs}} \ge L_{\text{label}} + N_{\text{repeat}} where :math:`N_{\text{repeat}}` is the number of consecutively repeated tokens. For example, in str `"aabbc"`, the number of repeats are `2`. Note: The current version only supports ``batch_size==1``. z4targets Tensor shouldn't contain blank index. Found .z2targets values must be less than the CTC dimensionNr)dtypedevice) ValueErrortorchmaxshapesizefullint64rops torchaudio forced_alignarange) r r r r r batch_sizelengthpathsscoress QC:\Projects\Historical\venv\Lib\site-packages\torchaudio/functional/_alignment.pyrr sOZ ZPWZZZ[[[ yY_R000MNNN&^^A.. q0A0AF  J=& T]Tdeee $\\!__gll1ooF ZM6U\Ucddd  $ $ $  % % %I(55i-YginooME6 &ELa9958CD DDcPeZdZUdZeed< eed< eed< eed< defdZdS) TokenSpanz[TokenSpan() Token with time stamps and score. Returned by :py:func:`merge_tokens`. tokenstartendscorerc |j|jz S)zReturns the time span)r*r))selfs r$__len__zTokenSpan.__len__[sx$*$$r%N)__name__ __module__ __qualname____doc__int__annotations__floatr.r%r$r'r'Lsm JJJ JJJ; HHH9 LLL&%%%%%%%r%r'tokensr#cJjdks jdkrtdttkrtdtjtjdgjtjdgj}tj|dk } fdt|d d|dd D}|S) aRemoves repeated tokens and blank tokens from the given CTC token sequence. Args: tokens (Tensor): Alignment tokens (unbatched) returned from :py:func:`forced_align`. Shape: `(time, )`. scores (Tensor): Alignment scores (unbatched) returned from :py:func:`forced_align`. Shape: `(time, )`. When computing the token-size score, the given score is averaged across the corresponding time span. Returns: list of TokenSpan Example: >>> aligned_tokens, scores = forced_align(emission, targets, input_lengths, target_lengths) >>> token_spans = merge_tokens(aligned_tokens[0], scores[0]) rz(`tokens` and `scores` must be 1D Tensor.z.`tokens` and `scores` must be the same length.r)r)prependappendrc g|]R\}}|xkt||||SS))r(r)r*r+)r'meanitem).0r)r*r r#r(r7s r$ z merge_tokens..{sm    E3E] "Eu , , U6%);L;Q;Q;S;S;X;X;Z;Z[[[ , , ,r%N) ndimrlenrdifftensorrnonzerosqueezetolistzip)r7r#r rBchanges_wo_blankspansr(s``` @r$ merge_tokensrJ`s4"{a6;!++CDDD 6{{c&kk!!IJJJ : bT&-@@@WYVZcicpIqIqIq   D}dai1199;;BBDD ]]__F       .ss35Eabb5IJJ   E Lr%)NNr)r) dataclassesrtypingrrrrrtorchaudio._extensionr__all__r3rr'rJr6r%r$rOsf!!!!!!(((((((((( 222222 '+'+ =E=E=E =EF#=EV$ =E  =E  66> =E=E=E=E@ %%%%%%% %&     DO      r%