hWSSKJr SSKJrJrJr SSKrSSKJr SSKJ r /r \ SS\S\S\\S \\S \ S \\\44 S jj5r \"S S55r SS\S\S \ S \\ 4Sjjrg)) dataclass)ListOptionalTupleN)Tensor)fail_if_no_align log_probstargets input_lengthstarget_lengthsblankreturncRXA;a[SUS35e[R"U5URS:a [S5eUcQUR S5UR S5pe[R "U4U[R URS9nUcQUR S5UR S5pe[R "U4U[R URS9nUceUce[RRRXX#U5upxXx4$)a2Align 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 dimensionr)dtypedevice) ValueErrortorchmaxshapesizefullint64rops torchaudio forced_align) r r r r r batch_sizelengthpathsscoress X/var/www/auris/envauris/lib/python3.13/site-packages/torchaudio/functional/_alignment.pyrr s Z OPWyXYZ[[ yyY__R00MNN&^^A. q0AF J=& T]TdTde $\\!_gll1oFZM6U\UcUcd  $$ $  %% %II((55i-inoME =cX\rSrSr%Sr\\S'\\S'\\S'\\S'S\4SjrS r g ) TokenSpanLzSTokenSpan() Token with time stamps and score. Returned by :py:func:`merge_tokens`. tokenstartendscorerc4URUR- $)zReturns the time span)r*r))selfs r#__len__TokenSpan.__len__[sxx$**$$r$N) __name__ __module__ __qualname____firstlineno____doc__int__annotations__floatr.__static_attributes__r0r$r#r&r&Ls5 J J; H9 L&%%r$r&tokensr"c URS:wdURS:wa [S5e[U5[U5:wa [S5e[R"U[R "S/UR S9[R "S/UR S9S9n[R"US:g5R5R5nUR5n[USSUSS5VVs/sH8upVX=or:wdM[XuXaXVR5R5S 9PM: nnnU$s snnf) asRemoves 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)prependappendrN)r(r)r*r+)ndimrlenrdifftensorrnonzerosqueezetolistzipr&meanitem) r:r"r r@changes_wo_blankr)r*r(spanss r# merge_tokensrJ`s&"{{a6;;!+CDD 6{c&k!IJJ :: bT&--@WYVZcicpcpIq D}}dai199;BBD ]]_F.s35Eab5IJ JJE] "E , \ %;L;Q;Q;S;X;X;Z[J   L  s ;D?,D?)NNr)r) dataclassesrtypingrrrrrtorchaudio._extensionr__all__r6rr&rJr0r$r#rOs!(( 2 '+'+ == =F#=V$ =  =  66> ==@ %% %&    DO r$