o iY@sDdZddlZddlZddlmZmZddlmZGdddeZ dS)z'ScorerInterface implementation for CTC.N)CTCPrefixScoreCTCPrefixScoreTH)BatchPartialScorerInterfacec@sxeZdZdZdejjdefddZdej fddZ dd d Z d d Z dej fddZ ddZdej fddZddZd S)CTCPrefixScorerz-Decoder interface wrapper for CTCPrefixScore.ctceoscCs||_||_d|_dS)zInitialize class. Args: ctc (torch.nn.Module): The CTC implementation. For example, :class:`espnet.nets.pytorch_backend.ctc.CTC` eos (int): The end-of-sequence id. N)rrimpl)selfrrr K/home/ubuntu/.local/lib/python3.10/site-packages/espnet/nets/scorers/ctc.py__init__ s  zCTCPrefixScorer.__init__xcCsD|j|dd}t|d|jt |_ d|j fS)Get an initial state for decoding. Args: x (torch.Tensor): The encoded feature tensor Returns: initial state r) r log_softmax unsqueezedetachsqueezecpunumpyrrnpr initial_stater r logpr r r init_states$ zCTCPrefixScorer.init_stateNc Cst|tkrSt|dkr|\}}||||fS|\}}}} } |||f|d} | durC|dddd|| ||ff| || fS|dddd||f| || fS|durYdS||S)a@Select state with relative ids in the main beam search. Args: state: Decoder state for prefix tokens i (int): Index to select a state in the main beam search new_id (int): New label id to select a state if necessary Returns: state: pruned state N)typetuplelenexpandsize) r stateinew_idscstrlog_psif_minf_max scoring_idmapsr r r select_state(s ( zCTCPrefixScorer.select_statec CsF|\}}||||\}}tj|||j|jd}|||ffS)aScore new token. Args: y (torch.Tensor): 1D prefix token next_tokens (torch.Tensor): torch.int64 next token to score state: decoder state for prefix tokens x (torch.Tensor): 2D encoder feature that generates ys Returns: tuple[torch.Tensor, Any]: Tuple of a score tensor for y that has a shape `(len(next_tokens),)` and next state for ys )devicedtype)rrtorch as_tensorr-r.) r yidsr!r prev_score presub_scorenew_sttscorer r r score_partialAs  zCTCPrefixScorer.score_partialcCs:|j|d}t|dg}t||d|j|_dS)rrrN) rrrr/tensorr rrr)r r rxlenr r r batch_init_stateWs z CTCPrefixScorer.batch_init_statecCs^|ddur&tjdd|Dddtdd|D|dd|ddfnd}||||S) aScore new token. Args: y (torch.Tensor): 1D prefix token ids (torch.Tensor): torch.int64 next token to score state: decoder state for prefix tokens x (torch.Tensor): 2D encoder feature that generates ys Returns: tuple[torch.Tensor, Any]: Tuple of a score tensor for y that has a shape `(len(next_tokens),)` and next state for ys rNcSg|]}|dqS)rr .0r+r r r vz7CTCPrefixScorer.batch_score_partial..r)dimcSr;)rr r<r r r r>wr?)r/stackr)r r1r2r!r batch_stater r r batch_score_partiales    z#CTCPrefixScorer.batch_score_partialcCs"|j|d}|j|dS)zExtend probs for decoding. This extension is for streaming decoding as in Eq (14) in https://arxiv.org/abs/2006.14941 Args: x (torch.Tensor): The encoded feature tensor rN)rrrr extend_probrr r r rEs zCTCPrefixScorer.extend_probcCs$g}|D] }||j|q|S)zExtend state for decoding. This extension is for streaming decoding as in Eq (14) in https://arxiv.org/abs/2006.14941 Args: state: The states of hyps Returns: exteded state )appendr extend_state)r r! new_stater+r r r rGs zCTCPrefixScorer.extend_state)N)__name__ __module__ __qualname____doc__r/nnModuleintr Tensorrr,r7r:rDrErGr r r r r s  r) rLrrr/espnet.nets.ctc_prefix_scorerrespnet.nets.scorer_interfacerrr r r r s