"""ScorerInterface implementation for CTC."""
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import numpy as np
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import torch
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from funasr.modules.scorers.ctc_prefix_score import CTCPrefixScore
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from funasr.modules.scorers.ctc_prefix_score import CTCPrefixScoreTH
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from funasr.modules.scorers.scorer_interface import BatchPartialScorerInterface
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class CTCPrefixScorer(BatchPartialScorerInterface):
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"""Decoder interface wrapper for CTCPrefixScore."""
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def __init__(self, ctc: torch.nn.Module, eos: int):
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"""Initialize class.
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Args:
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ctc (torch.nn.Module): The CTC implementation.
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For example, :class:`espnet.nets.pytorch_backend.ctc.CTC`
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eos (int): The end-of-sequence id.
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"""
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self.ctc = ctc
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self.eos = eos
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self.impl = None
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def init_state(self, x: torch.Tensor):
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"""Get an initial state for decoding.
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Args:
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x (torch.Tensor): The encoded feature tensor
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Returns: initial state
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"""
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logp = self.ctc.log_softmax(x.unsqueeze(0)).detach().squeeze(0).cpu().numpy()
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# TODO(karita): use CTCPrefixScoreTH
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self.impl = CTCPrefixScore(logp, 0, self.eos, np)
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return 0, self.impl.initial_state()
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def select_state(self, state, i, new_id=None):
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"""Select state with relative ids in the main beam search.
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Args:
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state: Decoder state for prefix tokens
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i (int): Index to select a state in the main beam search
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new_id (int): New label id to select a state if necessary
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Returns:
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state: pruned state
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"""
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if type(state) == tuple:
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if len(state) == 2: # for CTCPrefixScore
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sc, st = state
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return sc[i], st[i]
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else: # for CTCPrefixScoreTH (need new_id > 0)
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r, log_psi, f_min, f_max, scoring_idmap = state
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s = log_psi[i, new_id].expand(log_psi.size(1))
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if scoring_idmap is not None:
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return r[:, :, i, scoring_idmap[i, new_id]], s, f_min, f_max
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else:
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return r[:, :, i, new_id], s, f_min, f_max
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return None if state is None else state[i]
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def score_partial(self, y, ids, state, x):
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"""Score new token.
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Args:
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y (torch.Tensor): 1D prefix token
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next_tokens (torch.Tensor): torch.int64 next token to score
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state: decoder state for prefix tokens
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x (torch.Tensor): 2D encoder feature that generates ys
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Returns:
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tuple[torch.Tensor, Any]:
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Tuple of a score tensor for y that has a shape `(len(next_tokens),)`
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and next state for ys
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"""
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prev_score, state = state
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presub_score, new_st = self.impl(y.cpu(), ids.cpu(), state)
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tscore = torch.as_tensor(
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presub_score - prev_score, device=x.device, dtype=x.dtype
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)
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return tscore, (presub_score, new_st)
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def batch_init_state(self, x: torch.Tensor):
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"""Get an initial state for decoding.
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Args:
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x (torch.Tensor): The encoded feature tensor
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Returns: initial state
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"""
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logp = self.ctc.log_softmax(x.unsqueeze(0)) # assuming batch_size = 1
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xlen = torch.tensor([logp.size(1)])
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self.impl = CTCPrefixScoreTH(logp, xlen, 0, self.eos)
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return None
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def batch_score_partial(self, y, ids, state, x):
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"""Score new token.
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Args:
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y (torch.Tensor): 1D prefix token
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ids (torch.Tensor): torch.int64 next token to score
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state: decoder state for prefix tokens
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x (torch.Tensor): 2D encoder feature that generates ys
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Returns:
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tuple[torch.Tensor, Any]:
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Tuple of a score tensor for y that has a shape `(len(next_tokens),)`
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and next state for ys
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"""
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batch_state = (
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(
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torch.stack([s[0] for s in state], dim=2),
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torch.stack([s[1] for s in state]),
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state[0][2],
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state[0][3],
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)
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if state[0] is not None
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else None
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)
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return self.impl(y, batch_state, ids)
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def extend_prob(self, x: torch.Tensor):
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"""Extend probs for decoding.
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This extension is for streaming decoding
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as in Eq (14) in https://arxiv.org/abs/2006.14941
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Args:
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x (torch.Tensor): The encoded feature tensor
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"""
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logp = self.ctc.log_softmax(x.unsqueeze(0))
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self.impl.extend_prob(logp)
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def extend_state(self, state):
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"""Extend state for decoding.
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This extension is for streaming decoding
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as in Eq (14) in https://arxiv.org/abs/2006.14941
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Args:
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state: The states of hyps
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Returns: exteded state
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"""
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new_state = []
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for s in state:
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new_state.append(self.impl.extend_state(s))
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return new_state
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