mirror of
https://github.com/k2-fsa/icefall.git
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1281 lines
42 KiB
Python
1281 lines
42 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang
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# Xiaoyu Yang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import warnings
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional, Tuple, Union
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import k2
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import sentencepiece as spm
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import torch
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from model import SURT
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from icefall import NgramLm, NgramLmStateCost
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from icefall.decode import Nbest, one_best_decoding
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from icefall.lm_wrapper import LmScorer
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from icefall.utils import (
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DecodingResults,
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add_eos,
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add_sos,
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get_texts,
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get_texts_with_timestamp,
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)
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def fast_beam_search_one_best(
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model: SURT,
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decoding_graph: k2.Fsa,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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beam: float,
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max_states: int,
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max_contexts: int,
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temperature: float = 1.0,
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return_timestamps: bool = False,
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) -> Union[List[List[int]], DecodingResults]:
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"""It limits the maximum number of symbols per frame to 1.
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A lattice is first obtained using fast beam search, and then
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the shortest path within the lattice is used as the final output.
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Args:
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model:
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An instance of `SURT`.
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decoding_graph:
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Decoding graph used for decoding, may be a TrivialGraph or a LG.
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encoder_out:
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A tensor of shape (N, T, C) from the encoder.
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encoder_out_lens:
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A tensor of shape (N,) containing the number of frames in `encoder_out`
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before padding.
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beam:
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Beam value, similar to the beam used in Kaldi..
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max_states:
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Max states per stream per frame.
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max_contexts:
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Max contexts pre stream per frame.
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temperature:
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Softmax temperature.
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return_timestamps:
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Whether to return timestamps.
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Returns:
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If return_timestamps is False, return the decoded result.
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Else, return a DecodingResults object containing
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decoded result and corresponding timestamps.
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"""
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lattice = fast_beam_search(
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model=model,
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decoding_graph=decoding_graph,
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encoder_out=encoder_out,
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encoder_out_lens=encoder_out_lens,
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beam=beam,
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max_states=max_states,
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max_contexts=max_contexts,
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temperature=temperature,
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)
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best_path = one_best_decoding(lattice)
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if not return_timestamps:
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return get_texts(best_path)
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else:
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return get_texts_with_timestamp(best_path)
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def fast_beam_search_nbest_LG(
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model: SURT,
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decoding_graph: k2.Fsa,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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beam: float,
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max_states: int,
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max_contexts: int,
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num_paths: int,
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nbest_scale: float = 0.5,
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use_double_scores: bool = True,
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temperature: float = 1.0,
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return_timestamps: bool = False,
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) -> Union[List[List[int]], DecodingResults]:
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"""It limits the maximum number of symbols per frame to 1.
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The process to get the results is:
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- (1) Use fast beam search to get a lattice
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- (2) Select `num_paths` paths from the lattice using k2.random_paths()
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- (3) Unique the selected paths
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- (4) Intersect the selected paths with the lattice and compute the
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shortest path from the intersection result
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- (5) The path with the largest score is used as the decoding output.
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Args:
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model:
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An instance of `Transducer`.
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decoding_graph:
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Decoding graph used for decoding, may be a TrivialGraph or a LG.
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encoder_out:
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A tensor of shape (N, T, C) from the encoder.
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encoder_out_lens:
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A tensor of shape (N,) containing the number of frames in `encoder_out`
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before padding.
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beam:
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Beam value, similar to the beam used in Kaldi..
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max_states:
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Max states per stream per frame.
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max_contexts:
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Max contexts pre stream per frame.
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num_paths:
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Number of paths to extract from the decoded lattice.
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nbest_scale:
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It's the scale applied to the lattice.scores. A smaller value
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yields more unique paths.
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use_double_scores:
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True to use double precision for computation. False to use
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single precision.
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temperature:
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Softmax temperature.
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return_timestamps:
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Whether to return timestamps.
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Returns:
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If return_timestamps is False, return the decoded result.
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Else, return a DecodingResults object containing
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decoded result and corresponding timestamps.
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"""
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lattice = fast_beam_search(
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model=model,
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decoding_graph=decoding_graph,
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encoder_out=encoder_out,
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encoder_out_lens=encoder_out_lens,
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beam=beam,
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max_states=max_states,
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max_contexts=max_contexts,
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temperature=temperature,
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)
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nbest = Nbest.from_lattice(
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lattice=lattice,
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num_paths=num_paths,
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use_double_scores=use_double_scores,
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nbest_scale=nbest_scale,
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)
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# The following code is modified from nbest.intersect()
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word_fsa = k2.invert(nbest.fsa)
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if hasattr(lattice, "aux_labels"):
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# delete token IDs as it is not needed
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del word_fsa.aux_labels
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word_fsa.scores.zero_()
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word_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(word_fsa)
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path_to_utt_map = nbest.shape.row_ids(1)
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if hasattr(lattice, "aux_labels"):
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# lattice has token IDs as labels and word IDs as aux_labels.
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# inv_lattice has word IDs as labels and token IDs as aux_labels
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inv_lattice = k2.invert(lattice)
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inv_lattice = k2.arc_sort(inv_lattice)
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else:
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inv_lattice = k2.arc_sort(lattice)
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if inv_lattice.shape[0] == 1:
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path_lattice = k2.intersect_device(
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inv_lattice,
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word_fsa_with_epsilon_loops,
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b_to_a_map=torch.zeros_like(path_to_utt_map),
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sorted_match_a=True,
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)
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else:
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path_lattice = k2.intersect_device(
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inv_lattice,
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word_fsa_with_epsilon_loops,
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b_to_a_map=path_to_utt_map,
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sorted_match_a=True,
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)
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# path_lattice has word IDs as labels and token IDs as aux_labels
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path_lattice = k2.top_sort(k2.connect(path_lattice))
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tot_scores = path_lattice.get_tot_scores(
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use_double_scores=use_double_scores,
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log_semiring=True, # Note: we always use True
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)
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# See https://github.com/k2-fsa/icefall/pull/420 for why
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# we always use log_semiring=True
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ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
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best_hyp_indexes = ragged_tot_scores.argmax()
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best_path = k2.index_fsa(nbest.fsa, best_hyp_indexes)
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if not return_timestamps:
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return get_texts(best_path)
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else:
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return get_texts_with_timestamp(best_path)
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def fast_beam_search(
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model: SURT,
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decoding_graph: k2.Fsa,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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beam: float,
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max_states: int,
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max_contexts: int,
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temperature: float = 1.0,
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) -> k2.Fsa:
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"""It limits the maximum number of symbols per frame to 1.
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Args:
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model:
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An instance of `SURT`.
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decoding_graph:
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Decoding graph used for decoding, may be a TrivialGraph or a LG.
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encoder_out:
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A tensor of shape (N, T, C) from the encoder.
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encoder_out_lens:
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A tensor of shape (N,) containing the number of frames in `encoder_out`
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before padding.
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beam:
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Beam value, similar to the beam used in Kaldi..
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max_states:
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Max states per stream per frame.
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max_contexts:
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Max contexts pre stream per frame.
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temperature:
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Softmax temperature.
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Returns:
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Return an FsaVec with axes [utt][state][arc] containing the decoded
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lattice. Note: When the input graph is a TrivialGraph, the returned
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lattice is actually an acceptor.
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"""
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assert encoder_out.ndim == 3
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context_size = model.decoder.context_size
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vocab_size = model.decoder.vocab_size
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B, T, C = encoder_out.shape
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config = k2.RnntDecodingConfig(
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vocab_size=vocab_size,
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decoder_history_len=context_size,
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beam=beam,
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max_contexts=max_contexts,
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max_states=max_states,
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)
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individual_streams = []
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for i in range(B):
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individual_streams.append(k2.RnntDecodingStream(decoding_graph))
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decoding_streams = k2.RnntDecodingStreams(individual_streams, config)
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encoder_out = model.joiner.encoder_proj(encoder_out)
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for t in range(T):
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# shape is a RaggedShape of shape (B, context)
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# contexts is a Tensor of shape (shape.NumElements(), context_size)
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shape, contexts = decoding_streams.get_contexts()
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# `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
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contexts = contexts.to(torch.int64)
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# decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
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decoder_out = model.decoder(contexts, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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# current_encoder_out is of shape
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# (shape.NumElements(), 1, joiner_dim)
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# fmt: off
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current_encoder_out = torch.index_select(
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encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
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)
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# fmt: on
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logits = model.joiner(
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current_encoder_out.unsqueeze(2),
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decoder_out.unsqueeze(1),
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project_input=False,
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)
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logits = logits.squeeze(1).squeeze(1)
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log_probs = (logits / temperature).log_softmax(dim=-1)
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decoding_streams.advance(log_probs)
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decoding_streams.terminate_and_flush_to_streams()
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lattice = decoding_streams.format_output(encoder_out_lens.tolist())
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return lattice
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def greedy_search(
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model: SURT,
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encoder_out: torch.Tensor,
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max_sym_per_frame: int,
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return_timestamps: bool = False,
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) -> Union[List[int], DecodingResults]:
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"""Greedy search for a single utterance.
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Args:
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model:
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An instance of `SURT`.
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encoder_out:
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A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
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max_sym_per_frame:
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Maximum number of symbols per frame. If it is set to 0, the WER
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would be 100%.
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return_timestamps:
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Whether to return timestamps.
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Returns:
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If return_timestamps is False, return the decoded result.
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Else, return a DecodingResults object containing
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decoded result and corresponding timestamps.
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"""
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assert encoder_out.ndim == 4
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# support only batch_size == 1 for now
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assert encoder_out.size(0) == 1, encoder_out.size(0)
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blank_id = model.decoder.blank_id
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context_size = model.decoder.context_size
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unk_id = getattr(model, "unk_id", blank_id)
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device = next(model.parameters()).device
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decoder_input = torch.tensor(
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[-1] * (context_size - 1) + [blank_id], device=device, dtype=torch.int64
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).reshape(1, context_size)
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decoder_out = model.decoder(decoder_input, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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encoder_out = model.joiner.encoder_proj(encoder_out)
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T = encoder_out.size(1)
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t = 0
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hyp = [blank_id] * context_size
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# timestamp[i] is the frame index after subsampling
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# on which hyp[i] is decoded
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timestamp = []
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# Maximum symbols per utterance.
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max_sym_per_utt = 1000
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# symbols per frame
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sym_per_frame = 0
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# symbols per utterance decoded so far
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sym_per_utt = 0
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while t < T and sym_per_utt < max_sym_per_utt:
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if sym_per_frame >= max_sym_per_frame:
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sym_per_frame = 0
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t += 1
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continue
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# fmt: off
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current_encoder_out = encoder_out[:, t:t+1, :].unsqueeze(2)
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# fmt: on
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logits = model.joiner(
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current_encoder_out, decoder_out.unsqueeze(1), project_input=False
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)
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# logits is (1, 1, 1, vocab_size)
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y = logits.argmax().item()
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if y not in (blank_id, unk_id):
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hyp.append(y)
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timestamp.append(t)
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decoder_input = torch.tensor([hyp[-context_size:]], device=device).reshape(
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1, context_size
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)
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decoder_out = model.decoder(decoder_input, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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sym_per_utt += 1
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sym_per_frame += 1
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else:
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sym_per_frame = 0
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t += 1
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hyp = hyp[context_size:] # remove blanks
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if not return_timestamps:
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return hyp
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else:
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return DecodingResults(
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hyps=[hyp],
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timestamps=[timestamp],
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)
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def greedy_search_batch(
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model: SURT,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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return_timestamps: bool = False,
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) -> Union[List[List[int]], DecodingResults]:
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"""Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
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Args:
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model:
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The SURT model.
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encoder_out:
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Output from the encoder. Its shape is (N, T, C), where N >= 1.
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encoder_out_lens:
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A 1-D tensor of shape (N,), containing number of valid frames in
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encoder_out before padding.
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return_timestamps:
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Whether to return timestamps.
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Returns:
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If return_timestamps is False, return the decoded result.
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Else, return a DecodingResults object containing
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decoded result and corresponding timestamps.
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"""
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assert encoder_out.ndim == 3
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assert encoder_out.size(0) >= 1, encoder_out.size(0)
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packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
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input=encoder_out,
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lengths=encoder_out_lens.cpu(),
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batch_first=True,
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enforce_sorted=False,
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)
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device = next(model.parameters()).device
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blank_id = model.decoder.blank_id
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unk_id = getattr(model, "unk_id", blank_id)
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context_size = model.decoder.context_size
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batch_size_list = packed_encoder_out.batch_sizes.tolist()
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N = encoder_out.size(0)
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assert torch.all(encoder_out_lens > 0), encoder_out_lens
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assert N == batch_size_list[0], (N, batch_size_list)
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hyps = [[-1] * (context_size - 1) + [blank_id] for _ in range(N)]
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# timestamp[n][i] is the frame index after subsampling
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# on which hyp[n][i] is decoded
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timestamps = [[] for _ in range(N)]
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decoder_input = torch.tensor(
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hyps,
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device=device,
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dtype=torch.int64,
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) # (N, context_size)
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decoder_out = model.decoder(decoder_input, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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# decoder_out: (N, 1, decoder_out_dim)
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encoder_out = model.joiner.encoder_proj(packed_encoder_out.data)
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offset = 0
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for (t, batch_size) in enumerate(batch_size_list):
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start = offset
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end = offset + batch_size
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current_encoder_out = encoder_out.data[start:end]
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current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1)
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# current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim)
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offset = end
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decoder_out = decoder_out[:batch_size]
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logits = model.joiner(
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current_encoder_out, decoder_out.unsqueeze(1), project_input=False
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)
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# logits'shape (batch_size, 1, 1, vocab_size)
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logits = logits.squeeze(1).squeeze(1) # (batch_size, vocab_size)
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assert logits.ndim == 2, logits.shape
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y = logits.argmax(dim=1).tolist()
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emitted = False
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for i, v in enumerate(y):
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if v not in (blank_id, unk_id):
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hyps[i].append(v)
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timestamps[i].append(t)
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emitted = True
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if emitted:
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# update decoder output
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decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
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decoder_input = torch.tensor(
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decoder_input,
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device=device,
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dtype=torch.int64,
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)
|
|
decoder_out = model.decoder(decoder_input, need_pad=False)
|
|
decoder_out = model.joiner.decoder_proj(decoder_out)
|
|
|
|
sorted_ans = [h[context_size:] for h in hyps]
|
|
ans = []
|
|
ans_timestamps = []
|
|
unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
|
|
for i in range(N):
|
|
ans.append(sorted_ans[unsorted_indices[i]])
|
|
ans_timestamps.append(timestamps[unsorted_indices[i]])
|
|
|
|
if not return_timestamps:
|
|
return ans
|
|
else:
|
|
return DecodingResults(
|
|
hyps=ans,
|
|
timestamps=ans_timestamps,
|
|
)
|
|
|
|
|
|
def modified_beam_search(
|
|
model: SURT,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
beam: int = 4,
|
|
temperature: float = 1.0,
|
|
return_timestamps: bool = False,
|
|
) -> Union[List[List[int]], DecodingResults]:
|
|
"""Beam search in batch mode with --max-sym-per-frame=1 being hardcoded.
|
|
|
|
Args:
|
|
model:
|
|
The SURT model.
|
|
encoder_out:
|
|
Output from the encoder. Its shape is (N, T, C).
|
|
encoder_out_lens:
|
|
A 1-D tensor of shape (N,), containing number of valid frames in
|
|
encoder_out before padding.
|
|
beam:
|
|
Number of active paths during the beam search.
|
|
temperature:
|
|
Softmax temperature.
|
|
return_timestamps:
|
|
Whether to return timestamps.
|
|
Returns:
|
|
If return_timestamps is False, return the decoded result.
|
|
Else, return a DecodingResults object containing
|
|
decoded result and corresponding timestamps.
|
|
"""
|
|
assert encoder_out.ndim == 3, encoder_out.shape
|
|
assert encoder_out.size(0) >= 1, encoder_out.size(0)
|
|
|
|
packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
|
|
input=encoder_out,
|
|
lengths=encoder_out_lens.cpu(),
|
|
batch_first=True,
|
|
enforce_sorted=False,
|
|
)
|
|
|
|
blank_id = model.decoder.blank_id
|
|
unk_id = getattr(model, "unk_id", blank_id)
|
|
context_size = model.decoder.context_size
|
|
device = next(model.parameters()).device
|
|
|
|
batch_size_list = packed_encoder_out.batch_sizes.tolist()
|
|
N = encoder_out.size(0)
|
|
assert torch.all(encoder_out_lens > 0), encoder_out_lens
|
|
assert N == batch_size_list[0], (N, batch_size_list)
|
|
|
|
B = [HypothesisList() for _ in range(N)]
|
|
for i in range(N):
|
|
B[i].add(
|
|
Hypothesis(
|
|
ys=[blank_id] * context_size,
|
|
log_prob=torch.zeros(1, dtype=torch.float32, device=device),
|
|
timestamp=[],
|
|
)
|
|
)
|
|
|
|
encoder_out = model.joiner.encoder_proj(packed_encoder_out.data)
|
|
|
|
offset = 0
|
|
finalized_B = []
|
|
for (t, batch_size) in enumerate(batch_size_list):
|
|
start = offset
|
|
end = offset + batch_size
|
|
current_encoder_out = encoder_out.data[start:end]
|
|
current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1)
|
|
# current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim)
|
|
offset = end
|
|
|
|
finalized_B = B[batch_size:] + finalized_B
|
|
B = B[:batch_size]
|
|
|
|
hyps_shape = get_hyps_shape(B).to(device)
|
|
|
|
A = [list(b) for b in B]
|
|
B = [HypothesisList() for _ in range(batch_size)]
|
|
|
|
ys_log_probs = torch.cat(
|
|
[hyp.log_prob.reshape(1, 1) for hyps in A for hyp in hyps]
|
|
) # (num_hyps, 1)
|
|
|
|
decoder_input = torch.tensor(
|
|
[hyp.ys[-context_size:] for hyps in A for hyp in hyps],
|
|
device=device,
|
|
dtype=torch.int64,
|
|
) # (num_hyps, context_size)
|
|
|
|
decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1)
|
|
decoder_out = model.joiner.decoder_proj(decoder_out)
|
|
# decoder_out is of shape (num_hyps, 1, 1, joiner_dim)
|
|
|
|
# Note: For torch 1.7.1 and below, it requires a torch.int64 tensor
|
|
# as index, so we use `to(torch.int64)` below.
|
|
current_encoder_out = torch.index_select(
|
|
current_encoder_out,
|
|
dim=0,
|
|
index=hyps_shape.row_ids(1).to(torch.int64),
|
|
) # (num_hyps, 1, 1, encoder_out_dim)
|
|
|
|
logits = model.joiner(
|
|
current_encoder_out,
|
|
decoder_out,
|
|
project_input=False,
|
|
) # (num_hyps, 1, 1, vocab_size)
|
|
|
|
logits = logits.squeeze(1).squeeze(1) # (num_hyps, vocab_size)
|
|
|
|
log_probs = (logits / temperature).log_softmax(dim=-1) # (num_hyps, vocab_size)
|
|
|
|
log_probs.add_(ys_log_probs)
|
|
|
|
vocab_size = log_probs.size(-1)
|
|
|
|
log_probs = log_probs.reshape(-1)
|
|
|
|
row_splits = hyps_shape.row_splits(1) * vocab_size
|
|
log_probs_shape = k2.ragged.create_ragged_shape2(
|
|
row_splits=row_splits, cached_tot_size=log_probs.numel()
|
|
)
|
|
ragged_log_probs = k2.RaggedTensor(shape=log_probs_shape, value=log_probs)
|
|
|
|
for i in range(batch_size):
|
|
topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
|
|
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("ignore")
|
|
topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
|
|
topk_token_indexes = (topk_indexes % vocab_size).tolist()
|
|
|
|
for k in range(len(topk_hyp_indexes)):
|
|
hyp_idx = topk_hyp_indexes[k]
|
|
hyp = A[i][hyp_idx]
|
|
|
|
new_ys = hyp.ys[:]
|
|
new_token = topk_token_indexes[k]
|
|
new_timestamp = hyp.timestamp[:]
|
|
if new_token not in (blank_id, unk_id):
|
|
new_ys.append(new_token)
|
|
new_timestamp.append(t)
|
|
|
|
new_log_prob = topk_log_probs[k]
|
|
new_hyp = Hypothesis(
|
|
ys=new_ys, log_prob=new_log_prob, timestamp=new_timestamp
|
|
)
|
|
B[i].add(new_hyp)
|
|
|
|
B = B + finalized_B
|
|
best_hyps = [b.get_most_probable(length_norm=True) for b in B]
|
|
|
|
sorted_ans = [h.ys[context_size:] for h in best_hyps]
|
|
sorted_timestamps = [h.timestamp for h in best_hyps]
|
|
ans = []
|
|
ans_timestamps = []
|
|
unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
|
|
for i in range(N):
|
|
ans.append(sorted_ans[unsorted_indices[i]])
|
|
ans_timestamps.append(sorted_timestamps[unsorted_indices[i]])
|
|
|
|
if not return_timestamps:
|
|
return ans
|
|
else:
|
|
return DecodingResults(
|
|
hyps=ans,
|
|
timestamps=ans_timestamps,
|
|
)
|
|
|
|
|
|
def modified_beam_search_LODR(
|
|
model: SURT,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
LODR_lm: NgramLm,
|
|
LODR_lm_scale: float,
|
|
LM: LmScorer,
|
|
beam: int = 4,
|
|
) -> List[List[int]]:
|
|
"""This function implements LODR (https://arxiv.org/abs/2203.16776) with
|
|
`modified_beam_search`. It uses a bi-gram language model as the estimate
|
|
of the internal language model and subtracts its score during shallow fusion
|
|
with an external language model. This implementation uses a RNNLM as the
|
|
external language model.
|
|
|
|
Args:
|
|
model (Transducer):
|
|
The transducer model
|
|
encoder_out (torch.Tensor):
|
|
Encoder output in (N,T,C)
|
|
encoder_out_lens (torch.Tensor):
|
|
A 1-D tensor of shape (N,), containing the number of
|
|
valid frames in encoder_out before padding.
|
|
LODR_lm:
|
|
A low order n-gram LM, whose score will be subtracted during shallow fusion
|
|
LODR_lm_scale:
|
|
The scale of the LODR_lm
|
|
LM:
|
|
A neural net LM, e.g an RNNLM or transformer LM
|
|
beam (int, optional):
|
|
Beam size. Defaults to 4.
|
|
|
|
Returns:
|
|
Return a list-of-list of token IDs. ans[i] is the decoding results
|
|
for the i-th utterance.
|
|
|
|
"""
|
|
assert encoder_out.ndim == 3, encoder_out.shape
|
|
assert encoder_out.size(0) >= 1, encoder_out.size(0)
|
|
assert LM is not None
|
|
lm_scale = LM.lm_scale
|
|
|
|
packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
|
|
input=encoder_out,
|
|
lengths=encoder_out_lens.cpu(),
|
|
batch_first=True,
|
|
enforce_sorted=False,
|
|
)
|
|
|
|
blank_id = model.decoder.blank_id
|
|
sos_id = getattr(LM, "sos_id", 1)
|
|
unk_id = getattr(model, "unk_id", blank_id)
|
|
context_size = model.decoder.context_size
|
|
device = next(model.parameters()).device
|
|
|
|
batch_size_list = packed_encoder_out.batch_sizes.tolist()
|
|
N = encoder_out.size(0)
|
|
assert torch.all(encoder_out_lens > 0), encoder_out_lens
|
|
assert N == batch_size_list[0], (N, batch_size_list)
|
|
|
|
# get initial lm score and lm state by scoring the "sos" token
|
|
sos_token = torch.tensor([[sos_id]]).to(torch.int64).to(device)
|
|
lens = torch.tensor([1]).to(device)
|
|
init_score, init_states = LM.score_token(sos_token, lens)
|
|
|
|
B = [HypothesisList() for _ in range(N)]
|
|
for i in range(N):
|
|
B[i].add(
|
|
Hypothesis(
|
|
ys=[blank_id] * context_size,
|
|
log_prob=torch.zeros(1, dtype=torch.float32, device=device),
|
|
state=init_states, # state of the NN LM
|
|
lm_score=init_score.reshape(-1),
|
|
state_cost=NgramLmStateCost(
|
|
LODR_lm
|
|
), # state of the source domain ngram
|
|
)
|
|
)
|
|
|
|
encoder_out = model.joiner.encoder_proj(packed_encoder_out.data)
|
|
|
|
offset = 0
|
|
finalized_B = []
|
|
for batch_size in batch_size_list:
|
|
start = offset
|
|
end = offset + batch_size
|
|
current_encoder_out = encoder_out.data[start:end] # get batch
|
|
current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1)
|
|
# current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim)
|
|
offset = end
|
|
|
|
finalized_B = B[batch_size:] + finalized_B
|
|
B = B[:batch_size]
|
|
|
|
hyps_shape = get_hyps_shape(B).to(device)
|
|
|
|
A = [list(b) for b in B]
|
|
B = [HypothesisList() for _ in range(batch_size)]
|
|
|
|
ys_log_probs = torch.cat(
|
|
[hyp.log_prob.reshape(1, 1) for hyps in A for hyp in hyps]
|
|
)
|
|
|
|
decoder_input = torch.tensor(
|
|
[hyp.ys[-context_size:] for hyps in A for hyp in hyps],
|
|
device=device,
|
|
dtype=torch.int64,
|
|
) # (num_hyps, context_size)
|
|
|
|
decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1)
|
|
decoder_out = model.joiner.decoder_proj(decoder_out)
|
|
|
|
current_encoder_out = torch.index_select(
|
|
current_encoder_out,
|
|
dim=0,
|
|
index=hyps_shape.row_ids(1).to(torch.int64),
|
|
) # (num_hyps, 1, 1, encoder_out_dim)
|
|
|
|
logits = model.joiner(
|
|
current_encoder_out,
|
|
decoder_out,
|
|
project_input=False,
|
|
) # (num_hyps, 1, 1, vocab_size)
|
|
|
|
logits = logits.squeeze(1).squeeze(1) # (num_hyps, vocab_size)
|
|
|
|
log_probs = logits.log_softmax(dim=-1) # (num_hyps, vocab_size)
|
|
|
|
log_probs.add_(ys_log_probs)
|
|
|
|
vocab_size = log_probs.size(-1)
|
|
|
|
log_probs = log_probs.reshape(-1)
|
|
|
|
row_splits = hyps_shape.row_splits(1) * vocab_size
|
|
log_probs_shape = k2.ragged.create_ragged_shape2(
|
|
row_splits=row_splits, cached_tot_size=log_probs.numel()
|
|
)
|
|
ragged_log_probs = k2.RaggedTensor(shape=log_probs_shape, value=log_probs)
|
|
"""
|
|
for all hyps with a non-blank new token, score this token.
|
|
It is a little confusing here because this for-loop
|
|
looks very similar to the one below. Here, we go through all
|
|
top-k tokens and only add the non-blanks ones to the token_list.
|
|
LM will score those tokens given the LM states. Note that
|
|
the variable `scores` is the LM score after seeing the new
|
|
non-blank token.
|
|
"""
|
|
token_list = []
|
|
hs = []
|
|
cs = []
|
|
for i in range(batch_size):
|
|
topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
|
|
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("ignore")
|
|
topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
|
|
topk_token_indexes = (topk_indexes % vocab_size).tolist()
|
|
for k in range(len(topk_hyp_indexes)):
|
|
hyp_idx = topk_hyp_indexes[k]
|
|
hyp = A[i][hyp_idx]
|
|
|
|
new_token = topk_token_indexes[k]
|
|
if new_token not in (blank_id, unk_id):
|
|
if LM.lm_type == "rnn":
|
|
token_list.append([new_token])
|
|
# store the LSTM states
|
|
hs.append(hyp.state[0])
|
|
cs.append(hyp.state[1])
|
|
else:
|
|
# for transformer LM
|
|
token_list.append(
|
|
[sos_id] + hyp.ys[context_size:] + [new_token]
|
|
)
|
|
|
|
# forward NN LM to get new states and scores
|
|
if len(token_list) != 0:
|
|
x_lens = torch.tensor([len(tokens) for tokens in token_list]).to(device)
|
|
if LM.lm_type == "rnn":
|
|
tokens_to_score = (
|
|
torch.tensor(token_list).to(torch.int64).to(device).reshape(-1, 1)
|
|
)
|
|
hs = torch.cat(hs, dim=1).to(device)
|
|
cs = torch.cat(cs, dim=1).to(device)
|
|
state = (hs, cs)
|
|
else:
|
|
# for transformer LM
|
|
tokens_list = [torch.tensor(tokens) for tokens in token_list]
|
|
tokens_to_score = (
|
|
torch.nn.utils.rnn.pad_sequence(
|
|
tokens_list, batch_first=True, padding_value=0.0
|
|
)
|
|
.to(device)
|
|
.to(torch.int64)
|
|
)
|
|
|
|
state = None
|
|
|
|
scores, lm_states = LM.score_token(tokens_to_score, x_lens, state)
|
|
|
|
count = 0 # index, used to locate score and lm states
|
|
for i in range(batch_size):
|
|
topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
|
|
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("ignore")
|
|
topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
|
|
topk_token_indexes = (topk_indexes % vocab_size).tolist()
|
|
|
|
for k in range(len(topk_hyp_indexes)):
|
|
hyp_idx = topk_hyp_indexes[k]
|
|
hyp = A[i][hyp_idx]
|
|
|
|
ys = hyp.ys[:]
|
|
|
|
# current score of hyp
|
|
lm_score = hyp.lm_score
|
|
state = hyp.state
|
|
|
|
hyp_log_prob = topk_log_probs[k] # get score of current hyp
|
|
new_token = topk_token_indexes[k]
|
|
if new_token not in (blank_id, unk_id):
|
|
|
|
ys.append(new_token)
|
|
state_cost = hyp.state_cost.forward_one_step(new_token)
|
|
|
|
# calculate the score of the latest token
|
|
current_ngram_score = state_cost.lm_score - hyp.state_cost.lm_score
|
|
|
|
assert current_ngram_score <= 0.0, (
|
|
state_cost.lm_score,
|
|
hyp.state_cost.lm_score,
|
|
)
|
|
# score = score + TDLM_score - LODR_score
|
|
# LODR_LM_scale should be a negative number here
|
|
hyp_log_prob += (
|
|
lm_score[new_token] * lm_scale
|
|
+ LODR_lm_scale * current_ngram_score
|
|
) # add the lm score
|
|
|
|
lm_score = scores[count]
|
|
if LM.lm_type == "rnn":
|
|
state = (
|
|
lm_states[0][:, count, :].unsqueeze(1),
|
|
lm_states[1][:, count, :].unsqueeze(1),
|
|
)
|
|
count += 1
|
|
else:
|
|
state_cost = hyp.state_cost
|
|
|
|
new_hyp = Hypothesis(
|
|
ys=ys,
|
|
log_prob=hyp_log_prob,
|
|
state=state,
|
|
lm_score=lm_score,
|
|
state_cost=state_cost,
|
|
)
|
|
B[i].add(new_hyp)
|
|
|
|
B = B + finalized_B
|
|
best_hyps = [b.get_most_probable(length_norm=True) for b in B]
|
|
|
|
sorted_ans = [h.ys[context_size:] for h in best_hyps]
|
|
ans = []
|
|
unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
|
|
for i in range(N):
|
|
ans.append(sorted_ans[unsorted_indices[i]])
|
|
|
|
return ans
|
|
|
|
|
|
def beam_search(
|
|
model: SURT,
|
|
encoder_out: torch.Tensor,
|
|
beam: int = 4,
|
|
temperature: float = 1.0,
|
|
return_timestamps: bool = False,
|
|
) -> Union[List[int], DecodingResults]:
|
|
"""
|
|
It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf
|
|
|
|
espnet/nets/beam_search_SURT.py#L247 is used as a reference.
|
|
|
|
Args:
|
|
model:
|
|
An instance of `SURT`.
|
|
encoder_out:
|
|
A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
|
|
beam:
|
|
Beam size.
|
|
temperature:
|
|
Softmax temperature.
|
|
return_timestamps:
|
|
Whether to return timestamps.
|
|
|
|
Returns:
|
|
If return_timestamps is False, return the decoded result.
|
|
Else, return a DecodingResults object containing
|
|
decoded result and corresponding timestamps.
|
|
"""
|
|
assert encoder_out.ndim == 3
|
|
|
|
# support only batch_size == 1 for now
|
|
assert encoder_out.size(0) == 1, encoder_out.size(0)
|
|
blank_id = model.decoder.blank_id
|
|
unk_id = getattr(model, "unk_id", blank_id)
|
|
context_size = model.decoder.context_size
|
|
|
|
device = next(model.parameters()).device
|
|
|
|
decoder_input = torch.tensor(
|
|
[blank_id] * context_size,
|
|
device=device,
|
|
dtype=torch.int64,
|
|
).reshape(1, context_size)
|
|
|
|
decoder_out = model.decoder(decoder_input, need_pad=False)
|
|
decoder_out = model.joiner.decoder_proj(decoder_out)
|
|
|
|
encoder_out = model.joiner.encoder_proj(encoder_out)
|
|
|
|
T = encoder_out.size(1)
|
|
t = 0
|
|
|
|
B = HypothesisList()
|
|
B.add(Hypothesis(ys=[blank_id] * context_size, log_prob=0.0, timestamp=[]))
|
|
|
|
max_sym_per_utt = 20000
|
|
|
|
sym_per_utt = 0
|
|
|
|
decoder_cache: Dict[str, torch.Tensor] = {}
|
|
|
|
while t < T and sym_per_utt < max_sym_per_utt:
|
|
# fmt: off
|
|
current_encoder_out = encoder_out[:, t:t+1, :].unsqueeze(2)
|
|
# fmt: on
|
|
A = B
|
|
B = HypothesisList()
|
|
|
|
joint_cache: Dict[str, torch.Tensor] = {}
|
|
|
|
# TODO(fangjun): Implement prefix search to update the `log_prob`
|
|
# of hypotheses in A
|
|
|
|
while True:
|
|
y_star = A.get_most_probable()
|
|
A.remove(y_star)
|
|
|
|
cached_key = y_star.key
|
|
|
|
if cached_key not in decoder_cache:
|
|
decoder_input = torch.tensor(
|
|
[y_star.ys[-context_size:]],
|
|
device=device,
|
|
dtype=torch.int64,
|
|
).reshape(1, context_size)
|
|
|
|
decoder_out = model.decoder(decoder_input, need_pad=False)
|
|
decoder_out = model.joiner.decoder_proj(decoder_out)
|
|
decoder_cache[cached_key] = decoder_out
|
|
else:
|
|
decoder_out = decoder_cache[cached_key]
|
|
|
|
cached_key += f"-t-{t}"
|
|
if cached_key not in joint_cache:
|
|
logits = model.joiner(
|
|
current_encoder_out,
|
|
decoder_out.unsqueeze(1),
|
|
project_input=False,
|
|
)
|
|
|
|
# TODO(fangjun): Scale the blank posterior
|
|
log_prob = (logits / temperature).log_softmax(dim=-1)
|
|
# log_prob is (1, 1, 1, vocab_size)
|
|
log_prob = log_prob.squeeze()
|
|
# Now log_prob is (vocab_size,)
|
|
joint_cache[cached_key] = log_prob
|
|
else:
|
|
log_prob = joint_cache[cached_key]
|
|
|
|
# First, process the blank symbol
|
|
skip_log_prob = log_prob[blank_id]
|
|
new_y_star_log_prob = y_star.log_prob + skip_log_prob
|
|
|
|
# ys[:] returns a copy of ys
|
|
B.add(
|
|
Hypothesis(
|
|
ys=y_star.ys[:],
|
|
log_prob=new_y_star_log_prob,
|
|
timestamp=y_star.timestamp[:],
|
|
)
|
|
)
|
|
|
|
# Second, process other non-blank labels
|
|
values, indices = log_prob.topk(beam + 1)
|
|
for i, v in zip(indices.tolist(), values.tolist()):
|
|
if i in (blank_id, unk_id):
|
|
continue
|
|
new_ys = y_star.ys + [i]
|
|
new_log_prob = y_star.log_prob + v
|
|
new_timestamp = y_star.timestamp + [t]
|
|
A.add(
|
|
Hypothesis(
|
|
ys=new_ys,
|
|
log_prob=new_log_prob,
|
|
timestamp=new_timestamp,
|
|
)
|
|
)
|
|
|
|
# Check whether B contains more than "beam" elements more probable
|
|
# than the most probable in A
|
|
A_most_probable = A.get_most_probable()
|
|
|
|
kept_B = B.filter(A_most_probable.log_prob)
|
|
|
|
if len(kept_B) >= beam:
|
|
B = kept_B.topk(beam)
|
|
break
|
|
|
|
t += 1
|
|
|
|
best_hyp = B.get_most_probable(length_norm=True)
|
|
ys = best_hyp.ys[context_size:] # [context_size:] to remove blanks
|
|
|
|
if not return_timestamps:
|
|
return ys
|
|
else:
|
|
return DecodingResults(hyps=[ys], timestamps=[best_hyp.timestamp])
|
|
|
|
|
|
@dataclass
|
|
class Hypothesis:
|
|
# The predicted tokens so far.
|
|
# Newly predicted tokens are appended to `ys`.
|
|
ys: List[int]
|
|
|
|
# The log prob of ys.
|
|
# It contains only one entry.
|
|
log_prob: torch.Tensor
|
|
|
|
# timestamp[i] is the frame index after subsampling
|
|
# on which ys[i] is decoded
|
|
timestamp: List[int] = field(default_factory=list)
|
|
|
|
# the lm score for next token given the current ys
|
|
lm_score: Optional[torch.Tensor] = None
|
|
|
|
# the RNNLM states (h and c in LSTM)
|
|
state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
|
|
|
|
# N-gram LM state
|
|
state_cost: Optional[NgramLmStateCost] = None
|
|
|
|
@property
|
|
def key(self) -> str:
|
|
"""Return a string representation of self.ys"""
|
|
return "_".join(map(str, self.ys))
|
|
|
|
|
|
class HypothesisList(object):
|
|
def __init__(self, data: Optional[Dict[str, Hypothesis]] = None) -> None:
|
|
"""
|
|
Args:
|
|
data:
|
|
A dict of Hypotheses. Its key is its `value.key`.
|
|
"""
|
|
if data is None:
|
|
self._data = {}
|
|
else:
|
|
self._data = data
|
|
|
|
@property
|
|
def data(self) -> Dict[str, Hypothesis]:
|
|
return self._data
|
|
|
|
def add(self, hyp: Hypothesis) -> None:
|
|
"""Add a Hypothesis to `self`.
|
|
|
|
If `hyp` already exists in `self`, its probability is updated using
|
|
`log-sum-exp` with the existed one.
|
|
|
|
Args:
|
|
hyp:
|
|
The hypothesis to be added.
|
|
"""
|
|
key = hyp.key
|
|
if key in self:
|
|
old_hyp = self._data[key] # shallow copy
|
|
torch.logaddexp(old_hyp.log_prob, hyp.log_prob, out=old_hyp.log_prob)
|
|
else:
|
|
self._data[key] = hyp
|
|
|
|
def get_most_probable(self, length_norm: bool = False) -> Hypothesis:
|
|
"""Get the most probable hypothesis, i.e., the one with
|
|
the largest `log_prob`.
|
|
|
|
Args:
|
|
length_norm:
|
|
If True, the `log_prob` of a hypothesis is normalized by the
|
|
number of tokens in it.
|
|
Returns:
|
|
Return the hypothesis that has the largest `log_prob`.
|
|
"""
|
|
if length_norm:
|
|
return max(self._data.values(), key=lambda hyp: hyp.log_prob / len(hyp.ys))
|
|
else:
|
|
return max(self._data.values(), key=lambda hyp: hyp.log_prob)
|
|
|
|
def remove(self, hyp: Hypothesis) -> None:
|
|
"""Remove a given hypothesis.
|
|
|
|
Caution:
|
|
`self` is modified **in-place**.
|
|
|
|
Args:
|
|
hyp:
|
|
The hypothesis to be removed from `self`.
|
|
Note: It must be contained in `self`. Otherwise,
|
|
an exception is raised.
|
|
"""
|
|
key = hyp.key
|
|
assert key in self, f"{key} does not exist"
|
|
del self._data[key]
|
|
|
|
def filter(self, threshold: torch.Tensor) -> "HypothesisList":
|
|
"""Remove all Hypotheses whose log_prob is less than threshold.
|
|
|
|
Caution:
|
|
`self` is not modified. Instead, a new HypothesisList is returned.
|
|
|
|
Returns:
|
|
Return a new HypothesisList containing all hypotheses from `self`
|
|
with `log_prob` being greater than the given `threshold`.
|
|
"""
|
|
ans = HypothesisList()
|
|
for _, hyp in self._data.items():
|
|
if hyp.log_prob > threshold:
|
|
ans.add(hyp) # shallow copy
|
|
return ans
|
|
|
|
def topk(self, k: int) -> "HypothesisList":
|
|
"""Return the top-k hypothesis."""
|
|
hyps = list(self._data.items())
|
|
|
|
hyps = sorted(hyps, key=lambda h: h[1].log_prob, reverse=True)[:k]
|
|
|
|
ans = HypothesisList(dict(hyps))
|
|
return ans
|
|
|
|
def __contains__(self, key: str):
|
|
return key in self._data
|
|
|
|
def __iter__(self):
|
|
return iter(self._data.values())
|
|
|
|
def __len__(self) -> int:
|
|
return len(self._data)
|
|
|
|
def __str__(self) -> str:
|
|
s = []
|
|
for key in self:
|
|
s.append(key)
|
|
return ", ".join(s)
|
|
|
|
|
|
def get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape:
|
|
"""Return a ragged shape with axes [utt][num_hyps].
|
|
|
|
Args:
|
|
hyps:
|
|
len(hyps) == batch_size. It contains the current hypothesis for
|
|
each utterance in the batch.
|
|
Returns:
|
|
Return a ragged shape with 2 axes [utt][num_hyps]. Note that
|
|
the shape is on CPU.
|
|
"""
|
|
num_hyps = [len(h) for h in hyps]
|
|
|
|
# torch.cumsum() is inclusive sum, so we put a 0 at the beginning
|
|
# to get exclusive sum later.
|
|
num_hyps.insert(0, 0)
|
|
|
|
num_hyps = torch.tensor(num_hyps)
|
|
row_splits = torch.cumsum(num_hyps, dim=0, dtype=torch.int32)
|
|
ans = k2.ragged.create_ragged_shape2(
|
|
row_splits=row_splits, cached_tot_size=row_splits[-1].item()
|
|
)
|
|
return ans
|