# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang # Xiaoyu Yang) # # See ../../../../LICENSE for clarification regarding multiple authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import warnings from dataclasses import dataclass, field from typing import Dict, List, Optional, Tuple, Union import k2 import sentencepiece as spm import torch from torch import nn from icefall import ContextGraph, ContextState, NgramLm, NgramLmStateCost from icefall.decode import Nbest, one_best_decoding from icefall.lm_wrapper import LmScorer from icefall.rnn_lm.model import RnnLmModel from icefall.transformer_lm.model import TransformerLM from icefall.utils import ( DecodingResults, KeywordResult, add_eos, add_sos, get_texts, get_texts_with_timestamp, ) def fast_beam_search_one_best( model: nn.Module, decoding_graph: k2.Fsa, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, beam: float, max_states: int, max_contexts: int, temperature: float = 1.0, ilme_scale: float = 0.0, blank_penalty: float = 0.0, return_timestamps: bool = False, allow_partial: bool = False, ) -> Union[List[List[int]], DecodingResults]: """It limits the maximum number of symbols per frame to 1. A lattice is first obtained using fast beam search, and then the shortest path within the lattice is used as the final output. Args: model: An instance of `Transducer`. decoding_graph: Decoding graph used for decoding, may be a TrivialGraph or a LG. encoder_out: A tensor of shape (N, T, C) from the encoder. encoder_out_lens: A tensor of shape (N,) containing the number of frames in `encoder_out` before padding. beam: Beam value, similar to the beam used in Kaldi.. max_states: Max states per stream per frame. max_contexts: Max contexts pre stream per frame. 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. """ lattice = fast_beam_search( model=model, decoding_graph=decoding_graph, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, beam=beam, max_states=max_states, max_contexts=max_contexts, temperature=temperature, ilme_scale=ilme_scale, allow_partial=allow_partial, blank_penalty=blank_penalty, ) best_path = one_best_decoding(lattice) if not return_timestamps: return get_texts(best_path) else: return get_texts_with_timestamp(best_path) def fast_beam_search_nbest_LG( model: nn.Module, decoding_graph: k2.Fsa, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, beam: float, max_states: int, max_contexts: int, num_paths: int, nbest_scale: float = 0.5, use_double_scores: bool = True, temperature: float = 1.0, blank_penalty: float = 0.0, ilme_scale: float = 0.0, return_timestamps: bool = False, allow_partial: bool = False, ) -> Union[List[List[int]], DecodingResults]: """It limits the maximum number of symbols per frame to 1. The process to get the results is: - (1) Use fast beam search to get a lattice - (2) Select `num_paths` paths from the lattice using k2.random_paths() - (3) Unique the selected paths - (4) Intersect the selected paths with the lattice and compute the shortest path from the intersection result - (5) The path with the largest score is used as the decoding output. Args: model: An instance of `Transducer`. decoding_graph: Decoding graph used for decoding, may be a TrivialGraph or a LG. encoder_out: A tensor of shape (N, T, C) from the encoder. encoder_out_lens: A tensor of shape (N,) containing the number of frames in `encoder_out` before padding. beam: Beam value, similar to the beam used in Kaldi.. max_states: Max states per stream per frame. max_contexts: Max contexts pre stream per frame. num_paths: Number of paths to extract from the decoded lattice. nbest_scale: It's the scale applied to the lattice.scores. A smaller value yields more unique paths. use_double_scores: True to use double precision for computation. False to use single precision. 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. """ lattice = fast_beam_search( model=model, decoding_graph=decoding_graph, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, beam=beam, max_states=max_states, max_contexts=max_contexts, temperature=temperature, allow_partial=allow_partial, blank_penalty=blank_penalty, ilme_scale=ilme_scale, ) nbest = Nbest.from_lattice( lattice=lattice, num_paths=num_paths, use_double_scores=use_double_scores, nbest_scale=nbest_scale, ) # The following code is modified from nbest.intersect() word_fsa = k2.invert(nbest.fsa) if hasattr(lattice, "aux_labels"): # delete token IDs as it is not needed del word_fsa.aux_labels word_fsa.scores.zero_() word_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(word_fsa) path_to_utt_map = nbest.shape.row_ids(1) if hasattr(lattice, "aux_labels"): # lattice has token IDs as labels and word IDs as aux_labels. # inv_lattice has word IDs as labels and token IDs as aux_labels inv_lattice = k2.invert(lattice) inv_lattice = k2.arc_sort(inv_lattice) else: inv_lattice = k2.arc_sort(lattice) if inv_lattice.shape[0] == 1: path_lattice = k2.intersect_device( inv_lattice, word_fsa_with_epsilon_loops, b_to_a_map=torch.zeros_like(path_to_utt_map), sorted_match_a=True, ) else: path_lattice = k2.intersect_device( inv_lattice, word_fsa_with_epsilon_loops, b_to_a_map=path_to_utt_map, sorted_match_a=True, ) # path_lattice has word IDs as labels and token IDs as aux_labels path_lattice = k2.top_sort(k2.connect(path_lattice)) tot_scores = path_lattice.get_tot_scores( use_double_scores=use_double_scores, log_semiring=True, # Note: we always use True ) # See https://github.com/k2-fsa/icefall/pull/420 for why # we always use log_semiring=True ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) best_hyp_indexes = ragged_tot_scores.argmax() best_path = k2.index_fsa(nbest.fsa, best_hyp_indexes) if not return_timestamps: return get_texts(best_path) else: return get_texts_with_timestamp(best_path) def fast_beam_search_nbest( model: nn.Module, decoding_graph: k2.Fsa, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, beam: float, max_states: int, max_contexts: int, num_paths: int, nbest_scale: float = 0.5, use_double_scores: bool = True, temperature: float = 1.0, blank_penalty: float = 0.0, return_timestamps: bool = False, allow_partial: bool = False, ) -> Union[List[List[int]], DecodingResults]: """It limits the maximum number of symbols per frame to 1. The process to get the results is: - (1) Use fast beam search to get a lattice - (2) Select `num_paths` paths from the lattice using k2.random_paths() - (3) Unique the selected paths - (4) Intersect the selected paths with the lattice and compute the shortest path from the intersection result - (5) The path with the largest score is used as the decoding output. Args: model: An instance of `Transducer`. decoding_graph: Decoding graph used for decoding, may be a TrivialGraph or a LG. encoder_out: A tensor of shape (N, T, C) from the encoder. encoder_out_lens: A tensor of shape (N,) containing the number of frames in `encoder_out` before padding. beam: Beam value, similar to the beam used in Kaldi.. max_states: Max states per stream per frame. max_contexts: Max contexts pre stream per frame. num_paths: Number of paths to extract from the decoded lattice. nbest_scale: It's the scale applied to the lattice.scores. A smaller value yields more unique paths. use_double_scores: True to use double precision for computation. False to use single precision. 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. """ lattice = fast_beam_search( model=model, decoding_graph=decoding_graph, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, beam=beam, max_states=max_states, max_contexts=max_contexts, blank_penalty=blank_penalty, temperature=temperature, allow_partial=allow_partial, ) nbest = Nbest.from_lattice( lattice=lattice, num_paths=num_paths, use_double_scores=use_double_scores, nbest_scale=nbest_scale, ) # at this point, nbest.fsa.scores are all zeros. nbest = nbest.intersect(lattice) # Now nbest.fsa.scores contains acoustic scores max_indexes = nbest.tot_scores().argmax() best_path = k2.index_fsa(nbest.fsa, max_indexes) if not return_timestamps: return get_texts(best_path) else: return get_texts_with_timestamp(best_path) def fast_beam_search_nbest_oracle( model: nn.Module, decoding_graph: k2.Fsa, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, beam: float, max_states: int, max_contexts: int, num_paths: int, ref_texts: List[List[int]], use_double_scores: bool = True, nbest_scale: float = 0.5, temperature: float = 1.0, blank_penalty: float = 0.0, return_timestamps: bool = False, allow_partial: bool = False, ) -> Union[List[List[int]], DecodingResults]: """It limits the maximum number of symbols per frame to 1. A lattice is first obtained using fast beam search, and then we select `num_paths` linear paths from the lattice. The path that has the minimum edit distance with the given reference transcript is used as the output. This is the best result we can achieve for any nbest based rescoring methods. Args: model: An instance of `Transducer`. decoding_graph: Decoding graph used for decoding, may be a TrivialGraph or a LG. encoder_out: A tensor of shape (N, T, C) from the encoder. encoder_out_lens: A tensor of shape (N,) containing the number of frames in `encoder_out` before padding. beam: Beam value, similar to the beam used in Kaldi.. max_states: Max states per stream per frame. max_contexts: Max contexts pre stream per frame. num_paths: Number of paths to extract from the decoded lattice. ref_texts: A list-of-list of integers containing the reference transcripts. If the decoding_graph is a trivial_graph, the integer ID is the BPE token ID. use_double_scores: True to use double precision for computation. False to use single precision. nbest_scale: It's the scale applied to the lattice.scores. A smaller value yields more unique paths. 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. """ lattice = fast_beam_search( model=model, decoding_graph=decoding_graph, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, beam=beam, max_states=max_states, max_contexts=max_contexts, temperature=temperature, allow_partial=allow_partial, blank_penalty=blank_penalty, ) nbest = Nbest.from_lattice( lattice=lattice, num_paths=num_paths, use_double_scores=use_double_scores, nbest_scale=nbest_scale, ) hyps = nbest.build_levenshtein_graphs() refs = k2.levenshtein_graph(ref_texts, device=hyps.device) levenshtein_alignment = k2.levenshtein_alignment( refs=refs, hyps=hyps, hyp_to_ref_map=nbest.shape.row_ids(1), sorted_match_ref=True, ) tot_scores = levenshtein_alignment.get_tot_scores( use_double_scores=False, log_semiring=False ) ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) max_indexes = ragged_tot_scores.argmax() best_path = k2.index_fsa(nbest.fsa, max_indexes) if not return_timestamps: return get_texts(best_path) else: return get_texts_with_timestamp(best_path) def fast_beam_search( model: nn.Module, decoding_graph: k2.Fsa, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, beam: float, max_states: int, max_contexts: int, temperature: float = 1.0, subtract_ilme: bool = False, ilme_scale: float = 0.1, allow_partial: bool = False, blank_penalty: float = 0.0, ) -> k2.Fsa: """It limits the maximum number of symbols per frame to 1. Args: model: An instance of `Transducer`. decoding_graph: Decoding graph used for decoding, may be a TrivialGraph or a LG. encoder_out: A tensor of shape (N, T, C) from the encoder. encoder_out_lens: A tensor of shape (N,) containing the number of frames in `encoder_out` before padding. beam: Beam value, similar to the beam used in Kaldi.. max_states: Max states per stream per frame. max_contexts: Max contexts pre stream per frame. temperature: Softmax temperature. Returns: Return an FsaVec with axes [utt][state][arc] containing the decoded lattice. Note: When the input graph is a TrivialGraph, the returned lattice is actually an acceptor. """ assert encoder_out.ndim == 3 context_size = model.decoder.context_size vocab_size = model.decoder.vocab_size B, T, C = encoder_out.shape config = k2.RnntDecodingConfig( vocab_size=vocab_size, decoder_history_len=context_size, beam=beam, max_contexts=max_contexts, max_states=max_states, ) individual_streams = [] for i in range(B): individual_streams.append(k2.RnntDecodingStream(decoding_graph)) decoding_streams = k2.RnntDecodingStreams(individual_streams, config) encoder_out = model.joiner.encoder_proj(encoder_out) for t in range(T): # shape is a RaggedShape of shape (B, context) # contexts is a Tensor of shape (shape.NumElements(), context_size) shape, contexts = decoding_streams.get_contexts() # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64 contexts = contexts.to(torch.int64) # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim) decoder_out = model.decoder(contexts, need_pad=False) decoder_out = model.joiner.decoder_proj(decoder_out) # current_encoder_out is of shape # (shape.NumElements(), 1, joiner_dim) # fmt: off current_encoder_out = torch.index_select( encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64) ) # fmt: on logits = model.joiner( current_encoder_out.unsqueeze(2), decoder_out.unsqueeze(1), project_input=False, ) logits = logits.squeeze(1).squeeze(1) if blank_penalty != 0: logits[:, 0] -= blank_penalty log_probs = (logits / temperature).log_softmax(dim=-1) if ilme_scale != 0: ilme_logits = model.joiner( torch.zeros_like( current_encoder_out, device=current_encoder_out.device ).unsqueeze(2), decoder_out.unsqueeze(1), project_input=False, ) ilme_logits = ilme_logits.squeeze(1).squeeze(1) if blank_penalty != 0: ilme_logits[:, 0] -= blank_penalty ilme_log_probs = (ilme_logits / temperature).log_softmax(dim=-1) log_probs -= ilme_scale * ilme_log_probs decoding_streams.advance(log_probs) decoding_streams.terminate_and_flush_to_streams() lattice = decoding_streams.format_output( encoder_out_lens.tolist(), allow_partial=allow_partial ) return lattice def greedy_search( model: nn.Module, encoder_out: torch.Tensor, max_sym_per_frame: int, blank_penalty: float = 0.0, return_timestamps: bool = False, ) -> Union[List[int], DecodingResults]: """Greedy search for a single utterance. Args: model: An instance of `Transducer`. encoder_out: A tensor of shape (N, T, C) from the encoder. Support only N==1 for now. max_sym_per_frame: Maximum number of symbols per frame. If it is set to 0, the WER would be 100%. 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 context_size = model.decoder.context_size unk_id = getattr(model, "unk_id", blank_id) device = next(model.parameters()).device decoder_input = torch.tensor( [-1] * (context_size - 1) + [blank_id], 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 hyp = [blank_id] * context_size # timestamp[i] is the frame index after subsampling # on which hyp[i] is decoded timestamp = [] # Maximum symbols per utterance. max_sym_per_utt = 1000 # symbols per frame sym_per_frame = 0 # symbols per utterance decoded so far sym_per_utt = 0 while t < T and sym_per_utt < max_sym_per_utt: if sym_per_frame >= max_sym_per_frame: sym_per_frame = 0 t += 1 continue # fmt: off current_encoder_out = encoder_out[:, t:t+1, :].unsqueeze(2) # fmt: on logits = model.joiner( current_encoder_out, decoder_out.unsqueeze(1), project_input=False ) # logits is (1, 1, 1, vocab_size) if blank_penalty != 0: logits[:, :, :, 0] -= blank_penalty y = logits.argmax().item() if y not in (blank_id, unk_id): hyp.append(y) timestamp.append(t) decoder_input = torch.tensor([hyp[-context_size:]], device=device).reshape( 1, context_size ) decoder_out = model.decoder(decoder_input, need_pad=False) decoder_out = model.joiner.decoder_proj(decoder_out) sym_per_utt += 1 sym_per_frame += 1 else: sym_per_frame = 0 t += 1 hyp = hyp[context_size:] # remove blanks if not return_timestamps: return hyp else: return DecodingResults( hyps=[hyp], timestamps=[timestamp], ) def greedy_search_batch( model: nn.Module, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, blank_penalty: float = 0, return_timestamps: bool = False, ) -> Union[List[List[int]], DecodingResults]: """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1. Args: model: The transducer model. encoder_out: Output from the encoder. Its shape is (N, T, C), where N >= 1. encoder_out_lens: A 1-D tensor of shape (N,), containing number of valid frames in encoder_out before padding. 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 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, ) device = next(model.parameters()).device blank_id = model.decoder.blank_id unk_id = getattr(model, "unk_id", blank_id) context_size = model.decoder.context_size 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) hyps = [[-1] * (context_size - 1) + [blank_id] for _ in range(N)] # timestamp[n][i] is the frame index after subsampling # on which hyp[n][i] is decoded timestamps = [[] for _ in range(N)] # scores[n][i] is the logits on which hyp[n][i] is decoded scores = [[] for _ in range(N)] decoder_input = torch.tensor( hyps, device=device, dtype=torch.int64, ) # (N, context_size) decoder_out = model.decoder(decoder_input, need_pad=False) decoder_out = model.joiner.decoder_proj(decoder_out) # decoder_out: (N, 1, decoder_out_dim) encoder_out = model.joiner.encoder_proj(packed_encoder_out.data) offset = 0 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: (batch_size, 1, 1, encoder_out_dim) offset = end decoder_out = decoder_out[:batch_size] logits = model.joiner( current_encoder_out, decoder_out.unsqueeze(1), project_input=False ) # logits'shape (batch_size, 1, 1, vocab_size) logits = logits.squeeze(1).squeeze(1) # (batch_size, vocab_size) assert logits.ndim == 2, logits.shape if blank_penalty != 0: logits[:, 0] -= blank_penalty y = logits.argmax(dim=1).tolist() emitted = False for i, v in enumerate(y): if v not in (blank_id, unk_id): hyps[i].append(v) timestamps[i].append(t) scores[i].append(logits[i, v].item()) emitted = True if emitted: # update decoder output decoder_input = [h[-context_size:] for h in hyps[:batch_size]] decoder_input = torch.tensor( decoder_input, device=device, dtype=torch.int64, ) 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 = [] ans_scores = [] 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]]) ans_scores.append(scores[unsorted_indices[i]]) if not return_timestamps: return ans else: return DecodingResults( hyps=ans, timestamps=ans_timestamps, scores=ans_scores, ) @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 ac_probs: Optional[List[float]] = None # 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 # Context graph state context_state: Optional[ContextState] = None num_tailing_blanks: int = 0 @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, length_norm: bool = False) -> "HypothesisList": """Return the top-k hypothesis. Args: length_norm: If True, the `log_prob` of a hypothesis is normalized by the number of tokens in it. """ hyps = list(self._data.items()) if length_norm: hyps = sorted( hyps, key=lambda h: h[1].log_prob / len(h[1].ys), reverse=True )[:k] else: 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 def keywords_search( model: nn.Module, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, keywords_graph: ContextGraph, beam: int = 4, num_tailing_blanks: int = 0, blank_penalty: float = 0, ) -> List[List[KeywordResult]]: """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded. Args: model: The transducer 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. keywords_graph: A instance of ContextGraph containing keywords and their configurations. beam: Number of active paths during the beam search. num_tailing_blanks: The number of tailing blanks a keyword should be followed, this is for the scenario that a keyword will be the prefix of another. In most cases, you can just set it to 0. blank_penalty: The score used to penalize blank probability. Returns: Return a list of list of KeywordResult. """ assert encoder_out.ndim == 3, encoder_out.shape assert encoder_out.size(0) >= 1, encoder_out.size(0) assert keywords_graph is not None 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=[-1] * (context_size - 1) + [blank_id], log_prob=torch.zeros(1, dtype=torch.float32, device=device), context_state=keywords_graph.root, timestamp=[], ac_probs=[], ) ) encoder_out = model.joiner.encoder_proj(packed_encoder_out.data) offset = 0 finalized_B = [] sorted_ans = [[] for _ in range(N)] 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) if blank_penalty != 0: logits[:, 0] -= blank_penalty probs = logits.softmax(dim=-1) # (num_hyps, vocab_size) log_probs = probs.log() probs = probs.reshape(-1) 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) ragged_probs = k2.RaggedTensor(shape=log_probs_shape, value=probs) for i in range(batch_size): topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam) hyp_probs = ragged_probs[i].tolist() 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[:] new_ac_probs = hyp.ac_probs[:] context_score = 0 new_context_state = hyp.context_state new_num_tailing_blanks = hyp.num_tailing_blanks + 1 if new_token not in (blank_id, unk_id): new_ys.append(new_token) new_timestamp.append(t) new_ac_probs.append(hyp_probs[topk_indexes[k]]) ( context_score, new_context_state, _, ) = keywords_graph.forward_one_step(hyp.context_state, new_token) new_num_tailing_blanks = 0 if new_context_state.token == -1: # root new_ys[-context_size:] = [-1] * (context_size - 1) + [blank_id] new_log_prob = topk_log_probs[k] + context_score new_hyp = Hypothesis( ys=new_ys, log_prob=new_log_prob, timestamp=new_timestamp, ac_probs=new_ac_probs, context_state=new_context_state, num_tailing_blanks=new_num_tailing_blanks, ) B[i].add(new_hyp) top_hyp = B[i].get_most_probable(length_norm=True) matched, matched_state = keywords_graph.is_matched(top_hyp.context_state) if matched: ac_prob = ( sum(top_hyp.ac_probs[-matched_state.level :]) / matched_state.level ) if ( matched and top_hyp.num_tailing_blanks > num_tailing_blanks and ac_prob >= matched_state.ac_threshold ): keyword = KeywordResult( hyps=top_hyp.ys[-matched_state.level :], timestamps=top_hyp.timestamp[-matched_state.level :], phrase=matched_state.phrase, ) sorted_ans[i].append(keyword) B[i] = HypothesisList() B[i].add( Hypothesis( ys=[-1] * (context_size - 1) + [blank_id], log_prob=torch.zeros(1, dtype=torch.float32, device=device), context_state=keywords_graph.root, timestamp=[], ac_probs=[], ) ) B = B + finalized_B for i, hyps in enumerate(B): top_hyp = hyps.get_most_probable(length_norm=True) matched, matched_state = keywords_graph.is_matched(top_hyp.context_state) if matched: ac_prob = ( sum(top_hyp.ac_probs[-matched_state.level :]) / matched_state.level ) if matched and ac_prob >= matched_state.ac_threshold: keyword = KeywordResult( hyps=top_hyp.ys[-matched_state.level :], timestamps=top_hyp.timestamp[-matched_state.level :], phrase=matched_state.phrase, ) sorted_ans[i].append(keyword) ans = [] unsorted_indices = packed_encoder_out.unsorted_indices.tolist() for i in range(N): ans.append(sorted_ans[unsorted_indices[i]]) return ans def modified_beam_search( model: nn.Module, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, context_graph: Optional[ContextGraph] = None, beam: int = 4, temperature: float = 1.0, blank_penalty: float = 0.0, 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 transducer 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=[-1] * (context_size - 1) + [blank_id], log_prob=torch.zeros(1, dtype=torch.float32, device=device), context_state=None if context_graph is None else context_graph.root, 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) if blank_penalty != 0: logits[:, 0] -= blank_penalty 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[:] context_score = 0 new_context_state = None if context_graph is None else hyp.context_state if new_token not in (blank_id, unk_id): new_ys.append(new_token) new_timestamp.append(t) if context_graph is not None: ( context_score, new_context_state, _, ) = context_graph.forward_one_step( hyp.context_state, new_token, strict_mode=False ) new_log_prob = topk_log_probs[k] + context_score new_hyp = Hypothesis( ys=new_ys, log_prob=new_log_prob, timestamp=new_timestamp, context_state=new_context_state, ) B[i].add(new_hyp) B = B + finalized_B # finalize context_state, if the matched contexts do not reach final state # we need to add the score on the corresponding backoff arc if context_graph is not None: finalized_B = [HypothesisList() for _ in range(len(B))] for i, hyps in enumerate(B): for hyp in list(hyps): context_score, new_context_state = context_graph.finalize( hyp.context_state ) finalized_B[i].add( Hypothesis( ys=hyp.ys, log_prob=hyp.log_prob + context_score, timestamp=hyp.timestamp, context_state=new_context_state, ) ) 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_lm_rescore( model: nn.Module, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, LM: LmScorer, lm_scale_list: List[int], 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. Rescore the final results with RNNLM and return the one with the highest score Args: model: The transducer 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. LM: A neural network language model 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=[-1] * (context_size - 1) + [blank_id], 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 # get the am_scores for n-best list hyps_shape = get_hyps_shape(B) am_scores = torch.tensor([hyp.log_prob.item() for b in B for hyp in b]) am_scores = k2.RaggedTensor(value=am_scores, shape=hyps_shape).to(device) # now LM rescore # prepare input data to LM candidate_seqs = [hyp.ys[context_size:] for b in B for hyp in b] possible_seqs = k2.RaggedTensor(candidate_seqs) row_splits = possible_seqs.shape.row_splits(1) sentence_token_lengths = row_splits[1:] - row_splits[:-1] possible_seqs_with_sos = add_sos(possible_seqs, sos_id=1) possible_seqs_with_eos = add_eos(possible_seqs, eos_id=1) sentence_token_lengths += 1 x = possible_seqs_with_sos.pad(mode="constant", padding_value=blank_id) y = possible_seqs_with_eos.pad(mode="constant", padding_value=blank_id) x = x.to(device).to(torch.int64) y = y.to(device).to(torch.int64) sentence_token_lengths = sentence_token_lengths.to(device).to(torch.int64) lm_scores = LM.lm(x=x, y=y, lengths=sentence_token_lengths) assert lm_scores.ndim == 2 lm_scores = -1 * lm_scores.sum(dim=1) ans = {} unsorted_indices = packed_encoder_out.unsorted_indices.tolist() # get the best hyp with different lm_scale for lm_scale in lm_scale_list: key = f"nnlm_scale_{lm_scale:.2f}" tot_scores = am_scores.values + lm_scores * lm_scale ragged_tot_scores = k2.RaggedTensor(shape=am_scores.shape, value=tot_scores) max_indexes = ragged_tot_scores.argmax().tolist() unsorted_hyps = [candidate_seqs[idx] for idx in max_indexes] hyps = [] for idx in unsorted_indices: hyps.append(unsorted_hyps[idx]) ans[key] = hyps return ans def modified_beam_search_lm_rescore_LODR( model: nn.Module, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, LM: LmScorer, LODR_lm: NgramLm, sp: spm.SentencePieceProcessor, lm_scale_list: List[int], 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. Rescore the final results with RNNLM and return the one with the highest score Args: model: The transducer 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. LM: A neural network language model 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=[-1] * (context_size - 1) + [blank_id], 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 # get the am_scores for n-best list hyps_shape = get_hyps_shape(B) am_scores = torch.tensor([hyp.log_prob.item() for b in B for hyp in b]) am_scores = k2.RaggedTensor(value=am_scores, shape=hyps_shape).to(device) # now LM rescore # prepare input data to LM candidate_seqs = [hyp.ys[context_size:] for b in B for hyp in b] possible_seqs = k2.RaggedTensor(candidate_seqs) row_splits = possible_seqs.shape.row_splits(1) sentence_token_lengths = row_splits[1:] - row_splits[:-1] possible_seqs_with_sos = add_sos(possible_seqs, sos_id=1) possible_seqs_with_eos = add_eos(possible_seqs, eos_id=1) sentence_token_lengths += 1 x = possible_seqs_with_sos.pad(mode="constant", padding_value=blank_id) y = possible_seqs_with_eos.pad(mode="constant", padding_value=blank_id) x = x.to(device).to(torch.int64) y = y.to(device).to(torch.int64) sentence_token_lengths = sentence_token_lengths.to(device).to(torch.int64) lm_scores = LM.lm(x=x, y=y, lengths=sentence_token_lengths) assert lm_scores.ndim == 2 lm_scores = -1 * lm_scores.sum(dim=1) # now LODR scores import math LODR_scores = [] for seq in candidate_seqs: tokens = " ".join(sp.id_to_piece(seq)) LODR_scores.append(LODR_lm.score(tokens)) LODR_scores = torch.tensor(LODR_scores).to(device) * math.log( 10 ) # arpa scores are 10-based assert lm_scores.shape == LODR_scores.shape ans = {} unsorted_indices = packed_encoder_out.unsorted_indices.tolist() LODR_scale_list = [0.05 * i for i in range(1, 20)] # get the best hyp with different lm_scale and lodr_scale for lm_scale in lm_scale_list: for lodr_scale in LODR_scale_list: key = f"nnlm_scale_{lm_scale:.2f}_lodr_scale_{lodr_scale:.2f}" tot_scores = ( am_scores.values / lm_scale + lm_scores - LODR_scores * lodr_scale ) ragged_tot_scores = k2.RaggedTensor(shape=am_scores.shape, value=tot_scores) max_indexes = ragged_tot_scores.argmax().tolist() unsorted_hyps = [candidate_seqs[idx] for idx in max_indexes] hyps = [] for idx in unsorted_indices: hyps.append(unsorted_hyps[idx]) ans[key] = hyps return ans def _deprecated_modified_beam_search( model: nn.Module, encoder_out: torch.Tensor, beam: int = 4, return_timestamps: bool = False, ) -> Union[List[int], DecodingResults]: """It limits the maximum number of symbols per frame to 1. It decodes only one utterance at a time. We keep it only for reference. The function :func:`modified_beam_search` should be preferred as it supports batch decoding. Args: model: An instance of `Transducer`. encoder_out: A tensor of shape (N, T, C) from the encoder. Support only N==1 for now. beam: Beam size. 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 T = encoder_out.size(1) B = HypothesisList() B.add( Hypothesis( ys=[-1] * (context_size - 1) + [blank_id], log_prob=torch.zeros(1, dtype=torch.float32, device=device), timestamp=[], ) ) encoder_out = model.joiner.encoder_proj(encoder_out) for t in range(T): # fmt: off current_encoder_out = encoder_out[:, t:t+1, :].unsqueeze(2) # current_encoder_out is of shape (1, 1, 1, encoder_out_dim) # fmt: on A = list(B) B = HypothesisList() ys_log_probs = torch.cat([hyp.log_prob.reshape(1, 1) for hyp in A]) # ys_log_probs is of shape (num_hyps, 1) decoder_input = torch.tensor( [hyp.ys[-context_size:] for hyp in A], device=device, dtype=torch.int64, ) # decoder_input is of shape (num_hyps, context_size) decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1) decoder_out = model.joiner.decoder_proj(decoder_out) # decoder_output is of shape (num_hyps, 1, 1, joiner_dim) current_encoder_out = current_encoder_out.expand( decoder_out.size(0), 1, 1, -1 ) # (num_hyps, 1, 1, encoder_out_dim) logits = model.joiner( current_encoder_out, decoder_out, project_input=False, ) # logits is of shape (num_hyps, 1, 1, vocab_size) logits = logits.squeeze(1).squeeze(1) # now logits is of shape (num_hyps, vocab_size) log_probs = logits.log_softmax(dim=-1) log_probs.add_(ys_log_probs) log_probs = log_probs.reshape(-1) topk_log_probs, topk_indexes = log_probs.topk(beam) # topk_hyp_indexes are indexes into `A` topk_hyp_indexes = topk_indexes // logits.size(-1) topk_token_indexes = topk_indexes % logits.size(-1) with warnings.catch_warnings(): warnings.simplefilter("ignore") topk_hyp_indexes = topk_hyp_indexes.tolist() topk_token_indexes = topk_token_indexes.tolist() for i in range(len(topk_hyp_indexes)): hyp = A[topk_hyp_indexes[i]] new_ys = hyp.ys[:] new_timestamp = hyp.timestamp[:] new_token = topk_token_indexes[i] if new_token not in (blank_id, unk_id): new_ys.append(new_token) new_timestamp.append(t) new_log_prob = topk_log_probs[i] new_hyp = Hypothesis( ys=new_ys, log_prob=new_log_prob, timestamp=new_timestamp ) B.add(new_hyp) 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]) def beam_search( model: nn.Module, encoder_out: torch.Tensor, beam: int = 4, temperature: float = 1.0, blank_penalty: float = 0.0, return_timestamps: bool = False, ) -> Union[List[int], DecodingResults]: """ It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf espnet/nets/beam_search_transducer.py#L247 is used as a reference. Args: model: An instance of `Transducer`. 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=[-1] * (context_size - 1) + [blank_id], 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, ) if blank_penalty != 0: logits[:, :, :, 0] -= blank_penalty # 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]) def fast_beam_search_with_nbest_rescoring( model: nn.Module, decoding_graph: k2.Fsa, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, beam: float, max_states: int, max_contexts: int, ngram_lm_scale_list: List[float], num_paths: int, G: k2.Fsa, sp: spm.SentencePieceProcessor, word_table: k2.SymbolTable, oov_word: str = "", use_double_scores: bool = True, nbest_scale: float = 0.5, temperature: float = 1.0, return_timestamps: bool = False, ) -> Dict[str, Union[List[List[int]], DecodingResults]]: """It limits the maximum number of symbols per frame to 1. A lattice is first obtained using fast beam search, num_path are selected and rescored using a given language model. The shortest path within the lattice is used as the final output. Args: model: An instance of `Transducer`. decoding_graph: Decoding graph used for decoding, may be a TrivialGraph or a LG. encoder_out: A tensor of shape (N, T, C) from the encoder. encoder_out_lens: A tensor of shape (N,) containing the number of frames in `encoder_out` before padding. beam: Beam value, similar to the beam used in Kaldi. max_states: Max states per stream per frame. max_contexts: Max contexts pre stream per frame. ngram_lm_scale_list: A list of floats representing LM score scales. num_paths: Number of paths to extract from the decoded lattice. G: An FsaVec containing only a single FSA. It is an n-gram LM. sp: The BPE model. word_table: The word symbol table. oov_word: OOV words are replaced with this word. use_double_scores: True to use double precision for computation. False to use single precision. nbest_scale: It's the scale applied to the lattice.scores. A smaller value yields more unique paths. temperature: Softmax temperature. return_timestamps: Whether to return timestamps. Returns: Return the decoded result in a dict, where the key has the form 'ngram_lm_scale_xx' and the value is the decoded results optionally with timestamps. `xx` is the ngram LM scale value used during decoding, i.e., 0.1. """ lattice = fast_beam_search( model=model, decoding_graph=decoding_graph, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, beam=beam, max_states=max_states, max_contexts=max_contexts, temperature=temperature, ) nbest = Nbest.from_lattice( lattice=lattice, num_paths=num_paths, use_double_scores=use_double_scores, nbest_scale=nbest_scale, ) # at this point, nbest.fsa.scores are all zeros. nbest = nbest.intersect(lattice) # Now nbest.fsa.scores contains acoustic scores am_scores = nbest.tot_scores() # Now we need to compute the LM scores of each path. # (1) Get the token IDs of each Path. We assume the decoding_graph # is an acceptor, i.e., lattice is also an acceptor tokens_shape = nbest.fsa.arcs.shape().remove_axis(1) # [path][arc] tokens = k2.RaggedTensor(tokens_shape, nbest.fsa.labels.contiguous()) tokens = tokens.remove_values_leq(0) # remove -1 and 0 token_list: List[List[int]] = tokens.tolist() word_list: List[List[str]] = sp.decode(token_list) assert isinstance(oov_word, str), oov_word assert oov_word in word_table, oov_word oov_word_id = word_table[oov_word] word_ids_list: List[List[int]] = [] for words in word_list: this_word_ids = [] for w in words.split(): if w in word_table: this_word_ids.append(word_table[w]) else: this_word_ids.append(oov_word_id) word_ids_list.append(this_word_ids) word_fsas = k2.linear_fsa(word_ids_list, device=lattice.device) word_fsas_with_self_loops = k2.add_epsilon_self_loops(word_fsas) num_unique_paths = len(word_ids_list) b_to_a_map = torch.zeros( num_unique_paths, dtype=torch.int32, device=lattice.device, ) rescored_word_fsas = k2.intersect_device( a_fsas=G, b_fsas=word_fsas_with_self_loops, b_to_a_map=b_to_a_map, sorted_match_a=True, ret_arc_maps=False, ) rescored_word_fsas = k2.remove_epsilon_self_loops(rescored_word_fsas) rescored_word_fsas = k2.top_sort(k2.connect(rescored_word_fsas)) ngram_lm_scores = rescored_word_fsas.get_tot_scores( use_double_scores=True, log_semiring=False, ) ans: Dict[str, Union[List[List[int]], DecodingResults]] = {} for s in ngram_lm_scale_list: key = f"ngram_lm_scale_{s}" tot_scores = am_scores.values + s * ngram_lm_scores ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) max_indexes = ragged_tot_scores.argmax() best_path = k2.index_fsa(nbest.fsa, max_indexes) if not return_timestamps: ans[key] = get_texts(best_path) else: ans[key] = get_texts_with_timestamp(best_path) return ans def fast_beam_search_with_nbest_rnn_rescoring( model: nn.Module, decoding_graph: k2.Fsa, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, beam: float, max_states: int, max_contexts: int, ngram_lm_scale_list: List[float], num_paths: int, G: k2.Fsa, sp: spm.SentencePieceProcessor, word_table: k2.SymbolTable, rnn_lm_model: torch.nn.Module, rnn_lm_scale_list: List[float], oov_word: str = "", use_double_scores: bool = True, nbest_scale: float = 0.5, temperature: float = 1.0, return_timestamps: bool = False, ) -> Dict[str, Union[List[List[int]], DecodingResults]]: """It limits the maximum number of symbols per frame to 1. A lattice is first obtained using fast beam search, num_path are selected and rescored using a given language model and a rnn-lm. The shortest path within the lattice is used as the final output. Args: model: An instance of `Transducer`. decoding_graph: Decoding graph used for decoding, may be a TrivialGraph or a LG. encoder_out: A tensor of shape (N, T, C) from the encoder. encoder_out_lens: A tensor of shape (N,) containing the number of frames in `encoder_out` before padding. beam: Beam value, similar to the beam used in Kaldi. max_states: Max states per stream per frame. max_contexts: Max contexts pre stream per frame. ngram_lm_scale_list: A list of floats representing LM score scales. num_paths: Number of paths to extract from the decoded lattice. G: An FsaVec containing only a single FSA. It is an n-gram LM. sp: The BPE model. word_table: The word symbol table. rnn_lm_model: A rnn-lm model used for LM rescoring rnn_lm_scale_list: A list of floats representing RNN score scales. oov_word: OOV words are replaced with this word. use_double_scores: True to use double precision for computation. False to use single precision. nbest_scale: It's the scale applied to the lattice.scores. A smaller value yields more unique paths. temperature: Softmax temperature. return_timestamps: Whether to return timestamps. Returns: Return the decoded result in a dict, where the key has the form 'ngram_lm_scale_xx' and the value is the decoded results optionally with timestamps. `xx` is the ngram LM scale value used during decoding, i.e., 0.1. """ lattice = fast_beam_search( model=model, decoding_graph=decoding_graph, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, beam=beam, max_states=max_states, max_contexts=max_contexts, temperature=temperature, ) nbest = Nbest.from_lattice( lattice=lattice, num_paths=num_paths, use_double_scores=use_double_scores, nbest_scale=nbest_scale, ) # at this point, nbest.fsa.scores are all zeros. nbest = nbest.intersect(lattice) # Now nbest.fsa.scores contains acoustic scores am_scores = nbest.tot_scores() # Now we need to compute the LM scores of each path. # (1) Get the token IDs of each Path. We assume the decoding_graph # is an acceptor, i.e., lattice is also an acceptor tokens_shape = nbest.fsa.arcs.shape().remove_axis(1) # [path][arc] tokens = k2.RaggedTensor(tokens_shape, nbest.fsa.labels.contiguous()) tokens = tokens.remove_values_leq(0) # remove -1 and 0 token_list: List[List[int]] = tokens.tolist() word_list: List[List[str]] = sp.decode(token_list) assert isinstance(oov_word, str), oov_word assert oov_word in word_table, oov_word oov_word_id = word_table[oov_word] word_ids_list: List[List[int]] = [] for words in word_list: this_word_ids = [] for w in words.split(): if w in word_table: this_word_ids.append(word_table[w]) else: this_word_ids.append(oov_word_id) word_ids_list.append(this_word_ids) word_fsas = k2.linear_fsa(word_ids_list, device=lattice.device) word_fsas_with_self_loops = k2.add_epsilon_self_loops(word_fsas) num_unique_paths = len(word_ids_list) b_to_a_map = torch.zeros( num_unique_paths, dtype=torch.int32, device=lattice.device, ) rescored_word_fsas = k2.intersect_device( a_fsas=G, b_fsas=word_fsas_with_self_loops, b_to_a_map=b_to_a_map, sorted_match_a=True, ret_arc_maps=False, ) rescored_word_fsas = k2.remove_epsilon_self_loops(rescored_word_fsas) rescored_word_fsas = k2.top_sort(k2.connect(rescored_word_fsas)) ngram_lm_scores = rescored_word_fsas.get_tot_scores( use_double_scores=True, log_semiring=False, ) # Now RNN-LM blank_id = model.decoder.blank_id sos_id = sp.piece_to_id("sos_id") eos_id = sp.piece_to_id("eos_id") sos_tokens = add_sos(tokens, sos_id) tokens_eos = add_eos(tokens, eos_id) sos_tokens_row_splits = sos_tokens.shape.row_splits(1) sentence_lengths = sos_tokens_row_splits[1:] - sos_tokens_row_splits[:-1] x_tokens = sos_tokens.pad(mode="constant", padding_value=blank_id) y_tokens = tokens_eos.pad(mode="constant", padding_value=blank_id) x_tokens = x_tokens.to(torch.int64) y_tokens = y_tokens.to(torch.int64) sentence_lengths = sentence_lengths.to(torch.int64) rnn_lm_nll = rnn_lm_model(x=x_tokens, y=y_tokens, lengths=sentence_lengths) assert rnn_lm_nll.ndim == 2 assert rnn_lm_nll.shape[0] == len(token_list) rnn_lm_scores = -1 * rnn_lm_nll.sum(dim=1) ans: Dict[str, List[List[int]]] = {} for n_scale in ngram_lm_scale_list: for rnn_scale in rnn_lm_scale_list: key = f"ngram_lm_scale_{n_scale}_rnn_lm_scale_{rnn_scale}" tot_scores = ( am_scores.values + n_scale * ngram_lm_scores + rnn_scale * rnn_lm_scores ) ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) max_indexes = ragged_tot_scores.argmax() best_path = k2.index_fsa(nbest.fsa, max_indexes) if not return_timestamps: ans[key] = get_texts(best_path) else: ans[key] = get_texts_with_timestamp(best_path) return ans def modified_beam_search_ngram_rescoring( model: nn.Module, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, ngram_lm: NgramLm, ngram_lm_scale: float, beam: int = 4, temperature: float = 1.0, ) -> List[List[int]]: """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded. Args: model: The transducer 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. 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) 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 lm_scale = ngram_lm_scale 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=[-1] * (context_size - 1) + [blank_id], log_prob=torch.zeros(1, dtype=torch.float32, device=device), state_cost=NgramLmStateCost(ngram_lm), ) ) 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] 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) + hyp.state_cost.lm_score * lm_scale 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] if new_token not in (blank_id, unk_id): new_ys.append(new_token) state_cost = hyp.state_cost.forward_one_step(new_token) else: state_cost = hyp.state_cost # We only keep AM scores in new_hyp.log_prob new_log_prob = topk_log_probs[k] - hyp.state_cost.lm_score * lm_scale new_hyp = Hypothesis( ys=new_ys, log_prob=new_log_prob, 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 modified_beam_search_LODR( model: nn.Module, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, LODR_lm: NgramLm, LODR_lm_scale: float, LM: LmScorer, beam: int = 4, context_graph: Optional[ContextGraph] = None, ) -> 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=[-1] * (context_size - 1) + [blank_id], 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 context_state=None if context_graph is None else context_graph.root, ) ) 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] context_score = 0 new_context_state = None if context_graph is None else hyp.context_state if new_token not in (blank_id, unk_id): if context_graph is not None: ( context_score, new_context_state, _, ) = context_graph.forward_one_step( hyp.context_state, new_token, strict_mode=False ) 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 + context_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, context_state=new_context_state, ) B[i].add(new_hyp) B = B + finalized_B # finalize context_state, if the matched contexts do not reach final state # we need to add the score on the corresponding backoff arc if context_graph is not None: finalized_B = [HypothesisList() for _ in range(len(B))] for i, hyps in enumerate(B): for hyp in list(hyps): context_score, new_context_state = context_graph.finalize( hyp.context_state ) finalized_B[i].add( Hypothesis( ys=hyp.ys, log_prob=hyp.log_prob + context_score, timestamp=hyp.timestamp, context_state=new_context_state, ) ) 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 modified_beam_search_lm_shallow_fusion( model: nn.Module, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, LM: LmScorer, beam: int = 4, return_timestamps: bool = False, ) -> List[List[int]]: """Modified_beam_search + NN LM shallow fusion 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. sp: Sentence piece generator. LM (LmScorer): A neural net LM, e.g RNN or Transformer 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=[-1] * (context_size - 1) + [blank_id], log_prob=torch.zeros(1, dtype=torch.float32, device=device), state=init_states, lm_score=init_score.reshape(-1), 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] # 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] ) lm_scores = torch.cat( [hyp.lm_score.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 = [] # a list of 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] ) 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[:] 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] new_timestamp = hyp.timestamp[:] if new_token not in (blank_id, unk_id): ys.append(new_token) new_timestamp.append(t) hyp_log_prob += lm_score[new_token] * lm_scale # 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 new_hyp = Hypothesis( ys=ys, log_prob=hyp_log_prob, state=state, lm_score=lm_score, 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, )