# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # 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. # Copyright 2017 Johns Hopkins University (Shinji Watanabe) # # 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 copy from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import torch from tqdm import tqdm from nemo.collections.asr.modules import rnnt_abstract from nemo.collections.asr.parts.context_biasing import BoostingTreeModelConfig from nemo.collections.asr.parts.submodules.ngram_lm import DEFAULT_TOKEN_OFFSET, NGramGPULanguageModel from nemo.collections.asr.parts.submodules.rnnt_maes_batched_computer import ModifiedAESBatchedRNNTComputer from nemo.collections.asr.parts.submodules.rnnt_malsd_batched_computer import ModifiedALSDBatchedRNNTComputer from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceMethodMixin from nemo.collections.asr.parts.utils.batched_beam_decoding_utils import BlankLMScoreMode, PruningMode from nemo.collections.asr.parts.utils.rnnt_utils import ( HATJointOutput, Hypothesis, NBestHypotheses, is_prefix, select_k_expansions, ) from nemo.collections.common.parts.optional_cuda_graphs import WithOptionalCudaGraphs from nemo.core.classes import Typing, typecheck from nemo.core.neural_types import AcousticEncodedRepresentation, HypothesisType, LengthsType, NeuralType from nemo.utils import logging try: import kenlm KENLM_AVAILABLE = True except (ImportError, ModuleNotFoundError): KENLM_AVAILABLE = False def pack_hypotheses(hypotheses: List[Hypothesis]) -> List[Hypothesis]: """ Packs a list of hypotheses into a tensor and prepares decoder states. This function takes a list of token sequences (hypotheses) and converts it into a tensor format. If any decoder states are on the GPU, they are moved to the CPU. Additionally, the function removes any timesteps with a value of -1 from the sequences. Args: hypotheses (list): A list of token sequences representing hypotheses. Returns: list: A list of packed hypotheses in tensor format. """ for idx, hyp in enumerate(hypotheses): # type: rnnt_utils.Hypothesis hyp.y_sequence = torch.tensor(hyp.y_sequence, dtype=torch.long) if hyp.dec_state is not None: hyp.dec_state = _states_to_device(hyp.dec_state) # Remove -1 from timestep if hyp.timestamp is not None and len(hyp.timestamp) > 0 and hyp.timestamp[0] == -1: hyp.timestamp = hyp.timestamp[1:] return hypotheses def _states_to_device(dec_state, device='cpu'): """ Transfers decoder states to the specified device. This function moves the provided decoder states to the specified device (e.g., 'cpu' or 'cuda'). Args: dec_state (Tensor): The decoder states to be transferred. device (str): The target device to which the decoder states should be moved. Defaults to 'cpu'. Returns: Tensor: The decoder states on the specified device. """ if torch.is_tensor(dec_state): dec_state = dec_state.to(device) elif isinstance(dec_state, (list, tuple)): dec_state = tuple(_states_to_device(dec_i, device) for dec_i in dec_state) return dec_state class BeamRNNTInfer(Typing): """ Beam Search implementation ported from ESPNet implementation - https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py Sequence level beam decoding or batched-beam decoding, performed auto-repressively depending on the search type chosen. Args: decoder_model: rnnt_utils.AbstractRNNTDecoder implementation. joint_model: rnnt_utils.AbstractRNNTJoint implementation. beam_size: number of beams for beam search. Must be a positive integer >= 1. If beam size is 1, defaults to stateful greedy search. This greedy search might result in slightly different results than the greedy results obtained by GreedyRNNTInfer due to implementation differences. For accurate greedy results, please use GreedyRNNTInfer or GreedyBatchedRNNTInfer. search_type: str representing the type of beam search to perform. Must be one of ['beam', 'tsd', 'alsd']. 'nsc' is currently not supported. Algoritm used: `beam` - basic beam search strategy. Larger beams generally result in better decoding, however the time required for the search also grows steadily. `tsd` - time synchronous decoding. Please refer to the paper: [Alignment-Length Synchronous Decoding for RNN Transducer] (https://ieeexplore.ieee.org/document/9053040) for details on the algorithm implemented. Time synchronous decoding (TSD) execution time grows by the factor T * max_symmetric_expansions. For longer sequences, T is greater, and can therefore take a long time for beams to obtain good results. This also requires greater memory to execute. `alsd` - alignment-length synchronous decoding. Please refer to the paper: [Alignment-Length Synchronous Decoding for RNN Transducer] (https://ieeexplore.ieee.org/document/9053040) for details on the algorithm implemented. Alignment-length synchronous decoding (ALSD) execution time is faster than TSD, with growth factor of T + U_max, where U_max is the maximum target length expected during execution. Generally, T + U_max < T * max_symmetric_expansions. However, ALSD beams are non-unique, therefore it is required to use larger beam sizes to achieve the same (or close to the same) decoding accuracy as TSD. For a given decoding accuracy, it is possible to attain faster decoding via ALSD than TSD. `maes` = modified adaptive expansion searcn. Please refer to the paper: [Accelerating RNN Transducer Inference via Adaptive Expansion Search] (https://ieeexplore.ieee.org/document/9250505) Modified Adaptive Synchronous Decoding (mAES) execution time is adaptive w.r.t the number of expansions (for tokens) required per timestep. The number of expansions can usually be constrained to 1 or 2, and in most cases 2 is sufficient. This beam search technique can possibly obtain superior WER while sacrificing some evaluation time. score_norm: bool, whether to normalize the scores of the log probabilities. return_best_hypothesis: bool, decides whether to return a single hypothesis (the best out of N), or return all N hypothesis (sorted with best score first). The container class changes based this flag - When set to True (default), returns a single Hypothesis. When set to False, returns a NBestHypotheses container, which contains a list of Hypothesis. # The following arguments are specific to the chosen `search_type` tsd_max_sym_exp_per_step: Used for `search_type=tsd`. The maximum symmetric expansions allowed per timestep during beam search. Larger values should be used to attempt decoding of longer sequences, but this in turn increases execution time and memory usage. alsd_max_target_len: Used for `search_type=alsd`. The maximum expected target sequence length during beam search. Larger values allow decoding of longer sequences at the expense of execution time and memory. # The following two flags are placeholders and unused until `nsc` implementation is stabilized. nsc_max_timesteps_expansion: Unused int. nsc_prefix_alpha: Unused int. # mAES flags maes_num_steps: Number of adaptive steps to take. From the paper, 2 steps is generally sufficient. int > 1. maes_prefix_alpha: Maximum prefix length in prefix search. Must be an integer, and is advised to keep this as 1 in order to reduce expensive beam search cost later. int >= 0. maes_expansion_beta: Maximum number of prefix expansions allowed, in addition to the beam size. Effectively, the number of hypothesis = beam_size + maes_expansion_beta. Must be an int >= 0, and affects the speed of inference since large values will perform large beam search in the next step. maes_expansion_gamma: Float pruning threshold used in the prune-by-value step when computing the expansions. The default (2.3) is selected from the paper. It performs a comparison (max_log_prob - gamma <= log_prob[v]) where v is all vocabulary indices in the Vocab set and max_log_prob is the "most" likely token to be predicted. Gamma therefore provides a margin of additional tokens which can be potential candidates for expansion apart from the "most likely" candidate. Lower values will reduce the number of expansions (by increasing pruning-by-value, thereby improving speed but hurting accuracy). Higher values will increase the number of expansions (by reducing pruning-by-value, thereby reducing speed but potentially improving accuracy). This is a hyper parameter to be experimentally tuned on a validation set. softmax_temperature: Scales the logits of the joint prior to computing log_softmax. preserve_alignments: Bool flag which preserves the history of alignments generated during beam decoding (sample). When set to true, the Hypothesis will contain the non-null value for `alignments` in it. Here, `alignments` is a List of List of Tensor (of length V + 1) The length of the list corresponds to the Acoustic Length (T). Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. U is the number of target tokens for the current timestep Ti. NOTE: `preserve_alignments` is an invalid argument for any `search_type` other than basic beam search. ngram_lm_model: str The path to the N-gram LM ngram_lm_alpha: float Alpha weight of N-gram LM tokens_type: str Tokenization type ['subword', 'char'] """ @property def input_types(self): """Returns definitions of module input ports.""" return { "encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), "encoded_lengths": NeuralType(tuple('B'), LengthsType()), "partial_hypotheses": [NeuralType(elements_type=HypothesisType(), optional=True)], # must always be last } @property def output_types(self): """Returns definitions of module output ports.""" return {"predictions": [NeuralType(elements_type=HypothesisType())]} def __init__( self, decoder_model: rnnt_abstract.AbstractRNNTDecoder, joint_model: rnnt_abstract.AbstractRNNTJoint, beam_size: int, search_type: str = 'default', score_norm: bool = True, return_best_hypothesis: bool = True, tsd_max_sym_exp_per_step: Optional[int] = 50, alsd_max_target_len: Union[int, float] = 1.0, nsc_max_timesteps_expansion: int = 1, nsc_prefix_alpha: int = 1, maes_num_steps: int = 2, maes_prefix_alpha: int = 1, maes_expansion_gamma: float = 2.3, maes_expansion_beta: int = 2, language_model: Optional[Dict[str, Any]] = None, softmax_temperature: float = 1.0, preserve_alignments: bool = False, ngram_lm_model: Optional[str] = None, ngram_lm_alpha: float = 0.0, hat_subtract_ilm: bool = False, hat_ilm_weight: float = 0.0, max_symbols_per_step: Optional[int] = None, blank_lm_score_mode: Optional[str] = "no_score", pruning_mode: Optional[str] = "early", allow_cuda_graphs: bool = False, ): self.decoder = decoder_model self.joint = joint_model self.blank = decoder_model.blank_idx self.vocab_size = decoder_model.vocab_size self.search_type = search_type self.return_best_hypothesis = return_best_hypothesis if beam_size < 1: raise ValueError("Beam search size cannot be less than 1!") self.beam_size = beam_size self.score_norm = score_norm self.max_candidates = beam_size if self.beam_size == 1: logging.info("Beam size of 1 was used, switching to sample level `greedy_search`") self.search_algorithm = self.greedy_search elif search_type == "default": self.search_algorithm = self.default_beam_search elif search_type == "tsd": self.search_algorithm = self.time_sync_decoding elif search_type == "alsd": self.search_algorithm = self.align_length_sync_decoding elif search_type == "nsc": raise NotImplementedError("`nsc` (Constrained Beam Search) has not been implemented.") # self.search_algorithm = self.nsc_beam_search elif search_type == "maes": self.search_algorithm = self.modified_adaptive_expansion_search else: raise NotImplementedError( f"The search type ({search_type}) supplied is not supported!\n" f"Please use one of : (default, tsd, alsd, nsc)" ) if max_symbols_per_step is not None: logging.warning( f"Not supported parameter `max_symbols_per_step` for decoding strategy {self.search_algorithm }" ) if allow_cuda_graphs: logging.warning( f"""Cuda Graphs are not supported for the decoding strategy {self.search_algorithm}. Decoding will proceed without Cuda Graphs.""" ) strategies = ["default", "tsd", "alsd", "maes", "nsc"] strategies_batch = ["maes_batch", "malsd_batch"] if (pruning_mode, blank_lm_score_mode) != ("early", "no_score"): logging.warning( f"""Decoding strategies {strategies} support early pruning and the 'no_score' blank scoring mode. Please choose a strategy from {strategies_batch} for {pruning_mode} pruning and {blank_lm_score_mode} blank scoring mode." """ ) if tsd_max_sym_exp_per_step is None: tsd_max_sym_exp_per_step = -1 if search_type in ['tsd', 'alsd', 'nsc'] and not self.decoder.blank_as_pad: raise ValueError( f"Search type was chosen as '{search_type}', however the decoder module provided " f"does not support the `blank` token as a pad value. {search_type} requires " f"the blank token as pad value support in order to perform batched beam search." f"Please chose one of the other beam search methods, or re-train your model " f"with this support." ) self.tsd_max_symmetric_expansion_per_step = tsd_max_sym_exp_per_step self.alsd_max_target_length = alsd_max_target_len self.nsc_max_timesteps_expansion = nsc_max_timesteps_expansion self.nsc_prefix_alpha = int(nsc_prefix_alpha) self.maes_prefix_alpha = int(maes_prefix_alpha) self.maes_num_steps = int(maes_num_steps) self.maes_expansion_gamma = float(maes_expansion_gamma) self.maes_expansion_beta = int(maes_expansion_beta) if self.search_type == 'maes' and self.maes_prefix_alpha < 0: raise ValueError("`maes_prefix_alpha` must be a positive integer.") if self.search_type == 'maes' and self.vocab_size < beam_size + maes_expansion_beta: raise ValueError( f"beam_size ({beam_size}) + expansion_beta ({maes_expansion_beta}) " f"should be smaller or equal to vocabulary size ({self.vocab_size})." ) if search_type == 'maes': self.max_candidates += maes_expansion_beta if self.search_type == 'maes' and self.maes_num_steps < 2: raise ValueError("`maes_num_steps` must be greater than 1.") if softmax_temperature != 1.0 and language_model is not None: logging.warning( "Softmax temperature is not supported with LM decoding." "Setting softmax-temperature value to 1.0." ) self.softmax_temperature = 1.0 else: self.softmax_temperature = softmax_temperature self.language_model = language_model self.preserve_alignments = preserve_alignments self.token_offset = 0 if ngram_lm_model: self.ngram_lm = ngram_lm_model self.ngram_lm_alpha = ngram_lm_alpha else: self.ngram_lm = None if hat_subtract_ilm: assert hasattr(self.joint, "return_hat_ilm") assert search_type == "maes" self.hat_subtract_ilm = hat_subtract_ilm self.hat_ilm_weight = hat_ilm_weight @typecheck() def __call__( self, encoder_output: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[List[Hypothesis]] = None, ) -> Union[Hypothesis, NBestHypotheses]: """Perform general beam search. Args: encoder_output: Encoded speech features (B, D_enc, T_max) encoded_lengths: Lengths of the encoder outputs Returns: Either a list containing a single Hypothesis (when `return_best_hypothesis=True`, otherwise a list containing a single NBestHypotheses, which itself contains a list of Hypothesis. This list is sorted such that the best hypothesis is the first element. """ # Preserve decoder and joint training state decoder_training_state = self.decoder.training joint_training_state = self.joint.training # setup hat outputs mode return_hat_ilm_default = False if self.hat_subtract_ilm: assert hasattr(self.joint, "return_hat_ilm") return_hat_ilm_default = self.joint.return_hat_ilm self.joint.return_hat_ilm = self.hat_subtract_ilm with torch.inference_mode(): # Apply optional preprocessing encoder_output = encoder_output.transpose(1, 2) # (B, T, D) self.decoder.eval() self.joint.eval() hypotheses = [] with tqdm( range(encoder_output.size(0)), desc='Beam search progress:', total=encoder_output.size(0), unit='sample', ) as idx_gen: _p = next(self.joint.parameters()) dtype = _p.dtype # Decode every sample in the batch independently. for batch_idx in idx_gen: inseq = encoder_output[batch_idx : batch_idx + 1, : encoded_lengths[batch_idx], :] # [1, T, D] logitlen = encoded_lengths[batch_idx] if inseq.dtype != dtype: inseq = inseq.to(dtype=dtype) # Extract partial hypothesis if exists partial_hypothesis = partial_hypotheses[batch_idx] if partial_hypotheses is not None else None # Execute the specific search strategy nbest_hyps = self.search_algorithm( inseq, logitlen, partial_hypotheses=partial_hypothesis ) # sorted list of hypothesis # Prepare the list of hypotheses nbest_hyps = pack_hypotheses(nbest_hyps) # Pack the result if self.return_best_hypothesis: best_hypothesis = nbest_hyps[0] # type: Hypothesis else: best_hypothesis = NBestHypotheses(nbest_hyps) # type: NBestHypotheses hypotheses.append(best_hypothesis) self.decoder.train(decoder_training_state) self.joint.train(joint_training_state) if self.hat_subtract_ilm: self.joint.return_hat_ilm = return_hat_ilm_default return (hypotheses,) def sort_nbest(self, hyps: List[Hypothesis]) -> List[Hypothesis]: """Sort hypotheses by score or score given sequence length. Args: hyps: list of hypotheses Return: hyps: sorted list of hypotheses """ if self.score_norm: return sorted(hyps, key=lambda x: x.score / len(x.y_sequence), reverse=True) else: return sorted(hyps, key=lambda x: x.score, reverse=True) def greedy_search( self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None ) -> List[Hypothesis]: """Greedy search implementation for transducer. Generic case when beam size = 1. Results might differ slightly due to implementation details as compared to `GreedyRNNTInfer` and `GreedyBatchRNNTInfer`. Args: h: Encoded speech features (1, T_max, D_enc) Returns: hyp: 1-best decoding results """ if self.preserve_alignments: # Alignments is a 2-dimensional dangling list representing T x U alignments = [[]] else: alignments = None # Initialize zero state vectors dec_state = self.decoder.initialize_state(h) # Construct initial hypothesis hyp = Hypothesis( score=0.0, y_sequence=[self.blank], dec_state=dec_state, timestamp=[-1], length=encoded_lengths ) if partial_hypotheses is not None: if len(partial_hypotheses.y_sequence) > 0: hyp.y_sequence = [int(partial_hypotheses.y_sequence[-1].cpu().numpy())] hyp.dec_state = partial_hypotheses.dec_state hyp.dec_state = _states_to_device(hyp.dec_state, h.device) cache = {} # Initialize state and first token y, state, _ = self.decoder.score_hypothesis(hyp, cache) for i in range(int(encoded_lengths)): hi = h[:, i : i + 1, :] # [1, 1, D] not_blank = True symbols_added = 0 # TODO: Figure out how to remove this hard coding afterwords while not_blank and (symbols_added < 5): ytu = torch.log_softmax(self.joint.joint(hi, y) / self.softmax_temperature, dim=-1) # [1, 1, 1, V + 1] ytu = ytu[0, 0, 0, :] # [V + 1] # max() requires float if ytu.dtype != torch.float32: ytu = ytu.float() logp, pred = torch.max(ytu, dim=-1) # [1, 1] pred = pred.item() if self.preserve_alignments: # insert logprobs into last timestep alignments[-1].append((ytu.to('cpu'), torch.tensor(pred, dtype=torch.int32))) if pred == self.blank: not_blank = False if self.preserve_alignments: # convert Ti-th logits into a torch array alignments.append([]) # blank buffer for next timestep else: # Update state and current sequence hyp.y_sequence.append(int(pred)) hyp.score += float(logp) hyp.dec_state = state hyp.timestamp.append(i) # Compute next state and token y, state, _ = self.decoder.score_hypothesis(hyp, cache) symbols_added += 1 # Remove trailing empty list of alignments if self.preserve_alignments: if len(alignments[-1]) == 0: del alignments[-1] # attach alignments to hypothesis hyp.alignments = alignments # Remove the original input label if partial hypothesis was provided if partial_hypotheses is not None: hyp.y_sequence = hyp.y_sequence[1:] return [hyp] def default_beam_search( self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None ) -> List[Hypothesis]: """Beam search implementation. Args: x: Encoded speech features (1, T_max, D_enc) Returns: nbest_hyps: N-best decoding results """ # Initialize states beam = min(self.beam_size, self.vocab_size) beam_k = min(beam, (self.vocab_size - 1)) blank_tensor = torch.tensor([self.blank], device=h.device, dtype=torch.long) # Precompute some constants for blank position ids = list(range(self.vocab_size + 1)) ids.remove(self.blank) # Used when blank token is first vs last token if self.blank == 0: index_incr = 1 else: index_incr = 0 # Initialize zero vector states dec_state = self.decoder.initialize_state(h) # Initialize first hypothesis for the beam (blank) kept_hyps = [Hypothesis(score=0.0, y_sequence=[self.blank], dec_state=dec_state, timestamp=[-1], length=0)] cache = {} if partial_hypotheses is not None: if len(partial_hypotheses.y_sequence) > 0: kept_hyps[0].y_sequence = [int(partial_hypotheses.y_sequence[-1].cpu().numpy())] kept_hyps[0].dec_state = partial_hypotheses.dec_state kept_hyps[0].dec_state = _states_to_device(kept_hyps[0].dec_state, h.device) if self.preserve_alignments: kept_hyps[0].alignments = [[]] for i in range(int(encoded_lengths)): hi = h[:, i : i + 1, :] # [1, 1, D] hyps = kept_hyps kept_hyps = [] while True: max_hyp = max(hyps, key=lambda x: x.score) hyps.remove(max_hyp) # update decoder state and get next score y, state, lm_tokens = self.decoder.score_hypothesis(max_hyp, cache) # [1, 1, D] # get next token ytu = torch.log_softmax(self.joint.joint(hi, y) / self.softmax_temperature, dim=-1) # [1, 1, 1, V + 1] ytu = ytu[0, 0, 0, :] # [V + 1] # preserve alignments if self.preserve_alignments: logprobs = ytu.cpu().clone() # remove blank token before top k top_k = ytu[ids].topk(beam_k, dim=-1) # Two possible steps - blank token or non-blank token predicted ytu = ( torch.cat((top_k[0], ytu[self.blank].unsqueeze(0))), torch.cat((top_k[1] + index_incr, blank_tensor)), ) # for each possible step for logp, k in zip(*ytu): # construct hypothesis for step new_hyp = Hypothesis( score=(max_hyp.score + float(logp)), y_sequence=max_hyp.y_sequence[:], dec_state=max_hyp.dec_state, lm_state=max_hyp.lm_state, timestamp=max_hyp.timestamp[:], length=encoded_lengths, ) if self.preserve_alignments: new_hyp.alignments = copy.deepcopy(max_hyp.alignments) # if current token is blank, dont update sequence, just store the current hypothesis if k == self.blank: kept_hyps.append(new_hyp) else: # if non-blank token was predicted, update state and sequence and then search more hypothesis new_hyp.dec_state = state new_hyp.y_sequence.append(int(k)) new_hyp.timestamp.append(i) hyps.append(new_hyp) # Determine whether the alignment should be blank or token if self.preserve_alignments: if k == self.blank: new_hyp.alignments[-1].append( (logprobs.clone(), torch.tensor(self.blank, dtype=torch.int32)) ) else: new_hyp.alignments[-1].append( (logprobs.clone(), torch.tensor(new_hyp.y_sequence[-1], dtype=torch.int32)) ) # keep those hypothesis that have scores greater than next search generation hyps_max = float(max(hyps, key=lambda x: x.score).score) kept_most_prob = sorted( [hyp for hyp in kept_hyps if hyp.score > hyps_max], key=lambda x: x.score, ) # If enough hypothesis have scores greater than next search generation, # stop beam search. if len(kept_most_prob) >= beam: if self.preserve_alignments: # convert Ti-th logits into a torch array for kept_h in kept_most_prob: kept_h.alignments.append([]) # blank buffer for next timestep kept_hyps = kept_most_prob break # Remove trailing empty list of alignments if self.preserve_alignments: for h in kept_hyps: if len(h.alignments[-1]) == 0: del h.alignments[-1] # Remove the original input label if partial hypothesis was provided if partial_hypotheses is not None: for hyp in kept_hyps: if hyp.y_sequence[0] == partial_hypotheses.y_sequence[-1] and len(hyp.y_sequence) > 1: hyp.y_sequence = hyp.y_sequence[1:] return self.sort_nbest(kept_hyps) def time_sync_decoding( self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None ) -> List[Hypothesis]: """Time synchronous beam search implementation. Based on https://ieeexplore.ieee.org/document/9053040 Args: h: Encoded speech features (1, T_max, D_enc) Returns: nbest_hyps: N-best decoding results """ if partial_hypotheses is not None: raise NotImplementedError("`partial_hypotheses` support is not supported") # Precompute some constants for blank position ids = list(range(self.vocab_size + 1)) ids.remove(self.blank) # Used when blank token is first vs last token if self.blank == 0: index_incr = 1 else: index_incr = 0 # prepare the batched beam states beam = min(self.beam_size, self.vocab_size) beam_state = self.decoder.initialize_state( torch.zeros(beam, device=h.device, dtype=h.dtype) ) # [L, B, H], [L, B, H] (for LSTMs) # Initialize first hypothesis for the beam (blank) B = [ Hypothesis( y_sequence=[self.blank], score=0.0, dec_state=self.decoder.batch_select_state(beam_state, 0), timestamp=[-1], length=0, ) ] cache = {} # Initialize alignments if self.preserve_alignments: for hyp in B: hyp.alignments = [[]] for i in range(int(encoded_lengths)): hi = h[:, i : i + 1, :] # Update caches A = [] C = B h_enc = hi # For a limited number of symmetric expansions per timestep "i" for v in range(self.tsd_max_symmetric_expansion_per_step): D = [] # Decode a batch of beam states and scores beam_y, beam_state = self.decoder.batch_score_hypothesis(C, cache) # Extract the log probabilities and the predicted tokens beam_logp = torch.log_softmax( self.joint.joint(h_enc, torch.stack(beam_y)) / self.softmax_temperature, dim=-1 ) # [B, 1, 1, V + 1] beam_logp = beam_logp[:, 0, 0, :] # [B, V + 1] beam_topk = beam_logp[:, ids].topk(beam, dim=-1) seq_A = [h.y_sequence for h in A] for j, hyp in enumerate(C): # create a new hypothesis in A if hyp.y_sequence not in seq_A: # If the sequence is not in seq_A, add it as the blank token # In this step, we dont add a token but simply update score _temp_hyp = Hypothesis( score=(hyp.score + float(beam_logp[j, self.blank])), y_sequence=hyp.y_sequence[:], dec_state=hyp.dec_state, lm_state=hyp.lm_state, timestamp=hyp.timestamp[:], length=encoded_lengths, ) # Preserve the blank token alignment if self.preserve_alignments: _temp_hyp.alignments = copy.deepcopy(hyp.alignments) _temp_hyp.alignments[-1].append( (beam_logp[j].clone(), torch.tensor(self.blank, dtype=torch.int32)), ) A.append(_temp_hyp) else: # merge the existing blank hypothesis score with current score. dict_pos = seq_A.index(hyp.y_sequence) A[dict_pos].score = np.logaddexp( A[dict_pos].score, (hyp.score + float(beam_logp[j, self.blank])) ) if v < self.tsd_max_symmetric_expansion_per_step: for j, hyp in enumerate(C): # for each current hypothesis j # extract the top token score and top token id for the jth hypothesis for logp, k in zip(beam_topk[0][j], beam_topk[1][j] + index_incr): # create new hypothesis and store in D # Note: This loop does *not* include the blank token! new_hyp = Hypothesis( score=(hyp.score + float(logp)), y_sequence=(hyp.y_sequence + [int(k)]), dec_state=beam_state[j], lm_state=hyp.lm_state, timestamp=hyp.timestamp[:] + [i], length=encoded_lengths, ) # Preserve token alignment if self.preserve_alignments: new_hyp.alignments = copy.deepcopy(hyp.alignments) new_hyp.alignments[-1].append( (beam_topk[0].clone().cpu(), torch.tensor(k, dtype=torch.int32)), ) D.append(new_hyp) # Prune beam C = sorted(D, key=lambda x: x.score, reverse=True)[:beam] if self.preserve_alignments: # convert Ti-th logits into a torch array for C_i in C: # Check if the last token emitted at last timestep was a blank # If so, move to next timestep logp, label = C_i.alignments[-1][-1] # The last alignment of this step if int(label) == self.blank: C_i.alignments.append([]) # blank buffer for next timestep # Prune beam B = sorted(A, key=lambda x: x.score, reverse=True)[:beam] if self.preserve_alignments: # convert Ti-th logits into a torch array for B_i in B: # Check if the last token emitted at last timestep was a blank # If so, move to next timestep logp, label = B_i.alignments[-1][-1] # The last alignment of this step if int(label) == self.blank: B_i.alignments.append([]) # blank buffer for next timestep # Remove trailing empty list of alignments if self.preserve_alignments: for h in B: if len(h.alignments[-1]) == 0: del h.alignments[-1] return self.sort_nbest(B) def align_length_sync_decoding( self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None ) -> List[Hypothesis]: """Alignment-length synchronous beam search implementation. Based on https://ieeexplore.ieee.org/document/9053040 Args: h: Encoded speech features (1, T_max, D_enc) Returns: nbest_hyps: N-best decoding results """ # delay this import here instead of at the beginning to avoid circular imports. from nemo.collections.asr.modules.rnnt import RNNTDecoder, StatelessTransducerDecoder if partial_hypotheses is not None: raise NotImplementedError("`partial_hypotheses` support is not supported") # Precompute some constants for blank position ids = list(range(self.vocab_size + 1)) ids.remove(self.blank) # Used when blank token is first vs last token if self.blank == 0: index_incr = 1 else: index_incr = 0 # prepare the batched beam states beam = min(self.beam_size, self.vocab_size) h = h[0] # [T, D] h_length = int(encoded_lengths) beam_state = self.decoder.initialize_state( torch.zeros(beam, device=h.device, dtype=h.dtype) ) # [L, B, H], [L, B, H] for LSTMS beam_state = [self.decoder.batch_select_state(beam_state, 0)] # compute u_max as either a specific static limit, # or a multiple of current `h_length` dynamically. if type(self.alsd_max_target_length) == float: u_max = int(self.alsd_max_target_length * h_length) else: u_max = int(self.alsd_max_target_length) # Initialize first hypothesis for the beam (blank) B = [ Hypothesis( y_sequence=[self.blank], score=0.0, dec_state=beam_state[0], timestamp=[-1], length=0, ) ] # Initialize alignments if self.preserve_alignments: B[0].alignments = [[]] final = [] cache = {} # ALSD runs for T + U_max steps for i in range(h_length + u_max): # Update caches A = [] B_ = [] h_states = [] # preserve the list of batch indices which are added into the list # and those which are removed from the list # This is necessary to perform state updates in the correct batch indices later batch_ids = list(range(len(B))) # initialize as a list of all batch ids batch_removal_ids = [] # update with sample ids which are removed for bid, hyp in enumerate(B): u = len(hyp.y_sequence) - 1 t = i - u if t > (h_length - 1): batch_removal_ids.append(bid) continue B_.append(hyp) h_states.append((t, h[t])) if B_: # Compute the subset of batch ids which were *not* removed from the list above sub_batch_ids = None if len(B_) != beam: sub_batch_ids = batch_ids for id in batch_removal_ids: # sub_batch_ids contains list of ids *that were not removed* sub_batch_ids.remove(id) # extract the states of the sub batch only. if isinstance(self.decoder, RNNTDecoder) or isinstance(self.decoder, StatelessTransducerDecoder): beam_state_ = (beam_state[sub_batch_id] for sub_batch_id in sub_batch_ids) else: raise NotImplementedError("Unknown decoder type.") else: # If entire batch was used (none were removed), simply take all the states beam_state_ = beam_state # Decode a batch/sub-batch of beam states and scores beam_y, beam_state_ = self.decoder.batch_score_hypothesis(B_, cache) # If only a subset of batch ids were updated (some were removed) if sub_batch_ids is not None: # For each state in the RNN (2 for LSTM) # Update the current batch states with the sub-batch states (in the correct indices) # These indices are specified by sub_batch_ids, the ids of samples which were updated. if isinstance(self.decoder, RNNTDecoder) or isinstance(self.decoder, StatelessTransducerDecoder): # LSTM decoder, state is [layer x batch x hidden] index = 0 for sub_batch_id in sub_batch_ids: beam_state[sub_batch_id] = beam_state_[index] index += 1 else: raise NotImplementedError("Unknown decoder type.") else: # If entire batch was updated, simply update all the states beam_state = beam_state_ # h_states = list of [t, h[t]] # so h[1] here is a h[t] of shape [D] # Simply stack all of the h[t] within the sub_batch/batch (T <= beam) h_enc = torch.stack([h[1] for h in h_states]) # [T=beam, D] h_enc = h_enc.unsqueeze(1) # [B=beam, T=1, D]; batch over the beams # Extract the log probabilities and the predicted tokens beam_logp = torch.log_softmax( self.joint.joint(h_enc, torch.stack(beam_y)) / self.softmax_temperature, dim=-1 ) # [B=beam, 1, 1, V + 1] beam_logp = beam_logp[:, 0, 0, :] # [B=beam, V + 1] beam_topk = beam_logp[:, ids].topk(beam, dim=-1) for j, hyp in enumerate(B_): # For all updated samples in the batch, add it as the blank token # In this step, we dont add a token but simply update score new_hyp = Hypothesis( score=(hyp.score + float(beam_logp[j, self.blank])), y_sequence=hyp.y_sequence[:], dec_state=hyp.dec_state, lm_state=hyp.lm_state, timestamp=hyp.timestamp[:], length=i, ) if self.preserve_alignments: new_hyp.alignments = copy.deepcopy(hyp.alignments) # Add the alignment of blank at this step new_hyp.alignments[-1].append( (beam_logp[j].clone().cpu(), torch.tensor(self.blank, dtype=torch.int32)) ) # Add blank prediction to A A.append(new_hyp) # If the prediction "timestep" t has reached the length of the input sequence # we can add it to the "finished" hypothesis list. if h_states[j][0] == (h_length - 1): final.append(new_hyp) # Here, we carefully select the indices of the states that we want to preserve # for the next token (non-blank) update. if sub_batch_ids is not None: h_states_idx = sub_batch_ids[j] else: h_states_idx = j # for each current hypothesis j # extract the top token score and top token id for the jth hypothesis for logp, k in zip(beam_topk[0][j], beam_topk[1][j] + index_incr): # create new hypothesis and store in A # Note: This loop does *not* include the blank token! new_hyp = Hypothesis( score=(hyp.score + float(logp)), y_sequence=(hyp.y_sequence[:] + [int(k)]), dec_state=beam_state[h_states_idx], lm_state=hyp.lm_state, timestamp=hyp.timestamp[:] + [i], length=i, ) if self.preserve_alignments: new_hyp.alignments = copy.deepcopy(hyp.alignments) # Add the alignment of Uj for this beam candidate at this step new_hyp.alignments[-1].append( (beam_logp[j].clone().cpu(), torch.tensor(new_hyp.y_sequence[-1], dtype=torch.int32)) ) A.append(new_hyp) # Prune and recombine same hypothesis # This may cause next beam to be smaller than max beam size # Therefore larger beam sizes may be required for better decoding. B = sorted(A, key=lambda x: x.score, reverse=True)[:beam] B = self.recombine_hypotheses(B) if self.preserve_alignments: # convert Ti-th logits into a torch array for B_i in B: # Check if the last token emitted at last timestep was a blank # If so, move to next timestep logp, label = B_i.alignments[-1][-1] # The last alignment of this step if int(label) == self.blank: B_i.alignments.append([]) # blank buffer for next timestep # If B_ is empty list, then we may be able to early exit elif len(batch_ids) == len(batch_removal_ids): # break early break if final: # Remove trailing empty list of alignments if self.preserve_alignments: for h in final: if len(h.alignments[-1]) == 0: del h.alignments[-1] return self.sort_nbest(final) else: # Remove trailing empty list of alignments if self.preserve_alignments: for h in B: if len(h.alignments[-1]) == 0: del h.alignments[-1] return B def modified_adaptive_expansion_search( self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None ) -> List[Hypothesis]: """ Based on/modified from https://ieeexplore.ieee.org/document/9250505 Args: h: Encoded speech features (1, T_max, D_enc) Returns: nbest_hyps: N-best decoding results """ if partial_hypotheses is not None: raise NotImplementedError("`partial_hypotheses` support is not supported") h = h[0] # [T, D] # prepare the batched beam states beam = min(self.beam_size, self.vocab_size) beam_state = self.decoder.initialize_state( torch.zeros(1, device=h.device, dtype=h.dtype) ) # [L, B, H], [L, B, H] for LSTMS # Initialize first hypothesis for the beam (blank) init_tokens = [ Hypothesis( y_sequence=[self.blank], score=0.0, dec_state=self.decoder.batch_select_state(beam_state, 0), timestamp=[-1], length=0, ) ] cache = {} # Initialize alignment buffer if self.preserve_alignments: for hyp in init_tokens: hyp.alignments = [[]] # Decode a batch of beam states and scores beam_dec_out, beam_state = self.decoder.batch_score_hypothesis(init_tokens, cache) state = beam_state[0] # Setup ngram LM: if self.ngram_lm: init_lm_state = kenlm.State() self.ngram_lm.BeginSentenceWrite(init_lm_state) # TODO: Setup LM if self.language_model is not None: # beam_lm_states, beam_lm_scores = self.lm.buff_predict( # None, beam_lm_tokens, 1 # ) # lm_state = select_lm_state( # beam_lm_states, 0, self.lm_layers, self.is_wordlm # ) # lm_scores = beam_lm_scores[0] raise NotImplementedError() else: lm_state = None lm_scores = None # Initialize first hypothesis for the beam (blank) for kept hypotheses kept_hyps = [ Hypothesis( y_sequence=[self.blank], score=0.0, dec_state=state, dec_out=[beam_dec_out[0]], lm_state=lm_state, lm_scores=lm_scores, timestamp=[-1], length=0, ) ] if self.ngram_lm: kept_hyps[0].ngram_lm_state = init_lm_state # Initialize alignment buffer if self.preserve_alignments: for hyp in kept_hyps: hyp.alignments = [[]] for t in range(encoded_lengths): enc_out_t = h[t : t + 1].unsqueeze(0) # [1, 1, D] # Perform prefix search to obtain hypothesis hyps = self.prefix_search( sorted(kept_hyps, key=lambda x: len(x.y_sequence), reverse=True), enc_out_t, prefix_alpha=self.maes_prefix_alpha, ) # type: List[Hypothesis] kept_hyps = [] # Prepare output tensor beam_enc_out = enc_out_t # List that contains the blank token emisions list_b = [] duplication_check = [hyp.y_sequence for hyp in hyps] # Repeat for number of mAES steps for n in range(self.maes_num_steps): # Pack the decoder logits for all current hypothesis beam_dec_out = torch.stack([h.dec_out[-1] for h in hyps]) # [H, 1, D] # Extract the log probabilities ytm, ilm_ytm = self.resolve_joint_output(beam_enc_out, beam_dec_out) beam_logp, beam_idx = ytm.topk(self.max_candidates, dim=-1) beam_logp = beam_logp[:, 0, 0, :] # [B, V + 1] beam_idx = beam_idx[:, 0, 0, :] # [B, max_candidates] # Compute k expansions for all the current hypotheses k_expansions = select_k_expansions( hyps, beam_idx, beam_logp, self.maes_expansion_gamma, self.maes_expansion_beta ) # List that contains the hypothesis after prefix expansion list_exp = [] for i, hyp in enumerate(hyps): # For all hypothesis for k, new_score in k_expansions[i]: # for all expansion within these hypothesis new_hyp = Hypothesis( y_sequence=hyp.y_sequence[:], score=new_score, dec_out=hyp.dec_out[:], dec_state=hyp.dec_state, lm_state=hyp.lm_state, lm_scores=hyp.lm_scores, timestamp=hyp.timestamp[:], length=t, ) if self.ngram_lm: new_hyp.ngram_lm_state = hyp.ngram_lm_state # If the expansion was for blank if k == self.blank: list_b.append(new_hyp) else: # If the expansion was a token # new_hyp.y_sequence.append(int(k)) if (new_hyp.y_sequence + [int(k)]) not in duplication_check: new_hyp.y_sequence.append(int(k)) new_hyp.timestamp.append(t) # Setup ngram LM: if self.ngram_lm: lm_score, new_hyp.ngram_lm_state = self.compute_ngram_score( hyp.ngram_lm_state, int(k) ) if self.hat_subtract_ilm: new_hyp.score += self.ngram_lm_alpha * lm_score - float( self.hat_ilm_weight * ilm_ytm[i, 0, 0, k] ) else: new_hyp.score += self.ngram_lm_alpha * lm_score # TODO: Setup LM if self.language_model is not None: # new_hyp.score += self.lm_weight * float( # hyp.lm_scores[k] # ) pass list_exp.append(new_hyp) # Preserve alignments if self.preserve_alignments: new_hyp.alignments = copy.deepcopy(hyp.alignments) if k == self.blank: new_hyp.alignments[-1].append( (beam_logp[i].clone().cpu(), torch.tensor(self.blank, dtype=torch.int32)), ) else: new_hyp.alignments[-1].append( ( beam_logp[i].clone().cpu(), torch.tensor(new_hyp.y_sequence[-1], dtype=torch.int32), ), ) # If there were no token expansions in any of the hypotheses, # Early exit if not list_exp: kept_hyps = sorted(list_b, key=lambda x: x.score, reverse=True)[:beam] # Update aligments with next step if self.preserve_alignments: # convert Ti-th logits into a torch array for h_i in kept_hyps: # Check if the last token emitted at last timestep was a blank # If so, move to next timestep logp, label = h_i.alignments[-1][-1] # The last alignment of this step if int(label) == self.blank: h_i.alignments.append([]) # blank buffer for next timestep # Early exit break else: # Decode a batch of beam states and scores beam_dec_out, beam_state = self.decoder.batch_score_hypothesis( list_exp, cache, # self.language_model is not None, ) # TODO: Setup LM if self.language_model is not None: # beam_lm_states = create_lm_batch_states( # [hyp.lm_state for hyp in list_exp], # self.lm_layers, # self.is_wordlm, # ) # beam_lm_states, beam_lm_scores = self.lm.buff_predict( # beam_lm_states, beam_lm_tokens, len(list_exp) # ) pass # If this isnt the last mAES step if n < (self.maes_num_steps - 1): # For all expanded hypothesis for i, hyp in enumerate(list_exp): # Preserve the decoder logits for the current beam hyp.dec_out.append(beam_dec_out[i]) hyp.dec_state = beam_state[i] # TODO: Setup LM if self.language_model is not None: # hyp.lm_state = select_lm_state( # beam_lm_states, i, self.lm_layers, self.is_wordlm # ) # hyp.lm_scores = beam_lm_scores[i] pass # Copy the expanded hypothesis hyps = list_exp[:] # Update aligments with next step if self.preserve_alignments: # convert Ti-th logits into a torch array for h_i in hyps: # Check if the last token emitted at last timestep was a blank # If so, move to next timestep logp, label = h_i.alignments[-1][-1] # The last alignment of this step if int(label) == self.blank: h_i.alignments.append([]) # blank buffer for next timestep else: # Extract the log probabilities beam_logp, _ = self.resolve_joint_output(beam_enc_out, torch.stack(beam_dec_out)) beam_logp = beam_logp[:, 0, 0, :] # For all expansions, add the score for the blank label for i, hyp in enumerate(list_exp): hyp.score += float(beam_logp[i, self.blank]) # Preserve the decoder's output and state hyp.dec_out.append(beam_dec_out[i]) hyp.dec_state = beam_state[i] # TODO: Setup LM if self.language_model is not None: # hyp.lm_state = select_lm_state( # beam_lm_states, i, self.lm_layers, self.is_wordlm # ) # hyp.lm_scores = beam_lm_scores[i] pass # Finally, update the kept hypothesis of sorted top Beam candidates kept_hyps = sorted(list_b + list_exp, key=lambda x: x.score, reverse=True)[:beam] # Update aligments with next step if self.preserve_alignments: # convert Ti-th logits into a torch array for h_i in kept_hyps: # Check if the last token emitted at last timestep was a blank # If so, move to next timestep logp, label = h_i.alignments[-1][-1] # The last alignment of this step if int(label) == self.blank: h_i.alignments.append([]) # blank buffer for next timestep # Remove trailing empty list of alignments if self.preserve_alignments: for h in kept_hyps: if len(h.alignments[-1]) == 0: del h.alignments[-1] # Sort the hypothesis with best scores return self.sort_nbest(kept_hyps) def recombine_hypotheses(self, hypotheses: List[Hypothesis]) -> List[Hypothesis]: """Recombine hypotheses with equivalent output sequence. Args: hypotheses (list): list of hypotheses Returns: final (list): list of recombined hypotheses """ final = [] for hyp in hypotheses: seq_final = [f.y_sequence for f in final if f.y_sequence] if hyp.y_sequence in seq_final: seq_pos = seq_final.index(hyp.y_sequence) final[seq_pos].score = np.logaddexp(final[seq_pos].score, hyp.score) else: final.append(hyp) return final def resolve_joint_output(self, enc_out: torch.Tensor, dec_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ Resolve output types for RNNT and HAT joint models """ joint_output = self.joint.joint(enc_out, dec_out) if torch.is_tensor(joint_output): ytm = torch.log_softmax(joint_output / self.softmax_temperature, dim=-1) ilm_ytm = None elif self.hat_subtract_ilm and isinstance(joint_output, HATJointOutput): ytm, ilm_ytm = joint_output.hat_logprobs, joint_output.ilm_logprobs else: raise TypeError( f"Joint output ({type(joint_output)}) must be torch.Tensor or HATJointOutput in case of HAT joint" ) return ytm, ilm_ytm def prefix_search( self, hypotheses: List[Hypothesis], enc_out: torch.Tensor, prefix_alpha: int ) -> List[Hypothesis]: """ Prefix search for NSC and mAES strategies. Based on https://arxiv.org/pdf/1211.3711.pdf """ for j, hyp_j in enumerate(hypotheses[:-1]): for hyp_i in hypotheses[(j + 1) :]: curr_id = len(hyp_j.y_sequence) pref_id = len(hyp_i.y_sequence) if is_prefix(hyp_j.y_sequence, hyp_i.y_sequence) and (curr_id - pref_id) <= prefix_alpha: logp, ilm_logp = self.resolve_joint_output(enc_out, hyp_i.dec_out[-1]) logp = logp[0, 0, 0, :] curr_score = hyp_i.score + float(logp[hyp_j.y_sequence[pref_id]]) # Setup ngram LM: if self.ngram_lm: lm_score, next_state = self.compute_ngram_score( hyp_i.ngram_lm_state, int(hyp_j.y_sequence[pref_id]) ) if self.hat_subtract_ilm: curr_score += self.ngram_lm_alpha * lm_score - self.hat_ilm_weight * float( ilm_logp[0, 0, hyp_j.y_sequence[pref_id]] ) else: curr_score += self.ngram_lm_alpha * lm_score for k in range(pref_id, (curr_id - 1)): logp, ilm_logp = self.resolve_joint_output(enc_out, hyp_j.dec_out[k]) logp = logp[0, 0, 0, :] curr_score += float(logp[hyp_j.y_sequence[k + 1]]) # Setup ngram LM: if self.ngram_lm: lm_score, next_state = self.compute_ngram_score(next_state, int(hyp_j.y_sequence[k + 1])) if self.hat_subtract_ilm: curr_score += self.ngram_lm_alpha * lm_score - self.hat_ilm_weight * float( ilm_logp[0, 0, hyp_j.y_sequence[k + 1]] ) else: curr_score += self.ngram_lm_alpha * lm_score hyp_j.score = np.logaddexp(hyp_j.score, curr_score) return hypotheses def compute_ngram_score(self, current_lm_state: "kenlm.State", label: int) -> Tuple[float, "kenlm.State"]: """ Score computation for kenlm ngram language model. """ if self.token_offset: label = chr(label + self.token_offset) else: label = str(label) next_state = kenlm.State() lm_score = self.ngram_lm.BaseScore(current_lm_state, label, next_state) lm_score *= 1.0 / np.log10(np.e) return lm_score, next_state def set_decoding_type(self, decoding_type: str): """ Sets decoding type. Please check train_kenlm.py in scripts/asr_language_modeling/ to find out why we need Args: decoding_type: decoding type """ # TOKEN_OFFSET for BPE-based models if decoding_type == 'subword': self.token_offset = DEFAULT_TOKEN_OFFSET class BeamBatchedRNNTInfer(Typing, ConfidenceMethodMixin, WithOptionalCudaGraphs): @property def input_types(self): """Returns definitions of module input ports.""" return { "encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), "encoded_lengths": NeuralType(tuple('B'), LengthsType()), "partial_hypotheses": [NeuralType(elements_type=HypothesisType(), optional=True)], # must always be last } def __init__( self, decoder_model: rnnt_abstract.AbstractRNNTDecoder, joint_model: rnnt_abstract.AbstractRNNTJoint, blank_index: int, beam_size: int, search_type: str = 'malsd_batch', score_norm: bool = True, maes_num_steps: Optional[int] = 2, maes_expansion_gamma: Optional[float] = 2.3, maes_expansion_beta: Optional[int] = 2, max_symbols_per_step: Optional[int] = 10, preserve_alignments: bool = False, fusion_models: Optional[List[NGramGPULanguageModel]] = None, fusion_models_alpha: Optional[List[float]] = None, blank_lm_score_mode: Optional[str | BlankLMScoreMode] = BlankLMScoreMode.LM_WEIGHTED_FULL, pruning_mode: Optional[str | PruningMode] = PruningMode.LATE, allow_cuda_graphs: Optional[bool] = True, return_best_hypothesis: Optional[str] = True, ): """ Init method. Args: decoder: Prediction network from RNN-T joint: Joint module from RNN-T blank_index: index of blank symbol beam_size: beam size search_type: strategy from [`maes_batch`. `malsd_batch`]. Defaults to `malsd_batch` score_norm: whether to normalize scores before best hypothesis extraction maes_num_steps: Number of adaptive steps to take. From the paper, 2 steps is generally sufficient. int > 1. maes_expansion_gamma: Float pruning threshold used in the prune-by-value step when computing the expansions. The default (2.3) is selected from the paper. It performs a comparison (max_log_prob - gamma <= log_prob[v]) where v is all vocabulary indices in the Vocab set and max_log_prob is the "most" likely token to be predicted. Gamma therefore provides a margin of additional tokens which can be potential candidates for expansion apart from the "most likely" candidate. Lower values will reduce the number of expansions (by increasing pruning-by-value, thereby improving speed but hurting accuracy). Higher values will increase the number of expansions (by reducing pruning-by-value, thereby reducing speed but potentially improving accuracy). This is a hyper parameter to be experimentally tuned on a validation set. maes_expansion_beta: Maximum number of prefix expansions allowed, in addition to the beam size. Effectively, the number of hypothesis = beam_size + maes_expansion_beta. Must be an int >= 0, and affects the speed of inference since large values will perform large beam search in the next step. max_symbols_per_step: max symbols to emit on each step (to avoid infinite looping) preserve_alignments: if alignments are needed fusion_models: list of fusion models to use for decoding fusion_models_alpha: list of alpha values for fusion models blank_lm_score_mode: mode for scoring blank symbol with LM pruning_mode: mode for pruning hypotheses with LM allow_cuda_graphs: whether to allow CUDA graphs return_best_hypothesis: whether to return the best hypothesis or N-best hypotheses tokenizer: tokenizer for the model """ super().__init__() self.decoder = decoder_model self.joint = joint_model self._blank_index = blank_index self._SOS = blank_index # Start of single index self.beam_size = beam_size self.score_norm = score_norm self.return_best_hypothesis = return_best_hypothesis if max_symbols_per_step is not None and max_symbols_per_step <= 0: raise ValueError(f"Expected max_symbols_per_step > 0 (or None), got {max_symbols_per_step}") self.max_symbols = max_symbols_per_step self.preserve_alignments = preserve_alignments if search_type == "malsd_batch": # Depending on availability of `blank_as_pad` support # switch between more efficient batch decoding technique self._decoding_computer = ModifiedALSDBatchedRNNTComputer( decoder=self.decoder, joint=self.joint, beam_size=self.beam_size, blank_index=self._blank_index, max_symbols_per_step=self.max_symbols, preserve_alignments=preserve_alignments, fusion_models=fusion_models, fusion_models_alpha=fusion_models_alpha, blank_lm_score_mode=blank_lm_score_mode, pruning_mode=pruning_mode, allow_cuda_graphs=allow_cuda_graphs, ) elif search_type == "maes_batch": self._decoding_computer = ModifiedAESBatchedRNNTComputer( decoder=self.decoder, joint=self.joint, beam_size=self.beam_size, blank_index=self._blank_index, maes_num_steps=maes_num_steps, maes_expansion_beta=maes_expansion_beta, maes_expansion_gamma=maes_expansion_gamma, preserve_alignments=preserve_alignments, ngram_lm_model=fusion_models[0] if fusion_models is not None else None, ngram_lm_alpha=fusion_models_alpha[0] if fusion_models_alpha is not None else 0.0, blank_lm_score_mode=blank_lm_score_mode, pruning_mode=pruning_mode, allow_cuda_graphs=allow_cuda_graphs, ) def disable_cuda_graphs(self) -> bool: """Disable CUDA graphs (e.g., for decoding in training)""" if isinstance(self._decoding_computer, WithOptionalCudaGraphs): return self._decoding_computer.disable_cuda_graphs() return False def maybe_enable_cuda_graphs(self) -> bool: """Enable CUDA graphs (if allowed)""" if isinstance(self._decoding_computer, WithOptionalCudaGraphs): return self._decoding_computer.maybe_enable_cuda_graphs() return False @property def output_types(self): """Returns definitions of module output ports.""" return {"predictions": [NeuralType(elements_type=HypothesisType())]} def __call__(self, *args, **kwargs): return self.forward(*args, **kwargs) @typecheck() def forward( self, encoder_output: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[list[Hypothesis]] = None, ) -> Tuple[list[Hypothesis] | List[NBestHypotheses]]: """Returns a list of hypotheses given an input batch of the encoder hidden embedding. Output token is generated auto-regressively. Args: encoder_output: A tensor of size (batch, features, timesteps). encoded_lengths: list of int representing the length of each sequence output sequence. Returns: Tuple[list[Hypothesis] | List[NBestHypotheses]]: Tuple of a list of hypotheses for each batch. Each hypothesis contains the decoded sequence, timestamps and associated scores. The format of the returned hypotheses depends on the `return_best_hypothesis` attribute: - If `return_best_hypothesis` is True, returns the best hypothesis for each batch. - Otherwise, returns the N-best hypotheses for each batch. """ # Preserve decoder and joint training state decoder_training_state = self.decoder.training joint_training_state = self.joint.training with torch.inference_mode(): # Apply optional preprocessing encoder_output = encoder_output.transpose(1, 2) # (B, T, D) logitlen = encoded_lengths self.decoder.eval() self.joint.eval() inseq = encoder_output # [B, T, D] batched_beam_hyps = self._decoding_computer(x=inseq, out_len=logitlen) batch_size = encoder_output.shape[0] if self.return_best_hypothesis: hyps = batched_beam_hyps.to_hyps_list(score_norm=self.score_norm)[:batch_size] else: hyps = batched_beam_hyps.to_nbest_hyps_list(score_norm=self.score_norm)[:batch_size] self.decoder.train(decoder_training_state) self.joint.train(joint_training_state) return (hyps,) @dataclass class BeamRNNTInferConfig: """ Beam RNNT Inference config. """ beam_size: int search_type: str = 'default' score_norm: bool = True return_best_hypothesis: bool = True tsd_max_sym_exp_per_step: Optional[int] = 50 alsd_max_target_len: float = 1.0 nsc_max_timesteps_expansion: int = 1 nsc_prefix_alpha: int = 1 maes_num_steps: int = 2 maes_prefix_alpha: int = 1 maes_expansion_gamma: float = 2.3 maes_expansion_beta: int = 2 language_model: Optional[Dict[str, Any]] = None softmax_temperature: float = 1.0 preserve_alignments: bool = False ngram_lm_model: Optional[str] = None ngram_lm_alpha: Optional[float] = 0.0 boosting_tree: BoostingTreeModelConfig = field(default_factory=BoostingTreeModelConfig) boosting_tree_alpha: Optional[float] = 0.0 hat_subtract_ilm: bool = False hat_ilm_weight: float = 0.0 max_symbols_per_step: Optional[int] = 10 blank_lm_score_mode: Optional[str | BlankLMScoreMode] = BlankLMScoreMode.LM_WEIGHTED_FULL pruning_mode: Optional[str | PruningMode] = PruningMode.LATE allow_cuda_graphs: Optional[bool] = True