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| import torch |
|
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| from nemo.core.classes import NeuralModule, typecheck |
| from nemo.core.neural_types import LengthsType, LogprobsType, NeuralType, PredictionsType |
|
|
|
|
| class BeamSearchDecoderWithLM(NeuralModule): |
| """Neural Module that does CTC beam search with a N-gram language model. |
| It takes a batch of log_probabilities. Note the bigger the batch, the |
| better as processing is parallelized. Outputs a list of size batch_size. |
| Each element in the list is a list of size beam_search, and each element |
| in that list is a tuple of (final_log_prob, hyp_string). |
| Args: |
| vocab (list): List of characters that can be output by the ASR model. For English, this is the 28 character set |
| {a-z '}. The CTC blank symbol is automatically added. |
| beam_width (int): Size of beams to keep and expand upon. Larger beams result in more accurate but slower |
| predictions |
| alpha (float): The amount of importance to place on the N-gram language model. Larger alpha means more |
| importance on the LM and less importance on the acoustic model. |
| beta (float): A penalty term given to longer word sequences. Larger beta will result in shorter sequences. |
| lm_path (str): Path to N-gram language model |
| num_cpus (int): Number of CPUs to use |
| cutoff_prob (float): Cutoff probability in vocabulary pruning, default 1.0, no pruning |
| cutoff_top_n (int): Cutoff number in pruning, only top cutoff_top_n characters with highest probs in |
| vocabulary will be used in beam search, default 40. |
| input_tensor (bool): Set to True if you intend to pass PyTorch Tensors, set to False if you intend to pass |
| NumPy arrays. |
| """ |
|
|
| @property |
| def input_types(self): |
| """Returns definitions of module input ports. |
| """ |
| return { |
| "log_probs": NeuralType(('B', 'T', 'D'), LogprobsType()), |
| "log_probs_length": NeuralType(tuple('B'), LengthsType()), |
| } |
|
|
| @property |
| def output_types(self): |
| """Returns definitions of module output ports. |
| """ |
| return {"predictions": NeuralType(('B', 'T'), PredictionsType())} |
|
|
| def __init__( |
| self, vocab, beam_width, alpha, beta, lm_path, num_cpus, cutoff_prob=1.0, cutoff_top_n=40, input_tensor=False |
| ): |
|
|
| try: |
| from ctc_decoders import Scorer, ctc_beam_search_decoder_batch |
| except ModuleNotFoundError: |
| raise ModuleNotFoundError( |
| "BeamSearchDecoderWithLM requires the installation of ctc_decoders " |
| "from scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh" |
| ) |
|
|
| super().__init__() |
|
|
| if lm_path is not None: |
| self.scorer = Scorer(alpha, beta, model_path=lm_path, vocabulary=vocab) |
| else: |
| self.scorer = None |
| self.beam_search_func = ctc_beam_search_decoder_batch |
| self.vocab = vocab |
| self.beam_width = beam_width |
| self.num_cpus = num_cpus |
| self.cutoff_prob = cutoff_prob |
| self.cutoff_top_n = cutoff_top_n |
| self.input_tensor = input_tensor |
|
|
| @typecheck(ignore_collections=True) |
| @torch.no_grad() |
| def forward(self, log_probs, log_probs_length): |
| probs_list = log_probs |
| if self.input_tensor: |
| probs = torch.exp(log_probs) |
| probs_list = [] |
| for i, prob in enumerate(probs): |
| probs_list.append(prob[: log_probs_length[i], :]) |
| res = self.beam_search_func( |
| probs_list, |
| self.vocab, |
| beam_size=self.beam_width, |
| num_processes=self.num_cpus, |
| ext_scoring_func=self.scorer, |
| cutoff_prob=self.cutoff_prob, |
| cutoff_top_n=self.cutoff_top_n, |
| ) |
| return res |
|
|
