#!/usr/bin/env python3 # # Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, # Zengwei Yao, # 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. """ Usage: (1) greedy search ./zipformer_prompt_asr/decode_bert.py \ --epoch 28 \ --avg 15 \ --exp-dir ./zipformer_prompt_asr/exp \ --max-duration 1000 \ --decoding-method greedy_search \ --text-encoder-type BERT \ --memory-layer 0 \ --use-pre-text True \ --use-style-prompt True \ --max-prompt-lens 1000 \ --style-text-transform mixed-punc \ --pre-text-transform mixed-punc \ --compute-CER 0 (2) modified beam search ./zipformer_prompt_asr/decode_bert.py \ --epoch 28 \ --avg 15 \ --exp-dir ./zipformer_prompt_asr/exp \ --max-duration 1000 \ --decoding-method modified_beam_search \ --beam-size 4 \ --text-encoder-type BERT \ --memory-layer 0 \ --use-pre-text True \ --use-style-prompt True \ --max-prompt-lens 1000 \ --style-text-transform mixed-punc \ --pre-text-transform mixed-punc \ --compute-CER 0 (3) Decode LibriSpeech ./zipformer_prompt_asr/decode_bert.py \ --epoch 28 \ --avg 15 \ --exp-dir ./zipformer_prompt_asr/exp \ --max-duration 1000 \ --decoding-method modified_beam_search \ --use-ls-test-set True \ --beam-size 4 \ --text-encoder-type BERT \ --memory-layer 0 \ --use-pre-text True \ --use-style-prompt True \ --max-prompt-lens 1000 \ --style-text-transform mixed-punc \ --pre-text-transform mixed-punc \ --compute-CER 0 (4) Decode LibriSpeech + biasing list biasing_list=100 # could also be 0 ./zipformer_prompt_asr/decode_bert.py \ --epoch 28 \ --avg 15 \ --exp-dir ./zipformer_prompt_asr/exp \ --max-duration 1000 \ --decoding-method modified_beam_search \ --beam-size 4 \ --use-ls-test-set True \ --use-ls-context-list True \ --biasing-level utterance \ --ls-distractors $biasing_list \ --post-normalization True \ --text-encoder-type BERT \ --max-prompt-lens 1000 \ --style-text-transform mixed-punc \ --pre-text-transform mixed-punc """ import argparse import logging import math import warnings from collections import defaultdict from pathlib import Path from typing import Callable, Dict, List, Optional, Tuple import k2 import sentencepiece as spm import torch import torch.nn as nn from asr_datamodule import LibriHeavyAsrDataModule from beam_search import greedy_search, greedy_search_batch, modified_beam_search from dataset import naive_triplet_text_sampling, random_shuffle_subset from ls_text_normalization import word_normalization from text_normalization import ( _apply_style_transform, lower_all_char, lower_only_alpha, ref_text_normalization, remove_non_alphabetic, train_text_normalization, upper_all_char, upper_only_alpha, ) from train_bert_encoder import ( _encode_texts_as_bytes_with_tokenizer, add_model_arguments, get_params, get_tokenizer, get_transducer_model, ) from transformers import BertModel, BertTokenizer from utils import brian_biasing_list, get_facebook_biasing_list, write_error_stats from icefall.checkpoint import ( average_checkpoints, average_checkpoints_with_averaged_model, find_checkpoints, load_checkpoint, ) from icefall.lexicon import Lexicon from icefall.utils import AttributeDict, setup_logger, store_transcripts, str2bool LOG_EPS = math.log(1e-10) def get_parser(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument( "--epoch", type=int, default=30, help="""It specifies the checkpoint to use for decoding. Note: Epoch counts from 1. You can specify --avg to use more checkpoints for model averaging.""", ) parser.add_argument( "--iter", type=int, default=0, help="""If positive, --epoch is ignored and it will use the checkpoint exp_dir/checkpoint-iter.pt. You can specify --avg to use more checkpoints for model averaging. """, ) parser.add_argument( "--avg", type=int, default=9, help="Number of checkpoints to average. Automatically select " "consecutive checkpoints before the checkpoint specified by " "'--epoch' and '--iter'", ) parser.add_argument( "--use-averaged-model", type=str2bool, default=True, help="Whether to load averaged model. Currently it only supports " "using --epoch. If True, it would decode with the averaged model " "over the epoch range from `epoch-avg` (excluded) to `epoch`." "Actually only the models with epoch number of `epoch-avg` and " "`epoch` are loaded for averaging. ", ) parser.add_argument( "--exp-dir", type=str, default="pruned_transducer_stateless7/exp", help="The experiment dir", ) parser.add_argument( "--bpe-model", type=str, default="data/lang_bpe_500/bpe.model", help="Path to the BPE model", ) parser.add_argument( "--lang-dir", type=Path, default="data/lang_bpe_500", help="The lang dir containing word table and LG graph", ) parser.add_argument( "--decoding-method", type=str, default="greedy_search", help="""Possible values are: - greedy_search - beam_search - modified_beam_search - fast_beam_search - fast_beam_search_nbest - fast_beam_search_nbest_oracle - fast_beam_search_nbest_LG - modified_beam_search_lm_shallow_fusion # for rnn lm shallow fusion - modified_beam_search_LODR If you use fast_beam_search_nbest_LG, you have to specify `--lang-dir`, which should contain `LG.pt`. """, ) parser.add_argument( "--beam-size", type=int, default=4, help="""An integer indicating how many candidates we will keep for each frame. Used only when --decoding-method is beam_search or modified_beam_search.""", ) parser.add_argument( "--beam", type=float, default=20.0, help="""A floating point value to calculate the cutoff score during beam search (i.e., `cutoff = max-score - beam`), which is the same as the `beam` in Kaldi. Used only when --decoding-method is fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle """, ) parser.add_argument( "--ngram-lm-scale", type=float, default=0.01, help=""" Used only when --decoding_method is fast_beam_search_nbest_LG. It specifies the scale for n-gram LM scores. """, ) parser.add_argument( "--max-contexts", type=int, default=8, help="""Used only when --decoding-method is fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", ) parser.add_argument( "--max-states", type=int, default=64, help="""Used only when --decoding-method is fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", ) parser.add_argument( "--max-sym-per-frame", type=int, default=1, help="""Maximum number of symbols per frame. Used only when --decoding_method is greedy_search""", ) parser.add_argument( "--num-paths", type=int, default=200, help="""Number of paths for nbest decoding. Used only when the decoding method is fast_beam_search_nbest, fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", ) parser.add_argument( "--nbest-scale", type=float, default=0.5, help="""Scale applied to lattice scores when computing nbest paths. Used only when the decoding method is fast_beam_search_nbest, fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", ) parser.add_argument( "--use-pre-text", type=str2bool, default=True, help="Use pre-text is available during decoding", ) parser.add_argument( "--use-style-prompt", type=str2bool, default=True, help="Use style prompt when evaluation", ) parser.add_argument( "--max-prompt-lens", type=int, default=1000, ) parser.add_argument( "--post-normalization", type=str2bool, default=True, help="Normalized the recognition results by uppercasing and removing non-alphabetic symbols. ", ) parser.add_argument( "--compute-CER", type=str2bool, default=False, help="Reports CER. By default, only reports WER", ) parser.add_argument( "--style-text-transform", type=str, choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"], default="mixed-punc", help="The style of style prompt, i.e style_text", ) parser.add_argument( "--pre-text-transform", type=str, choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"], default="mixed-punc", help="The style of content prompt, i.e pre_text", ) parser.add_argument( "--use-ls-test-set", type=str2bool, default=False, help="Use librispeech test set for evaluation.", ) parser.add_argument( "--use-ls-context-list", type=str2bool, default=False, help="If use a fixed context list for LibriSpeech decoding", ) parser.add_argument( "--biasing-level", type=str, default="utterance", choices=["utterance", "Book", "Chapter"], ) parser.add_argument( "--ls-distractors", type=int, default=0, help="The number of distractors into context list for LibriSpeech decoding", ) add_model_arguments(parser) return parser def decode_one_batch( params: AttributeDict, model: nn.Module, sp: spm.SentencePieceProcessor, tokenizer: spm.SentencePieceProcessor, batch: dict, biasing_dict: dict = None, word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the following format: - key: It indicates the setting used for decoding. For example, if greedy_search is used, it would be "greedy_search" If beam search with a beam size of 7 is used, it would be "beam_7" - value: It contains the decoding result. `len(value)` equals to batch size. `value[i]` is the decoding result for the i-th utterance in the given batch. Args: params: It's the return value of :func:`get_params`. model: The neural model. sp: The BPE model. tokenizer: Tokenizer for the text encoder batch: It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. biasing_dict: A dictionary in the form `{cut_id: :w1 w2"}` that contains a list of biasing words (separated with space) word_table: The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used only when --decoding_method is fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. LM: A neural net LM for shallow fusion. Only used when `--use-shallow-fusion` set to true. ngram_lm: A ngram lm. Used in LODR decoding. ngram_lm_scale: The scale of the ngram language model. Returns: Return the decoding result. See above description for the format of the returned dict. """ device = next(model.parameters()).device feature = batch["inputs"] cuts = batch["supervisions"]["cut"] cut_ids = [c.supervisions[0].id for c in cuts] batch_size = feature.size(0) if "pre_text" in batch["supervisions"] and params.use_pre_text: pre_texts = batch["supervisions"]["pre_text"] pre_texts = [train_text_normalization(t) for t in pre_texts] else: pre_texts = ["" for _ in range(batch_size)] # get the librispeech biasing data if params.use_pre_text and (params.use_ls_context_list and params.use_ls_test_set): if params.biasing_level == "utterance": pre_texts = [biasing_dict[id] for id in cut_ids] elif params.biasing_level == "Chapter": chapter_ids = [c.split("-")[1] for c in cut_ids] pre_texts = [biasing_dict[id] for id in chapter_ids] elif params.biasing_level == "Book": chapter_ids = [c.split("-")[1] for c in cut_ids] pre_texts = [biasing_dict[id] for id in chapter_ids] else: raise ValueError(f"Unseen biasing level: {params.biasing_level}") if params.pre_text_transform == "mixed-punc": pre_texts = [t.lower() for t in pre_texts] # get style_text if params.use_style_prompt: fixed_sentence = "Mixed-case English transcription, with punctuation. Actually, it's fully not related." style_texts = batch["supervisions"].get( "style_text", [fixed_sentence for _ in range(batch_size)] ) style_texts = [train_text_normalization(t) for t in style_texts] else: style_texts = ["" for _ in range(batch_size)] # use empty string # Get the text embedding if params.use_pre_text or params.use_style_prompt: # apply style transform to the pre_text and style_text pre_texts = _apply_style_transform(pre_texts, params.pre_text_transform) if not params.use_ls_context_list: pre_texts = [t[-params.max_prompt_lens :] for t in pre_texts] if params.use_style_prompt: style_texts = _apply_style_transform( style_texts, params.style_text_transform ) with warnings.catch_warnings(): warnings.simplefilter("ignore") # Use tokenizer to prepare input for text encoder encoded_inputs, style_lens = _encode_texts_as_bytes_with_tokenizer( pre_texts=pre_texts, style_texts=style_texts, tokenizer=tokenizer, device=device, no_limit=True, ) logging.info( f"Shape of the encoded prompts: {encoded_inputs['input_ids'].shape}" ) memory, memory_key_padding_mask = model.encode_text( encoded_inputs=encoded_inputs, style_lens=style_lens, ) # (T,B,C) else: memory = None memory_key_padding_mask = None # Get the transducer encoder output assert feature.ndim == 3 feature = feature.to(device) # at entry, feature is (N, T, C) supervisions = batch["supervisions"] feature_lens = supervisions["num_frames"].to(device) with warnings.catch_warnings(): warnings.simplefilter("ignore") encoder_out, encoder_out_lens = model.encode_audio( feature=feature, feature_lens=feature_lens, memory=memory, memory_key_padding_mask=memory_key_padding_mask, ) hyps = [] if params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1: hyp_tokens = greedy_search_batch( model=model, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) elif params.decoding_method == "modified_beam_search": hyp_tokens = modified_beam_search( model=model, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, beam=params.beam_size, ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) else: batch_size = encoder_out.size(0) for i in range(batch_size): # fmt: off encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] # fmt: on if params.decoding_method == "greedy_search": hyp = greedy_search( model=model, encoder_out=encoder_out_i, max_sym_per_frame=params.max_sym_per_frame, ) else: raise ValueError( f"Unsupported decoding method: {params.decoding_method}" ) hyps.append(sp.decode(hyp).split()) if params.decoding_method == "greedy_search": return {"greedy_search": hyps} else: return {f"beam_size_{params.beam_size}": hyps} def decode_dataset( dl: torch.utils.data.DataLoader, params: AttributeDict, model: nn.Module, sp: spm.SentencePieceProcessor, tokenizer: spm.SentencePieceProcessor, biasing_dict: Dict = None, word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[str, List[str], List[str]]]]: """Decode dataset. Args: dl: PyTorch's dataloader containing the dataset to decode. params: It is returned by :func:`get_params`. model: The neural model. sp: The BPE model. tokenizer: Tokenizer for the text encoder biasing_dict: A dictionary in the form `{cut_id: :w1 w2"}` that contains a list of biasing words (separated with space) word_table: The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used only when --decoding_method is fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. LM: A neural network LM, used during shallow fusion Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. Its value is a list of tuples. Each tuple contains two elements: The first is the reference transcript, and the second is the predicted result. """ num_cuts = 0 try: num_batches = len(dl) except TypeError: num_batches = "?" if params.decoding_method == "greedy_search": log_interval = 50 else: log_interval = 20 results = defaultdict(list) for batch_idx, batch in enumerate(dl): texts = batch["supervisions"][ "text" ] # By default, this should be in mixed-punc format # the style of ref_text should match style_text texts = _apply_style_transform(texts, params.style_text_transform) if params.use_style_prompt: texts = _apply_style_transform(texts, params.style_text_transform) cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] if not params.use_ls_test_set: try: book_names = [ cut.text_path.split("/")[-2] for cut in batch["supervisions"]["cut"] ] except AttributeError: book_names = [ cut.id.split("/")[0] for cut in batch["supervisions"]["cut"] ] else: book_names = ["" for _ in cut_ids] hyps_dict = decode_one_batch( params=params, model=model, sp=sp, tokenizer=tokenizer, biasing_dict=biasing_dict, decoding_graph=decoding_graph, word_table=word_table, batch=batch, ) for name, hyps in hyps_dict.items(): this_batch = [] assert len(hyps) == len(texts) for cut_id, book_name, hyp_words, ref_text in zip( cut_ids, book_names, hyps, texts ): ref_text = ref_text_normalization( ref_text ) # remove full-width symbols & some book marks ref_words = ref_text.split() this_batch.append((cut_id, ref_words, hyp_words)) results[name].extend(this_batch) num_cuts += len(texts) if batch_idx % log_interval == 0: batch_str = f"{batch_idx}/{num_batches}" logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") return results def save_results( params: AttributeDict, test_set_name: str, results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]], biasing_words: List[str] = None, ): test_set_wers = dict() test_set_cers = dict() for key, results in results_dict.items(): recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" results = sorted(results) store_transcripts(filename=recog_path, texts=results) logging.info(f"The transcripts are stored in {recog_path}") # The following prints out WERs, per-word error statistics and aligned # ref/hyp pairs. errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True ) test_set_wers[key] = wer logging.info("Wrote detailed error stats to {}".format(errs_filename)) if params.compute_CER: # Write CER statistics recog_path = ( params.res_dir / f"recogs-{test_set_name}-char-{params.suffix}.txt" ) store_transcripts(filename=recog_path, texts=results, char_level=True) errs_filename = ( params.res_dir / f"errs-CER-{test_set_name}-{params.suffix}.txt" ) with open(errs_filename, "w") as f: cer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True, compute_CER=params.compute_CER, ) test_set_cers[key] = cer logging.info("Wrote detailed CER stats to {}".format(errs_filename)) test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: print("{}\t{}".format(key, val), file=f) s = "\nFor {}, WER of different settings are:\n".format(test_set_name) note = "\tbest for {}".format(test_set_name) for key, val in test_set_wers: s += "{}\t{}{}\n".format(key, val, note) note = "" logging.info(s) if params.compute_CER: test_set_cers = sorted(test_set_cers.items(), key=lambda x: x[1]) errs_info = params.res_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tCER", file=f) for key, val in test_set_cers: print("{}\t{}".format(key, val), file=f) s = "\nFor {}, CER of different settings are:\n".format(test_set_name) note = "\tbest for {}".format(test_set_name) for key, val in test_set_cers: s += "{} CER\t{}{}\n".format(key, val, note) note = "" logging.info(s) @torch.no_grad() def main(): parser = get_parser() LibriHeavyAsrDataModule.add_arguments(parser) args = parser.parse_args() args.exp_dir = Path(args.exp_dir) params = get_params() params.update(vars(args)) assert params.decoding_method in ( "greedy_search", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method if params.iter > 0: params.suffix = f"iter-{params.iter}-avg-{params.avg}" else: params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" if params.causal: assert ( "," not in params.chunk_size ), "chunk_size should be one value in decoding." assert ( "," not in params.left_context_frames ), "left_context_frames should be one value in decoding." params.suffix += f"-chunk-{params.chunk_size}" params.suffix += f"-left-context-{params.left_context_frames}" if "beam_search" in params.decoding_method: params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}" else: params.suffix += f"-context-{params.context_size}" params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" if params.use_pre_text: params.suffix += ( f"-pre-text-{params.pre_text_transform}-len-{params.max_prompt_lens}" ) if params.use_style_prompt: params.suffix += f"-style-prompt-{params.style_text_transform}" if params.use_ls_context_list: assert ( params.use_pre_text ), "Must set --use-pre-text to True if using context list" params.suffix += f"-use-{params.biasing_level}-level-ls-context-list" if params.biasing_level == "utterance" and params.ls_distractors: params.suffix += f"-ls-context-distractors-{params.ls_distractors}" if params.use_averaged_model: params.suffix += "-use-averaged-model" setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") logging.info("Decoding started") device = torch.device("cpu") if torch.cuda.is_available(): device = torch.device("cuda", 0) logging.info(f"Device: {device}") sp = spm.SentencePieceProcessor() sp.load(params.bpe_model) # and are defined in local/train_bpe_model.py params.blank_id = sp.piece_to_id("") params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() logging.info(params) logging.info("About to create model") model = get_transducer_model(params) tokenizer = get_tokenizer(params) if not params.use_averaged_model: if params.iter > 0: filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ : params.avg ] if len(filenames) == 0: raise ValueError( f"No checkpoints found for" f" --iter {params.iter}, --avg {params.avg}" ) elif len(filenames) < params.avg: raise ValueError( f"Not enough checkpoints ({len(filenames)}) found for" f" --iter {params.iter}, --avg {params.avg}" ) logging.info(f"averaging {filenames}") model.to(device) model.load_state_dict(average_checkpoints(filenames, device=device)) elif params.avg == 1: load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) else: start = params.epoch - params.avg + 1 filenames = [] for i in range(start, params.epoch + 1): if i >= 1: filenames.append(f"{params.exp_dir}/epoch-{i}.pt") logging.info(f"averaging {filenames}") model.to(device) model.load_state_dict(average_checkpoints(filenames, device=device)) else: if params.iter > 0: filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ : params.avg + 1 ] if len(filenames) == 0: raise ValueError( f"No checkpoints found for" f" --iter {params.iter}, --avg {params.avg}" ) elif len(filenames) < params.avg + 1: raise ValueError( f"Not enough checkpoints ({len(filenames)}) found for" f" --iter {params.iter}, --avg {params.avg}" ) filename_start = filenames[-1] filename_end = filenames[0] logging.info( "Calculating the averaged model over iteration checkpoints" f" from {filename_start} (excluded) to {filename_end}" ) model.to(device) model.load_state_dict( average_checkpoints_with_averaged_model( filename_start=filename_start, filename_end=filename_end, device=device, ) ) else: assert params.avg > 0, params.avg start = params.epoch - params.avg assert start >= 1, start filename_start = f"{params.exp_dir}/epoch-{start}.pt" filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" logging.info( f"Calculating the averaged model over epoch range from " f"{start} (excluded) to {params.epoch}" ) model.to(device) model.load_state_dict( average_checkpoints_with_averaged_model( filename_start=filename_start, filename_end=filename_end, device=device, ), strict=False, ) model.to(device) model.eval() LM = None decoding_graph = None word_table = None num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") # we need cut ids to display recognition results. args.return_cuts = True libriheavy = LibriHeavyAsrDataModule(args) test_clean_cuts = libriheavy.test_clean_cuts() test_other_cuts = libriheavy.test_other_cuts() ls_test_clean_cuts = libriheavy.librispeech_test_clean_cuts() ls_test_other_cuts = libriheavy.librispeech_test_other_cuts() test_clean_dl = libriheavy.valid_dataloaders( test_clean_cuts, text_sampling_func=naive_triplet_text_sampling ) test_other_dl = libriheavy.valid_dataloaders( test_other_cuts, text_sampling_func=naive_triplet_text_sampling ) ls_test_clean_dl = libriheavy.test_dataloaders(ls_test_clean_cuts) ls_test_other_dl = libriheavy.test_dataloaders(ls_test_other_cuts) if params.use_ls_test_set: test_sets = ["ls-test-clean", "ls-test-other"] test_dl = [ls_test_clean_dl, ls_test_other_dl] else: test_sets = ["test-clean", "test-other"] test_dl = [test_clean_dl, test_other_dl] for test_set, test_dl in zip(test_sets, test_dl): biasing_dict = None if params.use_ls_context_list: if test_set == "ls-test-clean": biasing_dict = get_facebook_biasing_list( test_set="test-clean", num_distractors=params.ls_distractors, ) elif test_set == "ls-test-other": biasing_dict = get_facebook_biasing_list( test_set="test-other", num_distractors=params.ls_distractors, ) results_dict = decode_dataset( dl=test_dl, params=params, model=model, sp=sp, tokenizer=tokenizer, biasing_dict=biasing_dict, word_table=word_table, decoding_graph=decoding_graph, ) save_results( params=params, test_set_name=test_set, results_dict=results_dict, ) if params.post_normalization: if "-post-normalization" not in params.suffix: params.suffix += "-post-normalization" new_res = {} for k in results_dict: new_ans = [] for item in results_dict[k]: id, ref, hyp = item if params.use_ls_test_set: hyp = ( " ".join(hyp).replace("-", " ").split() ) # handle the hypens hyp = upper_only_alpha(" ".join(hyp)).split() hyp = [word_normalization(w.upper()) for w in hyp] hyp = " ".join(hyp).split() hyp = [w for w in hyp if w != ""] ref = upper_only_alpha(" ".join(ref)).split() else: hyp = upper_only_alpha(" ".join(hyp)).split() ref = upper_only_alpha(" ".join(ref)).split() new_ans.append((id, ref, hyp)) new_res[k] = new_ans save_results( params=params, test_set_name=test_set, results_dict=new_res, ) if params.suffix.endswith("-post-normalization"): params.suffix = params.suffix.replace("-post-normalization", "") logging.info("Done!") if __name__ == "__main__": main()