From b070d04ae8c69995aec8ba5ce098da42da542e05 Mon Sep 17 00:00:00 2001 From: yifanyeung Date: Sun, 18 Feb 2024 12:36:47 +0800 Subject: [PATCH] fix flake8 --- egs/librispeech/SSL/hubert/ctc_decode.py | 839 ----------------------- 1 file changed, 839 deletions(-) delete mode 100644 egs/librispeech/SSL/hubert/ctc_decode.py diff --git a/egs/librispeech/SSL/hubert/ctc_decode.py b/egs/librispeech/SSL/hubert/ctc_decode.py deleted file mode 100644 index b184e2d012..0000000000 --- a/egs/librispeech/SSL/hubert/ctc_decode.py +++ /dev/null @@ -1,839 +0,0 @@ -#!/usr/bin/env python3 -# -# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, -# Liyong Guo, -# Quandong Wang, -# Zengwei Yao) -# -# 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) ctc-decoding -./zipformer/ctc_decode.py \ - --epoch 30 \ - --avg 15 \ - --exp-dir ./zipformer/exp \ - --use-ctc 1 \ - --max-duration 600 \ - --decoding-method ctc-decoding - -(2) 1best -./zipformer/ctc_decode.py \ - --epoch 30 \ - --avg 15 \ - --exp-dir ./zipformer/exp \ - --use-ctc 1 \ - --max-duration 600 \ - --hlg-scale 0.6 \ - --decoding-method 1best - -(3) nbest -./zipformer/ctc_decode.py \ - --epoch 30 \ - --avg 15 \ - --exp-dir ./zipformer/exp \ - --use-ctc 1 \ - --max-duration 600 \ - --hlg-scale 0.6 \ - --decoding-method nbest - -(4) nbest-rescoring -./zipformer/ctc_decode.py \ - --epoch 30 \ - --avg 15 \ - --exp-dir ./zipformer/exp \ - --use-ctc 1 \ - --max-duration 600 \ - --hlg-scale 0.6 \ - --nbest-scale 1.0 \ - --lm-dir data/lm \ - --decoding-method nbest-rescoring - -(5) whole-lattice-rescoring -./zipformer/ctc_decode.py \ - --epoch 30 \ - --avg 15 \ - --exp-dir ./zipformer/exp \ - --use-ctc 1 \ - --max-duration 600 \ - --hlg-scale 0.6 \ - --nbest-scale 1.0 \ - --lm-dir data/lm \ - --decoding-method whole-lattice-rescoring -""" - - -import argparse -import logging -import math -from collections import defaultdict -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import k2 -import sentencepiece as spm -import torch -import torch.nn as nn -from asr_datamodule import LibriSpeechAsrDataModule -from finetune import add_model_arguments, get_model, get_params - -from icefall.checkpoint import ( - average_checkpoints, - average_checkpoints_with_averaged_model, - find_checkpoints, - load_checkpoint, -) -from icefall.decode import ( - get_lattice, - nbest_decoding, - nbest_oracle, - one_best_decoding, - rescore_with_n_best_list, - rescore_with_whole_lattice, -) -from icefall.lexicon import Lexicon -from icefall.utils import ( - AttributeDict, - get_texts, - setup_logger, - store_transcripts, - str2bool, - write_error_stats, -) - -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=15, - 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="zipformer/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( - "--context-size", - type=int, - default=2, - help="The context size in the decoder. 1 means bigram; 2 means tri-gram", - ) - - parser.add_argument( - "--decoding-method", - type=str, - default="ctc-decoding", - help="""Decoding method. - Supported values are: - - (1) ctc-decoding. Use CTC decoding. It uses a sentence piece - model, i.e., lang_dir/bpe.model, to convert word pieces to words. - It needs neither a lexicon nor an n-gram LM. - - (2) 1best. Extract the best path from the decoding lattice as the - decoding result. - - (3) nbest. Extract n paths from the decoding lattice; the path - with the highest score is the decoding result. - - (4) nbest-rescoring. Extract n paths from the decoding lattice, - rescore them with an n-gram LM (e.g., a 4-gram LM), the path with - the highest score is the decoding result. - - (5) whole-lattice-rescoring. Rescore the decoding lattice with an - n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice - is the decoding result. - you have trained an RNN LM using ./rnn_lm/train.py - - (6) nbest-oracle. Its WER is the lower bound of any n-best - rescoring method can achieve. Useful for debugging n-best - rescoring method. - """, - ) - - parser.add_argument( - "--num-paths", - type=int, - default=100, - help="""Number of paths for n-best based decoding method. - Used only when "method" is one of the following values: - nbest, nbest-rescoring, and nbest-oracle - """, - ) - - parser.add_argument( - "--nbest-scale", - type=float, - default=1.0, - help="""The scale to be applied to `lattice.scores`. - It's needed if you use any kinds of n-best based rescoring. - Used only when "method" is one of the following values: - nbest, nbest-rescoring, and nbest-oracle - A smaller value results in more unique paths. - """, - ) - - parser.add_argument( - "--hlg-scale", - type=float, - default=0.6, - help="""The scale to be applied to `hlg.scores`. - """, - ) - - parser.add_argument( - "--lm-dir", - type=str, - default="data/lm", - help="""The n-gram LM dir. - It should contain either G_4_gram.pt or G_4_gram.fst.txt - """, - ) - - add_model_arguments(parser) - - return parser - - -def get_decoding_params() -> AttributeDict: - """Parameters for decoding.""" - params = AttributeDict( - { - "frame_shift_ms": 10, - "search_beam": 20, - "output_beam": 8, - "min_active_states": 30, - "max_active_states": 10000, - "use_double_scores": True, - } - ) - return params - - -def decode_one_batch( - params: AttributeDict, - model: nn.Module, - HLG: Optional[k2.Fsa], - H: Optional[k2.Fsa], - bpe_model: Optional[spm.SentencePieceProcessor], - batch: dict, - word_table: k2.SymbolTable, - G: 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 no rescoring is used, the key is the string `no_rescore`. - If LM rescoring is used, the key is the string `lm_scale_xxx`, - where `xxx` is the value of `lm_scale`. An example key is - `lm_scale_0.7` - - 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`. - - - params.decoding_method is "1best", it uses 1best decoding without LM rescoring. - - params.decoding_method is "nbest", it uses nbest decoding without LM rescoring. - - params.decoding_method is "nbest-rescoring", it uses nbest LM rescoring. - - params.decoding_method is "whole-lattice-rescoring", it uses whole lattice LM - rescoring. - - model: - The neural model. - HLG: - The decoding graph. Used only when params.decoding_method is NOT ctc-decoding. - H: - The ctc topo. Used only when params.decoding_method is ctc-decoding. - bpe_model: - The BPE model. Used only when params.decoding_method is ctc-decoding. - batch: - It is the return value from iterating - `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation - for the format of the `batch`. - word_table: - The word symbol table. - G: - An LM. It is not None when params.decoding_method is "nbest-rescoring" - or "whole-lattice-rescoring". In general, the G in HLG - is a 3-gram LM, while this G is a 4-gram LM. - Returns: - Return the decoding result. See above description for the format of - the returned dict. Note: If it decodes to nothing, then return None. - """ - if HLG is not None: - device = HLG.device - else: - device = H.device - - audio = batch["audio"].to(device) - encoder_out, encoder_out_lens = model.forward_encoder(audio) - ctc_output = model.ctc_output(encoder_out) - - num_frames = encoder_out_lens.cpu() - - supervision_segments = torch.stack( - ( - torch.arange(audio.shape[0], dtype=torch.int32), - torch.zeros_like(num_frames, dtype=torch.int32), - num_frames, - ), - 1, - ).to(torch.int32) - - if H is None: - assert HLG is not None - decoding_graph = HLG - else: - assert HLG is None - assert bpe_model is not None - decoding_graph = H - - lattice = get_lattice( - nnet_output=ctc_output, - decoding_graph=decoding_graph, - supervision_segments=supervision_segments, - search_beam=params.search_beam, - output_beam=params.output_beam, - min_active_states=params.min_active_states, - max_active_states=params.max_active_states, - ) - - if params.decoding_method == "ctc-decoding": - best_path = one_best_decoding( - lattice=lattice, use_double_scores=params.use_double_scores - ) - # Note: `best_path.aux_labels` contains token IDs, not word IDs - # since we are using H, not HLG here. - # - # token_ids is a lit-of-list of IDs - token_ids = get_texts(best_path) - - # hyps is a list of str, e.g., ['xxx yyy zzz', ...] - hyps = bpe_model.decode(token_ids) - - # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ] - hyps = [s.split() for s in hyps] - key = "ctc-decoding" - return {key: hyps} - - if params.decoding_method == "nbest-oracle": - # Note: You can also pass rescored lattices to it. - # We choose the HLG decoded lattice for speed reasons - # as HLG decoding is faster and the oracle WER - # is only slightly worse than that of rescored lattices. - best_path = nbest_oracle( - lattice=lattice, - num_paths=params.num_paths, - ref_texts=supervisions["text"], - word_table=word_table, - nbest_scale=params.nbest_scale, - oov="", - ) - hyps = get_texts(best_path) - hyps = [[word_table[i] for i in ids] for ids in hyps] - key = f"oracle_{params.num_paths}_nbest_scale_{params.nbest_scale}" # noqa - return {key: hyps} - - if params.decoding_method in ["1best", "nbest"]: - if params.decoding_method == "1best": - best_path = one_best_decoding( - lattice=lattice, use_double_scores=params.use_double_scores - ) - key = "no_rescore" - else: - best_path = nbest_decoding( - lattice=lattice, - num_paths=params.num_paths, - use_double_scores=params.use_double_scores, - nbest_scale=params.nbest_scale, - ) - key = f"no_rescore-nbest-scale-{params.nbest_scale}-{params.num_paths}" # noqa - - hyps = get_texts(best_path) - hyps = [[word_table[i] for i in ids] for ids in hyps] - return {key: hyps} - - assert params.decoding_method in [ - "nbest-rescoring", - "whole-lattice-rescoring", - ] - - lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7] - lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3] - lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0] - - if params.decoding_method == "nbest-rescoring": - best_path_dict = rescore_with_n_best_list( - lattice=lattice, - G=G, - num_paths=params.num_paths, - lm_scale_list=lm_scale_list, - nbest_scale=params.nbest_scale, - ) - elif params.decoding_method == "whole-lattice-rescoring": - best_path_dict = rescore_with_whole_lattice( - lattice=lattice, - G_with_epsilon_loops=G, - lm_scale_list=lm_scale_list, - ) - else: - assert False, f"Unsupported decoding method: {params.decoding_method}" - - ans = dict() - if best_path_dict is not None: - for lm_scale_str, best_path in best_path_dict.items(): - hyps = get_texts(best_path) - hyps = [[word_table[i] for i in ids] for ids in hyps] - ans[lm_scale_str] = hyps - else: - ans = None - return ans - - -def decode_dataset( - dl: torch.utils.data.DataLoader, - params: AttributeDict, - model: nn.Module, - HLG: Optional[k2.Fsa], - H: Optional[k2.Fsa], - bpe_model: Optional[spm.SentencePieceProcessor], - word_table: k2.SymbolTable, - G: 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. - HLG: - The decoding graph. Used only when params.decoding_method is NOT ctc-decoding. - H: - The ctc topo. Used only when params.decoding_method is ctc-decoding. - bpe_model: - The BPE model. Used only when params.decoding_method is ctc-decoding. - word_table: - It is the word symbol table. - G: - An LM. It is not None when params.decoding_method is "nbest-rescoring" - or "whole-lattice-rescoring". In general, the G in HLG - is a 3-gram LM, while this G is a 4-gram LM. - Returns: - Return a dict, whose key may be "no-rescore" if no LM rescoring - is used, or it may be "lm_scale_0.7" if LM rescoring 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 = "?" - - results = defaultdict(list) - for batch_idx, batch in enumerate(dl): - texts = batch["supervisions"]["text"] - cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] - - hyps_dict = decode_one_batch( - params=params, - model=model, - HLG=HLG, - H=H, - bpe_model=bpe_model, - batch=batch, - word_table=word_table, - G=G, - ) - - for name, hyps in hyps_dict.items(): - this_batch = [] - assert len(hyps) == len(texts) - for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): - 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 % 100 == 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]]]], -): - test_set_wers = 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) - test_set_wers[key] = wer - - logging.info("Wrote detailed error 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) - - -@torch.no_grad() -def main(): - parser = get_parser() - LibriSpeechAsrDataModule.add_arguments(parser) - args = parser.parse_args() - args.exp_dir = Path(args.exp_dir) - args.lang_dir = Path(args.lang_dir) - args.lm_dir = Path(args.lm_dir) - - params = get_params() - # add decoding params - params.update(get_decoding_params()) - params.update(vars(args)) - - assert params.decoding_method in ( - "ctc-decoding", - "1best", - "nbest", - "nbest-rescoring", - "whole-lattice-rescoring", - "nbest-oracle", - ) - 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 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}") - logging.info(params) - - lexicon = Lexicon(params.lang_dir) - max_token_id = max(lexicon.tokens) - num_classes = max_token_id + 1 # +1 for the blank - - params.vocab_size = num_classes - # and are defined in local/train_bpe_model.py - params.blank_id = 0 - - if params.decoding_method == "ctc-decoding": - HLG = None - H = k2.ctc_topo( - max_token=max_token_id, - modified=False, - device=device, - ) - bpe_model = spm.SentencePieceProcessor() - bpe_model.load(str(params.lang_dir / "bpe.model")) - else: - H = None - bpe_model = None - HLG = k2.Fsa.from_dict( - torch.load(f"{params.lang_dir}/HLG.pt", map_location=device) - ) - assert HLG.requires_grad is False - - HLG.scores *= params.hlg_scale - if not hasattr(HLG, "lm_scores"): - HLG.lm_scores = HLG.scores.clone() - - if params.decoding_method in ( - "nbest-rescoring", - "whole-lattice-rescoring", - ): - if not (params.lm_dir / "G_4_gram.pt").is_file(): - logging.info("Loading G_4_gram.fst.txt") - logging.warning("It may take 8 minutes.") - with open(params.lm_dir / "G_4_gram.fst.txt") as f: - first_word_disambig_id = lexicon.word_table["#0"] - - G = k2.Fsa.from_openfst(f.read(), acceptor=False) - # G.aux_labels is not needed in later computations, so - # remove it here. - del G.aux_labels - # CAUTION: The following line is crucial. - # Arcs entering the back-off state have label equal to #0. - # We have to change it to 0 here. - G.labels[G.labels >= first_word_disambig_id] = 0 - # See https://github.com/k2-fsa/k2/issues/874 - # for why we need to set G.properties to None - G.__dict__["_properties"] = None - G = k2.Fsa.from_fsas([G]).to(device) - G = k2.arc_sort(G) - # Save a dummy value so that it can be loaded in C++. - # See https://github.com/pytorch/pytorch/issues/67902 - # for why we need to do this. - G.dummy = 1 - - torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt") - else: - logging.info("Loading pre-compiled G_4_gram.pt") - d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device) - G = k2.Fsa.from_dict(d) - - if params.decoding_method == "whole-lattice-rescoring": - # Add epsilon self-loops to G as we will compose - # it with the whole lattice later - G = k2.add_epsilon_self_loops(G) - G = k2.arc_sort(G) - G = G.to(device) - - # G.lm_scores is used to replace HLG.lm_scores during - # LM rescoring. - G.lm_scores = G.scores.clone() - else: - G = None - - logging.info("About to create model") - model = get_model(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, - ) - ) - - model.to(device) - model.eval() - - 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 - librispeech = LibriSpeechAsrDataModule(args) - - dev_clean_cuts = librispeech.dev_clean_cuts() - dev_other_cuts = librispeech.dev_other_cuts() - - dev_clean_dl = librispeech.test_dataloaders( - dev_clean_cuts, - do_normalize=params.do_normalize, - ) - dev_other_dl = librispeech.test_dataloaders( - dev_other_cuts, - do_normalize=params.do_normalize, - ) - - test_sets = ["dev-clean", "dev-other"] - test_dl = [dev_clean_dl, dev_other_dl] - - # test_clean_cuts = librispeech.test_clean_cuts() - # test_other_cuts = librispeech.test_other_cuts() - - # test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) - # test_other_dl = librispeech.test_dataloaders(test_other_cuts) - - # 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): - results_dict = decode_dataset( - dl=test_dl, - params=params, - model=model, - HLG=HLG, - H=H, - bpe_model=bpe_model, - word_table=lexicon.word_table, - G=G, - ) - - save_results( - params=params, - test_set_name=test_set, - results_dict=results_dict, - ) - - logging.info("Done!") - - -if __name__ == "__main__": - main()