From a8ace5bb6196e15fb085a39c32d8f4411aa3c9dc Mon Sep 17 00:00:00 2001 From: jinzr <60612200+JinZr@users.noreply.github.com> Date: Thu, 13 Jul 2023 15:32:43 +0800 Subject: [PATCH] updated all `conformer_ctc*` recipes to use `tokens.txt` in `export.py` and `pretrained.py` --- egs/librispeech/ASR/conformer_ctc/export.py | 1 + .../ASR/conformer_ctc/pretrained.py | 25 ++++++----- egs/librispeech/ASR/conformer_ctc2/export.py | 19 +++++---- egs/librispeech/ASR/conformer_ctc3/export.py | 23 +++++----- .../ASR/conformer_ctc3/jit_pretrained.py | 42 ++++++++++--------- .../ASR/conformer_ctc3/pretrained.py | 42 ++++++++++--------- 6 files changed, 81 insertions(+), 71 deletions(-) diff --git a/egs/librispeech/ASR/conformer_ctc/export.py b/egs/librispeech/ASR/conformer_ctc/export.py index 42a756142..f0bb97560 100755 --- a/egs/librispeech/ASR/conformer_ctc/export.py +++ b/egs/librispeech/ASR/conformer_ctc/export.py @@ -66,6 +66,7 @@ def get_parser(): parser.add_argument( "--tokens", type=str, + required=True, help="Path to the tokens.txt.", ) diff --git a/egs/librispeech/ASR/conformer_ctc/pretrained.py b/egs/librispeech/ASR/conformer_ctc/pretrained.py index 6243ba52b..df3e4d819 100755 --- a/egs/librispeech/ASR/conformer_ctc/pretrained.py +++ b/egs/librispeech/ASR/conformer_ctc/pretrained.py @@ -80,10 +80,9 @@ def get_parser(): default="1best", help="""Decoding method. Possible values are: - (0) 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. + (0) ctc-decoding - Use CTC decoding. It uses a tokens.txt file + to convert tokens to actual words or characters. It needs + neither a lexicon nor an n-gram LM. (1) 1best - Use the best path as decoding output. Only the transformer encoder output is used for decoding. We call it HLG decoding. @@ -254,9 +253,6 @@ def main(): params.update(vars(args)) logging.info(f"{params}") - # Load tokens.txt here - token_table = k2.SymbolTable.from_file(params.tokens) - device = torch.device("cpu") if torch.cuda.is_available(): device = torch.device("cuda", 0) @@ -312,16 +308,19 @@ def main(): dtype=torch.int32, ) - def token_ids_to_words(token_ids: List[int]) -> str: - text = "" - for i in token_ids: - text += token_table[i] - return text.replace("▁", " ").strip() - if params.method == "ctc-decoding": logging.info("Use CTC decoding") max_token_id = params.num_classes - 1 + # Load tokens.txt here + token_table = k2.SymbolTable.from_file(params.tokens) + + def token_ids_to_words(token_ids: List[int]) -> str: + text = "" + for i in token_ids: + text += token_table[i] + return text.replace("▁", " ").strip() + H = k2.ctc_topo( max_token=max_token_id, modified=params.num_classes > 500, diff --git a/egs/librispeech/ASR/conformer_ctc2/export.py b/egs/librispeech/ASR/conformer_ctc2/export.py index 7892b03c6..26a95dbfa 100755 --- a/egs/librispeech/ASR/conformer_ctc2/export.py +++ b/egs/librispeech/ASR/conformer_ctc2/export.py @@ -23,6 +23,7 @@ Usage: ./conformer_ctc2/export.py \ --exp-dir ./conformer_ctc2/exp \ + --tokens ./data/lang_bpe_500/tokens.txt \ --epoch 20 \ --avg 10 @@ -46,6 +47,7 @@ import argparse import logging from pathlib import Path +import k2 import torch from conformer import Conformer from decode import get_params @@ -56,8 +58,7 @@ from icefall.checkpoint import ( find_checkpoints, load_checkpoint, ) -from icefall.lexicon import Lexicon -from icefall.utils import str2bool +from icefall.utils import num_tokens, str2bool def get_parser(): @@ -123,10 +124,10 @@ def get_parser(): ) parser.add_argument( - "--lang-dir", + "--tokens", type=str, - default="data/lang_bpe_500", - help="The lang dir", + required=True, + help="Path to the tokens.txt.", ) parser.add_argument( @@ -143,14 +144,14 @@ def get_parser(): def main(): args = get_parser().parse_args() args.exp_dir = Path(args.exp_dir) - args.lang_dir = Path(args.lang_dir) params = get_params() params.update(vars(args)) - lexicon = Lexicon(params.lang_dir) - max_token_id = max(lexicon.tokens) - num_classes = max_token_id + 1 # +1 for the blank + # Load tokens.txt here + token_table = k2.SymbolTable.from_file(params.tokens) + + num_classes = num_tokens(token_table) + 1 # +1 for the blank device = torch.device("cpu") if torch.cuda.is_available(): diff --git a/egs/librispeech/ASR/conformer_ctc3/export.py b/egs/librispeech/ASR/conformer_ctc3/export.py index c5b95d981..5cb9b4b6d 100755 --- a/egs/librispeech/ASR/conformer_ctc3/export.py +++ b/egs/librispeech/ASR/conformer_ctc3/export.py @@ -25,7 +25,7 @@ Usage: ./conformer_ctc3/export.py \ --exp-dir ./conformer_ctc3/exp \ - --lang-dir data/lang_bpe_500 \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 20 \ --avg 10 \ --jit-trace 1 @@ -36,7 +36,7 @@ It will generates the file: `jit_trace.pt`. ./conformer_ctc3/export.py \ --exp-dir ./conformer_ctc3/exp \ - --lang-dir data/lang_bpe_500 \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 20 \ --avg 10 @@ -62,6 +62,7 @@ import argparse import logging from pathlib import Path +import k2 import torch from scaling_converter import convert_scaled_to_non_scaled from train import add_model_arguments, get_ctc_model, get_params @@ -72,8 +73,7 @@ from icefall.checkpoint import ( find_checkpoints, load_checkpoint, ) -from icefall.lexicon import Lexicon -from icefall.utils import str2bool +from icefall.utils import num_tokens, str2bool def get_parser(): @@ -130,10 +130,10 @@ def get_parser(): ) parser.add_argument( - "--lang-dir", - type=Path, - default="data/lang_bpe_500", - help="The lang dir containing word table and LG graph", + "--tokens", + type=str, + required=True, + help="Path to the tokens.txt.", ) parser.add_argument( @@ -171,9 +171,10 @@ def main(): logging.info(f"device: {device}") - lexicon = Lexicon(params.lang_dir) - max_token_id = max(lexicon.tokens) - num_classes = max_token_id + 1 # +1 for the blank + # Load tokens.txt here + token_table = k2.SymbolTable.from_file(params.tokens) + + num_classes = num_tokens(token_table) + 1 # +1 for the blank params.vocab_size = num_classes if params.streaming_model: diff --git a/egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py b/egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py index 76db46cc8..dce62552a 100755 --- a/egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py +++ b/egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py @@ -24,7 +24,7 @@ Usage (for non-streaming mode): (1) ctc-decoding ./conformer_ctc3/pretrained.py \ --nn-model-filename ./conformer_ctc3/exp/cpu_jit.pt \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --method ctc-decoding \ --sample-rate 16000 \ /path/to/foo.wav \ @@ -71,7 +71,6 @@ from typing import List import k2 import kaldifeat -import sentencepiece as spm import torch import torchaudio from decode import get_decoding_params @@ -116,11 +115,9 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - help="""Path to bpe.model. - Used only when method is ctc-decoding. - """, + help="Path to the tokens.txt.", ) parser.add_argument( @@ -129,10 +126,9 @@ def get_parser(): default="1best", help="""Decoding method. Possible values are: - (0) 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. + (0) ctc-decoding - Use CTC decoding. It uses a tokens.txt file + to convert tokens to actual words or characters. It needs + neither a lexicon nor an n-gram LM. (1) 1best - Use the best path as decoding output. Only the transformer encoder output is used for decoding. We call it HLG decoding. @@ -281,6 +277,7 @@ def main(): waves = [w.to(device) for w in waves] logging.info("Decoding started") + hyps = [] features = fbank(waves) feature_lengths = [f.size(0) for f in features] @@ -300,10 +297,17 @@ def main(): if params.method == "ctc-decoding": logging.info("Use CTC decoding") - bpe_model = spm.SentencePieceProcessor() - bpe_model.load(params.bpe_model) max_token_id = params.num_classes - 1 + # Load tokens.txt here + token_table = k2.SymbolTable.from_file(params.tokens) + + def token_ids_to_words(token_ids: List[int]) -> str: + text = "" + for i in token_ids: + text += token_table[i] + return text.replace("▁", " ").strip() + H = k2.ctc_topo( max_token=max_token_id, modified=False, @@ -324,9 +328,9 @@ def main(): best_path = one_best_decoding( lattice=lattice, use_double_scores=params.use_double_scores ) - token_ids = get_texts(best_path) - hyps = bpe_model.decode(token_ids) - hyps = [s.split() for s in hyps] + hyp_tokens = get_texts(best_path) + for hyp in hyp_tokens: + hyps.append(token_ids_to_words(hyp)) elif params.method in [ "1best", "nbest-rescoring", @@ -391,16 +395,16 @@ def main(): ) best_path = next(iter(best_path_dict.values())) - hyps = get_texts(best_path) word_sym_table = k2.SymbolTable.from_file(params.words_file) - hyps = [[word_sym_table[i] for i in ids] for ids in hyps] + hyp_tokens = get_texts(best_path) + for hyp in hyp_tokens: + hyps.append(" ".join([word_sym_table[i] for i in hyp])) else: raise ValueError(f"Unsupported decoding method: {params.method}") s = "\n" for filename, hyp in zip(params.sound_files, hyps): - words = " ".join(hyp) - s += f"{filename}:\n{words}\n\n" + s += f"{filename}:\n{hyp}\n\n" logging.info(s) logging.info("Decoding Done") diff --git a/egs/librispeech/ASR/conformer_ctc3/pretrained.py b/egs/librispeech/ASR/conformer_ctc3/pretrained.py index 880945ea0..c37b99cce 100755 --- a/egs/librispeech/ASR/conformer_ctc3/pretrained.py +++ b/egs/librispeech/ASR/conformer_ctc3/pretrained.py @@ -24,7 +24,7 @@ Usage (for non-streaming mode): (1) ctc-decoding ./conformer_ctc3/pretrained.py \ --checkpoint conformer_ctc3/exp/pretrained.pt \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --method ctc-decoding \ --sample-rate 16000 \ test_wavs/1089-134686-0001.wav @@ -67,7 +67,6 @@ from typing import List import k2 import kaldifeat -import sentencepiece as spm import torch import torchaudio from decode import get_decoding_params @@ -114,11 +113,9 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - help="""Path to bpe.model. - Used only when method is ctc-decoding. - """, + help="Path to the tokens.txt.", ) parser.add_argument( @@ -127,10 +124,9 @@ def get_parser(): default="1best", help="""Decoding method. Possible values are: - (0) 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. + (0) ctc-decoding - Use CTC decoding. It uses a tokens.txt file + to convert tokens to actual words or characters. It needs + neither a lexicon nor an n-gram LM. (1) 1best - Use the best path as decoding output. Only the transformer encoder output is used for decoding. We call it HLG decoding. @@ -316,6 +312,7 @@ def main(): waves = [w.to(device) for w in waves] logging.info("Decoding started") + hyps = [] features = fbank(waves) feature_lengths = [f.size(0) for f in features] @@ -348,10 +345,17 @@ def main(): if params.method == "ctc-decoding": logging.info("Use CTC decoding") - bpe_model = spm.SentencePieceProcessor() - bpe_model.load(params.bpe_model) max_token_id = params.num_classes - 1 + # Load tokens.txt here + token_table = k2.SymbolTable.from_file(params.tokens) + + def token_ids_to_words(token_ids: List[int]) -> str: + text = "" + for i in token_ids: + text += token_table[i] + return text.replace("▁", " ").strip() + H = k2.ctc_topo( max_token=max_token_id, modified=False, @@ -372,9 +376,9 @@ def main(): best_path = one_best_decoding( lattice=lattice, use_double_scores=params.use_double_scores ) - token_ids = get_texts(best_path) - hyps = bpe_model.decode(token_ids) - hyps = [s.split() for s in hyps] + hyp_tokens = get_texts(best_path) + for hyp in hyp_tokens: + hyps.append(token_ids_to_words(hyp)) elif params.method in [ "1best", "nbest-rescoring", @@ -439,16 +443,16 @@ def main(): ) best_path = next(iter(best_path_dict.values())) - hyps = get_texts(best_path) word_sym_table = k2.SymbolTable.from_file(params.words_file) - hyps = [[word_sym_table[i] for i in ids] for ids in hyps] + hyp_tokens = get_texts(best_path) + for hyp in hyp_tokens: + hyps.append(" ".join([word_sym_table[i] for i in hyp])) else: raise ValueError(f"Unsupported decoding method: {params.method}") s = "\n" for filename, hyp in zip(params.sound_files, hyps): - words = " ".join(hyp) - s += f"{filename}:\n{words}\n\n" + s += f"{filename}:\n{hyp}\n\n" logging.info(s) logging.info("Decoding Done")