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updated all conformer_ctc* recipes to use tokens.txt in export.py and pretrained.py

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This commit is contained in:
jinzr 2023-07-13 15:32:43 +08:00
parent 2459ea0c3e
commit a8ace5bb61
6 changed files with 81 additions and 71 deletions
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1
egs/librispeech/ASR/conformer_ctc/export.py
View File

@ -66,6 +66,7 @@ def get_parser():
parser.add_argument(
"--tokens",
type=str,
required=True,
help="Path to the tokens.txt.",
)

25
egs/librispeech/ASR/conformer_ctc/pretrained.py
View File

@ -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,

19
egs/librispeech/ASR/conformer_ctc2/export.py
View File

@ -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():

23
egs/librispeech/ASR/conformer_ctc3/export.py
View File

@ -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:

42
egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py
View File

@ -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")

42
egs/librispeech/ASR/conformer_ctc3/pretrained.py
View File

@ -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")