Hui Zhang
2 years ago
parent
8c7859d3bc
commit
bac9e0b153
@ -0,0 +1,17 @@
|
|||||||
|
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
# s2t utils binaries.
|
||||||
|
from .build_vocab import main as build_vocab_main
|
||||||
|
from .compute_mean_std import main as compute_mean_std_main
|
||||||
|
from .format_data import main as format_data_main
|
||||||
@ -0,0 +1,166 @@
|
|||||||
|
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
"""Build vocabulary from manifest files.
|
||||||
|
Each item in vocabulary file is a character.
|
||||||
|
"""
|
||||||
|
import argparse
|
||||||
|
import functools
|
||||||
|
import os
|
||||||
|
import tempfile
|
||||||
|
from collections import Counter
|
||||||
|
|
||||||
|
import jsonlines
|
||||||
|
|
||||||
|
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
|
||||||
|
from paddlespeech.s2t.frontend.utility import BLANK
|
||||||
|
from paddlespeech.s2t.frontend.utility import SOS
|
||||||
|
from paddlespeech.s2t.frontend.utility import SPACE
|
||||||
|
from paddlespeech.s2t.frontend.utility import UNK
|
||||||
|
from paddlespeech.utils.argparse import add_arguments
|
||||||
|
from paddlespeech.utils.argparse import print_arguments
|
||||||
|
|
||||||
|
|
||||||
|
def count_manifest(counter, text_feature, manifest_path):
|
||||||
|
manifest_jsons = []
|
||||||
|
with jsonlines.open(manifest_path, 'r') as reader:
|
||||||
|
for json_data in reader:
|
||||||
|
manifest_jsons.append(json_data)
|
||||||
|
|
||||||
|
for line_json in manifest_jsons:
|
||||||
|
if isinstance(line_json['text'], str):
|
||||||
|
tokens = text_feature.tokenize(
|
||||||
|
line_json['text'], replace_space=False)
|
||||||
|
|
||||||
|
counter.update(tokens)
|
||||||
|
else:
|
||||||
|
assert isinstance(line_json['text'], list)
|
||||||
|
for text in line_json['text']:
|
||||||
|
tokens = text_feature.tokenize(text, replace_space=False)
|
||||||
|
counter.update(tokens)
|
||||||
|
|
||||||
|
|
||||||
|
def dump_text_manifest(fileobj, manifest_path, key='text'):
|
||||||
|
manifest_jsons = []
|
||||||
|
with jsonlines.open(manifest_path, 'r') as reader:
|
||||||
|
for json_data in reader:
|
||||||
|
manifest_jsons.append(json_data)
|
||||||
|
|
||||||
|
for line_json in manifest_jsons:
|
||||||
|
if isinstance(line_json[key], str):
|
||||||
|
fileobj.write(line_json[key] + "\n")
|
||||||
|
else:
|
||||||
|
assert isinstance(line_json[key], list)
|
||||||
|
for line in line_json[key]:
|
||||||
|
fileobj.write(line + "\n")
|
||||||
|
|
||||||
|
|
||||||
|
def build_vocab(manifest_paths="",
|
||||||
|
vocab_path="examples/librispeech/data/vocab.txt",
|
||||||
|
unit_type="char",
|
||||||
|
count_threshold=0,
|
||||||
|
text_keys='text',
|
||||||
|
spm_mode="unigram",
|
||||||
|
spm_vocab_size=0,
|
||||||
|
spm_model_prefix="",
|
||||||
|
spm_character_coverage=0.9995):
|
||||||
|
fout = open(vocab_path, 'w', encoding='utf-8')
|
||||||
|
fout.write(BLANK + "\n") # 0 will be used for "blank" in CTC
|
||||||
|
fout.write(UNK + '\n') # <unk> must be 1
|
||||||
|
|
||||||
|
if unit_type == 'spm':
|
||||||
|
# tools/spm_train --input=$wave_data/lang_char/input.txt
|
||||||
|
# --vocab_size=${nbpe} --model_type=${bpemode}
|
||||||
|
# --model_prefix=${bpemodel} --input_sentence_size=100000000
|
||||||
|
import sentencepiece as spm
|
||||||
|
|
||||||
|
fp = tempfile.NamedTemporaryFile(mode='w', delete=False)
|
||||||
|
for manifest_path in manifest_paths:
|
||||||
|
_text_keys = [text_keys] if type(
|
||||||
|
text_keys) is not list else text_keys
|
||||||
|
for text_key in _text_keys:
|
||||||
|
dump_text_manifest(fp, manifest_path, key=text_key)
|
||||||
|
fp.close()
|
||||||
|
# train
|
||||||
|
spm.SentencePieceTrainer.Train(
|
||||||
|
input=fp.name,
|
||||||
|
vocab_size=spm_vocab_size,
|
||||||
|
model_type=spm_mode,
|
||||||
|
model_prefix=spm_model_prefix,
|
||||||
|
input_sentence_size=100000000,
|
||||||
|
character_coverage=spm_character_coverage)
|
||||||
|
os.unlink(fp.name)
|
||||||
|
|
||||||
|
# encode
|
||||||
|
text_feature = TextFeaturizer(unit_type, "", spm_model_prefix)
|
||||||
|
counter = Counter()
|
||||||
|
|
||||||
|
for manifest_path in manifest_paths:
|
||||||
|
count_manifest(counter, text_feature, manifest_path)
|
||||||
|
|
||||||
|
count_sorted = sorted(counter.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
tokens = []
|
||||||
|
for token, count in count_sorted:
|
||||||
|
if count < count_threshold:
|
||||||
|
break
|
||||||
|
# replace space by `<space>`
|
||||||
|
token = SPACE if token == ' ' else token
|
||||||
|
tokens.append(token)
|
||||||
|
|
||||||
|
tokens = sorted(tokens)
|
||||||
|
for token in tokens:
|
||||||
|
fout.write(token + '\n')
|
||||||
|
|
||||||
|
fout.write(SOS + "\n") # <sos/eos>
|
||||||
|
fout.close()
|
||||||
|
|
||||||
|
|
||||||
|
def define_argparse():
|
||||||
|
parser = argparse.ArgumentParser(description=__doc__)
|
||||||
|
add_arg = functools.partial(add_arguments, argparser=parser)
|
||||||
|
|
||||||
|
# yapf: disable
|
||||||
|
add_arg('unit_type', str, "char", "Unit type, e.g. char, word, spm")
|
||||||
|
add_arg('count_threshold', int, 0,
|
||||||
|
"Truncation threshold for char/word counts.Default 0, no truncate.")
|
||||||
|
add_arg('vocab_path', str,
|
||||||
|
'examples/librispeech/data/vocab.txt',
|
||||||
|
"Filepath to write the vocabulary.")
|
||||||
|
add_arg('manifest_paths', str,
|
||||||
|
None,
|
||||||
|
"Filepaths of manifests for building vocabulary. "
|
||||||
|
"You can provide multiple manifest files.",
|
||||||
|
nargs='+',
|
||||||
|
required=True)
|
||||||
|
add_arg('text_keys', str,
|
||||||
|
'text',
|
||||||
|
"keys of the text in manifest for building vocabulary. "
|
||||||
|
"You can provide multiple k.",
|
||||||
|
nargs='+')
|
||||||
|
# bpe
|
||||||
|
add_arg('spm_vocab_size', int, 0, "Vocab size for spm.")
|
||||||
|
add_arg('spm_mode', str, 'unigram', "spm model type, e.g. unigram, spm, char, word. only need when `unit_type` is spm")
|
||||||
|
add_arg('spm_model_prefix', str, "", "spm_model_%(spm_mode)_%(count_threshold), spm model prefix, only need when `unit_type` is spm")
|
||||||
|
add_arg('spm_character_coverage', float, 0.9995, "character coverage to determine the minimum symbols")
|
||||||
|
# yapf: disable
|
||||||
|
|
||||||
|
args = parser.parse_args()
|
||||||
|
return args
|
||||||
|
|
||||||
|
def main():
|
||||||
|
args = define_argparse()
|
||||||
|
print_arguments(args, globals())
|
||||||
|
build_vocab(**vars(args))
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
@ -0,0 +1,106 @@
|
|||||||
|
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
"""Compute mean and std for feature normalizer, and save to file."""
|
||||||
|
import argparse
|
||||||
|
import functools
|
||||||
|
|
||||||
|
from paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline
|
||||||
|
from paddlespeech.s2t.frontend.featurizer.audio_featurizer import AudioFeaturizer
|
||||||
|
from paddlespeech.s2t.frontend.normalizer import FeatureNormalizer
|
||||||
|
from paddlespeech.utils.argparse import add_arguments
|
||||||
|
from paddlespeech.utils.argparse import print_arguments
|
||||||
|
|
||||||
|
|
||||||
|
def compute_cmvn(manifest_path="data/librispeech/manifest.train",
|
||||||
|
output_path="data/librispeech/mean_std.npz",
|
||||||
|
num_samples=2000,
|
||||||
|
num_workers=0,
|
||||||
|
spectrum_type="linear",
|
||||||
|
feat_dim=13,
|
||||||
|
delta_delta=False,
|
||||||
|
stride_ms=10,
|
||||||
|
window_ms=20,
|
||||||
|
sample_rate=16000,
|
||||||
|
use_dB_normalization=True,
|
||||||
|
target_dB=-20):
|
||||||
|
|
||||||
|
augmentation_pipeline = AugmentationPipeline('{}')
|
||||||
|
audio_featurizer = AudioFeaturizer(
|
||||||
|
spectrum_type=spectrum_type,
|
||||||
|
feat_dim=feat_dim,
|
||||||
|
delta_delta=delta_delta,
|
||||||
|
stride_ms=float(stride_ms),
|
||||||
|
window_ms=float(window_ms),
|
||||||
|
n_fft=None,
|
||||||
|
max_freq=None,
|
||||||
|
target_sample_rate=sample_rate,
|
||||||
|
use_dB_normalization=use_dB_normalization,
|
||||||
|
target_dB=target_dB,
|
||||||
|
dither=0.0)
|
||||||
|
|
||||||
|
def augment_and_featurize(audio_segment):
|
||||||
|
augmentation_pipeline.transform_audio(audio_segment)
|
||||||
|
return audio_featurizer.featurize(audio_segment)
|
||||||
|
|
||||||
|
normalizer = FeatureNormalizer(
|
||||||
|
mean_std_filepath=None,
|
||||||
|
manifest_path=manifest_path,
|
||||||
|
featurize_func=augment_and_featurize,
|
||||||
|
num_samples=num_samples,
|
||||||
|
num_workers=num_workers)
|
||||||
|
normalizer.write_to_file(output_path)
|
||||||
|
|
||||||
|
|
||||||
|
def define_argparse():
|
||||||
|
parser = argparse.ArgumentParser(description=__doc__)
|
||||||
|
add_arg = functools.partial(add_arguments, argparser=parser)
|
||||||
|
|
||||||
|
# yapf: disable
|
||||||
|
add_arg('manifest_path', str,
|
||||||
|
'data/librispeech/manifest.train',
|
||||||
|
"Filepath of manifest to compute normalizer's mean and stddev.")
|
||||||
|
|
||||||
|
add_arg('output_path', str,
|
||||||
|
'data/librispeech/mean_std.npz',
|
||||||
|
"Filepath of write mean and stddev to (.npz).")
|
||||||
|
add_arg('num_samples', int, 2000, "# of samples to for statistics.")
|
||||||
|
add_arg('num_workers',
|
||||||
|
default=0,
|
||||||
|
type=int,
|
||||||
|
help='num of subprocess workers for processing')
|
||||||
|
|
||||||
|
|
||||||
|
add_arg('spectrum_type', str,
|
||||||
|
'linear',
|
||||||
|
"Audio feature type. Options: linear, mfcc, fbank.",
|
||||||
|
choices=['linear', 'mfcc', 'fbank'])
|
||||||
|
add_arg('feat_dim', int, 13, "Audio feature dim.")
|
||||||
|
add_arg('delta_delta', bool, False, "Audio feature with delta delta.")
|
||||||
|
add_arg('stride_ms', int, 10, "stride length in ms.")
|
||||||
|
add_arg('window_ms', int, 20, "stride length in ms.")
|
||||||
|
add_arg('sample_rate', int, 16000, "target sample rate.")
|
||||||
|
add_arg('use_dB_normalization', bool, True, "do dB normalization.")
|
||||||
|
add_arg('target_dB', int, -20, "target dB.")
|
||||||
|
# yapf: disable
|
||||||
|
|
||||||
|
args = parser.parse_args()
|
||||||
|
return args
|
||||||
|
|
||||||
|
def main():
|
||||||
|
args = define_argparse()
|
||||||
|
print_arguments(args, globals())
|
||||||
|
compute_cmvn(**vars(args))
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
@ -0,0 +1,155 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
"""format manifest with more metadata."""
|
||||||
|
import argparse
|
||||||
|
import functools
|
||||||
|
import json
|
||||||
|
|
||||||
|
import jsonlines
|
||||||
|
|
||||||
|
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
|
||||||
|
from paddlespeech.s2t.frontend.utility import load_cmvn
|
||||||
|
from paddlespeech.s2t.io.utility import feat_type
|
||||||
|
from paddlespeech.utils.argparse import add_arguments
|
||||||
|
from paddlespeech.utils.argparse import print_arguments
|
||||||
|
|
||||||
|
|
||||||
|
def define_argparse():
|
||||||
|
parser = argparse.ArgumentParser(description=__doc__)
|
||||||
|
add_arg = functools.partial(add_arguments, argparser=parser)
|
||||||
|
# yapf: disable
|
||||||
|
add_arg('manifest_paths', str,
|
||||||
|
None,
|
||||||
|
"Filepaths of manifests for building vocabulary. "
|
||||||
|
"You can provide multiple manifest files.",
|
||||||
|
nargs='+',
|
||||||
|
required=True)
|
||||||
|
add_arg('output_path', str, None, "filepath of formated manifest.", required=True)
|
||||||
|
add_arg('cmvn_path', str,
|
||||||
|
'examples/librispeech/data/mean_std.json',
|
||||||
|
"Filepath of cmvn.")
|
||||||
|
add_arg('unit_type', str, "char", "Unit type, e.g. char, word, spm")
|
||||||
|
add_arg('vocab_path', str,
|
||||||
|
'examples/librispeech/data/vocab.txt',
|
||||||
|
"Filepath of the vocabulary.")
|
||||||
|
# bpe
|
||||||
|
add_arg('spm_model_prefix', str, None,
|
||||||
|
"spm model prefix, spm_model_%(bpe_mode)_%(count_threshold), only need when `unit_type` is spm")
|
||||||
|
|
||||||
|
# yapf: disable
|
||||||
|
args = parser.parse_args()
|
||||||
|
return args
|
||||||
|
|
||||||
|
def format_data(
|
||||||
|
manifest_paths="",
|
||||||
|
output_path="",
|
||||||
|
cmvn_path="examples/librispeech/data/mean_std.json",
|
||||||
|
unit_type="char",
|
||||||
|
vocab_path="examples/librispeech/data/vocab.txt",
|
||||||
|
spm_model_prefix=""):
|
||||||
|
|
||||||
|
fout = open(output_path, 'w', encoding='utf-8')
|
||||||
|
|
||||||
|
# get feat dim
|
||||||
|
filetype = cmvn_path.split(".")[-1]
|
||||||
|
mean, istd = load_cmvn(cmvn_path, filetype=filetype)
|
||||||
|
feat_dim = mean.shape[0] #(D)
|
||||||
|
print(f"Feature dim: {feat_dim}")
|
||||||
|
|
||||||
|
text_feature = TextFeaturizer(unit_type, vocab_path, spm_model_prefix)
|
||||||
|
vocab_size = text_feature.vocab_size
|
||||||
|
print(f"Vocab size: {vocab_size}")
|
||||||
|
|
||||||
|
# josnline like this
|
||||||
|
# {
|
||||||
|
# "input": [{"name": "input1", "shape": (100, 83), "feat": "xxx.ark:123"}],
|
||||||
|
# "output": [{"name":"target1", "shape": (40, 5002), "text": "a b c de"}],
|
||||||
|
# "utt2spk": "111-2222",
|
||||||
|
# "utt": "111-2222-333"
|
||||||
|
# }
|
||||||
|
count = 0
|
||||||
|
for manifest_path in manifest_paths:
|
||||||
|
with jsonlines.open(str(manifest_path), 'r') as reader:
|
||||||
|
manifest_jsons = list(reader)
|
||||||
|
|
||||||
|
for line_json in manifest_jsons:
|
||||||
|
output_json = {
|
||||||
|
"input": [],
|
||||||
|
"output": [],
|
||||||
|
'utt': line_json['utt'],
|
||||||
|
'utt2spk': line_json.get('utt2spk', 'global'),
|
||||||
|
}
|
||||||
|
|
||||||
|
# output
|
||||||
|
line = line_json['text']
|
||||||
|
if isinstance(line, str):
|
||||||
|
# only one target
|
||||||
|
tokens = text_feature.tokenize(line)
|
||||||
|
tokenids = text_feature.featurize(line)
|
||||||
|
output_json['output'].append({
|
||||||
|
'name': 'target1',
|
||||||
|
'shape': (len(tokenids), vocab_size),
|
||||||
|
'text': line,
|
||||||
|
'token': ' '.join(tokens),
|
||||||
|
'tokenid': ' '.join(map(str, tokenids)),
|
||||||
|
})
|
||||||
|
else:
|
||||||
|
# isinstance(line, list), multi target in one vocab
|
||||||
|
for i, item in enumerate(line, 1):
|
||||||
|
tokens = text_feature.tokenize(item)
|
||||||
|
tokenids = text_feature.featurize(item)
|
||||||
|
output_json['output'].append({
|
||||||
|
'name': f'target{i}',
|
||||||
|
'shape': (len(tokenids), vocab_size),
|
||||||
|
'text': item,
|
||||||
|
'token': ' '.join(tokens),
|
||||||
|
'tokenid': ' '.join(map(str, tokenids)),
|
||||||
|
})
|
||||||
|
|
||||||
|
# input
|
||||||
|
line = line_json['feat']
|
||||||
|
if isinstance(line, str):
|
||||||
|
# only one input
|
||||||
|
feat_shape = line_json['feat_shape']
|
||||||
|
assert isinstance(feat_shape, (list, tuple)), type(feat_shape)
|
||||||
|
filetype = feat_type(line)
|
||||||
|
if filetype == 'sound':
|
||||||
|
feat_shape.append(feat_dim)
|
||||||
|
else: # kaldi
|
||||||
|
raise NotImplementedError('no support kaldi feat now!')
|
||||||
|
|
||||||
|
output_json['input'].append({
|
||||||
|
"name": "input1",
|
||||||
|
"shape": feat_shape,
|
||||||
|
"feat": line,
|
||||||
|
"filetype": filetype,
|
||||||
|
})
|
||||||
|
else:
|
||||||
|
# isinstance(line, list), multi input
|
||||||
|
raise NotImplementedError("not support multi input now!")
|
||||||
|
|
||||||
|
fout.write(json.dumps(output_json) + '\n')
|
||||||
|
count += 1
|
||||||
|
|
||||||
|
print(f"{manifest_paths} Examples number: {count}")
|
||||||
|
fout.close()
|
||||||
|
|
||||||
|
def main():
|
||||||
|
args = define_argparse()
|
||||||
|
print_arguments(args, globals())
|
||||||
|
format_data(**vars(args))
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
Loading…
Reference in new issue