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D
DeepSpeech
提交 fd1116bd 编写于 作者: Y Yang Zhou
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fix conflict

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.gitignore
浏览文件 @ fd1116bd
......@@ -33,6 +33,12 @@ tools/Miniconda3-latest-Linux-x86_64.sh
tools/activate_python.sh
tools/miniconda.sh
tools/CRF++-0.58/
tools/liblbfgs-1.10/
tools/srilm/
tools/env.sh
tools/openfst-1.8.1/
tools/libsndfile/
tools/python-soundfile/
speechx/fc_patch/
......
examples/other/ngram_lm/s0/local/build_zh_lm.sh
浏览文件 @ fd1116bd
......@@ -27,7 +27,7 @@ arpa=$3
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ];then
# text tn & wordseg preprocess
echo "process text."
python3 ${MAIN_ROOT}/utils/zh_tn.py ${type} ${text} ${text}.${type}.tn
python3 ${MAIN_ROOT}/utils/zh_tn.py --token_type ${type} ${text} ${text}.${type}.tn
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
......
examples/other/ngram_lm/s0/local/download_lm_zh.sh
浏览文件 @ fd1116bd
......@@ -10,6 +10,11 @@ MD5="29e02312deb2e59b3c8686c7966d4fe3"
TARGET=${DIR}/zh_giga.no_cna_cmn.prune01244.klm
if [ -e $TARGET ];then
echo "already have lm"
exit 0;
fi
echo "Download language model ..."
download $URL $MD5 $TARGET
if [ $? -ne 0 ]; then
......
paddlespeech/cli/asr/infer.py
浏览文件 @ fd1116bd
......@@ -29,9 +29,10 @@ from ..download import get_path_from_url
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
......@@ -39,94 +40,14 @@ from paddlespeech.s2t.utils.utility import UpdateConfig
__all__ = ['ASRExecutor']
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"conformer_wenetspeech-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar.gz',
'md5':
'76cb19ed857e6623856b7cd7ebbfeda4',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/conformer/checkpoints/wenetspeech',
},
"transformer_librispeech-en-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz',
'md5':
'2c667da24922aad391eacafe37bc1660',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/transformer/checkpoints/avg_10',
},
"deepspeech2offline_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
'md5':
'932c3593d62fe5c741b59b31318aa314',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"deepspeech2online_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz',
'md5':
'23e16c69730a1cb5d735c98c83c21e16',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"deepspeech2offline_librispeech-en-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz',
'md5':
'f5666c81ad015c8de03aac2bc92e5762',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm',
'lm_md5':
'099a601759d467cd0a8523ff939819c5'
},
}
model_alias = {
"deepspeech2offline":
"paddlespeech.s2t.models.ds2:DeepSpeech2Model",
"deepspeech2online":
"paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline",
"conformer":
"paddlespeech.s2t.models.u2:U2Model",
"transformer":
"paddlespeech.s2t.models.u2:U2Model",
"wenetspeech":
"paddlespeech.s2t.models.u2:U2Model",
}
@cli_register(
name='paddlespeech.asr', description='Speech to text infer command.')
class ASRExecutor(BaseExecutor):
def __init__(self):
super(ASRExecutor, self).__init__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser(
prog='paddlespeech.asr', add_help=True)
......@@ -136,7 +57,9 @@ class ASRExecutor(BaseExecutor):
'--model',
type=str,
default='conformer_wenetspeech',
choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys()
],
help='Choose model type of asr task.')
self.parser.add_argument(
'--lang',
......@@ -192,23 +115,6 @@ class ASRExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(pretrained_models[tag],
res_path)
decompressed_path = os.path.abspath(decompressed_path)
logger.info(
'Use pretrained model stored in: {}'.format(decompressed_path))
return decompressed_path
def _init_from_path(self,
model_type: str='wenetspeech',
lang: str='zh',
......@@ -228,10 +134,11 @@ class ASRExecutor(BaseExecutor):
tag = model_type + '-' + lang + '-' + sample_rate_str
res_path = self._get_pretrained_path(tag) # wenetspeech_zh
self.res_path = res_path
self.cfg_path = os.path.join(res_path,
pretrained_models[tag]['cfg_path'])
self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path'])
self.ckpt_path = os.path.join(
res_path, pretrained_models[tag]['ckpt_path'] + ".pdparams")
res_path,
self.pretrained_models[tag]['ckpt_path'] + ".pdparams")
logger.info(res_path)
logger.info(self.cfg_path)
logger.info(self.ckpt_path)
......@@ -255,8 +162,8 @@ class ASRExecutor(BaseExecutor):
self.collate_fn_test = SpeechCollator.from_config(self.config)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = pretrained_models[tag]['lm_url']
lm_md5 = pretrained_models[tag]['lm_md5']
lm_url = self.pretrained_models[tag]['lm_url']
lm_md5 = self.pretrained_models[tag]['lm_md5']
self.download_lm(
lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
......@@ -274,7 +181,7 @@ class ASRExecutor(BaseExecutor):
raise Exception("wrong type")
model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset}
model_class = dynamic_import(model_name, model_alias)
model_class = dynamic_import(model_name, self.model_alias)
model_conf = self.config
model = model_class.from_config(model_conf)
self.model = model
......
paddlespeech/cli/asr/pretrained_models.py 0 → 100644
浏览文件 @ fd1116bd
# Copyright (c) 2022 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.
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"conformer_wenetspeech-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar.gz',
'md5':
'76cb19ed857e6623856b7cd7ebbfeda4',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/conformer/checkpoints/wenetspeech',
},
"transformer_librispeech-en-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz',
'md5':
'2c667da24922aad391eacafe37bc1660',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/transformer/checkpoints/avg_10',
},
"deepspeech2offline_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
'md5':
'932c3593d62fe5c741b59b31318aa314',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"deepspeech2online_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz',
'md5':
'23e16c69730a1cb5d735c98c83c21e16',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"deepspeech2offline_librispeech-en-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz',
'md5':
'f5666c81ad015c8de03aac2bc92e5762',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm',
'lm_md5':
'099a601759d467cd0a8523ff939819c5'
},
}
model_alias = {
"deepspeech2offline":
"paddlespeech.s2t.models.ds2:DeepSpeech2Model",
"deepspeech2online":
"paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline",
"conformer":
"paddlespeech.s2t.models.u2:U2Model",
"transformer":
"paddlespeech.s2t.models.u2:U2Model",
"wenetspeech":
"paddlespeech.s2t.models.u2:U2Model",
}
paddlespeech/cli/cls/infer.py
浏览文件 @ fd1116bd
......@@ -25,55 +25,23 @@ import yaml
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddleaudio import load
from paddleaudio.features import LogMelSpectrogram
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
__all__ = ['CLSExecutor']
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"panns_cnn6-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz',
'md5': '4cf09194a95df024fd12f84712cf0f9c',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn6.pdparams',
'label_file': 'audioset_labels.txt',
},
"panns_cnn10-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz',
'md5': 'cb8427b22176cc2116367d14847f5413',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn10.pdparams',
'label_file': 'audioset_labels.txt',
},
"panns_cnn14-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz',
'md5': 'e3b9b5614a1595001161d0ab95edee97',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn14.pdparams',
'label_file': 'audioset_labels.txt',
},
}
model_alias = {
"panns_cnn6": "paddlespeech.cls.models.panns:CNN6",
"panns_cnn10": "paddlespeech.cls.models.panns:CNN10",
"panns_cnn14": "paddlespeech.cls.models.panns:CNN14",
}
@cli_register(
name='paddlespeech.cls', description='Audio classification infer command.')
class CLSExecutor(BaseExecutor):
def __init__(self):
super(CLSExecutor, self).__init__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser(
prog='paddlespeech.cls', add_help=True)
......@@ -83,7 +51,9 @@ class CLSExecutor(BaseExecutor):
'--model',
type=str,
default='panns_cnn14',
choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys()
],
help='Choose model type of cls task.')
self.parser.add_argument(
'--config',
......@@ -121,23 +91,6 @@ class CLSExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(pretrained_models[tag],
res_path)
decompressed_path = os.path.abspath(decompressed_path)
logger.info(
'Use pretrained model stored in: {}'.format(decompressed_path))
return decompressed_path
def _init_from_path(self,
model_type: str='panns_cnn14',
cfg_path: Optional[os.PathLike]=None,
......@@ -153,12 +106,12 @@ class CLSExecutor(BaseExecutor):
if label_file is None or ckpt_path is None:
tag = model_type + '-' + '32k' # panns_cnn14-32k
self.res_path = self._get_pretrained_path(tag)
self.cfg_path = os.path.join(self.res_path,
pretrained_models[tag]['cfg_path'])
self.label_file = os.path.join(self.res_path,
pretrained_models[tag]['label_file'])
self.ckpt_path = os.path.join(self.res_path,
pretrained_models[tag]['ckpt_path'])
self.cfg_path = os.path.join(
self.res_path, self.pretrained_models[tag]['cfg_path'])
self.label_file = os.path.join(
self.res_path, self.pretrained_models[tag]['label_file'])
self.ckpt_path = os.path.join(
self.res_path, self.pretrained_models[tag]['ckpt_path'])
else:
self.cfg_path = os.path.abspath(cfg_path)
self.label_file = os.path.abspath(label_file)
......@@ -175,7 +128,7 @@ class CLSExecutor(BaseExecutor):
self._label_list.append(line.strip())
# model
model_class = dynamic_import(model_type, model_alias)
model_class = dynamic_import(model_type, self.model_alias)
model_dict = paddle.load(self.ckpt_path)
self.model = model_class(extract_embedding=False)
self.model.set_state_dict(model_dict)
......
paddlespeech/cli/cls/pretrained_models.py 0 → 100644
浏览文件 @ fd1116bd
# Copyright (c) 2022 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.
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"panns_cnn6-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz',
'md5': '4cf09194a95df024fd12f84712cf0f9c',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn6.pdparams',
'label_file': 'audioset_labels.txt',
},
"panns_cnn10-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz',
'md5': 'cb8427b22176cc2116367d14847f5413',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn10.pdparams',
'label_file': 'audioset_labels.txt',
},
"panns_cnn14-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz',
'md5': 'e3b9b5614a1595001161d0ab95edee97',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn14.pdparams',
'label_file': 'audioset_labels.txt',
},
}
model_alias = {
"panns_cnn6": "paddlespeech.cls.models.panns:CNN6",
"panns_cnn10": "paddlespeech.cls.models.panns:CNN10",
"panns_cnn14": "paddlespeech.cls.models.panns:CNN14",
}
paddlespeech/cli/executor.py
浏览文件 @ fd1116bd
......@@ -25,6 +25,8 @@ from typing import Union
import paddle
from .log import logger
from .utils import download_and_decompress
from .utils import MODEL_HOME
class BaseExecutor(ABC):
......@@ -35,19 +37,8 @@ class BaseExecutor(ABC):
def __init__(self):
self._inputs = OrderedDict()
self._outputs = OrderedDict()
@abstractmethod
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
Args:
tag (str): A tag of pretrained model.
Returns:
os.PathLike: The path on which resources of pretrained model locate.
"""
pass
self.pretrained_models = OrderedDict()
self.model_alias = OrderedDict()
@abstractmethod
def _init_from_path(self, *args, **kwargs):
......@@ -227,3 +218,20 @@ class BaseExecutor(ABC):
]
for l in loggers:
l.disabled = True
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(self.pretrained_models.keys())
assert tag in self.pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(self.pretrained_models[tag],
res_path)
decompressed_path = os.path.abspath(decompressed_path)
logger.info(
'Use pretrained model stored in: {}'.format(decompressed_path))
return decompressed_path
paddlespeech/cli/st/infer.py
浏览文件 @ fd1116bd
......@@ -32,40 +32,24 @@ from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import kaldi_bins
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.s2t.utils.utility import UpdateConfig
__all__ = ["STExecutor"]
pretrained_models = {
"fat_st_ted-en-zh": {
"url":
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz",
"md5":
"d62063f35a16d91210a71081bd2dd557",
"cfg_path":
"model.yaml",
"ckpt_path":
"exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams",
}
}
model_alias = {"fat_st": "paddlespeech.s2t.models.u2_st:U2STModel"}
kaldi_bins = {
"url":
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz",
"md5":
"c0682303b3f3393dbf6ed4c4e35a53eb",
}
@cli_register(
name="paddlespeech.st", description="Speech translation infer command.")
class STExecutor(BaseExecutor):
def __init__(self):
super(STExecutor, self).__init__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.kaldi_bins = kaldi_bins
self.parser = argparse.ArgumentParser(
prog="paddlespeech.st", add_help=True)
......@@ -75,7 +59,9 @@ class STExecutor(BaseExecutor):
"--model",
type=str,
default="fat_st_ted",
choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys()
],
help="Choose model type of st task.")
self.parser.add_argument(
"--src_lang",
......@@ -119,28 +105,11 @@ class STExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(pretrained_models[tag],
res_path)
decompressed_path = os.path.abspath(decompressed_path)
logger.info(
"Use pretrained model stored in: {}".format(decompressed_path))
return decompressed_path
def _set_kaldi_bins(self) -> os.PathLike:
"""
Download and returns kaldi_bins resources path of current task.
"""
decompressed_path = download_and_decompress(kaldi_bins, MODEL_HOME)
decompressed_path = download_and_decompress(self.kaldi_bins, MODEL_HOME)
decompressed_path = os.path.abspath(decompressed_path)
logger.info("Kaldi_bins stored in: {}".format(decompressed_path))
if "LD_LIBRARY_PATH" in os.environ:
......@@ -197,7 +166,7 @@ class STExecutor(BaseExecutor):
model_conf = self.config
model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset}
model_class = dynamic_import(model_name, model_alias)
model_class = dynamic_import(model_name, self.model_alias)
self.model = model_class.from_config(model_conf)
self.model.eval()
......
paddlespeech/cli/st/pretrained_models.py 0 → 100644
浏览文件 @ fd1116bd
# Copyright (c) 2022 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.
pretrained_models = {
"fat_st_ted-en-zh": {
"url":
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz",
"md5":
"d62063f35a16d91210a71081bd2dd557",
"cfg_path":
"model.yaml",
"ckpt_path":
"exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams",
}
}
model_alias = {"fat_st": "paddlespeech.s2t.models.u2_st:U2STModel"}
kaldi_bins = {
"url":
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz",
"md5":
"c0682303b3f3393dbf6ed4c4e35a53eb",
}
paddlespeech/cli/stats/infer.py
浏览文件 @ fd1116bd
......@@ -16,7 +16,6 @@ from typing import List
from prettytable import PrettyTable
from ..log import logger
from ..utils import cli_register
from ..utils import stats_wrapper
......@@ -27,7 +26,8 @@ model_name_format = {
'cls': 'Model-Sample Rate',
'st': 'Model-Source language-Target language',
'text': 'Model-Task-Language',
'tts': 'Model-Language'
'tts': 'Model-Language',
'vector': 'Model-Sample Rate'
}
......@@ -36,18 +36,18 @@ model_name_format = {
description='Get speech tasks support models list.')
class StatsExecutor():
def __init__(self):
super(StatsExecutor, self).__init__()
super().__init__()
self.parser = argparse.ArgumentParser(
prog='paddlespeech.stats', add_help=True)
self.task_choices = ['asr', 'cls', 'st', 'text', 'tts', 'vector']
self.parser.add_argument(
'--task',
type=str,
default='asr',
choices=['asr', 'cls', 'st', 'text', 'tts'],
choices=self.task_choices,
help='Choose speech task.',
required=True)
self.task_choices = ['asr', 'cls', 'st', 'text', 'tts']
def show_support_models(self, pretrained_models: dict):
fields = model_name_format[self.task].split("-")
......@@ -61,73 +61,15 @@ class StatsExecutor():
Command line entry.
"""
parser_args = self.parser.parse_args(argv)
self.task = parser_args.task
if self.task not in self.task_choices:
logger.error(
"Please input correct speech task, choices = ['asr', 'cls', 'st', 'text', 'tts']"
)
has_exceptions = False
try:
self(parser_args.task)
except Exception as e:
has_exceptions = True
if has_exceptions:
return False
elif self.task == 'asr':
try:
from ..asr.infer import pretrained_models
logger.info(
"Here is the list of ASR pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error("Failed to get the list of ASR pretrained models.")
return False
elif self.task == 'cls':
try:
from ..cls.infer import pretrained_models
logger.info(
"Here is the list of CLS pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error("Failed to get the list of CLS pretrained models.")
return False
elif self.task == 'st':
try:
from ..st.infer import pretrained_models
logger.info(
"Here is the list of ST pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error("Failed to get the list of ST pretrained models.")
return False
elif self.task == 'text':
try:
from ..text.infer import pretrained_models
logger.info(
"Here is the list of TEXT pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error(
"Failed to get the list of TEXT pretrained models.")
return False
elif self.task == 'tts':
try:
from ..tts.infer import pretrained_models
logger.info(
"Here is the list of TTS pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error("Failed to get the list of TTS pretrained models.")
return False
else:
return True
@stats_wrapper
def __call__(
......@@ -138,13 +80,12 @@ class StatsExecutor():
"""
self.task = task
if self.task not in self.task_choices:
print(
"Please input correct speech task, choices = ['asr', 'cls', 'st', 'text', 'tts']"
)
print("Please input correct speech task, choices = " + str(
self.task_choices))
elif self.task == 'asr':
try:
from ..asr.infer import pretrained_models
from ..asr.pretrained_models import pretrained_models
print(
"Here is the list of ASR pretrained models released by PaddleSpeech that can be used by command line and python API"
)
......@@ -154,7 +95,7 @@ class StatsExecutor():
elif self.task == 'cls':
try:
from ..cls.infer import pretrained_models
from ..cls.pretrained_models import pretrained_models
print(
"Here is the list of CLS pretrained models released by PaddleSpeech that can be used by command line and python API"
)
......@@ -164,7 +105,7 @@ class StatsExecutor():
elif self.task == 'st':
try:
from ..st.infer import pretrained_models
from ..st.pretrained_models import pretrained_models
print(
"Here is the list of ST pretrained models released by PaddleSpeech that can be used by command line and python API"
)
......@@ -174,7 +115,7 @@ class StatsExecutor():
elif self.task == 'text':
try:
from ..text.infer import pretrained_models
from ..text.pretrained_models import pretrained_models
print(
"Here is the list of TEXT pretrained models released by PaddleSpeech that can be used by command line and python API"
)
......@@ -184,10 +125,22 @@ class StatsExecutor():
elif self.task == 'tts':
try:
from ..tts.infer import pretrained_models
from ..tts.pretrained_models import pretrained_models
print(
"Here is the list of TTS pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
except BaseException:
print("Failed to get the list of TTS pretrained models.")
elif self.task == 'vector':
try:
from ..vector.pretrained_models import pretrained_models
print(
"Here is the list of Speaker Recognition pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
except BaseException:
print(
"Failed to get the list of Speaker Recognition pretrained models."
)
paddlespeech/cli/text/infer.py
浏览文件 @ fd1116bd
......@@ -25,58 +25,21 @@ from ...s2t.utils.dynamic_import import dynamic_import
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from .pretrained_models import tokenizer_alias
__all__ = ['TextExecutor']
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"ernie_linear_p7_wudao-punc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz',
'md5':
'12283e2ddde1797c5d1e57036b512746',
'cfg_path':
'ckpt/model_config.json',
'ckpt_path':
'ckpt/model_state.pdparams',
'vocab_file':
'punc_vocab.txt',
},
"ernie_linear_p3_wudao-punc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz',
'md5':
'448eb2fdf85b6a997e7e652e80c51dd2',
'cfg_path':
'ckpt/model_config.json',
'ckpt_path':
'ckpt/model_state.pdparams',
'vocab_file':
'punc_vocab.txt',
},
}
model_alias = {
"ernie_linear_p7": "paddlespeech.text.models:ErnieLinear",
"ernie_linear_p3": "paddlespeech.text.models:ErnieLinear",
}
tokenizer_alias = {
"ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer",
"ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer",
}
@cli_register(name='paddlespeech.text', description='Text infer command.')
class TextExecutor(BaseExecutor):
def __init__(self):
super(TextExecutor, self).__init__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.tokenizer_alias = tokenizer_alias
self.parser = argparse.ArgumentParser(
prog='paddlespeech.text', add_help=True)
......@@ -92,7 +55,9 @@ class TextExecutor(BaseExecutor):
'--model',
type=str,
default='ernie_linear_p7_wudao',
choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys()
],
help='Choose model type of text task.')
self.parser.add_argument(
'--lang',
......@@ -131,23 +96,6 @@ class TextExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(pretrained_models[tag],
res_path)
decompressed_path = os.path.abspath(decompressed_path)
logger.info(
'Use pretrained model stored in: {}'.format(decompressed_path))
return decompressed_path
def _init_from_path(self,
task: str='punc',
model_type: str='ernie_linear_p7_wudao',
......@@ -167,12 +115,12 @@ class TextExecutor(BaseExecutor):
if cfg_path is None or ckpt_path is None or vocab_file is None:
tag = '-'.join([model_type, task, lang])
self.res_path = self._get_pretrained_path(tag)
self.cfg_path = os.path.join(self.res_path,
pretrained_models[tag]['cfg_path'])
self.ckpt_path = os.path.join(self.res_path,
pretrained_models[tag]['ckpt_path'])
self.vocab_file = os.path.join(self.res_path,
pretrained_models[tag]['vocab_file'])
self.cfg_path = os.path.join(
self.res_path, self.pretrained_models[tag]['cfg_path'])
self.ckpt_path = os.path.join(
self.res_path, self.pretrained_models[tag]['ckpt_path'])
self.vocab_file = os.path.join(
self.res_path, self.pretrained_models[tag]['vocab_file'])
else:
self.cfg_path = os.path.abspath(cfg_path)
self.ckpt_path = os.path.abspath(ckpt_path)
......@@ -187,8 +135,8 @@ class TextExecutor(BaseExecutor):
self._punc_list.append(line.strip())
# model
model_class = dynamic_import(model_name, model_alias)
tokenizer_class = dynamic_import(model_name, tokenizer_alias)
model_class = dynamic_import(model_name, self.model_alias)
tokenizer_class = dynamic_import(model_name, self.tokenizer_alias)
self.model = model_class(
cfg_path=self.cfg_path, ckpt_path=self.ckpt_path)
self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0')
......
paddlespeech/cli/text/pretrained_models.py 0 → 100644
浏览文件 @ fd1116bd
# Copyright (c) 2022 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.
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"ernie_linear_p7_wudao-punc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz',
'md5':
'12283e2ddde1797c5d1e57036b512746',
'cfg_path':
'ckpt/model_config.json',
'ckpt_path':
'ckpt/model_state.pdparams',
'vocab_file':
'punc_vocab.txt',
},
"ernie_linear_p3_wudao-punc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz',
'md5':
'448eb2fdf85b6a997e7e652e80c51dd2',
'cfg_path':
'ckpt/model_config.json',
'ckpt_path':
'ckpt/model_state.pdparams',
'vocab_file':
'punc_vocab.txt',
},
}
model_alias = {
"ernie_linear_p7": "paddlespeech.text.models:ErnieLinear",
"ernie_linear_p3": "paddlespeech.text.models:ErnieLinear",
}
tokenizer_alias = {
"ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer",
"ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer",
}
paddlespeech/cli/tts/infer.py
浏览文件 @ fd1116bd
......@@ -29,9 +29,9 @@ from yacs.config import CfgNode
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.t2s.frontend import English
from paddlespeech.t2s.frontend.zh_frontend import Frontend
......@@ -39,299 +39,14 @@ from paddlespeech.t2s.modules.normalizer import ZScore
__all__ = ['TTSExecutor']
pretrained_models = {
# speedyspeech
"speedyspeech_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip',
'md5':
'6f6fa967b408454b6662c8c00c0027cb',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_30600.pdz',
'speech_stats':
'feats_stats.npy',
'phones_dict':
'phone_id_map.txt',
'tones_dict':
'tone_id_map.txt',
},
# fastspeech2
"fastspeech2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip',
'md5':
'637d28a5e53aa60275612ba4393d5f22',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_76000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip',
'md5':
'ffed800c93deaf16ca9b3af89bfcd747',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_100000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip',
'md5':
'f4dd4a5f49a4552b77981f544ab3392e',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_96400.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'speaker_dict':
'speaker_id_map.txt',
},
"fastspeech2_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip',
'md5':
'743e5024ca1e17a88c5c271db9779ba4',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_66200.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'speaker_dict':
'speaker_id_map.txt',
},
# tacotron2
"tacotron2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip',
'md5':
'0df4b6f0bcbe0d73c5ed6df8867ab91a',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_30600.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"tacotron2_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip',
'md5':
'6a5eddd81ae0e81d16959b97481135f3',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_60300.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
# pwgan
"pwgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip',
'md5':
'2e481633325b5bdf0a3823c714d2c117',
'config':
'pwg_default.yaml',
'ckpt':
'pwg_snapshot_iter_400000.pdz',
'speech_stats':
'pwg_stats.npy',
},
"pwgan_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip',
'md5':
'53610ba9708fd3008ccaf8e99dacbaf0',
'config':
'pwg_default.yaml',
'ckpt':
'pwg_snapshot_iter_400000.pdz',
'speech_stats':
'pwg_stats.npy',
},
"pwgan_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip',
'md5':
'd7598fa41ad362d62f85ffc0f07e3d84',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
"pwgan_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip',
'md5':
'b3da1defcde3e578be71eb284cb89f2c',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# mb_melgan
"mb_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'ee5f0604e20091f0d495b6ec4618b90d',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
# style_melgan
"style_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'5de2d5348f396de0c966926b8c462755',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# hifigan
"hifigan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip',
'md5':
'dd40a3d88dfcf64513fba2f0f961ada6',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip',
'md5':
'70e9131695decbca06a65fe51ed38a72',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip',
'md5':
'3bb49bc75032ed12f79c00c8cc79a09a',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip',
'md5':
'7da8f88359bca2457e705d924cf27bd4',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# wavernn
"wavernn_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip',
'md5':
'ee37b752f09bcba8f2af3b777ca38e13',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_400000.pdz',
'speech_stats':
'feats_stats.npy',
}
}
model_alias = {
# acoustic model
"speedyspeech":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeech",
"speedyspeech_inference":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference",
"fastspeech2":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
"fastspeech2_inference":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
"tacotron2":
"paddlespeech.t2s.models.tacotron2:Tacotron2",
"tacotron2_inference":
"paddlespeech.t2s.models.tacotron2:Tacotron2Inference",
# voc
"pwgan":
"paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
"pwgan_inference":
"paddlespeech.t2s.models.parallel_wavegan:PWGInference",
"mb_melgan":
"paddlespeech.t2s.models.melgan:MelGANGenerator",
"mb_melgan_inference":
"paddlespeech.t2s.models.melgan:MelGANInference",
"style_melgan":
"paddlespeech.t2s.models.melgan:StyleMelGANGenerator",
"style_melgan_inference":
"paddlespeech.t2s.models.melgan:StyleMelGANInference",
"hifigan":
"paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
"hifigan_inference":
"paddlespeech.t2s.models.hifigan:HiFiGANInference",
"wavernn":
"paddlespeech.t2s.models.wavernn:WaveRNN",
"wavernn_inference":
"paddlespeech.t2s.models.wavernn:WaveRNNInference",
}
@cli_register(
name='paddlespeech.tts', description='Text to Speech infer command.')
class TTSExecutor(BaseExecutor):
def __init__(self):
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser(
prog='paddlespeech.tts', add_help=True)
......@@ -449,22 +164,6 @@ class TTSExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(pretrained_models[tag],
res_path)
decompressed_path = os.path.abspath(decompressed_path)
logger.info(
'Use pretrained model stored in: {}'.format(decompressed_path))
return decompressed_path
def _init_from_path(
self,
am: str='fastspeech2_csmsc',
......@@ -490,16 +189,15 @@ class TTSExecutor(BaseExecutor):
if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
am_res_path = self._get_pretrained_path(am_tag)
self.am_res_path = am_res_path
self.am_config = os.path.join(am_res_path,
pretrained_models[am_tag]['config'])
self.am_config = os.path.join(
am_res_path, self.pretrained_models[am_tag]['config'])
self.am_ckpt = os.path.join(am_res_path,
pretrained_models[am_tag]['ckpt'])
self.pretrained_models[am_tag]['ckpt'])
self.am_stat = os.path.join(
am_res_path, pretrained_models[am_tag]['speech_stats'])
am_res_path, self.pretrained_models[am_tag]['speech_stats'])
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['phones_dict'])
print("self.phones_dict:", self.phones_dict)
am_res_path, self.pretrained_models[am_tag]['phones_dict'])
logger.info(am_res_path)
logger.info(self.am_config)
logger.info(self.am_ckpt)
......@@ -509,21 +207,20 @@ class TTSExecutor(BaseExecutor):
self.am_stat = os.path.abspath(am_stat)
self.phones_dict = os.path.abspath(phones_dict)
self.am_res_path = os.path.dirname(os.path.abspath(self.am_config))
print("self.phones_dict:", self.phones_dict)
# for speedyspeech
self.tones_dict = None
if 'tones_dict' in pretrained_models[am_tag]:
if 'tones_dict' in self.pretrained_models[am_tag]:
self.tones_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['tones_dict'])
am_res_path, self.pretrained_models[am_tag]['tones_dict'])
if tones_dict:
self.tones_dict = tones_dict
# for multi speaker fastspeech2
self.speaker_dict = None
if 'speaker_dict' in pretrained_models[am_tag]:
if 'speaker_dict' in self.pretrained_models[am_tag]:
self.speaker_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['speaker_dict'])
am_res_path, self.pretrained_models[am_tag]['speaker_dict'])
if speaker_dict:
self.speaker_dict = speaker_dict
......@@ -532,12 +229,12 @@ class TTSExecutor(BaseExecutor):
if voc_ckpt is None or voc_config is None or voc_stat is None:
voc_res_path = self._get_pretrained_path(voc_tag)
self.voc_res_path = voc_res_path
self.voc_config = os.path.join(voc_res_path,
pretrained_models[voc_tag]['config'])
self.voc_ckpt = os.path.join(voc_res_path,
pretrained_models[voc_tag]['ckpt'])
self.voc_config = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['config'])
self.voc_ckpt = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['ckpt'])
self.voc_stat = os.path.join(
voc_res_path, pretrained_models[voc_tag]['speech_stats'])
voc_res_path, self.pretrained_models[voc_tag]['speech_stats'])
logger.info(voc_res_path)
logger.info(self.voc_config)
logger.info(self.voc_ckpt)
......@@ -588,8 +285,9 @@ class TTSExecutor(BaseExecutor):
# model: {model_name}_{dataset}
am_name = am[:am.rindex('_')]
am_class = dynamic_import(am_name, model_alias)
am_inference_class = dynamic_import(am_name + '_inference', model_alias)
am_class = dynamic_import(am_name, self.model_alias)
am_inference_class = dynamic_import(am_name + '_inference',
self.model_alias)
if am_name == 'fastspeech2':
am = am_class(
......@@ -618,9 +316,9 @@ class TTSExecutor(BaseExecutor):
# vocoder
# model: {model_name}_{dataset}
voc_name = voc[:voc.rindex('_')]
voc_class = dynamic_import(voc_name, model_alias)
voc_class = dynamic_import(voc_name, self.model_alias)
voc_inference_class = dynamic_import(voc_name + '_inference',
model_alias)
self.model_alias)
if voc_name != 'wavernn':
voc = voc_class(**self.voc_config["generator_params"])
voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"])
......@@ -735,7 +433,6 @@ class TTSExecutor(BaseExecutor):
am_ckpt = args.am_ckpt
am_stat = args.am_stat
phones_dict = args.phones_dict
print("phones_dict:", phones_dict)
tones_dict = args.tones_dict
speaker_dict = args.speaker_dict
voc = args.voc
......
paddlespeech/cli/tts/pretrained_models.py 0 → 100644
浏览文件 @ fd1116bd
# Copyright (c) 2022 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.
pretrained_models = {
# speedyspeech
"speedyspeech_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip',
'md5':
'6f6fa967b408454b6662c8c00c0027cb',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_30600.pdz',
'speech_stats':
'feats_stats.npy',
'phones_dict':
'phone_id_map.txt',
'tones_dict':
'tone_id_map.txt',
},
# fastspeech2
"fastspeech2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip',
'md5':
'637d28a5e53aa60275612ba4393d5f22',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_76000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip',
'md5':
'ffed800c93deaf16ca9b3af89bfcd747',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_100000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip',
'md5':
'f4dd4a5f49a4552b77981f544ab3392e',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_96400.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'speaker_dict':
'speaker_id_map.txt',
},
"fastspeech2_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip',
'md5':
'743e5024ca1e17a88c5c271db9779ba4',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_66200.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'speaker_dict':
'speaker_id_map.txt',
},
# tacotron2
"tacotron2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip',
'md5':
'0df4b6f0bcbe0d73c5ed6df8867ab91a',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_30600.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"tacotron2_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip',
'md5':
'6a5eddd81ae0e81d16959b97481135f3',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_60300.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
# pwgan
"pwgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip',
'md5':
'2e481633325b5bdf0a3823c714d2c117',
'config':
'pwg_default.yaml',
'ckpt':
'pwg_snapshot_iter_400000.pdz',
'speech_stats':
'pwg_stats.npy',
},
"pwgan_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip',
'md5':
'53610ba9708fd3008ccaf8e99dacbaf0',
'config':
'pwg_default.yaml',
'ckpt':
'pwg_snapshot_iter_400000.pdz',
'speech_stats':
'pwg_stats.npy',
},
"pwgan_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip',
'md5':
'd7598fa41ad362d62f85ffc0f07e3d84',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
"pwgan_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip',
'md5':
'b3da1defcde3e578be71eb284cb89f2c',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# mb_melgan
"mb_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'ee5f0604e20091f0d495b6ec4618b90d',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
# style_melgan
"style_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'5de2d5348f396de0c966926b8c462755',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# hifigan
"hifigan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip',
'md5':
'dd40a3d88dfcf64513fba2f0f961ada6',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip',
'md5':
'70e9131695decbca06a65fe51ed38a72',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip',
'md5':
'3bb49bc75032ed12f79c00c8cc79a09a',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip',
'md5':
'7da8f88359bca2457e705d924cf27bd4',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# wavernn
"wavernn_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip',
'md5':
'ee37b752f09bcba8f2af3b777ca38e13',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_400000.pdz',
'speech_stats':
'feats_stats.npy',
}
}
model_alias = {
# acoustic model
"speedyspeech":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeech",
"speedyspeech_inference":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference",
"fastspeech2":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
"fastspeech2_inference":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
"tacotron2":
"paddlespeech.t2s.models.tacotron2:Tacotron2",
"tacotron2_inference":
"paddlespeech.t2s.models.tacotron2:Tacotron2Inference",
# voc
"pwgan":
"paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
"pwgan_inference":
"paddlespeech.t2s.models.parallel_wavegan:PWGInference",
"mb_melgan":
"paddlespeech.t2s.models.melgan:MelGANGenerator",
"mb_melgan_inference":
"paddlespeech.t2s.models.melgan:MelGANInference",
"style_melgan":
"paddlespeech.t2s.models.melgan:StyleMelGANGenerator",
"style_melgan_inference":
"paddlespeech.t2s.models.melgan:StyleMelGANInference",
"hifigan":
"paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
"hifigan_inference":
"paddlespeech.t2s.models.hifigan:HiFiGANInference",
"wavernn":
"paddlespeech.t2s.models.wavernn:WaveRNN",
"wavernn_inference":
"paddlespeech.t2s.models.wavernn:WaveRNNInference",
}
paddlespeech/cli/vector/infer.py
浏览文件 @ fd1116bd
......@@ -27,45 +27,24 @@ from yacs.config import CfgNode
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddleaudio.backends import load as load_audio
from paddleaudio.compliance.librosa import melspectrogram
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.vector.io.batch import feature_normalize
from paddlespeech.vector.modules.sid_model import SpeakerIdetification
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[-{dataset}][-{sr}][-...]".
# e.g. "ecapatdnn_voxceleb12-16k".
# Command line and python api use "{model_name}[-{dataset}]" as --model, usage:
# "paddlespeech vector --task spk --model ecapatdnn_voxceleb12-16k --sr 16000 --input ./input.wav"
"ecapatdnn_voxceleb12-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_0.tar.gz',
'md5':
'cc33023c54ab346cd318408f43fcaf95',
'cfg_path':
'conf/model.yaml', # the yaml config path
'ckpt_path':
'model/model', # the format is ${dir}/{model_name},
# so the first 'model' is dir, the second 'model' is the name
# this means we have a model stored as model/model.pdparams
},
}
model_alias = {
"ecapatdnn": "paddlespeech.vector.models.ecapa_tdnn:EcapaTdnn",
}
@cli_register(
name="paddlespeech.vector",
description="Speech to vector embedding infer command.")
class VectorExecutor(BaseExecutor):
def __init__(self):
super(VectorExecutor, self).__init__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser(
prog="paddlespeech.vector", add_help=True)
......@@ -128,8 +107,8 @@ class VectorExecutor(BaseExecutor):
Returns:
bool:
False: some audio occurs error
True: all audio process success
False: some audio occurs error
True: all audio process success
"""
# stage 0: parse the args and get the required args
parser_args = self.parser.parse_args(argv)
......@@ -289,32 +268,6 @@ class VectorExecutor(BaseExecutor):
return res
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""get the neural network path from the pretrained model list
we stored all the pretained mode in the variable `pretrained_models`
Args:
tag (str): model tag in the pretrained model list
Returns:
os.PathLike: the downloaded pretrained model path in the disk
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, \
'The model "{}" you want to use has not been supported,'\
'please choose other models.\n' \
'The support models includes\n\t\t{}'.format(tag, "\n\t\t".join(support_models))
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(pretrained_models[tag],
res_path)
decompressed_path = os.path.abspath(decompressed_path)
logger.info(
'Use pretrained model stored in: {}'.format(decompressed_path))
return decompressed_path
def _init_from_path(self,
model_type: str='ecapatdnn_voxceleb12',
sample_rate: int=16000,
......@@ -350,10 +303,11 @@ class VectorExecutor(BaseExecutor):
res_path = self._get_pretrained_path(tag)
self.res_path = res_path
self.cfg_path = os.path.join(res_path,
pretrained_models[tag]['cfg_path'])
self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path'])
self.ckpt_path = os.path.join(
res_path, pretrained_models[tag]['ckpt_path'] + '.pdparams')
res_path,
self.pretrained_models[tag]['ckpt_path'] + '.pdparams')
else:
# get the model from disk
self.cfg_path = os.path.abspath(cfg_path)
......@@ -373,7 +327,7 @@ class VectorExecutor(BaseExecutor):
logger.info("start to dynamic import the model class")
model_name = model_type[:model_type.rindex('_')]
logger.info(f"model name {model_name}")
model_class = dynamic_import(model_name, model_alias)
model_class = dynamic_import(model_name, self.model_alias)
model_conf = self.config.model
backbone = model_class(**model_conf)
model = SpeakerIdetification(
......
paddlespeech/cli/vector/pretrained_models.py 0 → 100644
浏览文件 @ fd1116bd
# Copyright (c) 2022 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.
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[-{dataset}][-{sr}][-...]".
# e.g. "ecapatdnn_voxceleb12-16k".
# Command line and python api use "{model_name}[-{dataset}]" as --model, usage:
# "paddlespeech vector --task spk --model ecapatdnn_voxceleb12-16k --sr 16000 --input ./input.wav"
"ecapatdnn_voxceleb12-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_0.tar.gz',
'md5':
'cc33023c54ab346cd318408f43fcaf95',
'cfg_path':
'conf/model.yaml', # the yaml config path
'ckpt_path':
'model/model', # the format is ${dir}/{model_name},
# so the first 'model' is dir, the second 'model' is the name
# this means we have a model stored as model/model.pdparams
},
}
model_alias = {
"ecapatdnn": "paddlespeech.vector.models.ecapa_tdnn:EcapaTdnn",
}
paddlespeech/server/utils/util.py
浏览文件 @ fd1116bd
......@@ -52,6 +52,10 @@ def get_chunks(data, block_size, pad_size, step):
Returns:
list: chunks list
"""
if block_size == -1:
return [data]
if step == "am":
data_len = data.shape[1]
elif step == "voc":
......
paddlespeech/vector/cluster/diarization.py
浏览文件 @ fd1116bd
# Copyright (c) 2022 SpeechBrain Authors. All Rights Reserved.
# Copyright (c) 2022 PaddlePaddle and SpeechBrain 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.
......@@ -18,12 +18,14 @@ This script has an optional dependency on open source sklearn library.
A few sklearn functions are modified in this script as per requirement.
"""
import argparse
import copy
import warnings
from distutils.util import strtobool
import numpy as np
import scipy
import sklearn
from scipy import linalg
from scipy import sparse
from scipy.sparse.csgraph import connected_components
from scipy.sparse.csgraph import laplacian as csgraph_laplacian
......@@ -346,6 +348,8 @@ class EmbeddingMeta:
---------
segset : list
List of session IDs as an array of strings.
modelset : list
List of model IDs as an array of strings.
stats : tensor
An ndarray of float64. Each line contains embedding
from the corresponding session.
......@@ -354,15 +358,20 @@ class EmbeddingMeta:
def __init__(
self,
segset=None,
modelset=None,
stats=None, ):
if segset is None:
self.segset = numpy.empty(0, dtype="|O")
self.stats = numpy.array([], dtype=np.float64)
self.segset = np.empty(0, dtype="|O")
self.modelset = np.empty(0, dtype="|O")
self.stats = np.array([], dtype=np.float64)
else:
self.segset = segset
self.modelset = modelset
self.stats = stats
self.stat0 = np.array([[1.0]] * self.stats.shape[0])
def norm_stats(self):
"""
Divide all first-order statistics by their Euclidean norm.
......@@ -371,6 +380,188 @@ class EmbeddingMeta:
vect_norm = np.clip(np.linalg.norm(self.stats, axis=1), 1e-08, np.inf)
self.stats = (self.stats.transpose() / vect_norm).transpose()
def get_mean_stats(self):
"""
Return the mean of first order statistics.
"""
mu = np.mean(self.stats, axis=0)
return mu
def get_total_covariance_stats(self):
"""
Compute and return the total covariance matrix of the first-order statistics.
"""
C = self.stats - self.stats.mean(axis=0)
return np.dot(C.transpose(), C) / self.stats.shape[0]
def get_model_stat0(self, mod_id):
"""Return zero-order statistics of a given model
Arguments
---------
mod_id : str
ID of the model which stat0 will be returned.
"""
S = self.stat0[self.modelset == mod_id, :]
return S
def get_model_stats(self, mod_id):
"""Return first-order statistics of a given model.
Arguments
---------
mod_id : str
ID of the model which stat1 will be returned.
"""
return self.stats[self.modelset == mod_id, :]
def sum_stat_per_model(self):
"""
Sum the zero- and first-order statistics per model and store them
in a new EmbeddingMeta.
Returns a EmbeddingMeta object with the statistics summed per model
and a numpy array with session_per_model.
"""
sts_per_model = EmbeddingMeta()
sts_per_model.modelset = np.unique(
self.modelset) # nd: get uniq spkr ids
sts_per_model.segset = copy.deepcopy(sts_per_model.modelset)
sts_per_model.stat0 = np.zeros(
(sts_per_model.modelset.shape[0], self.stat0.shape[1]),
dtype=np.float64, )
sts_per_model.stats = np.zeros(
(sts_per_model.modelset.shape[0], self.stats.shape[1]),
dtype=np.float64, )
session_per_model = np.zeros(np.unique(self.modelset).shape[0])
# For each model sum the stats
for idx, model in enumerate(sts_per_model.modelset):
sts_per_model.stat0[idx, :] = self.get_model_stat0(model).sum(
axis=0)
sts_per_model.stats[idx, :] = self.get_model_stats(model).sum(
axis=0)
session_per_model[idx] += self.get_model_stats(model).shape[0]
return sts_per_model, session_per_model
def center_stats(self, mu):
"""
Center first order statistics.
Arguments
---------
mu : array
Array to center on.
"""
dim = self.stats.shape[1] / self.stat0.shape[1]
index_map = np.repeat(np.arange(self.stat0.shape[1]), dim)
self.stats = self.stats - (self.stat0[:, index_map] *
mu.astype(np.float64))
def rotate_stats(self, R):
"""
Rotate first-order statistics by a right-product.
Arguments
---------
R : ndarray
Matrix to use for right product on the first order statistics.
"""
self.stats = np.dot(self.stats, R)
def whiten_stats(self, mu, sigma, isSqrInvSigma=False):
"""
Whiten first-order statistics
If sigma.ndim == 1, case of a diagonal covariance.
If sigma.ndim == 2, case of a single Gaussian with full covariance.
If sigma.ndim == 3, case of a full covariance UBM.
Arguments
---------
mu : array
Mean vector to be subtracted from the statistics.
sigma : narray
Co-variance matrix or covariance super-vector.
isSqrInvSigma : bool
True if the input Sigma matrix is the inverse of the square root of a covariance matrix.
"""
if sigma.ndim == 1:
self.center_stats(mu)
self.stats = self.stats / np.sqrt(sigma.astype(np.float64))
elif sigma.ndim == 2:
# Compute the inverse square root of the co-variance matrix Sigma
sqr_inv_sigma = sigma
if not isSqrInvSigma:
# eigen_values, eigen_vectors = scipy.linalg.eigh(sigma)
eigen_values, eigen_vectors = linalg.eigh(sigma)
ind = eigen_values.real.argsort()[::-1]
eigen_values = eigen_values.real[ind]
eigen_vectors = eigen_vectors.real[:, ind]
sqr_inv_eval_sigma = 1 / np.sqrt(eigen_values.real)
sqr_inv_sigma = np.dot(eigen_vectors,
np.diag(sqr_inv_eval_sigma))
else:
pass
# Whitening of the first-order statistics
self.center_stats(mu) # CENTERING
self.rotate_stats(sqr_inv_sigma)
elif sigma.ndim == 3:
# we assume that sigma is a 3D ndarray of size D x n x n
# where D is the number of distributions and n is the dimension of a single distribution
n = self.stats.shape[1] // self.stat0.shape[1]
sess_nb = self.stat0.shape[0]
self.center_stats(mu)
self.stats = (np.einsum("ikj,ikl->ilj",
self.stats.T.reshape(-1, n, sess_nb), sigma)
.reshape(-1, sess_nb).T)
else:
raise Exception("Wrong dimension of Sigma, must be 1 or 2")
def align_models(self, model_list):
"""
Align models of the current EmbeddingMeta to match a list of models
provided as input parameter. The size of the StatServer might be
reduced to match the input list of models.
Arguments
---------
model_list : ndarray of strings
List of models to match.
"""
indx = np.array(
[np.argwhere(self.modelset == v)[0][0] for v in model_list])
self.segset = self.segset[indx]
self.modelset = self.modelset[indx]
self.stat0 = self.stat0[indx, :]
self.stats = self.stats[indx, :]
def align_segments(self, segment_list):
"""
Align segments of the current EmbeddingMeta to match a list of segment
provided as input parameter. The size of the StatServer might be
reduced to match the input list of segments.
Arguments
---------
segment_list: ndarray of strings
list of segments to match
"""
indx = np.array(
[np.argwhere(self.segset == v)[0][0] for v in segment_list])
self.segset = self.segset[indx]
self.modelset = self.modelset[indx]
self.stat0 = self.stat0[indx, :]
self.stats = self.stats[indx, :]
class SpecClustUnorm:
"""
......
paddlespeech/vector/cluster/plda.py 0 → 100644
浏览文件 @ fd1116bd
此差异已折叠。
paddlespeech/vector/io/dataset_from_json.py
浏览文件 @ fd1116bd
......@@ -26,14 +26,14 @@ from paddleaudio.compliance.librosa import mfcc
class meta_info:
"""the audio meta info in the vector JSONDataset
Args:
id (str): the segment name
utt_id (str): the segment name
duration (float): segment time
wav (str): wav file path
start (int): start point in the original wav file
stop (int): stop point in the original wav file
lab_id (str): the record id
"""
id: str
utt_id: str
duration: float
wav: str
start: int
......
setup.py
浏览文件 @ fd1116bd
......@@ -65,6 +65,7 @@ base = [
"webrtcvad",
"yacs~=0.1.8",
"prettytable",
"zhon",
]
server = [
......@@ -91,7 +92,6 @@ requirements = {
"unidecode",
"yq",
"pre-commit",
"zhon",
]
}
......
speechx/examples/README.md
浏览文件 @ fd1116bd
# Examples for SpeechX
* dev - for speechx developer, using for test.
* ngram - using to build NGram ARPA lm.
* ds2_ol - ds2 streaming test under `aishell-1` test dataset.
The entrypoint is `ds2_ol/aishell/run.sh`
The entrypoint is `ds2_ol/aishell/run.sh`
## How to run
## How to run
`run.sh` is the entry point.
......@@ -17,9 +15,23 @@ pushd ds2_ol/aishell
bash run.sh
```
## Display Model with [Netron](https://github.com/lutzroeder/netron)
## Display Model with [Netron](https://github.com/lutzroeder/netron)
```
pip install netron
netron exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel --port 8022 --host 10.21.55.20
```
## For Developer
> Warning: Only for developer, make sure you know what's it.
* dev - for speechx developer, using for test.
## Build WFST
> Warning: Using below example when you know what's it.
* text_lm - process text for build lm
* ngram - using to build NGram ARPA lm.
* wfst - build wfst for TLG.
speechx/examples/ds2_ol/aishell/README.md
浏览文件 @ fd1116bd
......@@ -10,12 +10,18 @@ Other -> 0.00 % N=0 C=0 S=0 D=0 I=0
## CTC Prefix Beam Search w LM
LM: zh_giga.no_cna_cmn.prune01244.klm
```
Overall -> 7.86 % N=104768 C=96865 S=7573 D=330 I=327
Mandarin -> 7.86 % N=104768 C=96865 S=7573 D=330 I=327
Other -> 0.00 % N=0 C=0 S=0 D=0 I=0
```
## CTC WFST
LM: aishell train
```
Overall -> 11.14 % N=103017 C=93363 S=9583 D=71 I=1819
Mandarin -> 11.14 % N=103017 C=93363 S=9583 D=71 I=1818
Other -> 0.00 % N=0 C=0 S=0 D=0 I=1
```
```
\ No newline at end of file
speechx/examples/ds2_ol/aishell/run.sh
浏览文件 @ fd1116bd
......@@ -5,7 +5,10 @@ set -e
. path.sh
nj=40
stage=0
stop_stage=100
. utils/parse_options.sh
# 1. compile
if [ ! -d ${SPEECHX_EXAMPLES} ]; then
......@@ -26,102 +29,112 @@ vocb_dir=$ckpt_dir/data/lang_char/
mkdir -p exp
exp=$PWD/exp
aishell_wav_scp=aishell_test.scp
if [ ! -d $data/test ]; then
pushd $data
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
unzip aishell_test.zip
popd
realpath $data/test/*/*.wav > $data/wavlist
awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id
paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp
fi
if [ ! -d $ckpt_dir ]; then
mkdir -p $ckpt_dir
wget -P $ckpt_dir -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
tar xzfv $ckpt_dir/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz -C $ckpt_dir
fi
lm=$data/zh_giga.no_cna_cmn.prune01244.klm
if [ ! -f $lm ]; then
pushd $data
wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
popd
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ];then
aishell_wav_scp=aishell_test.scp
if [ ! -d $data/test ]; then
pushd $data
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
unzip aishell_test.zip
popd
realpath $data/test/*/*.wav > $data/wavlist
awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id
paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp
fi
if [ ! -d $ckpt_dir ]; then
mkdir -p $ckpt_dir
wget -P $ckpt_dir -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
tar xzfv $model_dir/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz -C $ckpt_dir
fi
lm=$data/zh_giga.no_cna_cmn.prune01244.klm
if [ ! -f $lm ]; then
pushd $data
wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
popd
fi
fi
# 3. make feature
text=$data/test/text
label_file=./aishell_result
wer=./aishell_wer
export GLOG_logtostderr=1
# 3. gen linear feat
cmvn=$PWD/cmvn.ark
cmvn-json2kaldi --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
# 3. gen linear feat
cmvn=$data/cmvn.ark
cmvn-json2kaldi --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn
./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/feat.log \
linear-spectrogram-wo-db-norm-ol \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
--feature_wspecifier=ark,scp:$data/split${nj}/JOB/feat.ark,$data/split${nj}/JOB/feat.scp \
--cmvn_file=$cmvn \
--streaming_chunk=0.36
text=$data/test/text
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/feat.log \
linear-spectrogram-wo-db-norm-ol \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
--feature_wspecifier=ark,scp:$data/split${nj}/JOB/feat.ark,$data/split${nj}/JOB/feat.scp \
--cmvn_file=$cmvn \
--streaming_chunk=0.36
fi
# 4. recognizer
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wolm.log \
ctc-prefix-beam-search-decoder-ol \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--params_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--dict_file=$vocb_dir/vocab.txt \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result
cat $data/split${nj}/*/result > ${label_file}
utils/compute-wer.py --char=1 --v=1 ${label_file} $text > ${wer}
# 4. decode with lm
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.lm.log \
ctc-prefix-beam-search-decoder-ol \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--params_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--dict_file=$vocb_dir/vocab.txt \
--lm_path=$lm \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_lm
cat $data/split${nj}/*/result_lm > ${label_file}_lm
utils/compute-wer.py --char=1 --v=1 ${label_file}_lm $text > ${wer}_lm
graph_dir=./aishell_graph
if [ ! -d $graph_dir ]; then
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph.zip
unzip aishell_graph.zip
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
# recognizer
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wolm.log \
ctc-prefix-beam-search-decoder-ol \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--params_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--dict_file=$vocb_dir/vocab.txt \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result
cat $data/split${nj}/*/result > $exp/${label_file}
utils/compute-wer.py --char=1 --v=1 $exp/${label_file} $text > $exp/${wer}
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
# decode with lm
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.lm.log \
ctc-prefix-beam-search-decoder-ol \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--params_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--dict_file=$vocb_dir/vocab.txt \
--lm_path=$lm \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_lm
cat $data/split${nj}/*/result_lm > $exp/${label_file}_lm
utils/compute-wer.py --char=1 --v=1 $exp/${label_file}_lm $text > $exp/${wer}_lm
fi
# 5. test TLG decoder
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wfst.log \
wfst-decoder-ol \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--params_path=$model_dir/avg_1.jit.pdiparams \
--word_symbol_table=$graph_dir/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--graph_path=$graph_dir/TLG.fst --max_active=7500 \
--acoustic_scale=1.2 \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_tlg
wfst=$data/wfst/
mkdir -p $wfst
if [ ! -f $wfst/aishell_graph.zip ]; then
pushd $wfst
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph.zip
unzip aishell_graph.zip
popd
fi
cat $data/split${nj}/*/result_tlg > ${label_file}_tlg
utils/compute-wer.py --char=1 --v=1 ${label_file}_tlg $text > ${wer}_tlg
\ No newline at end of file
graph_dir=$wfst/aishell_graph
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
# TLG decoder
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wfst.log \
wfst-decoder-ol \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--params_path=$model_dir/avg_1.jit.pdiparams \
--word_symbol_table=$graph_dir/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--graph_path=$graph_dir/TLG.fst --max_active=7500 \
--acoustic_scale=1.2 \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_tlg
cat $data/split${nj}/*/result_tlg > $exp/${label_file}_tlg
utils/compute-wer.py --char=1 --v=1 $exp/${label_file}_tlg $text > $exp/${wer}_tlg
fi
speechx/examples/ngram/zh/README.md 0 → 100644
浏览文件 @ fd1116bd
# ngram train for mandarin
Quick run:
```
bash run.sh --stage -1
```
## input
input files:
```
data/
├── lexicon.txt
├── text
└── vocab.txt
```
```
==> data/text <==
BAC009S0002W0122 而 对 楼市 成交 抑制 作用 最 大 的 限 购
BAC009S0002W0123 也 成为 地方 政府 的 眼中 钉
BAC009S0002W0124 自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后
BAC009S0002W0125 各地 政府 便 纷纷 跟进
BAC009S0002W0126 仅 一 个 多 月 的 时间 里
BAC009S0002W0127 除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外
BAC009S0002W0128 四十六 个 限 购 城市 当中
BAC009S0002W0129 四十一 个 已 正式 取消 或 变相 放松 了 限 购
BAC009S0002W0130 财政 金融 政策 紧随 其后 而来
BAC009S0002W0131 显示 出 了 极 强 的 威力
==> data/lexicon.txt <==
SIL sil
<SPOKEN_NOISE> sil
啊 aa a1
啊 aa a2
啊 aa a4
啊 aa a5
啊啊啊 aa a2 aa a2 aa a2
啊啊啊 aa a5 aa a5 aa a5
坐地 z uo4 d i4
坐实 z uo4 sh ix2
坐视 z uo4 sh ix4
坐稳 z uo4 uu un3
坐拥 z uo4 ii iong1
坐诊 z uo4 zh en3
坐庄 z uo4 zh uang1
坐姿 z uo4 z iy1
==> data/vocab.txt <==
<blank>
<unk>
A
B
C
D
E
龙
龚
龛
<eos>
```
## output
```
data/
├── local
│ ├── dict
│ │ ├── lexicon.txt
│ │ └── units.txt
│ └── lm
│ ├── heldout
│ ├── lm.arpa
│ ├── text
│ ├── text.no_oov
│ ├── train
│ ├── unigram.counts
│ ├── word.counts
│ └── wordlist
```
```
/workspace/srilm/bin/i686-m64/ngram-count
Namespace(bpemodel=None, in_lexicon='data/lexicon.txt', out_lexicon='data/local/dict/lexicon.txt', unit_file='data/vocab.txt')
Ignoring words 矽, which contains oov unit
Ignoring words 傩, which contains oov unit
Ignoring words 堀, which contains oov unit
Ignoring words 莼, which contains oov unit
Ignoring words 菰, which contains oov unit
Ignoring words 摭, which contains oov unit
Ignoring words 帙, which contains oov unit
Ignoring words 迨, which contains oov unit
Ignoring words 孥, which contains oov unit
Ignoring words 瑗, which contains oov unit
...
...
...
file data/local/lm/heldout: 10000 sentences, 89496 words, 0 OOVs
0 zeroprobs, logprob= -270337.9 ppl= 521.2819 ppl1= 1048.745
build LM done.
```
speechx/examples/ngram/zh/local/aishell_train_lms.sh 0 → 100755
浏览文件 @ fd1116bd
#!/bin/bash
# To be run from one directory above this script.
. ./path.sh
text=data/local/lm/text
lexicon=data/local/dict/lexicon.txt
for f in "$text" "$lexicon"; do
[ ! -f $x ] && echo "$0: No such file $f" && exit 1;
done
# Check SRILM tools
if ! which ngram-count > /dev/null; then
echo "srilm tools are not found, please download it and install it from: "
echo "http://www.speech.sri.com/projects/srilm/download.html"
echo "Then add the tools to your PATH"
exit 1
fi
# This script takes no arguments. It assumes you have already run
# aishell_data_prep.sh.
# It takes as input the files
# data/local/lm/text
# data/local/dict/lexicon.txt
dir=data/local/lm
mkdir -p $dir
cleantext=$dir/text.no_oov
# oov to <SPOKEN_NOISE>
# lexicon line: word char0 ... charn
# text line: utt word0 ... wordn -> line: <SPOKEN_NOISE> word0 ... wordn
cat $text | awk -v lex=$lexicon 'BEGIN{while((getline<lex) >0){ seen[$1]=1; } }
{for(n=1; n<=NF;n++) { if (seen[$n]) { printf("%s ", $n); } else {printf("<SPOKEN_NOISE> ");} } printf("\n");}' \
> $cleantext || exit 1;
# compute word counts, sort in descending order
# line: count word
cat $cleantext | awk '{for(n=2;n<=NF;n++) print $n; }' | sort | uniq -c | \
sort -nr > $dir/word.counts || exit 1;
# Get counts from acoustic training transcripts, and add one-count
# for each word in the lexicon (but not silence, we don't want it
# in the LM-- we'll add it optionally later).
cat $cleantext | awk '{for(n=2;n<=NF;n++) print $n; }' | \
cat - <(grep -w -v '!SIL' $lexicon | awk '{print $1}') | \
sort | uniq -c | sort -nr > $dir/unigram.counts || exit 1;
# word with <s> </s>
cat $dir/unigram.counts | awk '{print $2}' | cat - <(echo "<s>"; echo "</s>" ) > $dir/wordlist
# hold out to compute ppl
heldout_sent=10000 # Don't change this if you want result to be comparable with kaldi_lm results
mkdir -p $dir
cat $cleantext | awk '{for(n=2;n<=NF;n++){ printf $n; if(n<NF) printf " "; else print ""; }}' | \
head -$heldout_sent > $dir/heldout
cat $cleantext | awk '{for(n=2;n<=NF;n++){ printf $n; if(n<NF) printf " "; else print ""; }}' | \
tail -n +$heldout_sent > $dir/train
ngram-count -text $dir/train -order 3 -limit-vocab -vocab $dir/wordlist -unk \
-map-unk "<UNK>" -kndiscount -interpolate -lm $dir/lm.arpa
ngram -lm $dir/lm.arpa -ppl $dir/heldout
\ No newline at end of file
speechx/examples/ngram/zh/local/text_to_lexicon.py 0 → 100755
浏览文件 @ fd1116bd
#!/usr/bin/env python3
import argparse
from collections import Counter
def main(args):
counter = Counter()
with open(args.text, 'r') as fin, open(args.lexicon, 'w') as fout:
for line in fin:
line = line.strip()
if args.has_key:
utt, text = line.split(maxsplit=1)
words = text.split()
else:
words = line.split()
counter.update(words)
for word in counter:
val = " ".join(list(word))
fout.write(f"{word}\t{val}\n")
fout.flush()
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='text(line:utt1 中国 人) to lexicon（line:中国 中 国).')
parser.add_argument(
'--has_key',
default=True,
help='text path, with utt or not')
parser.add_argument(
'--text',
required=True,
help='text path. line: utt1 中国 人 or 中国 人')
parser.add_argument(
'--lexicon',
required=True,
help='lexicon path. line:中国 中 国')
args = parser.parse_args()
print(args)
main(args)
speechx/examples/ngram/zh/path.sh 0 → 100644
浏览文件 @ fd1116bd
# This contains the locations of binarys build required for running the examples.
MAIN_ROOT=`realpath $PWD/../../../../`
SPEECHX_ROOT=`realpath $MAIN_ROOT/speechx`
export LC_AL=C
# srilm
export LIBLBFGS=${MAIN_ROOT}/tools/liblbfgs-1.10
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:-}:${LIBLBFGS}/lib/.libs
export SRILM=${MAIN_ROOT}/tools/srilm
export PATH=${PATH}:${SRILM}/bin:${SRILM}/bin/i686-m64
speechx/examples/ngram/zh/run.sh 0 → 100755
浏览文件 @ fd1116bd
#!/bin/bash
set -eo pipefail
. path.sh
stage=-1
stop_stage=100
corpus=aishell
unit=data/vocab.txt # vocab file, line: char/spm_pice
lexicon=data/lexicon.txt # line: word ph0 ... phn, aishell/resource_aishell/lexicon.txt
text=data/text # line: utt text, aishell/data_aishell/transcript/aishell_transcript_v0.8.txt
. utils/parse_options.sh
data=$PWD/data
mkdir -p $data
if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
if [ ! -f $data/speech.ngram.zh.tar.gz ];then
pushd $data
wget -c http://paddlespeech.bj.bcebos.com/speechx/examples/ngram/zh/speech.ngram.zh.tar.gz
tar xvzf speech.ngram.zh.tar.gz
popd
fi
fi
if [ ! -f $unit ]; then
echo "$0: No such file $unit"
exit 1;
fi
if ! which ngram-count; then
pushd $MAIN_ROOT/tools
make srilm.done
popd
fi
mkdir -p data/local/dict
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# 7.1 Prepare dict
# line: char/spm_pices
cp $unit data/local/dict/units.txt
if [ ! -f $lexicon ];then
local/text_to_lexicon.py --has_key true --text $text --lexicon $lexicon
echo "Generate $lexicon from $text"
fi
# filter by vocab
# line: word ph0 ... phn -> line: word char0 ... charn
utils/fst/prepare_dict.py \
--unit_file $unit \
--in_lexicon ${lexicon} \
--out_lexicon data/local/dict/lexicon.txt
fi
lm=data/local/lm
mkdir -p $lm
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# 7.2 Train lm
cp $text $lm/text
local/aishell_train_lms.sh
fi
echo "build LM done."
exit 0
speechx/examples/ngram/zh/utils 0 → 120000
浏览文件 @ fd1116bd
../../../../utils/
\ No newline at end of file
speechx/examples/text_lm/README.md 0 → 100644
浏览文件 @ fd1116bd
# Text PreProcess for building ngram LM
Output `text` file like this:
```
BAC009S0002W0122 而 对 楼市 成交 抑制 作用 最 大 的 限 购
BAC009S0002W0123 也 成为 地方 政府 的 眼中 钉
BAC009S0002W0124 自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后
BAC009S0002W0125 各地 政府 便 纷纷 跟进
BAC009S0002W0126 仅 一 个 多 月 的 时间 里
BAC009S0002W0127 除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外
BAC009S0002W0128 四十六 个 限 购 城市 当中
BAC009S0002W0129 四十一 个 已 正式 取消 或 变相 放松 了 限 购
BAC009S0002W0130 财政 金融 政策 紧随 其后 而来
```
speechx/examples/text_lm/path.sh 0 → 100644
浏览文件 @ fd1116bd
MAIN_ROOT=`realpath $PWD/../../../../`
SPEECHX_ROOT=`realpath $MAIN_ROOT/speechx`
export LC_AL=C
speechx/examples/text_lm/run.sh 0 → 100755
浏览文件 @ fd1116bd
#!/bin/bash
set -eo pipefail
. path.sh
stage=0
stop_stage=100
has_key=true
token_type=word
. utils/parse_options.sh || exit -1;
text=data/text
if [ ! -f $text ]; then
echo "$0: Not find $1";
exit -1;
fi
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ];then
echo "text tn & wordseg preprocess"
rm -rf ${text}.tn
python3 utils/zh_tn.py --has_key $has_key --token_type $token_type ${text} ${text}.tn
fi
\ No newline at end of file
speechx/examples/text_lm/utils 0 → 120000
浏览文件 @ fd1116bd
../../../utils/
\ No newline at end of file
speechx/examples/wfst/README.md 0 → 100644
浏览文件 @ fd1116bd
# Built TLG wfst
## Input
```
data/local/
├── dict
│ ├── lexicon.txt
│ └── units.txt
└── lm
├── heldout
├── lm.arpa
├── text
├── text.no_oov
├── train
├── unigram.counts
├── word.counts
└── wordlist
```
```
==> data/local/dict/lexicon.txt <==
啊 啊
啊啊啊 啊 啊 啊
阿 阿
阿尔 阿 尔
阿根廷 阿 根 廷
阿九 阿 九
阿克 阿 克
阿拉伯数字 阿 拉 伯 数 字
阿拉法特 阿 拉 法 特
阿拉木图 阿 拉 木 图
==> data/local/dict/units.txt <==
<blank>
<unk>
A
B
C
D
E
F
G
H
==> data/local/lm/heldout <==
而 对 楼市 成交 抑制 作用 最 大 的 限 购
也 成为 地方 政府 的 眼中 钉
自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后
各地 政府 便 纷纷 跟进
仅 一 个 多 月 的 时间 里
除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外
四十六 个 限 购 城市 当中
四十一 个 已 正式 取消 或 变相 放松 了 限 购
财政 金融 政策 紧随 其后 而来
显示 出 了 极 强 的 威力
==> data/local/lm/lm.arpa <==
\data\
ngram 1=129356
ngram 2=504661
ngram 3=123455
\1-grams:
-1.531278 </s>
-3.828829 <SPOKEN_NOISE> -0.1600094
-6.157292 <UNK>
==> data/local/lm/text <==
BAC009S0002W0122 而 对 楼市 成交 抑制 作用 最 大 的 限 购
BAC009S0002W0123 也 成为 地方 政府 的 眼中 钉
BAC009S0002W0124 自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后
BAC009S0002W0125 各地 政府 便 纷纷 跟进
BAC009S0002W0126 仅 一 个 多 月 的 时间 里
BAC009S0002W0127 除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外
BAC009S0002W0128 四十六 个 限 购 城市 当中
BAC009S0002W0129 四十一 个 已 正式 取消 或 变相 放松 了 限 购
BAC009S0002W0130 财政 金融 政策 紧随 其后 而来
BAC009S0002W0131 显示 出 了 极 强 的 威力
==> data/local/lm/text.no_oov <==
<SPOKEN_NOISE> 而 对 楼市 成交 抑制 作用 最 大 的 限 购
<SPOKEN_NOISE> 也 成为 地方 政府 的 眼中 钉
<SPOKEN_NOISE> 自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后
<SPOKEN_NOISE> 各地 政府 便 纷纷 跟进
<SPOKEN_NOISE> 仅 一 个 多 月 的 时间 里
<SPOKEN_NOISE> 除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外
<SPOKEN_NOISE> 四十六 个 限 购 城市 当中
<SPOKEN_NOISE> 四十一 个 已 正式 取消 或 变相 放松 了 限 购
<SPOKEN_NOISE> 财政 ���融 政策 紧随 其后 而来
<SPOKEN_NOISE> 显示 出 了 极 强 的 威力
==> data/local/lm/train <==
汉莎 不 得 不 通过 这样 的 方式 寻求 新 的 发展 点
并 计划 朝云 计算 方面 发展
汉莎 的 基础 设施 部门 拥有 一千四百 名 员工
媒体 就 曾 披露 这笔 交易
虽然 双方 已经 正式 签署 了 外包 协议
但是 这笔 交易 还 需要 得到 反 垄断 部门 的 批准
陈 黎明 一九八九 年 获得 美国 康乃尔 大学 硕士 学位
并 于 二零零三 年 顺利 完成 美国 哈佛 商学 院 高级 管理 课程
曾 在 多家 国际 公司 任职
拥有 业务 开发 商务 及 企业 治理
==> data/local/lm/unigram.counts <==
57487 的
13099 在
11862 一
11397 了
10998 不
9913 是
7952 有
6250 和
6152 个
5422 将
==> data/local/lm/word.counts <==
57486 的
13098 在
11861 一
11396 了
10997 不
9912 是
7951 有
6249 和
6151 个
5421 将
==> data/local/lm/wordlist <==
的
在
一
了
不
是
有
和
个
将
```
## Output
```
fstaddselfloops 'echo 4234 |' 'echo 123660 |'
Lexicon and Token FSTs compiling succeeded
arpa2fst --read-symbol-table=data/lang_test/words.txt --keep-symbols=true -
LOG (arpa2fst[5.5.0~1-5a37]:Read():arpa-file-parser.cc:94) Reading \data\ section.
LOG (arpa2fst[5.5.0~1-5a37]:Read():arpa-file-parser.cc:149) Reading \1-grams: section.
LOG (arpa2fst[5.5.0~1-5a37]:Read():arpa-file-parser.cc:149) Reading \2-grams: section.
LOG (arpa2fst[5.5.0~1-5a37]:Read():arpa-file-parser.cc:149) Reading \3-grams: section.
Checking how stochastic G is (the first of these numbers should be small):
fstisstochastic data/lang_test/G.fst
0 -1.14386
fsttablecompose data/lang_test/L.fst data/lang_test/G.fst
fstminimizeencoded
fstdeterminizestar --use-log=true
fsttablecompose data/lang_test/T.fst data/lang_test/LG.fst
Composing decoding graph TLG.fst succeeded
Aishell build TLG done.
```
```
data/
├── lang_test
│ ├── G.fst
│ ├── L.fst
│ ├── LG.fst
│ ├── T.fst
│ ├── TLG.fst
│ ├── tokens.txt
│ ├── units.txt
│ └── words.txt
└── local
├── lang
│ ├── L.fst
│ ├── T.fst
│ ├── tokens.txt
│ ├── units.txt
│ └── words.txt
└── tmp
├── disambig.list
├── lexiconp_disambig.txt
├── lexiconp.txt
└── units.list
```
\ No newline at end of file
speechx/examples/wfst/path.sh 0 → 100644
浏览文件 @ fd1116bd
# This contains the locations of binarys build required for running the examples.
MAIN_ROOT=`realpath $PWD/../../../`
SPEECHX_ROOT=`realpath $MAIN_ROOT/speechx`
export LC_AL=C
# srilm
export LIBLBFGS=${MAIN_ROOT}/tools/liblbfgs-1.10
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:-}:${LIBLBFGS}/lib/.libs
export SRILM=${MAIN_ROOT}/tools/srilm
export PATH=${PATH}:${SRILM}/bin:${SRILM}/bin/i686-m64
# Kaldi
export KALDI_ROOT=${MAIN_ROOT}/tools/kaldi
[ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh
export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH
[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present, can not using Kaldi!"
[ -f $KALDI_ROOT/tools/config/common_path.sh ] && . $KALDI_ROOT/tools/config/common_path.sh
speechx/examples/wfst/run.sh 0 → 100755
浏览文件 @ fd1116bd
#!/bin/bash
set -eo pipefail
. path.sh
stage=-1
stop_stage=100
. utils/parse_options.sh
if ! which fstprint ; then
pushd $MAIN_ROOT/tools
make kaldi.done
popd
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# build T & L
# utils/fst/compile_lexicon_token_fst.sh <dict-src-dir> <tmp-dir> <lang-dir>
utils/fst/compile_lexicon_token_fst.sh \
data/local/dict data/local/tmp data/local/lang
# build G & LG & TLG
# utils/fst/make_tlg.sh <lm_dir> <src_lang> <tgt_lang>
utils/fst/make_tlg.sh data/local/lm data/local/lang data/lang_test || exit 1;
fi
echo "build TLG done."
exit 0
speechx/examples/wfst/utils 0 → 120000
浏览文件 @ fd1116bd
../../../utils/
\ No newline at end of file
speechx/tools/install_srilm.sh 已删除 100755 → 0
浏览文件 @ dc8efca2
#!/usr/bin/env bash
current_path=`pwd`
current_dir=`basename "$current_path"`
if [ "tools" != "$current_dir" ]; then
echo "You should run this script in tools/ directory!!"
exit 1
fi
if [ ! -d liblbfgs-1.10 ]; then
echo Installing libLBFGS library to support MaxEnt LMs
bash extras/install_liblbfgs.sh || exit 1
fi
! command -v gawk > /dev/null && \
echo "GNU awk is not installed so SRILM will probably not work correctly: refusing to install" && exit 1;
if [ $# -ne 3 ]; then
echo "SRILM download requires some information about you"
echo
echo "Usage: $0 <name> <organization> <email>"
exit 1
fi
srilm_url="http://www.speech.sri.com/projects/srilm/srilm_download.php"
post_data="WWW_file=srilm-1.7.3.tar.gz&WWW_name=$1&WWW_org=$2&WWW_email=$3"
if ! wget --post-data "$post_data" -O ./srilm.tar.gz "$srilm_url"; then
echo 'There was a problem downloading the file.'
echo 'Check you internet connection and try again.'
exit 1
fi
mkdir -p srilm
cd srilm
if [ -f ../srilm.tgz ]; then
tar -xvzf ../srilm.tgz # Old SRILM format
elif [ -f ../srilm.tar.gz ]; then
tar -xvzf ../srilm.tar.gz # Changed format type from tgz to tar.gz
fi
major=`gawk -F. '{ print $1 }' RELEASE`
minor=`gawk -F. '{ print $2 }' RELEASE`
micro=`gawk -F. '{ print $3 }' RELEASE`
if [ $major -le 1 ] && [ $minor -le 7 ] && [ $micro -le 1 ]; then
echo "Detected version 1.7.1 or earlier. Applying patch."
patch -p0 < ../extras/srilm.patch
fi
# set the SRILM variable in the top-level Makefile to this directory.
cp Makefile tmpf
cat tmpf | gawk -v pwd=`pwd` '/SRILM =/{printf("SRILM = %s\n", pwd); next;} {print;}' \
> Makefile || exit 1
rm tmpf
mtype=`sbin/machine-type`
echo HAVE_LIBLBFGS=1 >> common/Makefile.machine.$mtype
grep ADDITIONAL_INCLUDES common/Makefile.machine.$mtype | \
sed 's|$| -I$(SRILM)/../liblbfgs-1.10/include|' \
>> common/Makefile.machine.$mtype
grep ADDITIONAL_LDFLAGS common/Makefile.machine.$mtype | \
sed 's|$| -L$(SRILM)/../liblbfgs-1.10/lib/ -Wl,-rpath -Wl,$(SRILM)/../liblbfgs-1.10/lib/|' \
>> common/Makefile.machine.$mtype
make || exit
cd ..
(
[ ! -z "${SRILM}" ] && \
echo >&2 "SRILM variable is aleady defined. Undefining..." && \
unset SRILM
[ -f ./env.sh ] && . ./env.sh
[ ! -z "${SRILM}" ] && \
echo >&2 "SRILM config is already in env.sh" && exit
wd=`pwd`
wd=`readlink -f $wd || pwd`
echo "export SRILM=$wd/srilm"
dirs="\${PATH}"
for directory in $(cd srilm && find bin -type d ) ; do
dirs="$dirs:\${SRILM}/$directory"
done
echo "export PATH=$dirs"
) >> env.sh
echo >&2 "Installation of SRILM finished successfully"
echo >&2 "Please source the tools/env.sh in your path.sh to enable it"
tests/unit/cli/test_cli.sh
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#!/bin/bash
set -e
# Audio classification
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/cat.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/dog.wav
paddlespeech cls --input ./cat.wav --topk 10
......@@ -28,26 +29,16 @@ paddlespeech tts --am tacotron2_csmsc --input "你好，欢迎使用百度飞桨
paddlespeech tts --am tacotron2_csmsc --voc wavernn_csmsc --input "你好，欢迎使用百度飞桨深度学习框架！"
paddlespeech tts --am tacotron2_ljspeech --voc pwgan_ljspeech --lang en --input "Life was like a box of chocolates, you never know what you're gonna get."
# Speech Translation (only support linux)
paddlespeech st --input ./en.wav
# batch process
echo -e "1 欢迎光临。\n2 谢谢惠顾。" | paddlespeech tts
# shell pipeline
paddlespeech asr --input ./zh.wav | paddlespeech text --task punc
# stats
paddlespeech stats --task asr
paddlespeech stats --task tts
paddlespeech stats --task cls
# Speaker Verification
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
paddlespeech vector --task spk --input 85236145389.wav
# batch process
echo -e "1 欢迎光临。\n2 谢谢惠顾。" | paddlespeech tts
echo -e "demo1 85236145389.wav \n demo2 85236145389.wav" > vec.job
paddlespeech vector --task spk --input vec.job
......@@ -55,4 +46,13 @@ echo -e "demo3 85236145389.wav \n demo4 85236145389.wav" | paddlespeech vector -
rm 85236145389.wav
rm vec.job
# shell pipeline
paddlespeech asr --input ./zh.wav | paddlespeech text --task punc
# stats
paddlespeech stats --task asr
paddlespeech stats --task tts
paddlespeech stats --task cls
paddlespeech stats --task text
paddlespeech stats --task vector
paddlespeech stats --task st
tools/Makefile
浏览文件 @ fd1116bd
......@@ -25,7 +25,7 @@ clean:
apt.done:
apt update -y
apt install -y bc flac jq vim tig tree pkg-config libsndfile1 libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev
apt install -y bc flac jq vim tig tree sox pkg-config libsndfile1 libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev
echo "check_certificate = off" >> ~/.wgetrc
touch apt.done
......@@ -50,7 +50,7 @@ openblas.done:
bash extras/install_openblas.sh
touch openblas.done
kaldi.done: openblas.done
kaldi.done: apt.done openblas.done
bash extras/install_kaldi.sh
touch kaldi.done
......@@ -58,6 +58,11 @@ sctk.done:
./extras/install_sclite.sh
touch sctk.done
srilm.done:
./extras/install_liblbfgs.sh
extras/install_srilm.sh
touch srilm.done
######################
dev: python conda_packages.done sctk.done
......@@ -96,4 +101,4 @@ conda_packages.done: bc.done cmake.done flac.done ffmpeg.done sox.done sndfile.d
else
conda_packages.done:
endif
touch conda_packages.done
\ No newline at end of file
touch conda_packages.done
tools/extras/install_openfst.sh
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......@@ -7,8 +7,9 @@ set -x
# openfst
openfst=openfst-1.8.1
shared=true
WGET="wget -c --no-check-certificate"
test -e ${openfst}.tar.gz || wget http://www.openfst.org/twiki/pub/FST/FstDownload/${openfst}.tar.gz
test -e ${openfst}.tar.gz || $WGET http://www.openfst.org/twiki/pub/FST/FstDownload/${openfst}.tar.gz
test -d ${openfst} || tar -xvf ${openfst}.tar.gz && chown -R root:root ${openfst}
......
utils/compute-wer.py
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此差异已折叠。
utils/espnet_json_to_manifest.py 100644 → 100755
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文件模式从 100644 更改为 100755
utils/fst/prepare_dict.py
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......@@ -3,7 +3,8 @@ import argparse
def main(args):
# load `unit` or `vocab` file
# load vocab file
# line: token
unit_table = set()
with open(args.unit_file, 'r') as fin:
for line in fin:
......@@ -11,27 +12,41 @@ def main(args):
unit_table.add(unit)
def contain_oov(units):
"""token not in vocab
Args:
units (str): token
Returns:
bool: True token in voca, else False.
"""
for unit in units:
if unit not in unit_table:
return True
return False
# load spm model
# load spm model, for English
bpemode = args.bpemodel
if bpemode:
import sentencepiece as spm
sp = spm.SentencePieceProcessor()
sp.Load(sys.bpemodel)
# used to filter polyphone
# used to filter polyphone and invalid word
lexicon_table = set()
in_n = 0 # in lexicon word count
out_n = 0 # out lexicon word cout
with open(args.in_lexicon, 'r') as fin, \
open(args.out_lexicon, 'w') as fout:
for line in fin:
word = line.split()[0]
in_n += 1
if word == 'SIL' and not bpemode: # `sil` might be a valid piece in bpemodel
# filter 'SIL' for mandarin, keep it in English
continue
elif word == '<SPOKEN_NOISE>':
# filter <SPOKEN_NOISE>
continue
else:
# each word only has one pronunciation for e2e system
......@@ -39,12 +54,14 @@ def main(args):
continue
if bpemode:
# for english
pieces = sp.EncodeAsPieces(word)
if contain_oov(pieces):
print('Ignoring words {}, which contains oov unit'.
format(''.join(word).strip('▁')))
continue
# word is piece list, which not have <unk> piece, filter out by `contain_oov(pieces)`
chars = ' '.join(
[p if p in unit_table else '<unk>' for p in pieces])
else:
......@@ -58,11 +75,14 @@ def main(args):
# we assume the model unit of our e2e system is char now.
if word.encode('utf8').isalpha() and '▁' in unit_table:
word = '▁' + word
chars = ' '.join(word) # word is a char list
fout.write('{} {}\n'.format(word, chars))
lexicon_table.add(word)
out_n += 1
print(f"Filter lexicon by unit table: filter out {in_n - out_n}, {out_n}/{in_n}")
if __name__ == '__main__':
parser = argparse.ArgumentParser(
......
utils/generate_infer_yaml.py 100644 → 100755
浏览文件 @ fd1116bd
文件模式从 100644 更改为 100755
utils/link_wav.py 100644 → 100755
浏览文件 @ fd1116bd
文件模式从 100644 更改为 100755
utils/manifest_key_value.py
浏览文件 @ fd1116bd
......@@ -26,23 +26,39 @@ def main(args):
with wav_scp.open('w') as fwav, dur_scp.open('w') as fdur, text_scp.open(
'w') as ftxt:
for line_json in manifest_jsons:
# utt:str
# utt2spk:str
# input: [{name:str, shape:[dur_in_sec, feat_dim], feat:str, filetype:str}, ]
# output: [{name:str, shape:[tokenlen, vocab_dim], text:str, token:str, tokenid:str}, ]
utt = line_json['utt']
feat = line_json['feat']
utt2spk = line_json['utt2spk']
# input
assert (len(line_json['input']) == 1), "only support one input now"
input_json = line_json['input'][0]
feat = input_json['feat']
feat_shape = input_json['shape']
file_type = input_json['filetype']
file_ext = Path(feat).suffix # .wav
text = line_json['text']
feat_shape = line_json['feat_shape']
dur = feat_shape[0]
feat_dim = feat_shape[1]
if 'token' in line_json:
tokens = line_json['token']
tokenids = line_json['token_id']
token_shape = line_json['token_shape']
token_len = token_shape[0]
vocab_dim = token_shape[1]
if file_ext == '.wav':
fwav.write(f"{utt} {feat}\n")
fdur.write(f"{utt} {dur}\n")
# output
assert (
len(line_json['output']) == 1), "only support one output now"
output_json = line_json['output'][0]
text = output_json['text']
if 'token' in output_json:
tokens = output_json['token']
tokenids = output_json['tokenid']
token_shape = output_json['shape']
token_len = token_shape[0]
vocab_dim = token_shape[1]
ftxt.write(f"{utt} {text}\n")
count += 1
......
utils/zh_tn.py
浏览文件 @ fd1116bd
此差异已折叠。
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