未验证 提交 7c0ec3c2 编写于 作者: 小湉湉's avatar 小湉湉 提交者: GitHub

Merge pull request #1716 from yt605155624/update_cli

[CLI]update cli, test=doc
......@@ -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
......
# 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",
}
......@@ -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)
......
# 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",
}
......@@ -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
......@@ -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()
......
# 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",
}
......@@ -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."
)
......@@ -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')
......
# 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",
}
......@@ -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
......
# 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",
}
......@@ -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(
......
# 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",
}
#!/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
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