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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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Add paddlespeech.resource.

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pull/1917/head
KP 2 years ago
parent 775c4befbd
commit fa6e44e4ff
31 changed files with 1382 additions and 1507 deletions
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31
paddlespeech/cli/asr/infer.py
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@ -33,11 +33,8 @@ from ..utils import CLI_TIMER
from ..utils import MODEL_HOME from ..utils import MODEL_HOME
from ..utils import stats_wrapper from ..utils import stats_wrapper
from ..utils import timer_register from ..utils import timer_register
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.transform.transformation import Transformation from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.s2t.utils.utility import UpdateConfig from paddlespeech.s2t.utils.utility import UpdateConfig
__all__ = ['ASRExecutor'] __all__ = ['ASRExecutor']
@ -46,10 +43,7 @@ __all__ = ['ASRExecutor']
@timer_register @timer_register
class ASRExecutor(BaseExecutor): class ASRExecutor(BaseExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__(task='asr', inference_type='offline')
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser( self.parser = argparse.ArgumentParser(
prog='paddlespeech.asr', add_help=True) prog='paddlespeech.asr', add_help=True)
self.parser.add_argument( self.parser.add_argument(
@ -59,7 +53,8 @@ class ASRExecutor(BaseExecutor):
type=str, type=str,
default='conformer_wenetspeech', default='conformer_wenetspeech',
choices=[ choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys() tag[:tag.index('-')]
for tag in self.task_resource.pretrained_models.keys()
], ],
help='Choose model type of asr task.') help='Choose model type of asr task.')
self.parser.add_argument( self.parser.add_argument(
@ -141,14 +136,14 @@ class ASRExecutor(BaseExecutor):
if cfg_path is None or ckpt_path is None: if cfg_path is None or ckpt_path is None:
sample_rate_str = '16k' if sample_rate == 16000 else '8k' sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str tag = model_type + '-' + lang + '-' + sample_rate_str
res_path = self._get_pretrained_path(tag) # wenetspeech_zh self.task_resource.set_task_model(tag, version=None)
self.res_path = res_path self.res_path = self.task_resource.res_dir
self.cfg_path = os.path.join( self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path']) self.res_path, self.task_resource.res_dict['cfg_path'])
self.ckpt_path = os.path.join( self.ckpt_path = os.path.join(
res_path, self.res_path,
self.pretrained_models[tag]['ckpt_path'] + ".pdparams") self.task_resource.res_dict['ckpt_path'] + ".pdparams")
logger.info(res_path) logger.info(self.res_path)
else: else:
self.cfg_path = os.path.abspath(cfg_path) self.cfg_path = os.path.abspath(cfg_path)
@ -172,8 +167,8 @@ class ASRExecutor(BaseExecutor):
self.collate_fn_test = SpeechCollator.from_config(self.config) self.collate_fn_test = SpeechCollator.from_config(self.config)
self.text_feature = TextFeaturizer( self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab) unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = self.pretrained_models[tag]['lm_url'] lm_url = self.resource.res_dict['lm_url']
lm_md5 = self.pretrained_models[tag]['lm_md5'] lm_md5 = self.resource.res_dict['lm_md5']
self.download_lm( self.download_lm(
lm_url, lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5) os.path.dirname(self.config.decode.lang_model_path), lm_md5)
@ -191,7 +186,7 @@ class ASRExecutor(BaseExecutor):
raise Exception("wrong type") raise Exception("wrong type")
model_name = model_type[:model_type.rindex( model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset} '_')] # model_type: {model_name}_{dataset}
model_class = dynamic_import(model_name, self.model_alias) model_class = self.task_resource.get_model_class(model_name)
model_conf = self.config model_conf = self.config
model = model_class.from_config(model_conf) model = model_class.from_config(model_conf)
self.model = model self.model = model
@ -438,7 +433,7 @@ class ASRExecutor(BaseExecutor):
if not parser_args.verbose: if not parser_args.verbose:
self.disable_task_loggers() self.disable_task_loggers()
task_source = self.get_task_source(parser_args.input) task_source = self.get_input_source(parser_args.input)
task_results = OrderedDict() task_results = OrderedDict()
has_exceptions = False has_exceptions = False

151
paddlespeech/cli/asr/pretrained_models.py
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@ -1,151 +0,0 @@
# 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" 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',
},
"conformer_online_wenetspeech-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_wenetspeech_ckpt_1.0.0a.model.tar.gz',
'md5':
'b8c02632b04da34aca88459835be54a6',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/avg_10',
},
"conformer_online_multicn-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.0.model.tar.gz',
'md5':
'7989b3248c898070904cf042fd656003',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/multi_cn',
},
"conformer_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_0.1.2.model.tar.gz',
'md5':
'3f073eccfa7bb14e0c6867d65fc0dc3a',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/conformer/checkpoints/avg_30',
},
"conformer_online_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_chunk_conformer_aishell_ckpt_0.2.0.model.tar.gz',
'md5':
'b374cfb93537761270b6224fb0bfc26a',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/avg_30',
},
"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',
},
"deepspeech2online_wenetspeech-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr0/asr0_deepspeech2_online_wenetspeech_ckpt_1.0.0a.model.tar.gz',
'md5':
'e393d4d274af0f6967db24fc146e8074',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_10',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"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_fbank161_ckpt_0.2.1.model.tar.gz',
'md5':
'98b87b171b7240b7cae6e07d8d0bc9be',
'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",
"conformer_online":
"paddlespeech.s2t.models.u2:U2Model",
"transformer":
"paddlespeech.s2t.models.u2:U2Model",
"wenetspeech":
"paddlespeech.s2t.models.u2:U2Model",
}

63
paddlespeech/cli/base_commands.py
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@ -11,17 +11,18 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import argparse
from typing import List from typing import List
from prettytable import PrettyTable
from ..resource import CommonTaskResource
from .entry import commands from .entry import commands
from .utils import cli_register from .utils import cli_register
from .utils import explicit_command_register from .utils import explicit_command_register
from .utils import get_command from .utils import get_command
__all__ = [ __all__ = ['BaseCommand', 'HelpCommand', 'StatsCommand']
'BaseCommand',
'HelpCommand',
]
@cli_register(name='paddlespeech') @cli_register(name='paddlespeech')
@ -76,6 +77,59 @@ class VersionCommand:
return True return True
model_name_format = {
'asr': 'Model-Language-Sample Rate',
'cls': 'Model-Sample Rate',
'st': 'Model-Source language-Target language',
'text': 'Model-Task-Language',
'tts': 'Model-Language',
'vector': 'Model-Sample Rate'
}
@cli_register(
name='paddlespeech.stats',
description='Get speech tasks support models list.')
class StatsCommand:
def __init__(self):
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=self.task_choices,
help='Choose speech task.',
required=True)
def show_support_models(self, pretrained_models: dict):
fields = model_name_format[self.task].split("-")
table = PrettyTable(fields)
for key in pretrained_models:
table.add_row(key.split("-"))
print(table)
def execute(self, argv: List[str]) -> bool:
parser_args = self.parser.parse_args(argv)
self.task = parser_args.task
if self.task not in self.task_choices:
print("Please input correct speech task, choices = " + str(
self.task_choices))
return
pretrained_models = CommonTaskResource(task=self.task).pretrained_models
try:
print(
"Here is the list of {} pretrained models released by PaddleSpeech that can be used by command line and python API"
.format(self.task.upper()))
self.show_support_models(pretrained_models)
except BaseException:
print("Failed to get the list of {} pretrained models.".format(
self.task.upper()))
# Dynamic import when running specific command # Dynamic import when running specific command
_commands = { _commands = {
'asr': ['Speech to text infer command.', 'ASRExecutor'], 'asr': ['Speech to text infer command.', 'ASRExecutor'],
@ -91,3 +145,4 @@ for com, info in _commands.items():
name='paddlespeech.{}'.format(com), name='paddlespeech.{}'.format(com),
description=info[0], description=info[0],
cls='paddlespeech.cli.{}.{}'.format(com, info[1])) cls='paddlespeech.cli.{}.{}'.format(com, info[1]))

31
paddlespeech/cli/cls/infer.py
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@ -21,26 +21,19 @@ from typing import Union
import numpy as np import numpy as np
import paddle import paddle
import yaml import yaml
from paddleaudio import load
from paddleaudio.features import LogMelSpectrogram
from paddlespeech.utils.dynamic_import import dynamic_import
from ..executor import BaseExecutor from ..executor import BaseExecutor
from ..log import logger from ..log import logger
from ..utils import stats_wrapper from ..utils import stats_wrapper
from .pretrained_models import model_alias from paddleaudio import load
from .pretrained_models import pretrained_models from paddleaudio.features import LogMelSpectrogram
__all__ = ['CLSExecutor'] __all__ = ['CLSExecutor']
class CLSExecutor(BaseExecutor): class CLSExecutor(BaseExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__(task='cls')
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser( self.parser = argparse.ArgumentParser(
prog='paddlespeech.cls', add_help=True) prog='paddlespeech.cls', add_help=True)
self.parser.add_argument( self.parser.add_argument(
@ -50,7 +43,8 @@ class CLSExecutor(BaseExecutor):
type=str, type=str,
default='panns_cnn14', default='panns_cnn14',
choices=[ choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys() tag[:tag.index('-')]
for tag in self.task_resource.pretrained_models.keys()
], ],
help='Choose model type of cls task.') help='Choose model type of cls task.')
self.parser.add_argument( self.parser.add_argument(
@ -103,13 +97,16 @@ class CLSExecutor(BaseExecutor):
if label_file is None or ckpt_path is None: if label_file is None or ckpt_path is None:
tag = model_type + '-' + '32k' # panns_cnn14-32k tag = model_type + '-' + '32k' # panns_cnn14-32k
self.res_path = self._get_pretrained_path(tag) self.task_resource.set_task_model(tag, version=None)
self.cfg_path = os.path.join( self.cfg_path = os.path.join(
self.res_path, self.pretrained_models[tag]['cfg_path']) self.task_resource.res_dir,
self.task_resource.res_dict['cfg_path'])
self.label_file = os.path.join( self.label_file = os.path.join(
self.res_path, self.pretrained_models[tag]['label_file']) self.task_resource.res_dir,
self.task_resource.res_dict['label_file'])
self.ckpt_path = os.path.join( self.ckpt_path = os.path.join(
self.res_path, self.pretrained_models[tag]['ckpt_path']) self.task_resource.res_dir,
self.task_resource.res_dict['ckpt_path'])
else: else:
self.cfg_path = os.path.abspath(cfg_path) self.cfg_path = os.path.abspath(cfg_path)
self.label_file = os.path.abspath(label_file) self.label_file = os.path.abspath(label_file)
@ -126,7 +123,7 @@ class CLSExecutor(BaseExecutor):
self._label_list.append(line.strip()) self._label_list.append(line.strip())
# model # model
model_class = dynamic_import(model_type, self.model_alias) model_class = self.task_resource.get_model_class(model_type)
model_dict = paddle.load(self.ckpt_path) model_dict = paddle.load(self.ckpt_path)
self.model = model_class(extract_embedding=False) self.model = model_class(extract_embedding=False)
self.model.set_state_dict(model_dict) self.model.set_state_dict(model_dict)
@ -203,7 +200,7 @@ class CLSExecutor(BaseExecutor):
if not parser_args.verbose: if not parser_args.verbose:
self.disable_task_loggers() self.disable_task_loggers()
task_source = self.get_task_source(parser_args.input) task_source = self.get_input_source(parser_args.input)
task_results = OrderedDict() task_results = OrderedDict()
has_exceptions = False has_exceptions = False

47
paddlespeech/cli/cls/pretrained_models.py
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@ -1,47 +0,0 @@
# 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"
"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",
}

49
paddlespeech/cli/executor.py
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@ -24,9 +24,8 @@ from typing import Union
import paddle import paddle
from ..resource import CommonTaskResource
from .log import logger from .log import logger
from .utils import download_and_decompress
from .utils import MODEL_HOME
class BaseExecutor(ABC): class BaseExecutor(ABC):
@ -34,11 +33,10 @@ class BaseExecutor(ABC):
An abstract executor of paddlespeech tasks. An abstract executor of paddlespeech tasks.
""" """
def __init__(self): def __init__(self, task: str, **kwargs):
self._inputs = OrderedDict() self._inputs = OrderedDict()
self._outputs = OrderedDict() self._outputs = OrderedDict()
self.pretrained_models = OrderedDict() self.task_resource = CommonTaskResource(task=task, **kwargs)
self.model_alias = OrderedDict()
@abstractmethod @abstractmethod
def _init_from_path(self, *args, **kwargs): def _init_from_path(self, *args, **kwargs):
@ -98,8 +96,8 @@ class BaseExecutor(ABC):
""" """
pass pass
def get_task_source(self, input_: Union[str, os.PathLike, None] def get_input_source(self, input_: Union[str, os.PathLike, None]
) -> Dict[str, Union[str, os.PathLike]]: ) -> Dict[str, Union[str, os.PathLike]]:
""" """
Get task input source from command line input. Get task input source from command line input.
@ -115,15 +113,17 @@ class BaseExecutor(ABC):
ret = OrderedDict() ret = OrderedDict()
if input_ is None: # Take input from stdin if input_ is None: # Take input from stdin
for i, line in enumerate(sys.stdin): if not sys.stdin.isatty(
line = line.strip() ): # Avoid getting stuck when stdin is empty.
if len(line.split(' ')) == 1: for i, line in enumerate(sys.stdin):
ret[str(i + 1)] = line line = line.strip()
elif len(line.split(' ')) == 2: if len(line.split(' ')) == 1:
id_, info = line.split(' ') ret[str(i + 1)] = line
ret[id_] = info elif len(line.split(' ')) == 2:
else: # No valid input info from one line. id_, info = line.split(' ')
continue ret[id_] = info
else: # No valid input info from one line.
continue
else: else:
ret[1] = input_ ret[1] = input_
return ret return ret
@ -219,23 +219,6 @@ class BaseExecutor(ABC):
for l in loggers: for l in loggers:
l.disabled = True 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
def show_rtf(self, info: Dict[str, List[float]]): def show_rtf(self, info: Dict[str, List[float]]):
""" """
Calculate rft of current task and show results. Calculate rft of current task and show results.

36
paddlespeech/cli/st/infer.py
Unescape View File

@ -31,21 +31,22 @@ from ..log import logger
from ..utils import download_and_decompress from ..utils import download_and_decompress
from ..utils import MODEL_HOME from ..utils import MODEL_HOME
from ..utils import stats_wrapper 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.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.utils.utility import UpdateConfig from paddlespeech.s2t.utils.utility import UpdateConfig
from paddlespeech.utils.dynamic_import import dynamic_import
__all__ = ["STExecutor"] __all__ = ["STExecutor"]
kaldi_bins = {
"url":
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz",
"md5":
"c0682303b3f3393dbf6ed4c4e35a53eb",
}
class STExecutor(BaseExecutor): class STExecutor(BaseExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__(task='st')
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.kaldi_bins = kaldi_bins self.kaldi_bins = kaldi_bins
self.parser = argparse.ArgumentParser( self.parser = argparse.ArgumentParser(
@ -57,7 +58,8 @@ class STExecutor(BaseExecutor):
type=str, type=str,
default="fat_st_ted", default="fat_st_ted",
choices=[ choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys() tag[:tag.index('-')]
for tag in self.task_resource.pretrained_models.keys()
], ],
help="Choose model type of st task.") help="Choose model type of st task.")
self.parser.add_argument( self.parser.add_argument(
@ -131,14 +133,16 @@ class STExecutor(BaseExecutor):
if cfg_path is None or ckpt_path is None: if cfg_path is None or ckpt_path is None:
tag = model_type + "-" + src_lang + "-" + tgt_lang tag = model_type + "-" + src_lang + "-" + tgt_lang
res_path = self._get_pretrained_path(tag) self.task_resource.set_task_model(tag, version=None)
self.cfg_path = os.path.join(res_path, self.cfg_path = os.path.join(
pretrained_models[tag]["cfg_path"]) self.task_resource.res_dir,
self.ckpt_path = os.path.join(res_path, self.task_resource.res_dict['cfg_path'])
pretrained_models[tag]["ckpt_path"]) self.ckpt_path = os.path.join(
logger.info(res_path) self.task_resource.res_dir,
self.task_resource.res_dict['ckpt_path'])
logger.info(self.cfg_path) logger.info(self.cfg_path)
logger.info(self.ckpt_path) logger.info(self.ckpt_path)
res_path = self.task_resource.res_dir
else: else:
self.cfg_path = os.path.abspath(cfg_path) self.cfg_path = os.path.abspath(cfg_path)
self.ckpt_path = os.path.abspath(ckpt_path) self.ckpt_path = os.path.abspath(ckpt_path)
@ -163,7 +167,7 @@ class STExecutor(BaseExecutor):
model_conf = self.config model_conf = self.config
model_name = model_type[:model_type.rindex( model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset} '_')] # model_type: {model_name}_{dataset}
model_class = dynamic_import(model_name, self.model_alias) model_class = self.task_resource.get_model_class(model_name)
self.model = model_class.from_config(model_conf) self.model = model_class.from_config(model_conf)
self.model.eval() self.model.eval()
@ -301,7 +305,7 @@ class STExecutor(BaseExecutor):
if not parser_args.verbose: if not parser_args.verbose:
self.disable_task_loggers() self.disable_task_loggers()
task_source = self.get_task_source(parser_args.input) task_source = self.get_input_source(parser_args.input)
task_results = OrderedDict() task_results = OrderedDict()
has_exceptions = False has_exceptions = False

35
paddlespeech/cli/st/pretrained_models.py
Unescape View File

@ -1,35 +0,0 @@
# 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 = {
"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",
}

146
paddlespeech/cli/stats/infer.py
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@ -1,146 +0,0 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from typing import List
from prettytable import PrettyTable
from ..utils import cli_register
from ..utils import stats_wrapper
__all__ = ['StatsExecutor']
model_name_format = {
'asr': 'Model-Language-Sample Rate',
'cls': 'Model-Sample Rate',
'st': 'Model-Source language-Target language',
'text': 'Model-Task-Language',
'tts': 'Model-Language',
'vector': 'Model-Sample Rate'
}
@cli_register(
name='paddlespeech.stats',
description='Get speech tasks support models list.')
class StatsExecutor():
def __init__(self):
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=self.task_choices,
help='Choose speech task.',
required=True)
def show_support_models(self, pretrained_models: dict):
fields = model_name_format[self.task].split("-")
table = PrettyTable(fields)
for key in pretrained_models:
table.add_row(key.split("-"))
print(table)
def execute(self, argv: List[str]) -> bool:
"""
Command line entry.
"""
parser_args = self.parser.parse_args(argv)
has_exceptions = False
try:
self(parser_args.task)
except Exception as e:
has_exceptions = True
if has_exceptions:
return False
else:
return True
@stats_wrapper
def __call__(
self,
task: str=None, ):
"""
Python API to call an executor.
"""
self.task = task
if self.task not in self.task_choices:
print("Please input correct speech task, choices = " + str(
self.task_choices))
elif self.task == 'asr':
try:
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"
)
self.show_support_models(pretrained_models)
except BaseException:
print("Failed to get the list of ASR pretrained models.")
elif self.task == 'cls':
try:
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"
)
self.show_support_models(pretrained_models)
except BaseException:
print("Failed to get the list of CLS pretrained models.")
elif self.task == 'st':
try:
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"
)
self.show_support_models(pretrained_models)
except BaseException:
print("Failed to get the list of ST pretrained models.")
elif self.task == 'text':
try:
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"
)
self.show_support_models(pretrained_models)
except BaseException:
print("Failed to get the list of TEXT pretrained models.")
elif self.task == 'tts':
try:
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."
)

30
paddlespeech/cli/text/infer.py
Unescape View File

@ -24,21 +24,13 @@ import paddle
from ..executor import BaseExecutor from ..executor import BaseExecutor
from ..log import logger from ..log import logger
from ..utils import stats_wrapper from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from .pretrained_models import tokenizer_alias
from paddlespeech.utils.dynamic_import import dynamic_import
__all__ = ['TextExecutor'] __all__ = ['TextExecutor']
class TextExecutor(BaseExecutor): class TextExecutor(BaseExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__(task='text')
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.tokenizer_alias = tokenizer_alias
self.parser = argparse.ArgumentParser( self.parser = argparse.ArgumentParser(
prog='paddlespeech.text', add_help=True) prog='paddlespeech.text', add_help=True)
self.parser.add_argument( self.parser.add_argument(
@ -54,7 +46,8 @@ class TextExecutor(BaseExecutor):
type=str, type=str,
default='ernie_linear_p7_wudao', default='ernie_linear_p7_wudao',
choices=[ choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys() tag[:tag.index('-')]
for tag in self.task_resource.pretrained_models.keys()
], ],
help='Choose model type of text task.') help='Choose model type of text task.')
self.parser.add_argument( self.parser.add_argument(
@ -112,13 +105,16 @@ class TextExecutor(BaseExecutor):
if cfg_path is None or ckpt_path is None or vocab_file is None: if cfg_path is None or ckpt_path is None or vocab_file is None:
tag = '-'.join([model_type, task, lang]) tag = '-'.join([model_type, task, lang])
self.res_path = self._get_pretrained_path(tag) self.task_resource.set_task_model(tag, version=None)
self.cfg_path = os.path.join( self.cfg_path = os.path.join(
self.res_path, self.pretrained_models[tag]['cfg_path']) self.task_resource.res_dir,
self.task_resource.res_dict['cfg_path'])
self.ckpt_path = os.path.join( self.ckpt_path = os.path.join(
self.res_path, self.pretrained_models[tag]['ckpt_path']) self.task_resource.res_dir,
self.task_resource.res_dict['ckpt_path'])
self.vocab_file = os.path.join( self.vocab_file = os.path.join(
self.res_path, self.pretrained_models[tag]['vocab_file']) self.task_resource.res_dir,
self.task_resource.res_dict['vocab_file'])
else: else:
self.cfg_path = os.path.abspath(cfg_path) self.cfg_path = os.path.abspath(cfg_path)
self.ckpt_path = os.path.abspath(ckpt_path) self.ckpt_path = os.path.abspath(ckpt_path)
@ -133,8 +129,8 @@ class TextExecutor(BaseExecutor):
self._punc_list.append(line.strip()) self._punc_list.append(line.strip())
# model # model
model_class = dynamic_import(model_name, self.model_alias) model_class, tokenizer_class = self.task_resource.get_model_class(
tokenizer_class = dynamic_import(model_name, self.tokenizer_alias) model_name)
self.model = model_class( self.model = model_class(
cfg_path=self.cfg_path, ckpt_path=self.ckpt_path) cfg_path=self.cfg_path, ckpt_path=self.ckpt_path)
self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0') self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0')
@ -224,7 +220,7 @@ class TextExecutor(BaseExecutor):
if not parser_args.verbose: if not parser_args.verbose:
self.disable_task_loggers() self.disable_task_loggers()
task_source = self.get_task_source(parser_args.input) task_source = self.get_input_source(parser_args.input)
task_results = OrderedDict() task_results = OrderedDict()
has_exceptions = False has_exceptions = False

54
paddlespeech/cli/text/pretrained_models.py
Unescape View File

@ -1,54 +0,0 @@
# 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",
}

70
paddlespeech/cli/tts/infer.py
Unescape View File

@ -29,22 +29,16 @@ from yacs.config import CfgNode
from ..executor import BaseExecutor from ..executor import BaseExecutor
from ..log import logger from ..log import logger
from ..utils import stats_wrapper from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddlespeech.t2s.frontend import English from paddlespeech.t2s.frontend import English
from paddlespeech.t2s.frontend.zh_frontend import Frontend from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.t2s.modules.normalizer import ZScore from paddlespeech.t2s.modules.normalizer import ZScore
from paddlespeech.utils.dynamic_import import dynamic_import
__all__ = ['TTSExecutor'] __all__ = ['TTSExecutor']
class TTSExecutor(BaseExecutor): class TTSExecutor(BaseExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__('tts')
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser( self.parser = argparse.ArgumentParser(
prog='paddlespeech.tts', add_help=True) prog='paddlespeech.tts', add_help=True)
self.parser.add_argument( self.parser.add_argument(
@ -183,19 +177,23 @@ class TTSExecutor(BaseExecutor):
return return
# am # am
am_tag = am + '-' + lang am_tag = am + '-' + lang
self.task_resource.set_task_model(
model_tag=am_tag,
model_type=0, # am
version=None, # default version
)
if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None: 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 = self.task_resource.res_dir
self.am_res_path = am_res_path self.am_config = os.path.join(self.am_res_path,
self.am_config = os.path.join( self.task_resource.res_dict['config'])
am_res_path, self.pretrained_models[am_tag]['config']) self.am_ckpt = os.path.join(self.am_res_path,
self.am_ckpt = os.path.join(am_res_path, self.task_resource.res_dict['ckpt'])
self.pretrained_models[am_tag]['ckpt'])
self.am_stat = os.path.join( self.am_stat = os.path.join(
am_res_path, self.pretrained_models[am_tag]['speech_stats']) self.am_res_path, self.task_resource.res_dict['speech_stats'])
# must have phones_dict in acoustic # must have phones_dict in acoustic
self.phones_dict = os.path.join( self.phones_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['phones_dict']) self.am_res_path, self.task_resource.res_dict['phones_dict'])
logger.info(am_res_path) logger.info(self.am_res_path)
logger.info(self.am_config) logger.info(self.am_config)
logger.info(self.am_ckpt) logger.info(self.am_ckpt)
else: else:
@ -207,33 +205,37 @@ class TTSExecutor(BaseExecutor):
# for speedyspeech # for speedyspeech
self.tones_dict = None self.tones_dict = None
if 'tones_dict' in self.pretrained_models[am_tag]: if 'tones_dict' in self.task_resource.res_dict:
self.tones_dict = os.path.join( self.tones_dict = os.path.join(
self.am_res_path, self.pretrained_models[am_tag]['tones_dict']) self.am_res_path, self.task_resource.res_dict['tones_dict'])
if tones_dict: if tones_dict:
self.tones_dict = tones_dict self.tones_dict = tones_dict
# for multi speaker fastspeech2 # for multi speaker fastspeech2
self.speaker_dict = None self.speaker_dict = None
if 'speaker_dict' in self.pretrained_models[am_tag]: if 'speaker_dict' in self.task_resource.res_dict:
self.speaker_dict = os.path.join( self.speaker_dict = os.path.join(
self.am_res_path, self.am_res_path, self.task_resource.res_dict['speaker_dict'])
self.pretrained_models[am_tag]['speaker_dict'])
if speaker_dict: if speaker_dict:
self.speaker_dict = speaker_dict self.speaker_dict = speaker_dict
# voc # voc
voc_tag = voc + '-' + lang voc_tag = voc + '-' + lang
self.task_resource.set_task_model(
model_tag=voc_tag,
model_type=1, # vocoder
version=None, # default version
)
if voc_ckpt is None or voc_config is None or voc_stat is None: 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 = self.task_resource.voc_res_dir
self.voc_res_path = voc_res_path
self.voc_config = os.path.join( self.voc_config = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['config']) self.voc_res_path, self.task_resource.voc_res_dict['config'])
self.voc_ckpt = os.path.join( self.voc_ckpt = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['ckpt']) self.voc_res_path, self.task_resource.voc_res_dict['ckpt'])
self.voc_stat = os.path.join( self.voc_stat = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['speech_stats']) self.voc_res_path,
logger.info(voc_res_path) self.task_resource.voc_res_dict['speech_stats'])
logger.info(self.voc_res_path)
logger.info(self.voc_config) logger.info(self.voc_config)
logger.info(self.voc_ckpt) logger.info(self.voc_ckpt)
else: else:
@ -283,9 +285,9 @@ class TTSExecutor(BaseExecutor):
# model: {model_name}_{dataset} # model: {model_name}_{dataset}
am_name = am[:am.rindex('_')] am_name = am[:am.rindex('_')]
am_class = dynamic_import(am_name, self.model_alias) am_class = self.task_resource.get_model_class(am_name)
am_inference_class = dynamic_import(am_name + '_inference', am_inference_class = self.task_resource.get_model_class(am_name +
self.model_alias) '_inference')
if am_name == 'fastspeech2': if am_name == 'fastspeech2':
am = am_class( am = am_class(
@ -314,9 +316,9 @@ class TTSExecutor(BaseExecutor):
# vocoder # vocoder
# model: {model_name}_{dataset} # model: {model_name}_{dataset}
voc_name = voc[:voc.rindex('_')] voc_name = voc[:voc.rindex('_')]
voc_class = dynamic_import(voc_name, self.model_alias) voc_class = self.task_resource.get_model_class(voc_name)
voc_inference_class = dynamic_import(voc_name + '_inference', voc_inference_class = self.task_resource.get_model_class(voc_name +
self.model_alias) '_inference')
if voc_name != 'wavernn': if voc_name != 'wavernn':
voc = voc_class(**self.voc_config["generator_params"]) voc = voc_class(**self.voc_config["generator_params"])
voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"]) voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"])
@ -444,7 +446,7 @@ class TTSExecutor(BaseExecutor):
if not args.verbose: if not args.verbose:
self.disable_task_loggers() self.disable_task_loggers()
task_source = self.get_task_source(args.input) task_source = self.get_input_source(args.input)
task_results = OrderedDict() task_results = OrderedDict()
has_exceptions = False has_exceptions = False

300
paddlespeech/cli/tts/pretrained_models.py
Unescape View File

@ -1,300 +0,0 @@
# 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 = {
# 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",
}

29
paddlespeech/cli/vector/infer.py
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@ -22,26 +22,20 @@ from typing import Union
import paddle import paddle
import soundfile import soundfile
from paddleaudio.backends import load as load_audio
from paddleaudio.compliance.librosa import melspectrogram
from yacs.config import CfgNode from yacs.config import CfgNode
from ..executor import BaseExecutor from ..executor import BaseExecutor
from ..log import logger from ..log import logger
from ..utils import stats_wrapper from ..utils import stats_wrapper
from .pretrained_models import model_alias from paddleaudio.backends import load as load_audio
from .pretrained_models import pretrained_models from paddleaudio.compliance.librosa import melspectrogram
from paddlespeech.utils.dynamic_import import dynamic_import
from paddlespeech.vector.io.batch import feature_normalize from paddlespeech.vector.io.batch import feature_normalize
from paddlespeech.vector.modules.sid_model import SpeakerIdetification from paddlespeech.vector.modules.sid_model import SpeakerIdetification
class VectorExecutor(BaseExecutor): class VectorExecutor(BaseExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__('vector')
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser( self.parser = argparse.ArgumentParser(
prog="paddlespeech.vector", add_help=True) prog="paddlespeech.vector", add_help=True)
@ -49,7 +43,10 @@ class VectorExecutor(BaseExecutor):
"--model", "--model",
type=str, type=str,
default="ecapatdnn_voxceleb12", default="ecapatdnn_voxceleb12",
choices=["ecapatdnn_voxceleb12"], choices=[
tag[:tag.index('-')]
for tag in self.task_resource.pretrained_models.keys()
],
help="Choose model type of vector task.") help="Choose model type of vector task.")
self.parser.add_argument( self.parser.add_argument(
"--task", "--task",
@ -119,7 +116,7 @@ class VectorExecutor(BaseExecutor):
self.disable_task_loggers() self.disable_task_loggers()
# stage 2: read the input data and store them as a list # stage 2: read the input data and store them as a list
task_source = self.get_task_source(parser_args.input) task_source = self.get_input_source(parser_args.input)
logger.info(f"task source: {task_source}") logger.info(f"task source: {task_source}")
# stage 3: process the audio one by one # stage 3: process the audio one by one
@ -296,6 +293,7 @@ class VectorExecutor(BaseExecutor):
# get the mode from pretrained list # get the mode from pretrained list
sample_rate_str = "16k" if sample_rate == 16000 else "8k" sample_rate_str = "16k" if sample_rate == 16000 else "8k"
tag = model_type + "-" + sample_rate_str tag = model_type + "-" + sample_rate_str
self.task_resource.set_task_model(tag, version=None)
logger.info(f"load the pretrained model: {tag}") logger.info(f"load the pretrained model: {tag}")
# get the model from the pretrained list # get the model from the pretrained list
# we download the pretrained model and store it in the res_path # we download the pretrained model and store it in the res_path
@ -303,10 +301,11 @@ class VectorExecutor(BaseExecutor):
self.res_path = res_path self.res_path = res_path
self.cfg_path = os.path.join( self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path']) self.task_resource.res_dir,
self.task_resource.res_dict['cfg_path'])
self.ckpt_path = os.path.join( self.ckpt_path = os.path.join(
res_path, self.task_resource.res_dir,
self.pretrained_models[tag]['ckpt_path'] + '.pdparams') self.task_resource.res_dict['ckpt_path'] + '.pdparams')
else: else:
# get the model from disk # get the model from disk
self.cfg_path = os.path.abspath(cfg_path) self.cfg_path = os.path.abspath(cfg_path)
@ -325,8 +324,8 @@ class VectorExecutor(BaseExecutor):
# stage 3: get the model name to instance the model network with dynamic_import # stage 3: get the model name to instance the model network with dynamic_import
logger.info("start to dynamic import the model class") logger.info("start to dynamic import the model class")
model_name = model_type[:model_type.rindex('_')] model_name = model_type[:model_type.rindex('_')]
model_class = self.task_resource.get_model_class(model_name)
logger.info(f"model name {model_name}") logger.info(f"model name {model_name}")
model_class = dynamic_import(model_name, self.model_alias)
model_conf = self.config.model model_conf = self.config.model
backbone = model_class(**model_conf) backbone = model_class(**model_conf)
model = SpeakerIdetification( model = SpeakerIdetification(

36
paddlespeech/cli/vector/pretrained_models.py
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@ -1,36 +0,0 @@
# 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}][-{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_1.tar.gz',
'md5':
'67c7ff8885d5246bd16e0f5ac1cba99f',
'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",
}

4
paddlespeech/cli/stats/__init__.py → paddlespeech/resource/__init__.py
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@ -1,4 +1,4 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
@ -11,4 +11,4 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from .infer import StatsExecutor from .resource import CommonTaskResource

822
paddlespeech/resource/pretrained_models.py
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@ -0,0 +1,822 @@
# 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.
__all__ = [
'asr_dynamic_pretrained_models',
'asr_static_pretrained_models',
'cls_dynamic_pretrained_models',
'cls_static_pretrained_models',
'st_dynamic_pretrained_models',
'st_kaldi_bins',
'text_dynamic_pretrained_models',
'tts_dynamic_pretrained_models',
'tts_static_pretrained_models',
'tts_onnx_pretrained_models',
'vector_dynamic_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"
# ---------------------------------
# -------------- ASR --------------
# ---------------------------------
asr_dynamic_pretrained_models = {
"conformer_wenetspeech-zh-16k": {
'1.0': {
'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',
},
},
"conformer_online_wenetspeech-zh-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_wenetspeech_ckpt_1.0.0a.model.tar.gz',
'md5':
'b8c02632b04da34aca88459835be54a6',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/avg_10',
},
},
"conformer_online_multicn-zh-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.0.model.tar.gz',
'md5':
'7989b3248c898070904cf042fd656003',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/multi_cn',
},
'2.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.3.model.tar.gz',
'md5':
'0ac93d390552336f2a906aec9e33c5fa',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/multi_cn',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
},
"conformer_aishell-zh-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_0.1.2.model.tar.gz',
'md5':
'3f073eccfa7bb14e0c6867d65fc0dc3a',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/conformer/checkpoints/avg_30',
},
},
"conformer_online_aishell-zh-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_chunk_conformer_aishell_ckpt_0.2.0.model.tar.gz',
'md5':
'b374cfb93537761270b6224fb0bfc26a',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/avg_30',
},
},
"transformer_librispeech-en-16k": {
'1.0': {
'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',
},
},
"deepspeech2online_wenetspeech-zh-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr0/asr0_deepspeech2_online_wenetspeech_ckpt_1.0.0a.model.tar.gz',
'md5':
'e393d4d274af0f6967db24fc146e8074',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_10',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
},
"deepspeech2offline_aishell-zh-16k": {
'1.0': {
'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": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz',
'md5':
'98b87b171b7240b7cae6e07d8d0bc9be',
'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": {
'1.0': {
'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'
},
},
}
asr_static_pretrained_models = {
"deepspeech2offline_aishell-zh-16k": {
'1.0': {
'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',
'model':
'exp/deepspeech2/checkpoints/avg_1.jit.pdmodel',
'params':
'exp/deepspeech2/checkpoints/avg_1.jit.pdiparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
}
},
}
# ---------------------------------
# -------------- CLS --------------
# ---------------------------------
cls_dynamic_pretrained_models = {
"panns_cnn6-32k": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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',
},
},
}
cls_static_pretrained_models = {
"panns_cnn6-32k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz',
'md5':
'da087c31046d23281d8ec5188c1967da',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
},
"panns_cnn10-32k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz',
'md5':
'5460cc6eafbfaf0f261cc75b90284ae1',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
},
"panns_cnn14-32k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz',
'md5':
'ccc80b194821274da79466862b2ab00f',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
},
}
# ---------------------------------
# -------------- ST ---------------
# ---------------------------------
st_dynamic_pretrained_models = {
"fat_st_ted-en-zh": {
'1.0': {
"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",
},
},
}
st_kaldi_bins = {
"url":
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz",
"md5":
"c0682303b3f3393dbf6ed4c4e35a53eb",
}
# ---------------------------------
# -------------- TEXT -------------
# ---------------------------------
text_dynamic_pretrained_models = {
"ernie_linear_p7_wudao-punc-zh": {
'1.0': {
'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": {
'1.0': {
'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',
},
},
}
# ---------------------------------
# -------------- TTS --------------
# ---------------------------------
tts_dynamic_pretrained_models = {
# speedyspeech
"speedyspeech_csmsc-zh": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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": {
'1.0': {
'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',
},
},
"fastspeech2_cnndecoder_csmsc-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_ckpt_1.0.0.zip',
'md5':
'6eb28e22ace73e0ebe7845f86478f89f',
'config':
'cnndecoder.yaml',
'ckpt':
'snapshot_iter_153000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
},
}
tts_static_pretrained_models = {
# speedyspeech
"speedyspeech_csmsc-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip',
'md5':
'f10cbdedf47dc7a9668d2264494e1823',
'model':
'speedyspeech_csmsc.pdmodel',
'params':
'speedyspeech_csmsc.pdiparams',
'phones_dict':
'phone_id_map.txt',
'tones_dict':
'tone_id_map.txt',
'sample_rate':
24000,
},
},
# fastspeech2
"fastspeech2_csmsc-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip',
'md5':
'9788cd9745e14c7a5d12d32670b2a5a7',
'model':
'fastspeech2_csmsc.pdmodel',
'params':
'fastspeech2_csmsc.pdiparams',
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
},
# pwgan
"pwgan_csmsc-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip',
'md5':
'e3504aed9c5a290be12d1347836d2742',
'model':
'pwgan_csmsc.pdmodel',
'params':
'pwgan_csmsc.pdiparams',
'sample_rate':
24000,
},
},
# mb_melgan
"mb_melgan_csmsc-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_0.1.1.zip',
'md5':
'ac6eee94ba483421d750433f4c3b8d36',
'model':
'mb_melgan_csmsc.pdmodel',
'params':
'mb_melgan_csmsc.pdiparams',
'sample_rate':
24000,
},
},
# hifigan
"hifigan_csmsc-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_static_0.1.1.zip',
'md5':
'7edd8c436b3a5546b3a7cb8cff9d5a0c',
'model':
'hifigan_csmsc.pdmodel',
'params':
'hifigan_csmsc.pdiparams',
'sample_rate':
24000,
},
},
}
tts_onnx_pretrained_models = {
# fastspeech2
"fastspeech2_csmsc_onnx-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_onnx_0.2.0.zip',
'md5':
'fd3ad38d83273ad51f0ea4f4abf3ab4e',
'ckpt': ['fastspeech2_csmsc.onnx'],
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
},
"fastspeech2_cnndecoder_csmsc_onnx-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip',
'md5':
'5f70e1a6bcd29d72d54e7931aa86f266',
'ckpt': [
'fastspeech2_csmsc_am_encoder_infer.onnx',
'fastspeech2_csmsc_am_decoder.onnx',
'fastspeech2_csmsc_am_postnet.onnx',
],
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
},
# mb_melgan
"mb_melgan_csmsc_onnx-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip',
'md5':
'5b83ec746e8414bc29032d954ffd07ec',
'ckpt':
'mb_melgan_csmsc.onnx',
'sample_rate':
24000,
},
},
# hifigan
"hifigan_csmsc_onnx-zh": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_onnx_0.2.0.zip',
'md5':
'1a7dc0385875889e46952e50c0994a6b',
'ckpt':
'hifigan_csmsc.onnx',
'sample_rate':
24000,
},
},
}
# ---------------------------------
# ------------ Vector -------------
# ---------------------------------
vector_dynamic_pretrained_models = {
"ecapatdnn_voxceleb12-16k": {
'1.0': {
'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
},
},
}

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paddlespeech/resource/resource.py
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# 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.
import os
from collections import OrderedDict
from typing import Dict
from typing import List
from typing import Optional
from ..cli.utils import download_and_decompress
from ..cli.utils import MODEL_HOME
from ..utils.dynamic_import import dynamic_import
from .model_alias import model_alias
task_supported = ['asr', 'cls', 'st', 'text', 'tts', 'vector']
model_format_supported = ['dynamic', 'static', 'onnx']
inference_mode_supported = ['online', 'offline']
class CommonTaskResource:
def __init__(self, task: str, model_format: str='dynamic', **kwargs):
assert task in task_supported, 'Arg "task" must be one of {}.'.format(
task_supported)
assert model_format in model_format_supported, 'Arg "model_format" must be one of {}.'.format(
model_format_supported)
self.task = task
self.model_format = model_format
self.pretrained_models = self._get_pretrained_models()
if 'inference_mode' in kwargs:
assert kwargs[
'inference_mode'] in inference_mode_supported, 'Arg "inference_mode" must be one of {}.'.format(
inference_mode_supported)
self._inference_mode_filter(kwargs['inference_mode'])
# Initialize after model and version had been set.
self.model_tag = None
self.version = None
self.res_dict = None
self.res_dir = None
if self.task == 'tts':
# For vocoder
self.voc_model_tag = None
self.voc_version = None
self.voc_res_dict = None
self.voc_res_dir = None
def set_task_model(self,
model_tag: str,
model_type: int=0,
version: Optional[str]=None):
"""Set model tag and version of current task.
Args:
model_tag (str): Model tag.
model_type (int): 0 for acoustic model otherwise vocoder in tts task.
version (Optional[str], optional): Version of pretrained model. Defaults to None.
"""
assert model_tag in self.pretrained_models, \
"Can't find \"{}\" in resource. Model name must be one of {}".format(model_tag, list(self.pretrained_models.keys()))
if version is None:
version = self._get_default_version(model_tag)
assert version in self.pretrained_models[model_tag], \
"Can't find version \"{}\" in \"{}\". Model name must be one of {}".format(
version, model_tag, list(self.pretrained_models[model_tag].keys()))
if model_type == 0:
self.model_tag = model_tag
self.version = version
self.res_dict = self.pretrained_models[model_tag][version]
self.res_dir = self._fetch(self.res_dict,
self._get_model_dir(model_type))
else:
assert self.task == 'tts', 'Vocoder will only be used in tts task.'
self.voc_model_tag = model_tag
self.voc_version = version
self.voc_res_dict = self.pretrained_models[model_tag][version]
self.voc_res_dir = self._fetch(self.voc_res_dict,
self._get_model_dir(model_type))
@staticmethod
def get_model_class(model_name) -> List[object]:
"""Dynamic import model class.
Args:
model_name (str): Model name.
Returns:
List[object]: Return a list of model class.
"""
assert model_name in model_alias, 'No model classes found for "{}"'.format(
model_name)
ret = []
for import_path in model_alias[model_name]:
ret.append(dynamic_import(import_path))
if len(ret) == 1:
return ret[0]
else:
return ret
def get_versions(self, model_tag: str) -> List[str]:
"""List all available versions.
Args:
model_tag (str): Model tag.
Returns:
List[str]: Version list of model.
"""
return list(self.pretrained_models[model_tag].keys())
def _get_default_version(self, model_tag: str) -> str:
"""Get default version of model.
Args:
model_tag (str): Model tag.
Returns:
str: Default version.
"""
return self.get_versions(model_tag)[-1] # get latest version
def _get_model_dir(self, model_type: int=0) -> os.PathLike:
"""Get resource directory.
Args:
model_type (int): 0 for acoustic model otherwise vocoder in tts task.
Returns:
os.PathLike: Directory of model resource.
"""
if model_type == 0:
model_tag = self.model_tag
version = self.version
else:
model_tag = self.voc_model_tag
version = self.voc_version
return os.path.join(MODEL_HOME, model_tag, version)
def _get_pretrained_models(self) -> Dict[str, str]:
"""Get all available models for current task.
Returns:
Dict[str, str]: A dictionary with model tag and resources info.
"""
try:
import_models = '{}_{}_pretrained_models'.format(self.task,
self.model_format)
exec('from .pretrained_models import {}'.format(import_models))
models = OrderedDict(locals()[import_models])
except ImportError:
models = OrderedDict({}) # no models.
finally:
return models
def _inference_mode_filter(self, inference_mode: Optional[str]):
"""Filter models dict based on inference_mode.
Args:
inference_mode (Optional[str]): 'online', 'offline' or None.
"""
if inference_mode is None:
return
if self.task == 'asr':
online_flags = [
'online' in model_tag
for model_tag in self.pretrained_models.keys()
]
for online_flag, model_tag in zip(
online_flags, list(self.pretrained_models.keys())):
if inference_mode == 'online' and online_flag:
continue
elif inference_mode == 'offline' and not online_flag:
continue
else:
del self.pretrained_models[model_tag]
elif self.task == 'tts':
# Hardcode for tts online models.
tts_online_models = [
'fastspeech2_csmsc-zh', 'fastspeech2_cnndecoder_csmsc-zh',
'mb_melgan_csmsc-zh', 'hifigan_csmsc-zh'
]
for model_tag in list(self.pretrained_models.keys()):
if inference_mode == 'online' and model_tag in tts_online_models:
continue
elif inference_mode == 'offline':
continue
else:
del self.pretrained_models[model_tag]
else:
raise NotImplementedError('Only supports asr and tts task.')
@staticmethod
def _fetch(res_dict: Dict[str, str],
target_dir: os.PathLike) -> os.PathLike:
"""Fetch archive from url.
Args:
res_dict (Dict[str, str]): Info dict of a resource.
target_dir (os.PathLike): Directory to save archives.
Returns:
os.PathLike: Directory of model resource.
"""
return download_and_decompress(res_dict, target_dir)

110
paddlespeech/server/bin/paddlespeech_server.py
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@ -25,6 +25,7 @@ from ..executor import BaseExecutor
from ..util import cli_server_register from ..util import cli_server_register
from ..util import stats_wrapper from ..util import stats_wrapper
from paddlespeech.cli.log import logger from paddlespeech.cli.log import logger
from paddlespeech.resource import CommonTaskResource
from paddlespeech.server.engine.engine_pool import init_engine_pool from paddlespeech.server.engine.engine_pool import init_engine_pool
from paddlespeech.server.restful.api import setup_router as setup_http_router from paddlespeech.server.restful.api import setup_router as setup_http_router
from paddlespeech.server.utils.config import get_config from paddlespeech.server.utils.config import get_config
@ -152,101 +153,30 @@ class ServerStatsExecutor():
"Please input correct speech task, choices = ['asr', 'tts']") "Please input correct speech task, choices = ['asr', 'tts']")
return False return False
elif self.task.lower() == 'asr': try:
try: # Dynamic models
from paddlespeech.cli.asr.infer import pretrained_models dynamic_pretrained_models = CommonTaskResource(
logger.info( task=self.task, model_format='dynamic').pretrained_models
"Here is the table of ASR pretrained models supported in the service."
)
self.show_support_models(pretrained_models)
# show ASR static pretrained model
from paddlespeech.server.engine.asr.paddleinference.asr_engine import pretrained_models
logger.info(
"Here is the table of ASR static pretrained models supported in the service."
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error(
"Failed to get the table of ASR pretrained models supported in the service."
)
return False
elif self.task.lower() == 'tts':
try:
from paddlespeech.cli.tts.infer import pretrained_models
logger.info(
"Here is the table of TTS pretrained models supported in the service."
)
self.show_support_models(pretrained_models)
# show TTS static pretrained model
from paddlespeech.server.engine.tts.paddleinference.tts_engine import pretrained_models
logger.info(
"Here is the table of TTS static pretrained models supported in the service."
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error(
"Failed to get the table of TTS pretrained models supported in the service."
)
return False
elif self.task.lower() == 'cls': if len(dynamic_pretrained_models) > 0:
try:
from paddlespeech.cli.cls.infer import pretrained_models
logger.info( logger.info(
"Here is the table of CLS pretrained models supported in the service." "Here is the table of {} pretrained models supported in the service.".
) format(self.task.upper()))
self.show_support_models(pretrained_models) self.show_support_models(dynamic_pretrained_models)
# show CLS static pretrained model # Static models
from paddlespeech.server.engine.cls.paddleinference.cls_engine import pretrained_models static_pretrained_models = CommonTaskResource(
task=self.task, model_format='static').pretrained_models
if len(static_pretrained_models) > 0:
logger.info( logger.info(
"Here is the table of CLS static pretrained models supported in the service." "Here is the table of {} static pretrained models supported in the service.".
) format(self.task.upper()))
self.show_support_models(pretrained_models) self.show_support_models(pretrained_models)
return True return True
except BaseException:
logger.error(
"Failed to get the table of CLS pretrained models supported in the service."
)
return False
elif self.task.lower() == 'text':
try:
from paddlespeech.cli.text.infer import pretrained_models
logger.info(
"Here is the table of Text pretrained models supported in the service."
)
self.show_support_models(pretrained_models)
return True except BaseException:
except BaseException:
logger.error(
"Failed to get the table of Text pretrained models supported in the service."
)
return False
elif self.task.lower() == 'vector':
try:
from paddlespeech.cli.vector.infer import pretrained_models
logger.info(
"Here is the table of Vector pretrained models supported in the service."
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error(
"Failed to get the table of Vector pretrained models supported in the service."
)
return False
else:
logger.error( logger.error(
f"Failed to get the table of {self.task} pretrained models supported in the service." "Failed to get the table of {} pretrained models supported in the service.".
) format(self.task.upper()))
return False return False

40
paddlespeech/server/engine/asr/online/asr_engine.py
Unescape View File

@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import copy
import os import os
import sys import sys
from typing import Optional from typing import Optional
@ -21,15 +20,14 @@ import paddle
from numpy import float32 from numpy import float32
from yacs.config import CfgNode from yacs.config import CfgNode
from .pretrained_models import pretrained_models
from paddlespeech.cli.asr.infer import ASRExecutor from paddlespeech.cli.asr.infer import ASRExecutor
from paddlespeech.cli.log import logger from paddlespeech.cli.log import logger
from paddlespeech.cli.utils import MODEL_HOME from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.resource import CommonTaskResource
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.frontend.speech import SpeechSegment from paddlespeech.s2t.frontend.speech import SpeechSegment
from paddlespeech.s2t.modules.ctc import CTCDecoder from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.s2t.transform.transformation import Transformation from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.s2t.utils.tensor_utils import add_sos_eos from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
from paddlespeech.s2t.utils.tensor_utils import pad_sequence from paddlespeech.s2t.utils.tensor_utils import pad_sequence
from paddlespeech.s2t.utils.utility import UpdateConfig from paddlespeech.s2t.utils.utility import UpdateConfig
@ -53,7 +51,7 @@ class PaddleASRConnectionHanddler:
logger.info( logger.info(
"create an paddle asr connection handler to process the websocket connection" "create an paddle asr connection handler to process the websocket connection"
) )
self.config = asr_engine.config # server config self.config = asr_engine.config # server config
self.model_config = asr_engine.executor.config self.model_config = asr_engine.executor.config
self.asr_engine = asr_engine self.asr_engine = asr_engine
@ -251,10 +249,12 @@ class PaddleASRConnectionHanddler:
# for deepspeech2 # for deepspeech2
# init state # init state
self.chunk_state_h_box = np.zeros( self.chunk_state_h_box = np.zeros(
(self.model_config .num_rnn_layers, 1, self.model_config.rnn_layer_size), (self.model_config.num_rnn_layers, 1,
self.model_config.rnn_layer_size),
dtype=float32) dtype=float32)
self.chunk_state_c_box = np.zeros( self.chunk_state_c_box = np.zeros(
(self.model_config.num_rnn_layers, 1, self.model_config.rnn_layer_size), (self.model_config.num_rnn_layers, 1,
self.model_config.rnn_layer_size),
dtype=float32) dtype=float32)
self.decoder.reset_decoder(batch_size=1) self.decoder.reset_decoder(batch_size=1)
@ -699,7 +699,8 @@ class PaddleASRConnectionHanddler:
class ASRServerExecutor(ASRExecutor): class ASRServerExecutor(ASRExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__()
self.pretrained_models = pretrained_models self.task_resource = CommonTaskResource(
task='asr', model_format='dynamic', inference_mode='online')
def _init_from_path(self, def _init_from_path(self,
model_type: str=None, model_type: str=None,
@ -723,20 +724,19 @@ class ASRServerExecutor(ASRExecutor):
self.sample_rate = sample_rate self.sample_rate = sample_rate
sample_rate_str = '16k' if sample_rate == 16000 else '8k' sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str tag = model_type + '-' + lang + '-' + sample_rate_str
self.task_resource.set_task_model(model_tag=tag)
if cfg_path is None or am_model is None or am_params is None: if cfg_path is None or am_model is None or am_params is None:
logger.info(f"Load the pretrained model, tag = {tag}") logger.info(f"Load the pretrained model, tag = {tag}")
res_path = self._get_pretrained_path(tag) # wenetspeech_zh self.res_path = self.task_resource.res_dir
self.res_path = res_path
self.cfg_path = os.path.join( self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path']) self.res_path, self.task_resource.res_dict['cfg_path'])
self.am_model = os.path.join(res_path, self.am_model = os.path.join(self.res_path,
self.pretrained_models[tag]['model']) self.task_resource.res_dict['model'])
self.am_params = os.path.join(res_path, self.am_params = os.path.join(self.res_path,
self.pretrained_models[tag]['params']) self.task_resource.res_dict['params'])
logger.info(res_path) logger.info(self.res_path)
else: else:
self.cfg_path = os.path.abspath(cfg_path) self.cfg_path = os.path.abspath(cfg_path)
self.am_model = os.path.abspath(am_model) self.am_model = os.path.abspath(am_model)
@ -763,8 +763,8 @@ class ASRServerExecutor(ASRExecutor):
self.text_feature = TextFeaturizer( self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab) unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = self.pretrained_models[tag]['lm_url'] lm_url = self.task_resource.res_dict['lm_url']
lm_md5 = self.pretrained_models[tag]['lm_md5'] lm_md5 = self.task_resource.res_dict['lm_md5']
logger.info(f"Start to load language model {lm_url}") logger.info(f"Start to load language model {lm_url}")
self.download_lm( self.download_lm(
lm_url, lm_url,
@ -810,7 +810,7 @@ class ASRServerExecutor(ASRExecutor):
model_name = model_type[:model_type.rindex( model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset} '_')] # model_type: {model_name}_{dataset}
logger.info(f"model name: {model_name}") logger.info(f"model name: {model_name}")
model_class = dynamic_import(model_name, self.model_alias) model_class = self.task_resource.get_model_class(model_name)
model_conf = self.config model_conf = self.config
model = model_class.from_config(model_conf) model = model_class.from_config(model_conf)
self.model = model self.model = model
@ -824,7 +824,7 @@ class ASRServerExecutor(ASRExecutor):
raise ValueError(f"Not support: {model_type}") raise ValueError(f"Not support: {model_type}")
return True return True
class ASREngine(BaseEngine): class ASREngine(BaseEngine):
"""ASR server resource """ASR server resource

70
paddlespeech/server/engine/asr/online/pretrained_models.py
Unescape View File

@ -1,70 +0,0 @@
# 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 = {
"deepspeech2online_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz',
'md5':
'98b87b171b7240b7cae6e07d8d0bc9be',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_1',
'model':
'exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel',
'params':
'exp/deepspeech2_online/checkpoints/avg_1.jit.pdiparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"conformer_online_multicn-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.3.model.tar.gz',
'md5':
'0ac93d390552336f2a906aec9e33c5fa',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/multi_cn',
'model':
'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
'params':
'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"conformer_online_wenetspeech-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_wenetspeech_ckpt_1.0.0a.model.tar.gz',
'md5':
'b8c02632b04da34aca88459835be54a6',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/avg_10',
'model':
'exp/chunk_conformer/checkpoints/avg_10.pdparams',
'params':
'exp/chunk_conformer/checkpoints/avg_10.pdparams',
'lm_url':
'',
'lm_md5':
'',
},
}

25
paddlespeech/server/engine/asr/paddleinference/asr_engine.py
Unescape View File

@ -19,10 +19,10 @@ from typing import Optional
import paddle import paddle
from yacs.config import CfgNode from yacs.config import CfgNode
from .pretrained_models import pretrained_models
from paddlespeech.cli.asr.infer import ASRExecutor from paddlespeech.cli.asr.infer import ASRExecutor
from paddlespeech.cli.log import logger from paddlespeech.cli.log import logger
from paddlespeech.cli.utils import MODEL_HOME from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.resource import CommonTaskResource
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.modules.ctc import CTCDecoder from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.s2t.utils.utility import UpdateConfig from paddlespeech.s2t.utils.utility import UpdateConfig
@ -36,7 +36,8 @@ __all__ = ['ASREngine']
class ASRServerExecutor(ASRExecutor): class ASRServerExecutor(ASRExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__()
self.pretrained_models = pretrained_models self.task_resource = CommonTaskResource(
task='asr', model_format='static', inference_mode='online')
def _init_from_path(self, def _init_from_path(self,
model_type: str='wenetspeech', model_type: str='wenetspeech',
@ -53,17 +54,17 @@ class ASRServerExecutor(ASRExecutor):
sample_rate_str = '16k' if sample_rate == 16000 else '8k' sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str tag = model_type + '-' + lang + '-' + sample_rate_str
self.task_resource.set_task_model(model_tag=tag)
if cfg_path is None or am_model is None or am_params is None: if cfg_path is None or am_model is None or am_params is None:
res_path = self._get_pretrained_path(tag) # wenetspeech_zh self.res_path = self.task_resource.res_dir
self.res_path = res_path
self.cfg_path = os.path.join( self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path']) self.res_path, self.task_resource.res_dict['cfg_path'])
self.am_model = os.path.join(res_path, self.am_model = os.path.join(self.res_path,
self.pretrained_models[tag]['model']) self.task_resource.res_dict['model'])
self.am_params = os.path.join(res_path, self.am_params = os.path.join(self.res_path,
self.pretrained_models[tag]['params']) self.task_resource.res_dict['params'])
logger.info(res_path) logger.info(self.res_path)
logger.info(self.cfg_path) logger.info(self.cfg_path)
logger.info(self.am_model) logger.info(self.am_model)
logger.info(self.am_params) logger.info(self.am_params)
@ -89,8 +90,8 @@ class ASRServerExecutor(ASRExecutor):
self.text_feature = TextFeaturizer( self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab) unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = self.pretrained_models[tag]['lm_url'] lm_url = self.task_resource.res_dict['lm_url']
lm_md5 = self.pretrained_models[tag]['lm_md5'] lm_md5 = self.task_resource.res_dict['lm_md5']
self.download_lm( self.download_lm(
lm_url, lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5) os.path.dirname(self.config.decode.lang_model_path), lm_md5)

34
paddlespeech/server/engine/asr/paddleinference/pretrained_models.py
Unescape View File

@ -1,34 +0,0 @@
# 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 = {
"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',
'model':
'exp/deepspeech2/checkpoints/avg_1.jit.pdmodel',
'params':
'exp/deepspeech2/checkpoints/avg_1.jit.pdiparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
}

18
paddlespeech/server/engine/cls/paddleinference/cls_engine.py
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@ -20,9 +20,9 @@ import numpy as np
import paddle import paddle
import yaml import yaml
from .pretrained_models import pretrained_models
from paddlespeech.cli.cls.infer import CLSExecutor from paddlespeech.cli.cls.infer import CLSExecutor
from paddlespeech.cli.log import logger from paddlespeech.cli.log import logger
from paddlespeech.resource import CommonTaskResource
from paddlespeech.server.engine.base_engine import BaseEngine from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.paddle_predictor import init_predictor from paddlespeech.server.utils.paddle_predictor import init_predictor
from paddlespeech.server.utils.paddle_predictor import run_model from paddlespeech.server.utils.paddle_predictor import run_model
@ -33,11 +33,12 @@ __all__ = ['CLSEngine']
class CLSServerExecutor(CLSExecutor): class CLSServerExecutor(CLSExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__()
self.pretrained_models = pretrained_models self.task_resource = CommonTaskResource(
task='cls', model_format='static')
def _init_from_path( def _init_from_path(
self, self,
model_type: str='panns_cnn14', model_type: str='panns_cnn14_audioset',
cfg_path: Optional[os.PathLike]=None, cfg_path: Optional[os.PathLike]=None,
model_path: Optional[os.PathLike]=None, model_path: Optional[os.PathLike]=None,
params_path: Optional[os.PathLike]=None, params_path: Optional[os.PathLike]=None,
@ -49,15 +50,16 @@ class CLSServerExecutor(CLSExecutor):
if cfg_path is None or model_path is None or params_path is None or label_file is None: if cfg_path is None or model_path is None or params_path is None or label_file is None:
tag = model_type + '-' + '32k' tag = model_type + '-' + '32k'
self.res_path = self._get_pretrained_path(tag) self.task_resource.set_task_model(model_tag=tag)
self.res_path = self.task_resource.res_dir
self.cfg_path = os.path.join( self.cfg_path = os.path.join(
self.res_path, self.pretrained_models[tag]['cfg_path']) self.res_path, self.task_resource.res_dict['cfg_path'])
self.model_path = os.path.join( self.model_path = os.path.join(
self.res_path, self.pretrained_models[tag]['model_path']) self.res_path, self.task_resource.res_dict['model_path'])
self.params_path = os.path.join( self.params_path = os.path.join(
self.res_path, self.pretrained_models[tag]['params_path']) self.res_path, self.task_resource.res_dict['params_path'])
self.label_file = os.path.join( self.label_file = os.path.join(
self.res_path, self.pretrained_models[tag]['label_file']) self.res_path, self.task_resource.res_dict['label_file'])
else: else:
self.cfg_path = os.path.abspath(cfg_path) self.cfg_path = os.path.abspath(cfg_path)
self.model_path = os.path.abspath(model_path) self.model_path = os.path.abspath(model_path)

58
paddlespeech/server/engine/cls/paddleinference/pretrained_models.py
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@ -1,58 +0,0 @@
# 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 = {
"panns_cnn6-32k": {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz',
'md5':
'da087c31046d23281d8ec5188c1967da',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
"panns_cnn10-32k": {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz',
'md5':
'5460cc6eafbfaf0f261cc75b90284ae1',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
"panns_cnn14-32k": {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz',
'md5':
'ccc80b194821274da79466862b2ab00f',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
}

69
paddlespeech/server/engine/tts/online/onnx/pretrained_models.py
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@ -1,69 +0,0 @@
# 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.
# support online model
pretrained_models = {
# fastspeech2
"fastspeech2_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_onnx_0.2.0.zip',
'md5':
'fd3ad38d83273ad51f0ea4f4abf3ab4e',
'ckpt': ['fastspeech2_csmsc.onnx'],
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
"fastspeech2_cnndecoder_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip',
'md5':
'5f70e1a6bcd29d72d54e7931aa86f266',
'ckpt': [
'fastspeech2_csmsc_am_encoder_infer.onnx',
'fastspeech2_csmsc_am_decoder.onnx',
'fastspeech2_csmsc_am_postnet.onnx',
],
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
# mb_melgan
"mb_melgan_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip',
'md5':
'5b83ec746e8414bc29032d954ffd07ec',
'ckpt':
'mb_melgan_csmsc.onnx',
'sample_rate':
24000,
},
# hifigan
"hifigan_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_onnx_0.2.0.zip',
'md5':
'1a7dc0385875889e46952e50c0994a6b',
'ckpt':
'hifigan_csmsc.onnx',
'sample_rate':
24000,
},
}

41
paddlespeech/server/engine/tts/online/onnx/tts_engine.py
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@ -20,9 +20,9 @@ from typing import Optional
import numpy as np import numpy as np
import paddle import paddle
from .pretrained_models import pretrained_models
from paddlespeech.cli.log import logger from paddlespeech.cli.log import logger
from paddlespeech.cli.tts.infer import TTSExecutor from paddlespeech.cli.tts.infer import TTSExecutor
from paddlespeech.resource import CommonTaskResource
from paddlespeech.server.engine.base_engine import BaseEngine from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.audio_process import float2pcm from paddlespeech.server.utils.audio_process import float2pcm
from paddlespeech.server.utils.onnx_infer import get_sess from paddlespeech.server.utils.onnx_infer import get_sess
@ -43,7 +43,7 @@ class TTSServerExecutor(TTSExecutor):
self.voc_pad = voc_pad self.voc_pad = voc_pad
self.voc_upsample = voc_upsample self.voc_upsample = voc_upsample
self.pretrained_models = pretrained_models self.task_resource = CommonTaskResource(task='tts', model_format='onnx')
def _init_from_path( def _init_from_path(
self, self,
@ -72,16 +72,21 @@ class TTSServerExecutor(TTSExecutor):
return return
# am # am
am_tag = am + '-' + lang am_tag = am + '-' + lang
self.task_resource.set_task_model(
model_tag=am_tag,
model_type=0, # am
version=None, # default version
)
self.am_res_path = self.task_resource.res_dir
if am == "fastspeech2_csmsc_onnx": if am == "fastspeech2_csmsc_onnx":
# get model info # get model info
if am_ckpt is None or phones_dict is None: if am_ckpt 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_ckpt = os.path.join( self.am_ckpt = os.path.join(
am_res_path, self.pretrained_models[am_tag]['ckpt'][0]) self.am_res_path, self.task_resource.res_dict['ckpt'][0])
# must have phones_dict in acoustic # must have phones_dict in acoustic
self.phones_dict = os.path.join( self.phones_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['phones_dict']) self.am_res_path,
self.task_resource.res_dict['phones_dict'])
else: else:
self.am_ckpt = os.path.abspath(am_ckpt[0]) self.am_ckpt = os.path.abspath(am_ckpt[0])
@ -94,19 +99,19 @@ class TTSServerExecutor(TTSExecutor):
elif am == "fastspeech2_cnndecoder_csmsc_onnx": elif am == "fastspeech2_cnndecoder_csmsc_onnx":
if am_ckpt is None or am_stat is None or phones_dict is None: if am_ckpt 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_encoder_infer = os.path.join( self.am_encoder_infer = os.path.join(
am_res_path, self.pretrained_models[am_tag]['ckpt'][0]) self.am_res_path, self.task_resource.res_dict['ckpt'][0])
self.am_decoder = os.path.join( self.am_decoder = os.path.join(
am_res_path, self.pretrained_models[am_tag]['ckpt'][1]) self.am_res_path, self.task_resource.res_dict['ckpt'][1])
self.am_postnet = os.path.join( self.am_postnet = os.path.join(
am_res_path, self.pretrained_models[am_tag]['ckpt'][2]) self.am_res_path, self.task_resource.res_dict['ckpt'][2])
# must have phones_dict in acoustic # must have phones_dict in acoustic
self.phones_dict = os.path.join( self.phones_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['phones_dict']) self.am_res_path,
self.task_resource.res_dict['phones_dict'])
self.am_stat = os.path.join( self.am_stat = os.path.join(
am_res_path, self.pretrained_models[am_tag]['speech_stats']) self.am_res_path,
self.task_resource.res_dict['speech_stats'])
else: else:
self.am_encoder_infer = os.path.abspath(am_ckpt[0]) self.am_encoder_infer = os.path.abspath(am_ckpt[0])
@ -131,11 +136,15 @@ class TTSServerExecutor(TTSExecutor):
# voc model info # voc model info
voc_tag = voc + '-' + lang voc_tag = voc + '-' + lang
self.task_resource.set_task_model(
model_tag=voc_tag,
model_type=1, # vocoder
version=None, # default version
)
if voc_ckpt is None: if voc_ckpt is None:
voc_res_path = self._get_pretrained_path(voc_tag) self.voc_res_path = self.task_resource.voc_res_dir
self.voc_res_path = voc_res_path
self.voc_ckpt = os.path.join( self.voc_ckpt = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['ckpt']) self.voc_res_path, self.task_resource.voc_res_dict['ckpt'])
else: else:
self.voc_ckpt = os.path.abspath(voc_ckpt) self.voc_ckpt = os.path.abspath(voc_ckpt)
self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_ckpt)) self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_ckpt))

73
paddlespeech/server/engine/tts/online/python/pretrained_models.py
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@ -1,73 +0,0 @@
# 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.
# support online model
pretrained_models = {
# 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_cnndecoder_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_ckpt_1.0.0.zip',
'md5':
'6eb28e22ace73e0ebe7845f86478f89f',
'config':
'cnndecoder.yaml',
'ckpt':
'snapshot_iter_153000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
# 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',
},
# 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',
},
}

57
paddlespeech/server/engine/tts/online/python/tts_engine.py
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@ -22,9 +22,9 @@ import paddle
import yaml import yaml
from yacs.config import CfgNode from yacs.config import CfgNode
from .pretrained_models import pretrained_models
from paddlespeech.cli.log import logger from paddlespeech.cli.log import logger
from paddlespeech.cli.tts.infer import TTSExecutor from paddlespeech.cli.tts.infer import TTSExecutor
from paddlespeech.resource import CommonTaskResource
from paddlespeech.server.engine.base_engine import BaseEngine from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.audio_process import float2pcm from paddlespeech.server.utils.audio_process import float2pcm
from paddlespeech.server.utils.util import denorm from paddlespeech.server.utils.util import denorm
@ -32,7 +32,6 @@ from paddlespeech.server.utils.util import get_chunks
from paddlespeech.t2s.frontend import English from paddlespeech.t2s.frontend import English
from paddlespeech.t2s.frontend.zh_frontend import Frontend from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.t2s.modules.normalizer import ZScore from paddlespeech.t2s.modules.normalizer import ZScore
from paddlespeech.utils.dynamic_import import dynamic_import
__all__ = ['TTSEngine'] __all__ = ['TTSEngine']
@ -44,7 +43,8 @@ class TTSServerExecutor(TTSExecutor):
self.am_pad = am_pad self.am_pad = am_pad
self.voc_block = voc_block self.voc_block = voc_block
self.voc_pad = voc_pad self.voc_pad = voc_pad
self.pretrained_models = pretrained_models self.task_resource = CommonTaskResource(
task='tts', model_format='static', inference_mode='online')
def get_model_info(self, def get_model_info(self,
field: str, field: str,
@ -65,7 +65,7 @@ class TTSServerExecutor(TTSExecutor):
[Tensor]: standard deviation [Tensor]: standard deviation
""" """
model_class = dynamic_import(model_name, self.model_alias) model_class = self.task_resource.get_model_class(model_name)
if field == "am": if field == "am":
odim = self.am_config.n_mels odim = self.am_config.n_mels
@ -110,20 +110,24 @@ class TTSServerExecutor(TTSExecutor):
return return
# am model info # am model info
am_tag = am + '-' + lang am_tag = am + '-' + lang
self.task_resource.set_task_model(
model_tag=am_tag,
model_type=0, # am
version=None, # default version
)
if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None: 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 = self.task_resource.res_dir
self.am_res_path = am_res_path self.am_config = os.path.join(self.am_res_path,
self.am_config = os.path.join( self.task_resource.res_dict['config'])
am_res_path, self.pretrained_models[am_tag]['config']) self.am_ckpt = os.path.join(self.am_res_path,
self.am_ckpt = os.path.join(am_res_path, self.task_resource.res_dict['ckpt'])
self.pretrained_models[am_tag]['ckpt'])
self.am_stat = os.path.join( self.am_stat = os.path.join(
am_res_path, self.pretrained_models[am_tag]['speech_stats']) self.am_res_path, self.task_resource.res_dict['speech_stats'])
# must have phones_dict in acoustic # must have phones_dict in acoustic
self.phones_dict = os.path.join( self.phones_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['phones_dict']) self.am_res_path, self.task_resource.res_dict['phones_dict'])
print("self.phones_dict:", self.phones_dict) print("self.phones_dict:", self.phones_dict)
logger.info(am_res_path) logger.info(self.am_res_path)
logger.info(self.am_config) logger.info(self.am_config)
logger.info(self.am_ckpt) logger.info(self.am_ckpt)
else: else:
@ -139,16 +143,21 @@ class TTSServerExecutor(TTSExecutor):
# voc model info # voc model info
voc_tag = voc + '-' + lang voc_tag = voc + '-' + lang
self.task_resource.set_task_model(
model_tag=voc_tag,
model_type=1, # vocoder
version=None, # default version
)
if voc_ckpt is None or voc_config is None or voc_stat is None: 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 = self.task_resource.voc_res_dir
self.voc_res_path = voc_res_path
self.voc_config = os.path.join( self.voc_config = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['config']) self.voc_res_path, self.task_resource.voc_res_dict['config'])
self.voc_ckpt = os.path.join( self.voc_ckpt = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['ckpt']) self.voc_res_path, self.task_resource.voc_res_dict['ckpt'])
self.voc_stat = os.path.join( self.voc_stat = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['speech_stats']) self.voc_res_path,
logger.info(voc_res_path) self.task_resource.voc_res_dict['speech_stats'])
logger.info(self.voc_res_path)
logger.info(self.voc_config) logger.info(self.voc_config)
logger.info(self.voc_ckpt) logger.info(self.voc_ckpt)
else: else:
@ -188,8 +197,8 @@ class TTSServerExecutor(TTSExecutor):
am, am_mu, am_std = self.get_model_info("am", self.am_name, am, am_mu, am_std = self.get_model_info("am", self.am_name,
self.am_ckpt, self.am_stat) self.am_ckpt, self.am_stat)
am_normalizer = ZScore(am_mu, am_std) am_normalizer = ZScore(am_mu, am_std)
am_inference_class = dynamic_import(self.am_name + '_inference', am_inference_class = self.task_resource.get_model_class(
self.model_alias) self.am_name + '_inference')
self.am_inference = am_inference_class(am_normalizer, am) self.am_inference = am_inference_class(am_normalizer, am)
self.am_inference.eval() self.am_inference.eval()
print("acoustic model done!") print("acoustic model done!")
@ -199,8 +208,8 @@ class TTSServerExecutor(TTSExecutor):
voc, voc_mu, voc_std = self.get_model_info("voc", self.voc_name, voc, voc_mu, voc_std = self.get_model_info("voc", self.voc_name,
self.voc_ckpt, self.voc_stat) self.voc_ckpt, self.voc_stat)
voc_normalizer = ZScore(voc_mu, voc_std) voc_normalizer = ZScore(voc_mu, voc_std)
voc_inference_class = dynamic_import(self.voc_name + '_inference', voc_inference_class = self.task_resource.get_model_class(self.voc_name +
self.model_alias) '_inference')
self.voc_inference = voc_inference_class(voc_normalizer, voc) self.voc_inference = voc_inference_class(voc_normalizer, voc)
self.voc_inference.eval() self.voc_inference.eval()
print("voc done!") print("voc done!")
@ -505,4 +514,4 @@ class TTSEngine(BaseEngine):
logger.info(f"RTF: {self.executor.final_response_time / duration}") logger.info(f"RTF: {self.executor.final_response_time / duration}")
logger.info( logger.info(
f"Other info: front time: {self.executor.frontend_time} s, first am infer time: {self.executor.first_am_infer} s, first voc infer time: {self.executor.first_voc_infer} s," f"Other info: front time: {self.executor.frontend_time} s, first am infer time: {self.executor.first_am_infer} s, first voc infer time: {self.executor.first_voc_infer} s,"
) )

87
paddlespeech/server/engine/tts/paddleinference/pretrained_models.py
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@ -1,87 +0,0 @@
# 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.
# Static model applied on paddle inference
pretrained_models = {
# speedyspeech
"speedyspeech_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip',
'md5':
'f10cbdedf47dc7a9668d2264494e1823',
'model':
'speedyspeech_csmsc.pdmodel',
'params':
'speedyspeech_csmsc.pdiparams',
'phones_dict':
'phone_id_map.txt',
'tones_dict':
'tone_id_map.txt',
'sample_rate':
24000,
},
# fastspeech2
"fastspeech2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip',
'md5':
'9788cd9745e14c7a5d12d32670b2a5a7',
'model':
'fastspeech2_csmsc.pdmodel',
'params':
'fastspeech2_csmsc.pdiparams',
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
# pwgan
"pwgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip',
'md5':
'e3504aed9c5a290be12d1347836d2742',
'model':
'pwgan_csmsc.pdmodel',
'params':
'pwgan_csmsc.pdiparams',
'sample_rate':
24000,
},
# mb_melgan
"mb_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_0.1.1.zip',
'md5':
'ac6eee94ba483421d750433f4c3b8d36',
'model':
'mb_melgan_csmsc.pdmodel',
'params':
'mb_melgan_csmsc.pdiparams',
'sample_rate':
24000,
},
# hifigan
"hifigan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_static_0.1.1.zip',
'md5':
'7edd8c436b3a5546b3a7cb8cff9d5a0c',
'model':
'hifigan_csmsc.pdmodel',
'params':
'hifigan_csmsc.pdiparams',
'sample_rate':
24000,
},
}

51
paddlespeech/server/engine/tts/paddleinference/tts_engine.py
Unescape View File

@ -23,9 +23,9 @@ import paddle
import soundfile as sf import soundfile as sf
from scipy.io import wavfile from scipy.io import wavfile
from .pretrained_models import pretrained_models
from paddlespeech.cli.log import logger from paddlespeech.cli.log import logger
from paddlespeech.cli.tts.infer import TTSExecutor from paddlespeech.cli.tts.infer import TTSExecutor
from paddlespeech.resource import CommonTaskResource
from paddlespeech.server.engine.base_engine import BaseEngine from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.audio_process import change_speed from paddlespeech.server.utils.audio_process import change_speed
from paddlespeech.server.utils.errors import ErrorCode from paddlespeech.server.utils.errors import ErrorCode
@ -41,7 +41,8 @@ __all__ = ['TTSEngine']
class TTSServerExecutor(TTSExecutor): class TTSServerExecutor(TTSExecutor):
def __init__(self): def __init__(self):
super().__init__() super().__init__()
self.pretrained_models = pretrained_models self.task_resource = CommonTaskResource(
task='tts', model_format='static')
def _init_from_path( def _init_from_path(
self, self,
@ -67,19 +68,23 @@ class TTSServerExecutor(TTSExecutor):
return return
# am # am
am_tag = am + '-' + lang am_tag = am + '-' + lang
self.task_resource.set_task_model(
model_tag=am_tag,
model_type=0, # am
version=None, # default version
)
if am_model is None or am_params is None or phones_dict is None: if am_model is None or am_params is None or phones_dict is None:
am_res_path = self._get_pretrained_path(am_tag) self.am_res_path = self.task_resource.res_dir
self.am_res_path = am_res_path self.am_model = os.path.join(self.am_res_path,
self.am_model = os.path.join( self.task_resource.res_dict['model'])
am_res_path, self.pretrained_models[am_tag]['model']) self.am_params = os.path.join(self.am_res_path,
self.am_params = os.path.join( self.task_resource.res_dict['params'])
am_res_path, self.pretrained_models[am_tag]['params'])
# must have phones_dict in acoustic # must have phones_dict in acoustic
self.phones_dict = os.path.join( self.phones_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['phones_dict']) self.am_res_path, self.task_resource.res_dict['phones_dict'])
self.am_sample_rate = self.pretrained_models[am_tag]['sample_rate'] self.am_sample_rate = self.task_resource.res_dict['sample_rate']
logger.info(am_res_path) logger.info(self.am_res_path)
logger.info(self.am_model) logger.info(self.am_model)
logger.info(self.am_params) logger.info(self.am_params)
else: else:
@ -92,32 +97,36 @@ class TTSServerExecutor(TTSExecutor):
# for speedyspeech # for speedyspeech
self.tones_dict = None self.tones_dict = None
if 'tones_dict' in self.pretrained_models[am_tag]: if 'tones_dict' in self.task_resource.res_dict:
self.tones_dict = os.path.join( self.tones_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['tones_dict']) self.am_res_path, self.task_resource.res_dict['tones_dict'])
if tones_dict: if tones_dict:
self.tones_dict = tones_dict self.tones_dict = tones_dict
# for multi speaker fastspeech2 # for multi speaker fastspeech2
self.speaker_dict = None self.speaker_dict = None
if 'speaker_dict' in self.pretrained_models[am_tag]: if 'speaker_dict' in self.task_resource.res_dict:
self.speaker_dict = os.path.join( self.speaker_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['speaker_dict']) self.am_res_path, self.task_resource.res_dict['speaker_dict'])
if speaker_dict: if speaker_dict:
self.speaker_dict = speaker_dict self.speaker_dict = speaker_dict
# voc # voc
voc_tag = voc + '-' + lang voc_tag = voc + '-' + lang
self.task_resource.set_task_model(
model_tag=voc_tag,
model_type=1, # vocoder
version=None, # default version
)
if voc_model is None or voc_params is None: if voc_model is None or voc_params is None:
voc_res_path = self._get_pretrained_path(voc_tag) self.voc_res_path = self.task_resource.voc_res_dir
self.voc_res_path = voc_res_path
self.voc_model = os.path.join( self.voc_model = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['model']) self.voc_res_path, self.task_resource.voc_res_dict['model'])
self.voc_params = os.path.join( self.voc_params = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['params']) self.voc_res_path, self.task_resource.voc_res_dict['params'])
self.voc_sample_rate = self.pretrained_models[voc_tag][ self.voc_sample_rate = self.task_resource.voc_res_dict[
'sample_rate'] 'sample_rate']
logger.info(voc_res_path) logger.info(self.voc_res_path)
logger.info(self.voc_model) logger.info(self.voc_model)
logger.info(self.voc_params) logger.info(self.voc_params)
else: else:

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