Add paddlespeech.resource.

paddlespeech/cli/asr/infer.py

@@ -33,11 +33,8 @@ from ..utils import CLI_TIMER
 from ..utils import MODEL_HOME
 from ..utils import stats_wrapper
 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.transform.transformation import Transformation
-from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 from paddlespeech.s2t.utils.utility import UpdateConfig
 
 __all__ = ['ASRExecutor']
@@ -46,10 +43,7 @@ __all__ = ['ASRExecutor']
 @timer_register
 class ASRExecutor(BaseExecutor):
     def __init__(self):
-        super().__init__()
-        self.model_alias = model_alias
-        self.pretrained_models = pretrained_models
-
+        super().__init__(task='asr', inference_type='offline')
         self.parser = argparse.ArgumentParser(
             prog='paddlespeech.asr', add_help=True)
         self.parser.add_argument(
@@ -59,7 +53,8 @@ class ASRExecutor(BaseExecutor):
             type=str,
             default='conformer_wenetspeech',
             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.')
         self.parser.add_argument(
@@ -141,14 +136,14 @@ class ASRExecutor(BaseExecutor):
         if cfg_path is None or ckpt_path is None:
             sample_rate_str = '16k' if sample_rate == 16000 else '8k'
             tag = model_type + '-' + lang + '-' + sample_rate_str
-            res_path = self._get_pretrained_path(tag)  # wenetspeech_zh
-            self.res_path = res_path
+            self.task_resource.set_task_model(tag, version=None)
+            self.res_path = self.task_resource.res_dir
             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(
-                res_path,
-                self.pretrained_models[tag]['ckpt_path'] + ".pdparams")
-            logger.info(res_path)
+                self.res_path,
+                self.task_resource.res_dict['ckpt_path'] + ".pdparams")
+            logger.info(self.res_path)
 
         else:
             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.text_feature = TextFeaturizer(
                     unit_type=self.config.unit_type, vocab=self.vocab)
-                lm_url = self.pretrained_models[tag]['lm_url']
-                lm_md5 = self.pretrained_models[tag]['lm_md5']
+                lm_url = self.resource.res_dict['lm_url']
+                lm_md5 = self.resource.res_dict['lm_md5']
                 self.download_lm(
                     lm_url,
                     os.path.dirname(self.config.decode.lang_model_path), lm_md5)
@@ -191,7 +186,7 @@ class ASRExecutor(BaseExecutor):
                 raise Exception("wrong type")
         model_name = model_type[:model_type.rindex(
             '_')]  # model_type: {model_name}_{dataset}
-        model_class = dynamic_import(model_name, self.model_alias)
+        model_class = self.task_resource.get_model_class(model_name)
         model_conf = self.config
         model = model_class.from_config(model_conf)
         self.model = model
@@ -438,7 +433,7 @@ class ASRExecutor(BaseExecutor):
         if not parser_args.verbose:
             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()
         has_exceptions = False
 

paddlespeech/cli/asr/pretrained_models.py

-# 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",
-}

paddlespeech/cli/base_commands.py

@@ -11,17 +11,18 @@
 # 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 ..resource import CommonTaskResource
 from .entry import commands
 from .utils import cli_register
 from .utils import explicit_command_register
 from .utils import get_command
 
-__all__ = [
-    'BaseCommand',
-    'HelpCommand',
-]
+__all__ = ['BaseCommand', 'HelpCommand', 'StatsCommand']
 
 
 @cli_register(name='paddlespeech')
@@ -76,6 +77,59 @@ class VersionCommand:
         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
 _commands = {
     'asr': ['Speech to text infer command.', 'ASRExecutor'],
@@ -91,3 +145,4 @@ for com, info in _commands.items():
         name='paddlespeech.{}'.format(com),
         description=info[0],
         cls='paddlespeech.cli.{}.{}'.format(com, info[1]))
+        
\ No newline at end of file

paddlespeech/cli/cls/infer.py

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

paddlespeech/cli/cls/pretrained_models.py

-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-pretrained_models = {
-    # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
-    # e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
-    # Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
-    # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
-    "panns_cnn6-32k": {
-        'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz',
-        'md5': '4cf09194a95df024fd12f84712cf0f9c',
-        'cfg_path': 'panns.yaml',
-        'ckpt_path': 'cnn6.pdparams',
-        'label_file': 'audioset_labels.txt',
-    },
-    "panns_cnn10-32k": {
-        'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz',
-        'md5': 'cb8427b22176cc2116367d14847f5413',
-        'cfg_path': 'panns.yaml',
-        'ckpt_path': 'cnn10.pdparams',
-        'label_file': 'audioset_labels.txt',
-    },
-    "panns_cnn14-32k": {
-        'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz',
-        'md5': 'e3b9b5614a1595001161d0ab95edee97',
-        'cfg_path': 'panns.yaml',
-        'ckpt_path': 'cnn14.pdparams',
-        'label_file': 'audioset_labels.txt',
-    },
-}
-
-model_alias = {
-    "panns_cnn6": "paddlespeech.cls.models.panns:CNN6",
-    "panns_cnn10": "paddlespeech.cls.models.panns:CNN10",
-    "panns_cnn14": "paddlespeech.cls.models.panns:CNN14",
-}

paddlespeech/cli/executor.py

@@ -24,9 +24,8 @@ from typing import Union
 
 import paddle
 
+from ..resource import CommonTaskResource
 from .log import logger
-from .utils import download_and_decompress
-from .utils import MODEL_HOME
 
 
 class BaseExecutor(ABC):
@@ -34,11 +33,10 @@ class BaseExecutor(ABC):
         An abstract executor of paddlespeech tasks.
     """
 
-    def __init__(self):
+    def __init__(self, task: str, **kwargs):
         self._inputs = OrderedDict()
         self._outputs = OrderedDict()
-        self.pretrained_models = OrderedDict()
-        self.model_alias = OrderedDict()
+        self.task_resource = CommonTaskResource(task=task, **kwargs)
 
     @abstractmethod
     def _init_from_path(self, *args, **kwargs):
@@ -98,7 +96,7 @@ class BaseExecutor(ABC):
         """
         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]]:
         """
         Get task input source from command line input.
@@ -115,6 +113,8 @@ class BaseExecutor(ABC):
             ret = OrderedDict()
 
             if input_ is None:  # Take input from stdin
+                if not sys.stdin.isatty(
+                ):  # Avoid getting stuck when stdin is empty.
                     for i, line in enumerate(sys.stdin):
                         line = line.strip()
                         if len(line.split(' ')) == 1:
@@ -219,23 +219,6 @@ class BaseExecutor(ABC):
         for l in loggers:
             l.disabled = True
 
-    def _get_pretrained_path(self, tag: str) -> os.PathLike:
-        """
-        Download and returns pretrained resources path of current task.
-        """
-        support_models = list(self.pretrained_models.keys())
-        assert tag in self.pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
-            tag, '\n\t\t'.join(support_models))
-
-        res_path = os.path.join(MODEL_HOME, tag)
-        decompressed_path = download_and_decompress(self.pretrained_models[tag],
-                                                    res_path)
-        decompressed_path = os.path.abspath(decompressed_path)
-        logger.info(
-            'Use pretrained model stored in: {}'.format(decompressed_path))
-
-        return decompressed_path
-
     def show_rtf(self, info: Dict[str, List[float]]):
         """
         Calculate rft of current task and show results.

paddlespeech/cli/st/infer.py

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

paddlespeech/cli/st/pretrained_models.py

-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-pretrained_models = {
-    "fat_st_ted-en-zh": {
-        "url":
-        "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz",
-        "md5":
-        "d62063f35a16d91210a71081bd2dd557",
-        "cfg_path":
-        "model.yaml",
-        "ckpt_path":
-        "exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams",
-    }
-}
-
-model_alias = {"fat_st": "paddlespeech.s2t.models.u2_st:U2STModel"}
-
-kaldi_bins = {
-    "url":
-    "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz",
-    "md5":
-    "c0682303b3f3393dbf6ed4c4e35a53eb",
-}

paddlespeech/cli/stats/infer.py

-# 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."
-                )

paddlespeech/cli/text/infer.py

@@ -24,21 +24,13 @@ import paddle
 from ..executor import BaseExecutor
 from ..log import logger
 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']
 
 
 class TextExecutor(BaseExecutor):
     def __init__(self):
-        super().__init__()
-        self.model_alias = model_alias
-        self.pretrained_models = pretrained_models
-        self.tokenizer_alias = tokenizer_alias
-
+        super().__init__(task='text')
         self.parser = argparse.ArgumentParser(
             prog='paddlespeech.text', add_help=True)
         self.parser.add_argument(
@@ -54,7 +46,8 @@ class TextExecutor(BaseExecutor):
             type=str,
             default='ernie_linear_p7_wudao',
             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.')
         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:
             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.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.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.res_path, self.pretrained_models[tag]['vocab_file'])
+                self.task_resource.res_dir,
+                self.task_resource.res_dict['vocab_file'])
         else:
             self.cfg_path = os.path.abspath(cfg_path)
             self.ckpt_path = os.path.abspath(ckpt_path)
@@ -133,8 +129,8 @@ class TextExecutor(BaseExecutor):
                     self._punc_list.append(line.strip())
 
             # model
-            model_class = dynamic_import(model_name, self.model_alias)
-            tokenizer_class = dynamic_import(model_name, self.tokenizer_alias)
+            model_class, tokenizer_class = self.task_resource.get_model_class(
+                model_name)
             self.model = model_class(
                 cfg_path=self.cfg_path, ckpt_path=self.ckpt_path)
             self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0')
@@ -224,7 +220,7 @@ class TextExecutor(BaseExecutor):
         if not parser_args.verbose:
             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()
         has_exceptions = False
 

paddlespeech/cli/text/pretrained_models.py

-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-pretrained_models = {
-    # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
-    # e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
-    # Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
-    # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
-    "ernie_linear_p7_wudao-punc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz',
-        'md5':
-        '12283e2ddde1797c5d1e57036b512746',
-        'cfg_path':
-        'ckpt/model_config.json',
-        'ckpt_path':
-        'ckpt/model_state.pdparams',
-        'vocab_file':
-        'punc_vocab.txt',
-    },
-    "ernie_linear_p3_wudao-punc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz',
-        'md5':
-        '448eb2fdf85b6a997e7e652e80c51dd2',
-        'cfg_path':
-        'ckpt/model_config.json',
-        'ckpt_path':
-        'ckpt/model_state.pdparams',
-        'vocab_file':
-        'punc_vocab.txt',
-    },
-}
-
-model_alias = {
-    "ernie_linear_p7": "paddlespeech.text.models:ErnieLinear",
-    "ernie_linear_p3": "paddlespeech.text.models:ErnieLinear",
-}
-
-tokenizer_alias = {
-    "ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer",
-    "ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer",
-}

paddlespeech/cli/tts/infer.py

@@ -29,22 +29,16 @@ from yacs.config import CfgNode
 from ..executor import BaseExecutor
 from ..log import logger
 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.zh_frontend import Frontend
 from paddlespeech.t2s.modules.normalizer import ZScore
-from paddlespeech.utils.dynamic_import import dynamic_import
 
 __all__ = ['TTSExecutor']
 
 
 class TTSExecutor(BaseExecutor):
     def __init__(self):
-        super().__init__()
-        self.model_alias = model_alias
-        self.pretrained_models = pretrained_models
-
+        super().__init__('tts')
         self.parser = argparse.ArgumentParser(
             prog='paddlespeech.tts', add_help=True)
         self.parser.add_argument(
@@ -183,19 +177,23 @@ class TTSExecutor(BaseExecutor):
             return
         # am
         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:
-            am_res_path = self._get_pretrained_path(am_tag)
-            self.am_res_path = am_res_path
-            self.am_config = os.path.join(
-                am_res_path, self.pretrained_models[am_tag]['config'])
-            self.am_ckpt = os.path.join(am_res_path,
-                                        self.pretrained_models[am_tag]['ckpt'])
+            self.am_res_path = self.task_resource.res_dir
+            self.am_config = os.path.join(self.am_res_path,
+                                          self.task_resource.res_dict['config'])
+            self.am_ckpt = os.path.join(self.am_res_path,
+                                        self.task_resource.res_dict['ckpt'])
             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
             self.phones_dict = os.path.join(
-                am_res_path, self.pretrained_models[am_tag]['phones_dict'])
-            logger.info(am_res_path)
+                self.am_res_path, self.task_resource.res_dict['phones_dict'])
+            logger.info(self.am_res_path)
             logger.info(self.am_config)
             logger.info(self.am_ckpt)
         else:
@@ -207,33 +205,37 @@ class TTSExecutor(BaseExecutor):
 
         # for speedyspeech
         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.am_res_path, self.pretrained_models[am_tag]['tones_dict'])
+                self.am_res_path, self.task_resource.res_dict['tones_dict'])
             if tones_dict:
                 self.tones_dict = tones_dict
 
         # for multi speaker fastspeech2
         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.am_res_path,
-                self.pretrained_models[am_tag]['speaker_dict'])
+                self.am_res_path, self.task_resource.res_dict['speaker_dict'])
             if speaker_dict:
                 self.speaker_dict = speaker_dict
 
         # voc
         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:
-            voc_res_path = self._get_pretrained_path(voc_tag)
-            self.voc_res_path = voc_res_path
+            self.voc_res_path = self.task_resource.voc_res_dir
             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(
-                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(
-                voc_res_path, self.pretrained_models[voc_tag]['speech_stats'])
-            logger.info(voc_res_path)
+                self.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_ckpt)
         else:
@@ -283,9 +285,9 @@ class TTSExecutor(BaseExecutor):
         # model: {model_name}_{dataset}
         am_name = am[:am.rindex('_')]
 
-        am_class = dynamic_import(am_name, self.model_alias)
-        am_inference_class = dynamic_import(am_name + '_inference',
-                                            self.model_alias)
+        am_class = self.task_resource.get_model_class(am_name)
+        am_inference_class = self.task_resource.get_model_class(am_name +
+                                                                '_inference')
 
         if am_name == 'fastspeech2':
             am = am_class(
@@ -314,9 +316,9 @@ class TTSExecutor(BaseExecutor):
         # vocoder
         # model: {model_name}_{dataset}
         voc_name = voc[:voc.rindex('_')]
-        voc_class = dynamic_import(voc_name, self.model_alias)
-        voc_inference_class = dynamic_import(voc_name + '_inference',
-                                             self.model_alias)
+        voc_class = self.task_resource.get_model_class(voc_name)
+        voc_inference_class = self.task_resource.get_model_class(voc_name +
+                                                                 '_inference')
         if voc_name != 'wavernn':
             voc = voc_class(**self.voc_config["generator_params"])
             voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"])
@@ -444,7 +446,7 @@ class TTSExecutor(BaseExecutor):
         if not args.verbose:
             self.disable_task_loggers()
 
-        task_source = self.get_task_source(args.input)
+        task_source = self.get_input_source(args.input)
         task_results = OrderedDict()
         has_exceptions = False
 

paddlespeech/cli/tts/pretrained_models.py

-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-pretrained_models = {
-    # speedyspeech
-    "speedyspeech_csmsc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip',
-        'md5':
-        '6f6fa967b408454b6662c8c00c0027cb',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_30600.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-        'phones_dict':
-        'phone_id_map.txt',
-        'tones_dict':
-        'tone_id_map.txt',
-    },
-
-    # fastspeech2
-    "fastspeech2_csmsc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip',
-        'md5':
-        '637d28a5e53aa60275612ba4393d5f22',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_76000.pdz',
-        'speech_stats':
-        'speech_stats.npy',
-        'phones_dict':
-        'phone_id_map.txt',
-    },
-    "fastspeech2_ljspeech-en": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip',
-        'md5':
-        'ffed800c93deaf16ca9b3af89bfcd747',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_100000.pdz',
-        'speech_stats':
-        'speech_stats.npy',
-        'phones_dict':
-        'phone_id_map.txt',
-    },
-    "fastspeech2_aishell3-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip',
-        'md5':
-        'f4dd4a5f49a4552b77981f544ab3392e',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_96400.pdz',
-        'speech_stats':
-        'speech_stats.npy',
-        'phones_dict':
-        'phone_id_map.txt',
-        'speaker_dict':
-        'speaker_id_map.txt',
-    },
-    "fastspeech2_vctk-en": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip',
-        'md5':
-        '743e5024ca1e17a88c5c271db9779ba4',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_66200.pdz',
-        'speech_stats':
-        'speech_stats.npy',
-        'phones_dict':
-        'phone_id_map.txt',
-        'speaker_dict':
-        'speaker_id_map.txt',
-    },
-    # tacotron2
-    "tacotron2_csmsc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip',
-        'md5':
-        '0df4b6f0bcbe0d73c5ed6df8867ab91a',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_30600.pdz',
-        'speech_stats':
-        'speech_stats.npy',
-        'phones_dict':
-        'phone_id_map.txt',
-    },
-    "tacotron2_ljspeech-en": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip',
-        'md5':
-        '6a5eddd81ae0e81d16959b97481135f3',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_60300.pdz',
-        'speech_stats':
-        'speech_stats.npy',
-        'phones_dict':
-        'phone_id_map.txt',
-    },
-
-    # pwgan
-    "pwgan_csmsc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip',
-        'md5':
-        '2e481633325b5bdf0a3823c714d2c117',
-        'config':
-        'pwg_default.yaml',
-        'ckpt':
-        'pwg_snapshot_iter_400000.pdz',
-        'speech_stats':
-        'pwg_stats.npy',
-    },
-    "pwgan_ljspeech-en": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip',
-        'md5':
-        '53610ba9708fd3008ccaf8e99dacbaf0',
-        'config':
-        'pwg_default.yaml',
-        'ckpt':
-        'pwg_snapshot_iter_400000.pdz',
-        'speech_stats':
-        'pwg_stats.npy',
-    },
-    "pwgan_aishell3-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip',
-        'md5':
-        'd7598fa41ad362d62f85ffc0f07e3d84',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_1000000.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-    },
-    "pwgan_vctk-en": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip',
-        'md5':
-        'b3da1defcde3e578be71eb284cb89f2c',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_1500000.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-    },
-    # mb_melgan
-    "mb_melgan_csmsc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip',
-        'md5':
-        'ee5f0604e20091f0d495b6ec4618b90d',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_1000000.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-    },
-    # style_melgan
-    "style_melgan_csmsc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip',
-        'md5':
-        '5de2d5348f396de0c966926b8c462755',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_1500000.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-    },
-    # hifigan
-    "hifigan_csmsc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip',
-        'md5':
-        'dd40a3d88dfcf64513fba2f0f961ada6',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_2500000.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-    },
-    "hifigan_ljspeech-en": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip',
-        'md5':
-        '70e9131695decbca06a65fe51ed38a72',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_2500000.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-    },
-    "hifigan_aishell3-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip',
-        'md5':
-        '3bb49bc75032ed12f79c00c8cc79a09a',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_2500000.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-    },
-    "hifigan_vctk-en": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip',
-        'md5':
-        '7da8f88359bca2457e705d924cf27bd4',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_2500000.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-    },
-
-    # wavernn
-    "wavernn_csmsc-zh": {
-        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip',
-        'md5':
-        'ee37b752f09bcba8f2af3b777ca38e13',
-        'config':
-        'default.yaml',
-        'ckpt':
-        'snapshot_iter_400000.pdz',
-        'speech_stats':
-        'feats_stats.npy',
-    }
-}
-
-model_alias = {
-    # acoustic model
-    "speedyspeech":
-    "paddlespeech.t2s.models.speedyspeech:SpeedySpeech",
-    "speedyspeech_inference":
-    "paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference",
-    "fastspeech2":
-    "paddlespeech.t2s.models.fastspeech2:FastSpeech2",
-    "fastspeech2_inference":
-    "paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
-    "tacotron2":
-    "paddlespeech.t2s.models.tacotron2:Tacotron2",
-    "tacotron2_inference":
-    "paddlespeech.t2s.models.tacotron2:Tacotron2Inference",
-    # voc
-    "pwgan":
-    "paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
-    "pwgan_inference":
-    "paddlespeech.t2s.models.parallel_wavegan:PWGInference",
-    "mb_melgan":
-    "paddlespeech.t2s.models.melgan:MelGANGenerator",
-    "mb_melgan_inference":
-    "paddlespeech.t2s.models.melgan:MelGANInference",
-    "style_melgan":
-    "paddlespeech.t2s.models.melgan:StyleMelGANGenerator",
-    "style_melgan_inference":
-    "paddlespeech.t2s.models.melgan:StyleMelGANInference",
-    "hifigan":
-    "paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
-    "hifigan_inference":
-    "paddlespeech.t2s.models.hifigan:HiFiGANInference",
-    "wavernn":
-    "paddlespeech.t2s.models.wavernn:WaveRNN",
-    "wavernn_inference":
-    "paddlespeech.t2s.models.wavernn:WaveRNNInference",
-}

paddlespeech/cli/vector/infer.py

@@ -22,26 +22,20 @@ from typing import Union
 
 import paddle
 import soundfile
-from paddleaudio.backends import load as load_audio
-from paddleaudio.compliance.librosa import melspectrogram
 from yacs.config import CfgNode
 
 from ..executor import BaseExecutor
 from ..log import logger
 from ..utils import stats_wrapper
-from .pretrained_models import model_alias
-from .pretrained_models import pretrained_models
-from paddlespeech.utils.dynamic_import import dynamic_import
+from paddleaudio.backends import load as load_audio
+from paddleaudio.compliance.librosa import melspectrogram
 from paddlespeech.vector.io.batch import feature_normalize
 from paddlespeech.vector.modules.sid_model import SpeakerIdetification
 
 
 class VectorExecutor(BaseExecutor):
     def __init__(self):
-        super().__init__()
-        self.model_alias = model_alias
-        self.pretrained_models = pretrained_models
-
+        super().__init__('vector')
         self.parser = argparse.ArgumentParser(
             prog="paddlespeech.vector", add_help=True)
 
@@ -49,7 +43,10 @@ class VectorExecutor(BaseExecutor):
             "--model",
             type=str,
             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.")
         self.parser.add_argument(
             "--task",
@@ -119,7 +116,7 @@ class VectorExecutor(BaseExecutor):
             self.disable_task_loggers()
 
         # 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}")
 
         # stage 3: process the audio one by one
@@ -296,6 +293,7 @@ class VectorExecutor(BaseExecutor):
             # get the mode from pretrained list
             sample_rate_str = "16k" if sample_rate == 16000 else "8k"
             tag = model_type + "-" + sample_rate_str
+            self.task_resource.set_task_model(tag, version=None)
             logger.info(f"load the pretrained model: {tag}")
             # get the model from the pretrained list
             # 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.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(
-                res_path,
-                self.pretrained_models[tag]['ckpt_path'] + '.pdparams')
+                self.task_resource.res_dir,
+                self.task_resource.res_dict['ckpt_path'] + '.pdparams')
         else:
             # get the model from disk
             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
         logger.info("start to dynamic import the model class")
         model_name = model_type[:model_type.rindex('_')]
+        model_class = self.task_resource.get_model_class(model_name)
         logger.info(f"model name {model_name}")
-        model_class = dynamic_import(model_name, self.model_alias)
         model_conf = self.config.model
         backbone = model_class(**model_conf)
         model = SpeakerIdetification(

paddlespeech/cli/vector/pretrained_models.py

-# 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",
-}

paddlespeech/cli/stats/__init__.py → paddlespeech/resource/__init__.py

-# 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");
 # 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.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from .infer import StatsExecutor
+from .resource import CommonTaskResource

paddlespeech/resource/pretrained_models.py

+# 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
+        },
+    },
+}

paddlespeech/resource/resource.py

+# 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)

paddlespeech/server/bin/paddlespeech_server.py

@@ -25,6 +25,7 @@ from ..executor import BaseExecutor
 from ..util import cli_server_register
 from ..util import stats_wrapper
 from paddlespeech.cli.log import logger
+from paddlespeech.resource import CommonTaskResource
 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.utils.config import get_config
@@ -152,101 +153,30 @@ class ServerStatsExecutor():
                 "Please input correct speech task, choices = ['asr', 'tts']")
             return False
 
-        elif self.task.lower() == 'asr':
         try:
-                from paddlespeech.cli.asr.infer import pretrained_models
-                logger.info(
-                    "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)
+            # Dynamic models
+            dynamic_pretrained_models = CommonTaskResource(
+                task=self.task, model_format='dynamic').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
+            if len(dynamic_pretrained_models) > 0:
                 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
+                    "Here is the table of {} pretrained models supported in the service.".
+                    format(self.task.upper()))
+                self.show_support_models(dynamic_pretrained_models)
+
+            # Static models
+            static_pretrained_models = CommonTaskResource(
+                task=self.task, model_format='static').pretrained_models
+            if len(static_pretrained_models) > 0:
                 logger.info(
-                    "Here is the table of TTS 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)
 
             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':
-            try:
-                from paddlespeech.cli.cls.infer import pretrained_models
-                logger.info(
-                    "Here is the table of CLS pretrained models supported in the service."
-                )
-                self.show_support_models(pretrained_models)
-
-                # show CLS static pretrained model
-                from paddlespeech.server.engine.cls.paddleinference.cls_engine import pretrained_models
-                logger.info(
-                    "Here is the table of CLS 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 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:
-                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(
-                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

paddlespeech/server/engine/asr/online/asr_engine.py

@@ -11,7 +11,6 @@
 # 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 copy
 import os
 import sys
 from typing import Optional
@@ -21,15 +20,14 @@ import paddle
 from numpy import float32
 from yacs.config import CfgNode
 
-from .pretrained_models import pretrained_models
 from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.log import logger
 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.speech import SpeechSegment
 from paddlespeech.s2t.modules.ctc import CTCDecoder
 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 pad_sequence
 from paddlespeech.s2t.utils.utility import UpdateConfig
@@ -251,10 +249,12 @@ class PaddleASRConnectionHanddler:
             # for deepspeech2 
             # init state
             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)
             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)
             self.decoder.reset_decoder(batch_size=1)
 
@@ -699,7 +699,8 @@ class PaddleASRConnectionHanddler:
 class ASRServerExecutor(ASRExecutor):
     def __init__(self):
         super().__init__()
-        self.pretrained_models = pretrained_models
+        self.task_resource = CommonTaskResource(
+            task='asr', model_format='dynamic', inference_mode='online')
 
     def _init_from_path(self,
                         model_type: str=None,
@@ -723,20 +724,19 @@ class ASRServerExecutor(ASRExecutor):
         self.sample_rate = sample_rate
         sample_rate_str = '16k' if sample_rate == 16000 else '8k'
         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:
             logger.info(f"Load the pretrained model, tag = {tag}")
-            res_path = self._get_pretrained_path(tag)  # wenetspeech_zh
-            self.res_path = res_path
+            self.res_path = self.task_resource.res_dir
 
             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.pretrained_models[tag]['model'])
-            self.am_params = os.path.join(res_path,
-                                          self.pretrained_models[tag]['params'])
-            logger.info(res_path)
+            self.am_model = os.path.join(self.res_path,
+                                         self.task_resource.res_dict['model'])
+            self.am_params = os.path.join(self.res_path,
+                                          self.task_resource.res_dict['params'])
+            logger.info(self.res_path)
         else:
             self.cfg_path = os.path.abspath(cfg_path)
             self.am_model = os.path.abspath(am_model)
@@ -763,8 +763,8 @@ class ASRServerExecutor(ASRExecutor):
                 self.text_feature = TextFeaturizer(
                     unit_type=self.config.unit_type, vocab=self.vocab)
 
-                lm_url = self.pretrained_models[tag]['lm_url']
-                lm_md5 = self.pretrained_models[tag]['lm_md5']
+                lm_url = self.task_resource.res_dict['lm_url']
+                lm_md5 = self.task_resource.res_dict['lm_md5']
                 logger.info(f"Start to load language model {lm_url}")
                 self.download_lm(
                     lm_url,
@@ -810,7 +810,7 @@ class ASRServerExecutor(ASRExecutor):
             model_name = model_type[:model_type.rindex(
                 '_')]  # model_type: {model_name}_{dataset}
             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 = model_class.from_config(model_conf)
             self.model = model

paddlespeech/server/engine/asr/online/pretrained_models.py

-# 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':
-        '',
-    },
-}

paddlespeech/server/engine/asr/paddleinference/asr_engine.py

@@ -19,10 +19,10 @@ from typing import Optional
 import paddle
 from yacs.config import CfgNode
 
-from .pretrained_models import pretrained_models
 from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.log import logger
 from paddlespeech.cli.utils import MODEL_HOME
+from paddlespeech.resource import CommonTaskResource
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.modules.ctc import CTCDecoder
 from paddlespeech.s2t.utils.utility import UpdateConfig
@@ -36,7 +36,8 @@ __all__ = ['ASREngine']
 class ASRServerExecutor(ASRExecutor):
     def __init__(self):
         super().__init__()
-        self.pretrained_models = pretrained_models
+        self.task_resource = CommonTaskResource(
+            task='asr', model_format='static', inference_mode='online')
 
     def _init_from_path(self,
                         model_type: str='wenetspeech',
@@ -53,17 +54,17 @@ class ASRServerExecutor(ASRExecutor):
 
         sample_rate_str = '16k' if sample_rate == 16000 else '8k'
         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:
-            res_path = self._get_pretrained_path(tag)  # wenetspeech_zh
-            self.res_path = res_path
+            self.res_path = self.task_resource.res_dir
             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.pretrained_models[tag]['model'])
-            self.am_params = os.path.join(res_path,
-                                          self.pretrained_models[tag]['params'])
-            logger.info(res_path)
+            self.am_model = os.path.join(self.res_path,
+                                         self.task_resource.res_dict['model'])
+            self.am_params = os.path.join(self.res_path,
+                                          self.task_resource.res_dict['params'])
+            logger.info(self.res_path)
             logger.info(self.cfg_path)
             logger.info(self.am_model)
             logger.info(self.am_params)
@@ -89,8 +90,8 @@ class ASRServerExecutor(ASRExecutor):
                 self.text_feature = TextFeaturizer(
                     unit_type=self.config.unit_type, vocab=self.vocab)
 
-                lm_url = self.pretrained_models[tag]['lm_url']
-                lm_md5 = self.pretrained_models[tag]['lm_md5']
+                lm_url = self.task_resource.res_dict['lm_url']
+                lm_md5 = self.task_resource.res_dict['lm_md5']
                 self.download_lm(
                     lm_url,
                     os.path.dirname(self.config.decode.lang_model_path), lm_md5)

paddlespeech/server/engine/asr/paddleinference/pretrained_models.py

-# 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'
-    },
-}

paddlespeech/server/engine/cls/paddleinference/cls_engine.py

@@ -20,9 +20,9 @@ import numpy as np
 import paddle
 import yaml
 
-from .pretrained_models import pretrained_models
 from paddlespeech.cli.cls.infer import CLSExecutor
 from paddlespeech.cli.log import logger
+from paddlespeech.resource import CommonTaskResource
 from paddlespeech.server.engine.base_engine import BaseEngine
 from paddlespeech.server.utils.paddle_predictor import init_predictor
 from paddlespeech.server.utils.paddle_predictor import run_model
@@ -33,11 +33,12 @@ __all__ = ['CLSEngine']
 class CLSServerExecutor(CLSExecutor):
     def __init__(self):
         super().__init__()
-        self.pretrained_models = pretrained_models
+        self.task_resource = CommonTaskResource(
+            task='cls', model_format='static')
 
     def _init_from_path(
             self,
-            model_type: str='panns_cnn14',
+            model_type: str='panns_cnn14_audioset',
             cfg_path: Optional[os.PathLike]=None,
             model_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:
             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.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.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.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.res_path, self.pretrained_models[tag]['label_file'])
+                self.res_path, self.task_resource.res_dict['label_file'])
         else:
             self.cfg_path = os.path.abspath(cfg_path)
             self.model_path = os.path.abspath(model_path)

paddlespeech/server/engine/cls/paddleinference/pretrained_models.py

-# 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',
-    },
-}

paddlespeech/server/engine/tts/online/onnx/pretrained_models.py

-# 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,
-    },
-}

paddlespeech/server/engine/tts/online/onnx/tts_engine.py

@@ -20,9 +20,9 @@ from typing import Optional
 import numpy as np
 import paddle
 
-from .pretrained_models import pretrained_models
 from paddlespeech.cli.log import logger
 from paddlespeech.cli.tts.infer import TTSExecutor
+from paddlespeech.resource import CommonTaskResource
 from paddlespeech.server.engine.base_engine import BaseEngine
 from paddlespeech.server.utils.audio_process import float2pcm
 from paddlespeech.server.utils.onnx_infer import get_sess
@@ -43,7 +43,7 @@ class TTSServerExecutor(TTSExecutor):
         self.voc_pad = voc_pad
         self.voc_upsample = voc_upsample
 
-        self.pretrained_models = pretrained_models
+        self.task_resource = CommonTaskResource(task='tts', model_format='onnx')
 
     def _init_from_path(
             self,
@@ -72,16 +72,21 @@ class TTSServerExecutor(TTSExecutor):
             return
         # am
         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":
             # get model info
             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(
-                    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
                 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:
                 self.am_ckpt = os.path.abspath(am_ckpt[0])
@@ -94,19 +99,19 @@ class TTSServerExecutor(TTSExecutor):
 
         elif am == "fastspeech2_cnndecoder_csmsc_onnx":
             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(
-                    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(
-                    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(
-                    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
                 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(
-                    am_res_path, self.pretrained_models[am_tag]['speech_stats'])
+                    self.am_res_path,
+                    self.task_resource.res_dict['speech_stats'])
 
             else:
                 self.am_encoder_infer = os.path.abspath(am_ckpt[0])
@@ -131,11 +136,15 @@ class TTSServerExecutor(TTSExecutor):
 
         # voc model info
         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:
-            voc_res_path = self._get_pretrained_path(voc_tag)
-            self.voc_res_path = voc_res_path
+            self.voc_res_path = self.task_resource.voc_res_dir
             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:
             self.voc_ckpt = os.path.abspath(voc_ckpt)
             self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_ckpt))

paddlespeech/server/engine/tts/online/python/pretrained_models.py

-# 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',
-    },
-}

paddlespeech/server/engine/tts/online/python/tts_engine.py

@@ -22,9 +22,9 @@ import paddle
 import yaml
 from yacs.config import CfgNode
 
-from .pretrained_models import pretrained_models
 from paddlespeech.cli.log import logger
 from paddlespeech.cli.tts.infer import TTSExecutor
+from paddlespeech.resource import CommonTaskResource
 from paddlespeech.server.engine.base_engine import BaseEngine
 from paddlespeech.server.utils.audio_process import float2pcm
 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.zh_frontend import Frontend
 from paddlespeech.t2s.modules.normalizer import ZScore
-from paddlespeech.utils.dynamic_import import dynamic_import
 
 __all__ = ['TTSEngine']
 
@@ -44,7 +43,8 @@ class TTSServerExecutor(TTSExecutor):
         self.am_pad = am_pad
         self.voc_block = voc_block
         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,
                        field: str,
@@ -65,7 +65,7 @@ class TTSServerExecutor(TTSExecutor):
             [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":
             odim = self.am_config.n_mels
@@ -110,20 +110,24 @@ class TTSServerExecutor(TTSExecutor):
             return
         # am model info
         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:
-            am_res_path = self._get_pretrained_path(am_tag)
-            self.am_res_path = am_res_path
-            self.am_config = os.path.join(
-                am_res_path, self.pretrained_models[am_tag]['config'])
-            self.am_ckpt = os.path.join(am_res_path,
-                                        self.pretrained_models[am_tag]['ckpt'])
+            self.am_res_path = self.task_resource.res_dir
+            self.am_config = os.path.join(self.am_res_path,
+                                          self.task_resource.res_dict['config'])
+            self.am_ckpt = os.path.join(self.am_res_path,
+                                        self.task_resource.res_dict['ckpt'])
             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
             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)
-            logger.info(am_res_path)
+            logger.info(self.am_res_path)
             logger.info(self.am_config)
             logger.info(self.am_ckpt)
         else:
@@ -139,16 +143,21 @@ class TTSServerExecutor(TTSExecutor):
 
         # voc model info
         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:
-            voc_res_path = self._get_pretrained_path(voc_tag)
-            self.voc_res_path = voc_res_path
+            self.voc_res_path = self.task_resource.voc_res_dir
             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(
-                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(
-                voc_res_path, self.pretrained_models[voc_tag]['speech_stats'])
-            logger.info(voc_res_path)
+                self.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_ckpt)
         else:
@@ -188,8 +197,8 @@ class TTSServerExecutor(TTSExecutor):
             am, am_mu, am_std = self.get_model_info("am", self.am_name,
                                                     self.am_ckpt, self.am_stat)
             am_normalizer = ZScore(am_mu, am_std)
-            am_inference_class = dynamic_import(self.am_name + '_inference',
-                                                self.model_alias)
+            am_inference_class = self.task_resource.get_model_class(
+                self.am_name + '_inference')
             self.am_inference = am_inference_class(am_normalizer, am)
             self.am_inference.eval()
         print("acoustic model done!")
@@ -199,8 +208,8 @@ class TTSServerExecutor(TTSExecutor):
         voc, voc_mu, voc_std = self.get_model_info("voc", self.voc_name,
                                                    self.voc_ckpt, self.voc_stat)
         voc_normalizer = ZScore(voc_mu, voc_std)
-        voc_inference_class = dynamic_import(self.voc_name + '_inference',
-                                             self.model_alias)
+        voc_inference_class = self.task_resource.get_model_class(self.voc_name +
+                                                                 '_inference')
         self.voc_inference = voc_inference_class(voc_normalizer, voc)
         self.voc_inference.eval()
         print("voc done!")

paddlespeech/server/engine/tts/paddleinference/pretrained_models.py

-# 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,
-    },
-}

paddlespeech/server/engine/tts/paddleinference/tts_engine.py

@@ -23,9 +23,9 @@ import paddle
 import soundfile as sf
 from scipy.io import wavfile
 
-from .pretrained_models import pretrained_models
 from paddlespeech.cli.log import logger
 from paddlespeech.cli.tts.infer import TTSExecutor
+from paddlespeech.resource import CommonTaskResource
 from paddlespeech.server.engine.base_engine import BaseEngine
 from paddlespeech.server.utils.audio_process import change_speed
 from paddlespeech.server.utils.errors import ErrorCode
@@ -41,7 +41,8 @@ __all__ = ['TTSEngine']
 class TTSServerExecutor(TTSExecutor):
     def __init__(self):
         super().__init__()
-        self.pretrained_models = pretrained_models
+        self.task_resource = CommonTaskResource(
+            task='tts', model_format='static')
 
     def _init_from_path(
             self,
@@ -67,19 +68,23 @@ class TTSServerExecutor(TTSExecutor):
             return
         # am
         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:
-            am_res_path = self._get_pretrained_path(am_tag)
-            self.am_res_path = am_res_path
-            self.am_model = os.path.join(
-                am_res_path, self.pretrained_models[am_tag]['model'])
-            self.am_params = os.path.join(
-                am_res_path, self.pretrained_models[am_tag]['params'])
+            self.am_res_path = self.task_resource.res_dir
+            self.am_model = os.path.join(self.am_res_path,
+                                         self.task_resource.res_dict['model'])
+            self.am_params = os.path.join(self.am_res_path,
+                                          self.task_resource.res_dict['params'])
             # must have phones_dict in acoustic
             self.phones_dict = os.path.join(
-                am_res_path, self.pretrained_models[am_tag]['phones_dict'])
-            self.am_sample_rate = self.pretrained_models[am_tag]['sample_rate']
+                self.am_res_path, self.task_resource.res_dict['phones_dict'])
+            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_params)
         else:
@@ -92,32 +97,36 @@ class TTSServerExecutor(TTSExecutor):
 
         # for speedyspeech
         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(
-                am_res_path, self.pretrained_models[am_tag]['tones_dict'])
+                self.am_res_path, self.task_resource.res_dict['tones_dict'])
             if tones_dict:
                 self.tones_dict = tones_dict
 
         # for multi speaker fastspeech2
         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(
-                am_res_path, self.pretrained_models[am_tag]['speaker_dict'])
+                self.am_res_path, self.task_resource.res_dict['speaker_dict'])
             if speaker_dict:
                 self.speaker_dict = speaker_dict
 
         # voc
         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:
-            voc_res_path = self._get_pretrained_path(voc_tag)
-            self.voc_res_path = voc_res_path
+            self.voc_res_path = self.task_resource.voc_res_dir
             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(
-                voc_res_path, self.pretrained_models[voc_tag]['params'])
-            self.voc_sample_rate = self.pretrained_models[voc_tag][
+                self.voc_res_path, self.task_resource.voc_res_dict['params'])
+            self.voc_sample_rate = self.task_resource.voc_res_dict[
                 'sample_rate']
-            logger.info(voc_res_path)
+            logger.info(self.voc_res_path)
             logger.info(self.voc_model)
             logger.info(self.voc_params)
         else: