Merge branch 'develop' of https://github.com/lym0302/PaddleSpeech into paddlespeech_stats

demos/speech_server/conf/application.yaml

@@ -9,9 +9,17 @@ port: 8090
 ##################################################################
 #                     CONFIG FILE                                #
 ##################################################################
-# add engine type (Options: asr, tts) and config file here.
+# The engine_type of speech task needs to keep the same type as the config file of speech task.
+# E.g: The engine_type of asr is 'python', the engine_backend of asr is 'XX/asr.yaml'
+# E.g: The engine_type of asr is 'inference', the engine_backend of asr is 'XX/asr_pd.yaml'
+#
+# add engine type (Options: python, inference) 
+engine_type:
+    asr: 'inference'
+    tts: 'inference'
 
+# add engine backend type (Options: asr, tts) and config file here.
+# Adding a speech task to engine_backend means starting the service.
 engine_backend:
-    asr: 'conf/asr/asr.yaml'
+    asr: 'conf/asr/asr_pd.yaml'
-    tts: 'conf/tts/tts.yaml'
+    tts: 'conf/tts/tts_pd.yaml'
-

demos/speech_server/conf/asr/asr.yaml

@@ -1,7 +1,8 @@
 model: 'conformer_wenetspeech'
 lang: 'zh'
 sample_rate: 16000
-cfg_path: 
+cfg_path: # [optional]
-ckpt_path: 
+ckpt_path: # [optional]
 decode_method: 'attention_rescoring'
-force_yes: False
+force_yes: True
+device: 'gpu:3'  # set 'gpu:id' or 'cpu'

demos/speech_server/conf/asr/asr_pd.yaml

@@ -0,0 +1,25 @@
+# This is the parameter configuration file for ASR server.
+# These are the static models that support paddle inference.
+
+##################################################################
+#                  ACOUSTIC MODEL SETTING                        #
+# am choices=['deepspeech2offline_aishell'] TODO
+##################################################################
+model_type: 'deepspeech2offline_aishell'
+am_model: # the pdmodel file of am static model [optional]
+am_params:  # the pdiparams file of am static model [optional]
+lang: 'zh'
+sample_rate: 16000
+cfg_path: 
+decode_method: 
+force_yes: True
+
+am_predictor_conf:
+  device: 'gpu:3'  # set 'gpu:id' or 'cpu'
+  enable_mkldnn: True
+  switch_ir_optim: True
+
+
+##################################################################
+#                            OTHERS                              #
+##################################################################

demos/speech_server/conf/tts/tts.yaml

@@ -29,4 +29,4 @@ voc_stat:
 #                            OTHERS                              #
 ##################################################################
 lang: 'zh'
-device: 'gpu:2'
+device: 'gpu:3'  # set 'gpu:id' or 'cpu'

demos/speech_server/conf/tts/tts_pd.yaml

@@ -6,8 +6,8 @@
 # am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
 ##################################################################
 am: 'fastspeech2_csmsc'   
-am_model: # the pdmodel file of am static model
+am_model: # the pdmodel file of your am static model (XX.pdmodel)
-am_params: # the pdiparams file of am static model
+am_params: # the pdiparams file of your am static model (XX.pdipparams)
 am_sample_rate: 24000
 phones_dict: 
 tones_dict: 
@@ -15,9 +15,9 @@ speaker_dict:
 spk_id: 0
 
 am_predictor_conf:
-  use_gpu: True
+  device: 'gpu:3'  # set 'gpu:id' or 'cpu'
-  enable_mkldnn: True
+  enable_mkldnn: False
-  switch_ir_optim: True
+  switch_ir_optim: False
 
 
 ##################################################################
@@ -25,17 +25,16 @@ am_predictor_conf:
 # voc choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
 ##################################################################
 voc: 'pwgan_csmsc'
-voc_model: # the pdmodel file of vocoder static model
+voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
-voc_params: # the pdiparams file of vocoder static model 
+voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
 voc_sample_rate: 24000
 
 voc_predictor_conf:
-  use_gpu: True
+  device: 'gpu:3'  # set 'gpu:id' or 'cpu'
-  enable_mkldnn: True  
+  enable_mkldnn: False  
-  switch_ir_optim: True  
+  switch_ir_optim: False  
 
 ##################################################################
 #                            OTHERS                              #
 ##################################################################
 lang: 'zh'
-device: paddle.get_device()

paddlespeech/__init__.py

@@ -11,3 +11,14 @@
 # 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.
+
+
+
+
+
+
+
+
+
+
+

paddlespeech/cli/asr/infer.py

@@ -291,6 +291,7 @@ class ASRExecutor(BaseExecutor):
         """
 
         audio_file = input
+        if isinstance(audio_file, (str, os.PathLike)):
             logger.info("Preprocess audio_file:" + audio_file)
 
         # Get the object for feature extraction
@@ -412,13 +413,13 @@ class ASRExecutor(BaseExecutor):
     def _check(self, audio_file: str, sample_rate: int, force_yes: bool):
         self.sample_rate = sample_rate
         if self.sample_rate != 16000 and self.sample_rate != 8000:
-            logger.error("please input --sr 8000 or --sr 16000")
+            logger.error("invalid sample rate, please input --sr 8000 or --sr 16000")
-            raise Exception("invalid sample rate")
+            return False
-            sys.exit(-1)
 
+        if isinstance(audio_file, (str, os.PathLike)):
             if not os.path.isfile(audio_file):
                 logger.error("Please input the right audio file path")
-            sys.exit(-1)
+                return False
 
         logger.info("checking the audio file format......")
         try:
@@ -435,7 +436,7 @@ class ASRExecutor(BaseExecutor):
                  sample rate: 8k \n \
                  sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \
                  ")
-            sys.exit(-1)
+            return False
         logger.info("The sample rate is %d" % audio_sample_rate)
         if audio_sample_rate != self.sample_rate:
             logger.warning("The sample rate of the input file is not {}.\n \
@@ -469,6 +470,8 @@ class ASRExecutor(BaseExecutor):
             logger.info("The audio file format is right")
             self.change_format = False
 
+        return True
+
     def execute(self, argv: List[str]) -> bool:
         """
             Command line entry.
@@ -523,7 +526,8 @@ class ASRExecutor(BaseExecutor):
         Python API to call an executor.
         """
         audio_file = os.path.abspath(audio_file)
-        self._check(audio_file, sample_rate, force_yes)
+        if not self._check(audio_file, sample_rate, force_yes):
+            sys.exit(-1)
         paddle.set_device(device)
         self._init_from_path(model, lang, sample_rate, config, decode_method,
                              ckpt_path)

paddlespeech/s2t/io/utility.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from typing import List
+from io import BytesIO
 
 import numpy as np
 
@@ -88,6 +89,10 @@ def pad_sequence(sequences: List[np.ndarray],
 
 
 def feat_type(filepath):
+    # deal with Byteio type for paddlespeech server
+    if isinstance(filepath, BytesIO):
+        return 'sound'
+
     suffix = filepath.split(":")[0].split('.')[-1].lower()
     if suffix == 'ark':
         return 'mat'

paddlespeech/server/bin/main.py

@@ -16,10 +16,9 @@ import uvicorn
 import yaml
 from fastapi import FastAPI
 
-from paddlespeech.server.engine.engine_factory import EngineFactory
+from paddlespeech.server.engine.engine_pool import init_engine_pool
 from paddlespeech.server.restful.api import setup_router
 from paddlespeech.server.utils.config import get_config
-from paddlespeech.server.utils.log import logger
 
 app = FastAPI(
     title="PaddleSpeech Serving API", description="Api", version="0.0.1")
@@ -39,11 +38,7 @@ def init(config):
     api_router = setup_router(api_list)
     app.include_router(api_router)
 
-    # init engine
+    if not init_engine_pool(config):
-    engine_pool = []
-    for engine in config.engine_backend:
-        engine_pool.append(EngineFactory.get_engine(engine_name=engine))
-        if not engine_pool[-1].init(config_file=config.engine_backend[engine]):
         return False
 
     return True

paddlespeech/server/bin/paddlespeech_server.py

@@ -20,7 +20,7 @@ from fastapi import FastAPI
 from ..executor import BaseExecutor
 from ..util import cli_server_register
 from ..util import stats_wrapper
-from paddlespeech.server.engine.engine_factory import EngineFactory
+from paddlespeech.server.engine.engine_pool import init_engine_pool
 from paddlespeech.server.restful.api import setup_router
 from paddlespeech.server.utils.config import get_config
 
@@ -63,12 +63,7 @@ class ServerExecutor(BaseExecutor):
         api_router = setup_router(api_list)
         app.include_router(api_router)
 
-        # init engine
+        if not init_engine_pool(config):
-        engine_pool = []
-        for engine in config.engine_backend:
-            engine_pool.append(EngineFactory.get_engine(engine_name=engine))
-            if not engine_pool[-1].init(
-                    config_file=config.engine_backend[engine]):
             return False
 
         return True

paddlespeech/server/conf/application.yaml

@@ -9,9 +9,17 @@ port: 8090
 ##################################################################
 #                     CONFIG FILE                                #
 ##################################################################
-# add engine type (Options: asr, tts) and config file here.
+# The engine_type of speech task needs to keep the same type as the config file of speech task.
+# E.g: The engine_type of asr is 'python', the engine_backend of asr is 'XX/asr.yaml'
+# E.g: The engine_type of asr is 'inference', the engine_backend of asr is 'XX/asr_pd.yaml'
+#
+# add engine type (Options: python, inference) 
+engine_type:
+    asr: 'python'
+    tts: 'python'
 
+# add engine backend type (Options: asr, tts) and config file here.
+# Adding a speech task to engine_backend means starting the service.
 engine_backend:
     asr: 'conf/asr/asr.yaml'
-    tts: 'conf/tts/tts_pd.yaml'
+    tts: 'conf/tts/tts.yaml'
-

paddlespeech/server/conf/asr/asr.yaml

@@ -1,7 +1,8 @@
 model: 'conformer_wenetspeech'
 lang: 'zh'
 sample_rate: 16000
-cfg_path: 
+cfg_path: # [optional]
-ckpt_path: 
+ckpt_path: # [optional]
 decode_method: 'attention_rescoring'
-force_yes: False
+force_yes: True
+device: 'gpu:3'  # set 'gpu:id' or 'cpu'

paddlespeech/server/conf/asr/asr_pd.yaml

@@ -0,0 +1,25 @@
+# This is the parameter configuration file for ASR server.
+# These are the static models that support paddle inference.
+
+##################################################################
+#                  ACOUSTIC MODEL SETTING                        #
+# am choices=['deepspeech2offline_aishell'] TODO
+##################################################################
+model_type: 'deepspeech2offline_aishell'
+am_model: # the pdmodel file of am static model [optional]
+am_params:  # the pdiparams file of am static model [optional]
+lang: 'zh'
+sample_rate: 16000
+cfg_path: 
+decode_method: 
+force_yes: True
+
+am_predictor_conf:
+  device: 'gpu:3'  # set 'gpu:id' or 'cpu'
+  enable_mkldnn: True
+  switch_ir_optim: True
+
+
+##################################################################
+#                            OTHERS                              #
+##################################################################

paddlespeech/server/conf/tts/tts.yaml

@@ -29,4 +29,4 @@ voc_stat:
 #                            OTHERS                              #
 ##################################################################
 lang: 'zh'
-device: paddle.get_device()
+device: 'gpu:3'  # set 'gpu:id' or 'cpu'

paddlespeech/server/conf/tts/tts_pd.yaml

@@ -6,18 +6,18 @@
 # am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
 ##################################################################
 am: 'fastspeech2_csmsc'   
-am_model: # the pdmodel file of am static model
+am_model: # the pdmodel file of your am static model (XX.pdmodel)
-am_params: # the pdiparams file of am static model
+am_params: # the pdiparams file of your am static model (XX.pdipparams)
-am_sample_rate: 24000
+am_sample_rate: 24000    # must match the model
 phones_dict: 
 tones_dict: 
 speaker_dict: 
 spk_id: 0
 
 am_predictor_conf:
-  use_gpu: True
+  device: 'gpu:3'  # set 'gpu:id' or 'cpu'
-  enable_mkldnn: True
+  enable_mkldnn: False
-  switch_ir_optim: True
+  switch_ir_optim: False
 
 
 ##################################################################
@@ -25,17 +25,16 @@ am_predictor_conf:
 # voc choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
 ##################################################################
 voc: 'pwgan_csmsc'
-voc_model: # the pdmodel file of vocoder static model
+voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
-voc_params: # the pdiparams file of vocoder static model 
+voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
-voc_sample_rate: 24000
+voc_sample_rate: 24000    #must match the model
 
 voc_predictor_conf:
-  use_gpu: True
+  device: 'gpu:3'   # set 'gpu:id' or 'cpu'
-  enable_mkldnn: True  
+  enable_mkldnn: False
-  switch_ir_optim: True  
+  switch_ir_optim: False  
 
 ##################################################################
 #                            OTHERS                              #
 ##################################################################
 lang: 'zh'
-device: paddle.get_device()

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

@@ -0,0 +1,13 @@
+# 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.

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

@@ -0,0 +1,244 @@
+# 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 io
+import os
+from typing import List
+from typing import Optional
+from typing import Union
+
+import librosa
+import paddle
+import soundfile
+from yacs.config import CfgNode
+
+from paddlespeech.cli.utils import MODEL_HOME
+from paddlespeech.s2t.modules.ctc import CTCDecoder
+from paddlespeech.cli.asr.infer import ASRExecutor
+from paddlespeech.cli.log import logger
+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
+from paddlespeech.server.utils.config import get_config
+from paddlespeech.server.utils.paddle_predictor import init_predictor
+from paddlespeech.server.utils.paddle_predictor import run_model
+from paddlespeech.server.engine.base_engine import BaseEngine
+
+__all__ = ['ASREngine']
+
+
+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'
+    },
+}
+
+
+class ASRServerExecutor(ASRExecutor):
+    def __init__(self):
+        super().__init__()
+        pass
+
+    def _init_from_path(self,
+                        model_type: str='wenetspeech',
+                        am_model: Optional[os.PathLike]=None,
+                        am_params: Optional[os.PathLike]=None,
+                        lang: str='zh',
+                        sample_rate: int=16000,
+                        cfg_path: Optional[os.PathLike]=None,
+                        decode_method: str='attention_rescoring',
+                        am_predictor_conf: dict=None):
+        """
+        Init model and other resources from a specific path.
+        """
+
+        if cfg_path is None or am_model is None or am_params 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.cfg_path = os.path.join(res_path,
+                                         pretrained_models[tag]['cfg_path'])
+
+            self.am_model = os.path.join(res_path,
+                                         pretrained_models[tag]['model'])
+            self.am_params = os.path.join(res_path,
+                                          pretrained_models[tag]['params'])
+            logger.info(res_path)
+            logger.info(self.cfg_path)
+            logger.info(self.am_model)
+            logger.info(self.am_params)
+        else:
+            self.cfg_path = os.path.abspath(cfg_path)
+            self.am_model = os.path.abspath(am_model)
+            self.am_params = os.path.abspath(am_params)
+            self.res_path = os.path.dirname(
+                os.path.dirname(os.path.abspath(self.cfg_path)))
+
+        #Init body.
+        self.config = CfgNode(new_allowed=True)
+        self.config.merge_from_file(self.cfg_path)
+
+        with UpdateConfig(self.config):
+            if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
+                from paddlespeech.s2t.io.collator import SpeechCollator
+                self.vocab = self.config.vocab_filepath
+                self.config.decode.lang_model_path = os.path.join(
+                    MODEL_HOME, 'language_model',
+                    self.config.decode.lang_model_path)
+                self.collate_fn_test = SpeechCollator.from_config(self.config)
+                self.text_feature = TextFeaturizer(
+                    unit_type=self.config.unit_type, vocab=self.vocab)
+
+                lm_url = pretrained_models[tag]['lm_url']
+                lm_md5 = pretrained_models[tag]['lm_md5']
+                self.download_lm(
+                    lm_url,
+                    os.path.dirname(self.config.decode.lang_model_path), lm_md5)
+            elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
+                raise Exception("wrong type")
+            else:
+                raise Exception("wrong type")
+
+        # AM predictor
+        self.am_predictor_conf = am_predictor_conf
+        self.am_predictor = init_predictor(
+            model_file=self.am_model,
+            params_file=self.am_params,
+            predictor_conf=self.am_predictor_conf)
+
+        # decoder
+        self.decoder = CTCDecoder(
+            odim=self.config.output_dim,  # <blank> is in  vocab
+            enc_n_units=self.config.rnn_layer_size * 2,
+            blank_id=self.config.blank_id,
+            dropout_rate=0.0,
+            reduction=True,  # sum
+            batch_average=True,  # sum / batch_size
+            grad_norm_type=self.config.get('ctc_grad_norm_type', None))
+
+
+    @paddle.no_grad()
+    def infer(self, model_type: str):
+        """
+        Model inference and result stored in self.output.
+        """
+        cfg = self.config.decode
+        audio = self._inputs["audio"]
+        audio_len = self._inputs["audio_len"]
+        if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
+            decode_batch_size = audio.shape[0]
+            # init once
+            self.decoder.init_decoder(
+                decode_batch_size, self.text_feature.vocab_list,
+                cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta,
+                cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n,
+                cfg.num_proc_bsearch)
+
+            output_data = run_model(
+                                self.am_predictor,
+                                [audio.numpy(), audio_len.numpy()])
+
+            probs = output_data[0]
+            eouts_len = output_data[1]
+
+            batch_size = probs.shape[0]
+            self.decoder.reset_decoder(batch_size=batch_size)
+            self.decoder.next(probs, eouts_len)
+            trans_best, trans_beam = self.decoder.decode()
+
+            # self.model.decoder.del_decoder()
+            self._outputs["result"] = trans_best[0]
+
+        elif "conformer" in model_type or "transformer" in model_type:
+            raise Exception("invalid model name")
+        else:
+            raise Exception("invalid model name")
+
+
+class ASREngine(BaseEngine):
+    """ASR server engine
+
+    Args:
+        metaclass: Defaults to Singleton.
+    """
+
+    def __init__(self):
+        super(ASREngine, self).__init__()
+
+    def init(self, config_file: str) -> bool:
+        """init engine resource
+
+        Args:
+            config_file (str): config file
+
+        Returns:
+            bool: init failed or success
+        """
+        self.input = None
+        self.output = None
+        self.executor = ASRServerExecutor()
+        self.config = get_config(config_file)
+
+        paddle.set_device(paddle.get_device())
+        self.executor._init_from_path(
+                    model_type=self.config.model_type,
+                    am_model=self.config.am_model,
+                    am_params=self.config.am_params,
+                    lang=self.config.lang,
+                    sample_rate=self.config.sample_rate,
+                    cfg_path=self.config.cfg_path,
+                    decode_method=self.config.decode_method,
+                    am_predictor_conf=self.config.am_predictor_conf)
+
+        logger.info("Initialize ASR server engine successfully.")
+        return True
+
+    def run(self, audio_data):
+        """engine run 
+
+        Args:
+            audio_data (bytes): base64.b64decode
+        """
+        if self.executor._check(
+                io.BytesIO(audio_data), self.config.sample_rate,
+                self.config.force_yes):
+            logger.info("start running asr engine")
+            self.executor.preprocess(self.config.model_type, io.BytesIO(audio_data))
+            self.executor.infer(self.config.model_type)
+            self.output = self.executor.postprocess()  # Retrieve result of asr.
+            logger.info("end inferring asr engine")
+        else:
+            logger.info("file check failed!")
+            self.output = None
+
+    def postprocess(self):
+        """postprocess
+        """
+        return self.output

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

@@ -12,21 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import io
-import os
-from typing import List
-from typing import Optional
-from typing import Union
 
-import librosa
 import paddle
-import soundfile
 
 from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.log import logger
-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
 from paddlespeech.server.engine.base_engine import BaseEngine
 from paddlespeech.server.utils.config import get_config
 
@@ -38,101 +28,6 @@ class ASRServerExecutor(ASRExecutor):
         super().__init__()
         pass
 
-    def _check(self, audio_file: str, sample_rate: int, force_yes: bool):
-        self.sample_rate = sample_rate
-        if self.sample_rate != 16000 and self.sample_rate != 8000:
-            logger.error("please input --sr 8000 or --sr 16000")
-            return False
-
-        logger.info("checking the audio file format......")
-        try:
-            audio, audio_sample_rate = soundfile.read(
-                audio_file, dtype="int16", always_2d=True)
-        except Exception as e:
-            logger.exception(e)
-            logger.error(
-                "can not open the audio file, please check the audio file format is 'wav'. \n \
-                 you can try to use sox to change the file format.\n \
-                 For example: \n \
-                 sample rate: 16k \n \
-                 sox input_audio.xx --rate 16k --bits 16 --channels 1 output_audio.wav \n \
-                 sample rate: 8k \n \
-                 sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \
-                 ")
-
-        logger.info("The sample rate is %d" % audio_sample_rate)
-        if audio_sample_rate != self.sample_rate:
-            logger.warning("The sample rate of the input file is not {}.\n \
-                            The program will resample the wav file to {}.\n \
-                            If the result does not meet your expectations，\n \
-                            Please input the 16k 16 bit 1 channel wav file. \
-                        ".format(self.sample_rate, self.sample_rate))
-            self.change_format = True
-        else:
-            logger.info("The audio file format is right")
-            self.change_format = False
-
-        return True
-
-    def preprocess(self, model_type: str, input: Union[str, os.PathLike]):
-        """
-        Input preprocess and return paddle.Tensor stored in self.input.
-        Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
-        """
-
-        audio_file = input
-
-        # Get the object for feature extraction
-        if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
-            audio, _ = self.collate_fn_test.process_utterance(
-                audio_file=audio_file, transcript=" ")
-            audio_len = audio.shape[0]
-            audio = paddle.to_tensor(audio, dtype='float32')
-            audio_len = paddle.to_tensor(audio_len)
-            audio = paddle.unsqueeze(audio, axis=0)
-            # vocab_list = collate_fn_test.vocab_list
-            self._inputs["audio"] = audio
-            self._inputs["audio_len"] = audio_len
-            logger.info(f"audio feat shape: {audio.shape}")
-
-        elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
-            logger.info("get the preprocess conf")
-            preprocess_conf = self.config.preprocess_config
-            preprocess_args = {"train": False}
-            preprocessing = Transformation(preprocess_conf)
-            logger.info("read the audio file")
-            audio, audio_sample_rate = soundfile.read(
-                audio_file, dtype="int16", always_2d=True)
-
-            if self.change_format:
-                if audio.shape[1] >= 2:
-                    audio = audio.mean(axis=1, dtype=np.int16)
-                else:
-                    audio = audio[:, 0]
-                # pcm16 -> pcm 32
-                audio = self._pcm16to32(audio)
-                audio = librosa.resample(audio, audio_sample_rate,
-                                         self.sample_rate)
-                audio_sample_rate = self.sample_rate
-                # pcm32 -> pcm 16
-                audio = self._pcm32to16(audio)
-            else:
-                audio = audio[:, 0]
-
-            logger.info(f"audio shape: {audio.shape}")
-            # fbank
-            audio = preprocessing(audio, **preprocess_args)
-
-            audio_len = paddle.to_tensor(audio.shape[0])
-            audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
-
-            self._inputs["audio"] = audio
-            self._inputs["audio_len"] = audio_len
-            logger.info(f"audio feat shape: {audio.shape}")
-
-        else:
-            raise Exception("wrong type")
-
 
 class ASREngine(BaseEngine):
     """ASR server engine
@@ -157,16 +52,12 @@ class ASREngine(BaseEngine):
         self.output = None
         self.executor = ASRServerExecutor()
 
-        try:
         self.config = get_config(config_file)
-            paddle.set_device(paddle.get_device())
+        paddle.set_device(self.config.device)
         self.executor._init_from_path(
             self.config.model, self.config.lang, self.config.sample_rate,
             self.config.cfg_path, self.config.decode_method,
             self.config.ckpt_path)
-        except:
-            logger.info("Initialize ASR server engine Failed.")
-            return False
 
         logger.info("Initialize ASR server engine successfully.")
         return True

paddlespeech/server/engine/engine_factory.py

@@ -13,20 +13,24 @@
 # limitations under the License.
 from typing import Text
 
-from paddlespeech.server.engine.asr.python.asr_engine import ASREngine
-#from paddlespeech.server.engine.tts.python.tts_engine import TTSEngine
-from paddlespeech.server.engine.tts.paddleinference.tts_engine import TTSEngine
-
 
 __all__ = ['EngineFactory']
 
 
 class EngineFactory(object):
     @staticmethod
-    def get_engine(engine_name: Text):
+    def get_engine(engine_name: Text, engine_type: Text):
-        if engine_name == 'asr':
+        if engine_name == 'asr' and engine_type == 'inference':
+            from paddlespeech.server.engine.asr.paddleinference.asr_engine import ASREngine
+            return ASREngine()
+        elif engine_name == 'asr' and engine_type == 'python':
+            from paddlespeech.server.engine.asr.python.asr_engine import ASREngine
             return ASREngine()
-        elif engine_name == 'tts':
+        elif engine_name == 'tts' and engine_type == 'inference':
+            from paddlespeech.server.engine.tts.paddleinference.tts_engine import TTSEngine
+            return TTSEngine()
+        elif engine_name == 'tts' and engine_type == 'python':
+            from paddlespeech.server.engine.tts.python.tts_engine import TTSEngine
             return TTSEngine()
         else:
             return None

paddlespeech/server/engine/engine_pool.py

@@ -0,0 +1,36 @@
+# 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.
+from paddlespeech.server.engine.engine_factory import EngineFactory
+
+# global value
+ENGINE_POOL = {}
+
+
+def get_engine_pool() -> dict:
+    """ Get engine pool
+    """
+    global ENGINE_POOL
+    return ENGINE_POOL
+
+
+def init_engine_pool(config) -> bool:
+    """ Init engine pool
+    """
+    global ENGINE_POOL
+    for engine in config.engine_backend:
+        ENGINE_POOL[engine] = EngineFactory.get_engine(engine_name=engine, engine_type=config.engine_type[engine])
+        if not ENGINE_POOL[engine].init(config_file=config.engine_backend[engine]):
+            return False
+
+    return True

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

@@ -344,7 +344,6 @@ class TTSEngine(BaseEngine):
 
         try:
             self.config = get_config(config_file)
-
             self.executor._init_from_path(
                 am=self.config.am,
                 am_model=self.config.am_model,

paddlespeech/server/restful/api.py

@@ -22,7 +22,14 @@ _router = APIRouter()
 
 
 def setup_router(api_list: List):
+    """setup router for fastapi
 
+    Args:
+        api_list (List): [asr, tts]
+
+    Returns:
+        APIRouter
+    """
     for api_name in api_list:
         if api_name == 'asr':
             _router.include_router(asr_router)

paddlespeech/server/restful/asr_api.py

@@ -16,7 +16,7 @@ import traceback
 from typing import Union
 from fastapi import APIRouter
 
-from paddlespeech.server.engine.asr.python.asr_engine import ASREngine
+from paddlespeech.server.engine.engine_pool import get_engine_pool
 from paddlespeech.server.restful.request import ASRRequest
 from paddlespeech.server.restful.response import ASRResponse
 from paddlespeech.server.restful.response import ErrorResponse
@@ -61,9 +61,12 @@ def asr(request_body: ASRRequest):
         json: [description]
     """
     try:
-        # single 
         audio_data = base64.b64decode(request_body.audio)
-        asr_engine = ASREngine()
+
+        # get single engine from engine pool
+        engine_pool = get_engine_pool()
+        asr_engine = engine_pool['asr']
+
         asr_engine.run(audio_data)
         asr_results = asr_engine.postprocess()
 

paddlespeech/server/restful/tts_api.py

@@ -16,7 +16,7 @@ from typing import Union
 
 from fastapi import APIRouter
 
-from paddlespeech.server.engine.tts.paddleinference.tts_engine import TTSEngine
+from paddlespeech.server.engine.engine_pool import get_engine_pool
 from paddlespeech.server.restful.request import TTSRequest
 from paddlespeech.server.restful.response import ErrorResponse
 from paddlespeech.server.restful.response import TTSResponse
@@ -60,28 +60,41 @@ def tts(request_body: TTSRequest):
     Returns:
         json: [description]
     """
-    # json to dict 
+    # get params
-    item_dict = request_body.dict()
+    text = request_body.text
-    sentence = item_dict['text']
+    spk_id = request_body.spk_id
-    spk_id = item_dict['spk_id']
+    speed = request_body.speed
-    speed = item_dict['speed']
+    volume = request_body.volume
-    volume = item_dict['volume']
+    sample_rate = request_body.sample_rate
-    sample_rate = item_dict['sample_rate']
+    save_path = request_body.save_path
-    save_path = item_dict['save_path']
 
     # Check parameters
-    if speed <=0 or speed > 3 or volume <=0 or volume > 3 or \
+    if speed <= 0 or speed > 3:
-        sample_rate not in [0, 16000, 8000] or \
+        return failed_response(
-        (save_path is not None and not save_path.endswith("pcm") and not save_path.endswith("wav")):
+            ErrorCode.SERVER_PARAM_ERR,
-        return failed_response(ErrorCode.SERVER_PARAM_ERR)
+            "invalid speed value, the value should be between 0 and 3.")
-
+    if volume <= 0 or volume > 3:
-    # single
+        return failed_response(
-    tts_engine = TTSEngine()
+            ErrorCode.SERVER_PARAM_ERR,
+            "invalid volume value, the value should be between 0 and 3.")
+    if sample_rate not in [0, 16000, 8000]:
+        return failed_response(
+            ErrorCode.SERVER_PARAM_ERR,
+            "invalid sample_rate value, the choice of value is 0, 8000, 16000.")
+    if save_path is not None and not save_path.endswith(
+            "pcm") and not save_path.endswith("wav"):
+        return failed_response(
+            ErrorCode.SERVER_PARAM_ERR,
+            "invalid save_path, saved audio formats support pcm and wav")
 
     # run
     try:
+        # get single engine from engine pool
+        engine_pool = get_engine_pool()
+        tts_engine = engine_pool['tts']
+
         lang, target_sample_rate, wav_base64 = tts_engine.run(
-            sentence, spk_id, speed, volume, sample_rate, save_path)
+            text, spk_id, speed, volume, sample_rate, save_path)
 
         response = {
             "success": True,

paddlespeech/server/utils/paddle_predictor.py

@@ -41,8 +41,9 @@ def init_predictor(model_dir: Optional[os.PathLike]=None,
         config = Config(model_file, params_file)
 
     config.enable_memory_optim()
-    if predictor_conf["use_gpu"]:
+    if "gpu" in predictor_conf["device"]:
-        config.enable_use_gpu(1000, 0)
+        gpu_id = predictor_conf["device"].split(":")[-1]
+        config.enable_use_gpu(1000, int(gpu_id))
     if predictor_conf["enable_mkldnn"]:
         config.enable_mkldnn()
     if predictor_conf["switch_ir_optim"]: