fix ds2 server config

.gitignore

@@ -39,6 +39,9 @@ tools/env.sh
 tools/openfst-1.8.1/
 tools/libsndfile/
 tools/python-soundfile/
+tools/onnx
+tools/onnxruntime
+tools/Paddle2ONNX
 
 speechx/fc_patch/
 

.mergify.yml

@@ -52,7 +52,7 @@ pull_request_rules:
         add: ["T2S"]
   - name: "auto add label=Audio"
     conditions:
-      - files~=^paddleaudio/
+      - files~=^paddlespeech/audio/
     actions:
       label:
         add: ["Audio"]
@@ -100,7 +100,7 @@ pull_request_rules:
         add: ["README"]
   - name: "auto add label=Documentation"
     conditions:
-      - files~=^(docs/|CHANGELOG.md|paddleaudio/CHANGELOG.md)
+      - files~=^(docs/|CHANGELOG.md)
     actions:
       label:
         add: ["Documentation"]

audio/.gitignore

-.eggs
-*.wav

audio/CHANGELOG.md

-# Changelog
-
-Date: 2022-3-15, Author: Xiaojie Chen.
-  - kaldi and librosa mfcc, fbank, spectrogram.
-  - unit test and benchmark.
-
-Date: 2022-2-25, Author: Hui Zhang.
-  - Refactor architecture.
-  - dtw distance and mcd style dtw.

audio/README.md

-# PaddleAudio
-
-PaddleAudio is an audio library for PaddlePaddle.
-
-## Install
-
-`pip install .`

audio/docs/Makefile

-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file

audio/docs/README.md

-# Build docs for PaddleAudio
-
-Execute the following steps in **current directory**.
-
-## 1. Install
-
-`pip install Sphinx sphinx_rtd_theme`
-
-
-## 2. Generate API docs
-
-Generate API docs from doc string.
-
-`sphinx-apidoc -fMeT -o source ../paddleaudio ../paddleaudio/utils --templatedir source/_templates`
-
-
-## 3. Build
-
-`sphinx-build source _html`
-
-
-## 4. Preview
-
-Open `_html/index.html` for page preview.

audio/docs/make.bat

-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
-
-:end
-popd

audio/paddleaudio/utils/env.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.
-'''
-This module is used to store environmental variables in PaddleAudio.
-PPAUDIO_HOME     -->  the root directory for storing PaddleAudio related data. Default to ~/.paddleaudio. Users can change the
-├                            default value through the PPAUDIO_HOME environment variable.
-├─ MODEL_HOME    -->  Store model files.
-└─ DATA_HOME     -->  Store automatically downloaded datasets.
-'''
-import os
-
-__all__ = [
-    'USER_HOME',
-    'PPAUDIO_HOME',
-    'MODEL_HOME',
-    'DATA_HOME',
-]
-
-
-def _get_user_home():
-    return os.path.expanduser('~')
-
-
-def _get_ppaudio_home():
-    if 'PPAUDIO_HOME' in os.environ:
-        home_path = os.environ['PPAUDIO_HOME']
-        if os.path.exists(home_path):
-            if os.path.isdir(home_path):
-                return home_path
-            else:
-                raise RuntimeError(
-                    'The environment variable PPAUDIO_HOME {} is not a directory.'.
-                    format(home_path))
-        else:
-            return home_path
-    return os.path.join(_get_user_home(), '.paddleaudio')
-
-
-def _get_sub_home(directory):
-    home = os.path.join(_get_ppaudio_home(), directory)
-    if not os.path.exists(home):
-        os.makedirs(home)
-    return home
-
-
-USER_HOME = _get_user_home()
-PPAUDIO_HOME = _get_ppaudio_home()
-MODEL_HOME = _get_sub_home('models')
-DATA_HOME = _get_sub_home('datasets')

audio/setup.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 glob
-import os
-
-import setuptools
-from setuptools.command.install import install
-from setuptools.command.test import test
-
-# set the version here
-VERSION = '0.0.0'
-
-
-# Inspired by the example at https://pytest.org/latest/goodpractises.html
-class TestCommand(test):
-    def finalize_options(self):
-        test.finalize_options(self)
-        self.test_args = []
-        self.test_suite = True
-
-    def run(self):
-        self.run_benchmark()
-        super(TestCommand, self).run()
-
-    def run_tests(self):
-        # Run nose ensuring that argv simulates running nosetests directly
-        import nose
-        nose.run_exit(argv=['nosetests', '-w', 'tests'])
-
-    def run_benchmark(self):
-        for benchmark_item in glob.glob('tests/benchmark/*py'):
-            os.system(f'pytest {benchmark_item}')
-
-
-class InstallCommand(install):
-    def run(self):
-        install.run(self)
-
-
-def write_version_py(filename='paddleaudio/__init__.py'):
-    with open(filename, "a") as f:
-        f.write(f"__version__ = '{VERSION}'")
-
-
-def remove_version_py(filename='paddleaudio/__init__.py'):
-    with open(filename, "r") as f:
-        lines = f.readlines()
-    with open(filename, "w") as f:
-        for line in lines:
-            if "__version__" not in line:
-                f.write(line)
-
-
-remove_version_py()
-write_version_py()
-
-setuptools.setup(
-    name="paddleaudio",
-    version=VERSION,
-    author="",
-    author_email="",
-    description="PaddleAudio, in development",
-    long_description="",
-    long_description_content_type="text/markdown",
-    url="",
-    packages=setuptools.find_packages(include=['paddleaudio*']),
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: MIT License",
-        "Operating System :: OS Independent",
-    ],
-    python_requires='>=3.6',
-    install_requires=[
-        'numpy >= 1.15.0', 'scipy >= 1.0.0', 'resampy >= 0.2.2',
-        'soundfile >= 0.9.0', 'colorlog', 'pathos == 0.2.8'
-    ],
-    extras_require={
-        'test': [
-            'nose', 'librosa==0.8.1', 'soundfile==0.10.3.post1',
-            'torchaudio==0.10.2', 'pytest-benchmark'
-        ],
-    },
-    cmdclass={
-        'install': InstallCommand,
-        'test': TestCommand,
-    }, )
-
-remove_version_py()

demos/audio_searching/README.md

@@ -89,7 +89,7 @@ Then to start the system server, and it provides HTTP backend services.
   Then start the server with Fastapi.
 
   ```bash
-  export PYTHONPATH=$PYTHONPATH:./src:../../paddleaudio
+  export PYTHONPATH=$PYTHONPATH:./src
   python src/audio_search.py
   ```
 

demos/audio_searching/README_cn.md

@@ -91,7 +91,7 @@ ffce340b3790  minio/minio:RELEASE.2020-12-03T00-03-10Z  "/usr/bin/docker-ent…"
   启动用 Fastapi 构建的服务
 
   ```bash
-  export PYTHONPATH=$PYTHONPATH:./src:../../paddleaudio
+  export PYTHONPATH=$PYTHONPATH:./src
   python src/audio_search.py
   ```
 

demos/streaming_asr_server/conf/ws_ds2_application.yaml

@@ -7,11 +7,11 @@ host: 0.0.0.0
 port: 8090
 
 # The task format in the engin_list is: <speech task>_<engine type>
-# task choices = ['asr_online']
+# task choices = ['asr_online-inference', 'asr_online-onnx']
 # protocol = ['websocket'] (only one can be selected).
 # websocket only support online engine type.
 protocol: 'websocket'
-engine_list: ['asr_online']
+engine_list: ['asr_online-inference']
 
 
 #################################################################################
@@ -19,8 +19,8 @@ engine_list: ['asr_online']
 #################################################################################
 
 ################################### ASR #########################################
-################### speech task: asr; engine_type: online #######################
-asr_online:
+################### speech task: asr; engine_type: online-inference #######################
+asr_online-inference:
     model_type: 'deepspeech2online_aishell'
     am_model:    # the pdmodel file of am static model [optional]
     am_params:   # the pdiparams file of am static model [optional]
@@ -47,3 +47,38 @@ asr_online:
         shift_n: 4      # frame
         window_ms: 25   # ms
         shift_ms: 10    # ms
+
+
+
+################################### ASR #########################################
+################### speech task: asr; engine_type: online-onnx #######################
+asr_online-onnx:
+    model_type: 'deepspeech2online_aishell'
+    am_model:  # the pdmodel file of onnx am static model [optional]
+    am_params:  # the pdiparams file of am static model [optional]
+    lang: 'zh'
+    sample_rate: 16000
+    cfg_path: 
+    decode_method: 
+    num_decoding_left_chunks: 
+    force_yes: True
+    device: 'cpu' # cpu or gpu:id
+
+    # https://onnxruntime.ai/docs/api/python/api_summary.html#inferencesession
+    am_predictor_conf:
+        device: 'cpu' # set 'gpu:id' or 'cpu'
+        graph_optimization_level: 0 
+        intra_op_num_threads: 0 # Sets the number of threads used to parallelize the execution within nodes.
+        inter_op_num_threads: 0 # Sets the number of threads used to parallelize the execution of the graph (across nodes).
+        log_severity_level: 2   # Log severity level. Applies to session load, initialization, etc. 0:Verbose, 1:Info, 2:Warning. 3:Error, 4:Fatal. Default is 2.
+        log_verbosity_level: 0  # VLOG level if DEBUG build and session_log_severity_level is 0. Applies to session load, initialization, etc. Default is 0.
+
+    chunk_buffer_conf:
+        frame_duration_ms: 80
+        shift_ms: 40
+        sample_rate: 16000
+        sample_width: 2
+        window_n: 7     # frame
+        shift_n: 4      # frame
+        window_ms: 20   # ms
+        shift_ms: 10    # ms

demos/streaming_asr_server/server.sh

@@ -4,6 +4,6 @@ export CUDA_VISIBLE_DEVICE=0,1,2,3
 # nohup python3 punc_server.py --config_file conf/punc_application.yaml > punc.log 2>&1 &
 paddlespeech_server start --config_file conf/punc_application.yaml &> punc.log &
 
-# nohup python3 streaming_asr_server.py --config_file conf/ws_conformer_application.yaml > streaming_asr.log 2>&1 &
-paddlespeech_server start --config_file conf/ws_conformer_application.yaml &> streaming_asr.log  &
+# nohup python3 streaming_asr_server.py --config_file conf/ws_conformer_wenetspeech_application.yaml > streaming_asr.log 2>&1 &
+paddlespeech_server start --config_file conf/ws_conformer_wenetspeech_application.yaml &> streaming_asr.log  &
 

demos/streaming_asr_server/test.sh

@@ -4,7 +4,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
 # read the wav and pass it to only streaming asr service
 # If `127.0.0.1` is not accessible, you need to use the actual service IP address.
 # python3 websocket_client.py --server_ip 127.0.0.1 --port 8290 --wavfile ./zh.wav
-paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8290 --input ./zh.wav
+paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
 
 # read the wav and call streaming and punc service
 # If `127.0.0.1` is not accessible, you need to use the actual service IP address.

docs/source/cls/custom_dataset.md

 # Customize Dataset for Audio Classification
 
-Following this tutorial you can customize your dataset for audio classification task by using `paddlespeech` and `paddleaudio`.
+Following this tutorial you can customize your dataset for audio classification task by using `paddlespeech`.
 
-A base class of classification dataset is `paddleaudio.dataset.AudioClassificationDataset`. To customize your dataset you should write a dataset class derived from `AudioClassificationDataset`. 
+A base class of classification dataset is `paddlespeech.audio.dataset.AudioClassificationDataset`. To customize your dataset you should write a dataset class derived from `AudioClassificationDataset`. 
 
 Assuming you have some wave files that stored in your own directory. You should prepare a meta file with the information of filepaths and labels. For example the absolute path of it is `/PATH/TO/META_FILE.txt`:
 ```
@@ -14,7 +14,7 @@ Assuming you have some wave files that stored in your own directory. You should
 Here is an example to build your custom dataset in `custom_dataset.py`:
 
 ```python
-from paddleaudio.datasets.dataset import AudioClassificationDataset
+from paddlespeech.audio.datasets.dataset import AudioClassificationDataset
 
 class CustomDataset(AudioClassificationDataset):
     meta_file = '/PATH/TO/META_FILE.txt'
@@ -48,7 +48,7 @@ class CustomDataset(AudioClassificationDataset):
 Then you can build dataset and data loader from `CustomDataset`:
 ```python
 import paddle
-from paddleaudio.features import LogMelSpectrogram
+from paddlespeech.audio.features import LogMelSpectrogram
 
 from custom_dataset import CustomDataset
 

docs/source/released_model.md

@@ -8,13 +8,13 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER |
 :-------------:| :------------:| :-----: | -----: | :-----: |:-----:| :-----:  | :-----:  | :-----: 
 [Ds2 Online Wenetspeech ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr0/asr0_deepspeech2_online_wenetspeech_ckpt_1.0.2.model.tar.gz) | Wenetspeech Dataset | Char-based | 1.2 GB  | 2 Conv + 5 LSTM layers | 0.152 (test\_net, w/o LM) <br> 0.2417 (test\_meeting, w/o LM) <br> 0.053 (aishell, w/ LM) |-| 10000 h |- 
 [Ds2 Online Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz) | Aishell Dataset | Char-based | 491 MB  | 2 Conv + 5 LSTM layers | 0.0666 |-| 151 h | [D2 Online Aishell ASR0](../../examples/aishell/asr0) 
-[Ds2 Offline Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz)| Aishell Dataset | Char-based | 306 MB | 2 Conv + 3 bidirectional GRU layers| 0.064 |-| 151 h | [Ds2 Offline Aishell ASR0](../../examples/aishell/asr0) 
+[Ds2 Offline Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz)| Aishell Dataset | Char-based | 1.4 GB | 2 Conv + 5 bidirectional LSTM layers| 0.0554 |-| 151 h | [Ds2 Offline Aishell ASR0](../../examples/aishell/asr0) 
 [Conformer Online Wenetspeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_wenetspeech_ckpt_1.0.0a.model.tar.gz) | WenetSpeech Dataset | Char-based | 457 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring| 0.11 (test\_net) 0.1879 (test\_meeting) |-| 10000 h |- 
 [Conformer Online Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_chunk_conformer_aishell_ckpt_0.2.0.model.tar.gz) | Aishell Dataset | Char-based | 189 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring| 0.0544 |-| 151 h | [Conformer Online Aishell ASR1](../../examples/aishell/asr1) 
 [Conformer Offline Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_0.1.2.model.tar.gz) | Aishell Dataset | Char-based | 189 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0464 |-| 151 h | [Conformer Offline Aishell ASR1](../../examples/aishell/asr1) 
 [Transformer Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz) | Aishell Dataset | Char-based | 128 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0523 || 151 h | [Transformer  Aishell ASR1](../../examples/aishell/asr1) 
-[Ds2 Offline Librispeech ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz)| Librispeech Dataset | Char-based | 518 MB | 2 Conv + 3 bidirectional LSTM layers| - |0.0725| 960 h | [Ds2 Offline Librispeech ASR0](../../examples/librispeech/asr0) 
-[Conformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 191 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0337 | 960 h | [Conformer Librispeech ASR1](../../examples/librispeech/asr1) 
+[Ds2 Offline Librispeech ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_offline_librispeech_ckpt_1.0.1.model.tar.gz)| Librispeech Dataset | Char-based | 1.3 GB | 2 Conv + 5 bidirectional LSTM layers| - |0.0467| 960 h | [Ds2 Offline Librispeech ASR0](../../examples/librispeech/asr0) 
+[Conformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 191 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0338 | 960 h | [Conformer Librispeech ASR1](../../examples/librispeech/asr1) 
 [Transformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB  | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0381 | 960 h | [Transformer Librispeech ASR1](../../examples/librispeech/asr1) 
 [Transformer Librispeech ASR2 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB  | Encoder:Transformer, Decoder:Transformer, Decoding method: JoinCTC w/ LM |-| 0.0240 | 960 h | [Transformer Librispeech ASR2](../../examples/librispeech/asr2) 
 

examples/aishell/asr0/RESULTS.md

@@ -13,6 +13,7 @@
 
 | Model | Number of Params | Release | Config | Test set | Valid Loss | CER |  
 | --- | --- | --- | --- | --- | --- | --- |
+| DeepSpeech2 | 122.3M | r1.0.1 | conf/deepspeech2.yaml + U2 Data pipline and spec aug + fbank161 | test | 5.780756044387817 | 0.055400 | 
 | DeepSpeech2 | 58.4M | v2.2.0 | conf/deepspeech2.yaml + spec aug | test | 5.738585948944092 | 0.064000 |  
 | DeepSpeech2 | 58.4M | v2.1.0 | conf/deepspeech2.yaml + spec aug | test | 7.483316898345947 | 0.077860 |  
 | DeepSpeech2 | 58.4M | v2.1.0 | conf/deepspeech2.yaml | test | 7.299022197723389 | 0.078671 |

examples/esc50/cls0/conf/panns.yaml

 data:
-  dataset: 'paddleaudio.datasets:ESC50'
+  dataset: 'paddlespeech.audio.datasets:ESC50'
   num_classes: 50
   train:
     mode: 'train'

examples/hey_snips/kws0/conf/mdtc.yaml

@@ -2,7 +2,7 @@
 ###########################################
 #                   Data                  #
 ###########################################
-dataset: 'paddleaudio.datasets:HeySnips'
+dataset: 'paddlespeech.audio.datasets:HeySnips'
 data_dir: '/PATH/TO/DATA/hey_snips_research_6k_en_train_eval_clean_ter'
 
 ############################################

examples/librispeech/asr0/RESULTS.md

@@ -3,6 +3,7 @@
 ## Deepspeech2 Non-Streaming
 | Model | Params | release |  Config | Test set | Loss | WER |  
 | --- | --- | --- | --- | --- | --- | --- |  
+| DeepSpeech2 | 113.96M | r1.0.1 | conf/deepspeech2.yaml + U2 Data pipline and spec aug + fbank161 | test-clean | 10.76069622039795 | 0.046700 | 
 | DeepSpeech2 | 42.96M | 2.2.0 | conf/deepspeech2.yaml + spec_aug | test-clean | 14.49190807 | 0.067283 |  
 | DeepSpeech2 | 42.96M | 2.1.0 | conf/deepspeech2.yaml | test-clean | 15.184467315673828 | 0.072154 |  
 | DeepSpeech2 | 42.96M | 2.0.0 | conf/deepspeech2.yaml | test-clean | - | 0.073973 |  

examples/voxceleb/sv0/local/data_prepare.py

@@ -14,9 +14,9 @@
 import argparse
 
 import paddle
-from paddleaudio.datasets.voxceleb import VoxCeleb
 from yacs.config import CfgNode
 
+from paddlespeech.audio.datasets.voxceleb import VoxCeleb
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.vector.io.augment import build_augment_pipeline
 from paddlespeech.vector.training.seeding import seed_everything

examples/voxceleb/sv0/local/make_rirs_noise_csv_dataset_from_json.py

@@ -21,9 +21,9 @@ import os
 from typing import List
 
 import tqdm
-from paddleaudio import load as load_audio
 from yacs.config import CfgNode
 
+from paddlespeech.audio import load as load_audio
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.vector.utils.vector_utils import get_chunks
 

examples/voxceleb/sv0/local/make_vox_csv_dataset_from_json.py

@@ -22,9 +22,9 @@ import os
 import random
 
 import tqdm
-from paddleaudio import load as load_audio
 from yacs.config import CfgNode
 
+from paddlespeech.audio import load as load_audio
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.vector.utils.vector_utils import get_chunks
 

audio/paddleaudio/datasets/esc50.py → paddlespeech/audio/datasets/esc50.py

@@ -16,8 +16,8 @@ import os
 from typing import List
 from typing import Tuple
 
+from ..utils import DATA_HOME
 from ..utils.download import download_and_decompress
-from ..utils.env import DATA_HOME
 from .dataset import AudioClassificationDataset
 
 __all__ = ['ESC50']

audio/paddleaudio/datasets/gtzan.py → paddlespeech/audio/datasets/gtzan.py

@@ -17,8 +17,8 @@ import random
 from typing import List
 from typing import Tuple
 
+from ..utils import DATA_HOME
 from ..utils.download import download_and_decompress
-from ..utils.env import DATA_HOME
 from .dataset import AudioClassificationDataset
 
 __all__ = ['GTZAN']

audio/paddleaudio/datasets/tess.py → paddlespeech/audio/datasets/tess.py

@@ -17,8 +17,8 @@ import random
 from typing import List
 from typing import Tuple
 
+from ..utils import DATA_HOME
 from ..utils.download import download_and_decompress
-from ..utils.env import DATA_HOME
 from .dataset import AudioClassificationDataset
 
 __all__ = ['TESS']

audio/paddleaudio/datasets/urban_sound.py → paddlespeech/audio/datasets/urban_sound.py

@@ -16,8 +16,8 @@ import os
 from typing import List
 from typing import Tuple
 
+from ..utils import DATA_HOME
 from ..utils.download import download_and_decompress
-from ..utils.env import DATA_HOME
 from .dataset import AudioClassificationDataset
 
 __all__ = ['UrbanSound8K']

audio/paddleaudio/utils/__init__.py → paddlespeech/audio/utils/__init__.py

@@ -11,13 +11,11 @@
 # 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 ...cli.utils import DATA_HOME
+from ...cli.utils import MODEL_HOME
 from .download import decompress
 from .download import download_and_decompress
 from .download import load_state_dict_from_url
-from .env import DATA_HOME
-from .env import MODEL_HOME
-from .env import PPAUDIO_HOME
-from .env import USER_HOME
 from .error import ParameterError
 from .log import Logger
 from .log import logger

paddlespeech/cli/asr/infer.py

@@ -187,7 +187,7 @@ class ASRExecutor(BaseExecutor):
             elif "conformer" in model_type or "transformer" in model_type:
                 self.config.decode.decoding_method = decode_method
                 if num_decoding_left_chunks:
-                    assert num_decoding_left_chunks == -1 or num_decoding_left_chunks >= 0, f"num_decoding_left_chunks should be -1 or >=0"
+                    assert num_decoding_left_chunks == -1 or num_decoding_left_chunks >= 0, "num_decoding_left_chunks should be -1 or >=0"
                     self.config.num_decoding_left_chunks = num_decoding_left_chunks
 
             else:

paddlespeech/cli/cls/infer.py

@@ -21,12 +21,12 @@ from typing import Union
 import numpy as np
 import paddle
 import yaml
-from paddleaudio import load
-from paddleaudio.features import LogMelSpectrogram
 
 from ..executor import BaseExecutor
 from ..log import logger
 from ..utils import stats_wrapper
+from paddlespeech.audio import load
+from paddlespeech.audio.features import LogMelSpectrogram
 
 __all__ = ['CLSExecutor']
 

paddlespeech/cli/utils.py

@@ -24,11 +24,11 @@ from typing import Any
 from typing import Dict
 
 import paddle
-import paddleaudio
 import requests
 import yaml
 from paddle.framework import load
 
+import paddlespeech.audio
 from . import download
 from .entry import commands
 try:
@@ -190,6 +190,7 @@ def _get_sub_home(directory):
 PPSPEECH_HOME = _get_paddlespcceh_home()
 MODEL_HOME = _get_sub_home('models')
 CONF_HOME = _get_sub_home('conf')
+DATA_HOME = _get_sub_home('datasets')
 
 
 def _md5(text: str):
@@ -281,7 +282,7 @@ def _note_one_stat(cls_name, params={}):
 
     if 'audio_file' in params:
         try:
-            _, sr = paddleaudio.load(params['audio_file'])
+            _, sr = paddlespeech.audio.load(params['audio_file'])
         except Exception:
             sr = -1
 

paddlespeech/cli/vector/infer.py

@@ -22,13 +22,13 @@ 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 paddlespeech.audio.backends import load as load_audio
+from paddlespeech.audio.compliance.librosa import melspectrogram
 from paddlespeech.vector.io.batch import feature_normalize
 from paddlespeech.vector.modules.sid_model import SpeakerIdetification
 

paddlespeech/cls/exps/panns/deploy/predict.py

@@ -16,11 +16,12 @@ import os
 
 import numpy as np
 from paddle import inference
-from paddleaudio.backends import load as load_audio
-from paddleaudio.datasets import ESC50
-from paddleaudio.features import melspectrogram
 from scipy.special import softmax
 
+from paddlespeech.audio.backends import load as load_audio
+from paddlespeech.audio.datasets import ESC50
+from paddlespeech.audio.features import melspectrogram
+
 # yapf: disable
 parser = argparse.ArgumentParser()
 parser.add_argument("--model_dir", type=str, required=True, default="./export", help="The directory to static model.")

paddlespeech/cls/exps/panns/export_model.py

@@ -15,8 +15,8 @@ import argparse
 import os
 
 import paddle
-from paddleaudio.datasets import ESC50
 
+from paddlespeech.audio.datasets import ESC50
 from paddlespeech.cls.models import cnn14
 from paddlespeech.cls.models import SoundClassifier
 

paddlespeech/cls/exps/panns/predict.py

@@ -17,10 +17,10 @@ import os
 import paddle
 import paddle.nn.functional as F
 import yaml
-from paddleaudio.backends import load as load_audio
-from paddleaudio.features import LogMelSpectrogram
-from paddleaudio.utils import logger
 
+from paddlespeech.audio.backends import load as load_audio
+from paddlespeech.audio.features import LogMelSpectrogram
+from paddlespeech.audio.utils import logger
 from paddlespeech.cls.models import SoundClassifier
 from paddlespeech.utils.dynamic_import import dynamic_import
 

paddlespeech/cls/exps/panns/train.py

@@ -16,10 +16,10 @@ import os
 
 import paddle
 import yaml
-from paddleaudio.features import LogMelSpectrogram
-from paddleaudio.utils import logger
-from paddleaudio.utils import Timer
 
+from paddlespeech.audio.features import LogMelSpectrogram
+from paddlespeech.audio.utils import logger
+from paddlespeech.audio.utils import Timer
 from paddlespeech.cls.models import SoundClassifier
 from paddlespeech.utils.dynamic_import import dynamic_import
 

paddlespeech/cls/models/panns/panns.py

@@ -15,8 +15,9 @@ import os
 
 import paddle.nn as nn
 import paddle.nn.functional as F
-from paddleaudio.utils.download import load_state_dict_from_url
-from paddleaudio.utils.env import MODEL_HOME
+
+from paddlespeech.audio.utils import MODEL_HOME
+from paddlespeech.audio.utils.download import load_state_dict_from_url
 
 __all__ = ['CNN14', 'CNN10', 'CNN6', 'cnn14', 'cnn10', 'cnn6']
 

paddlespeech/kws/exps/mdtc/train.py

@@ -14,10 +14,10 @@
 import os
 
 import paddle
-from paddleaudio.utils import logger
-from paddleaudio.utils import Timer
 from yacs.config import CfgNode
 
+from paddlespeech.audio.utils import logger
+from paddlespeech.audio.utils import Timer
 from paddlespeech.kws.exps.mdtc.collate import collate_features
 from paddlespeech.kws.models.loss import max_pooling_loss
 from paddlespeech.kws.models.mdtc import KWSModel

paddlespeech/resource/pretrained_models.py

@@ -15,6 +15,7 @@
 __all__ = [
     'asr_dynamic_pretrained_models',
     'asr_static_pretrained_models',
+    'asr_onnx_pretrained_models',
     'cls_dynamic_pretrained_models',
     'cls_static_pretrained_models',
     'st_dynamic_pretrained_models',
@@ -166,23 +167,17 @@ asr_dynamic_pretrained_models = {
         },
     },
     "deepspeech2online_aishell-zh-16k": {
-        '1.0': {
+        '1.0.2': {
             'url':
-            'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_1.0.1.model.tar.gz',
-            'md5':
-            'df5ddeac8b679a470176649ac4b78726',
-            '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'
+            'http://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_1.0.2.model.tar.gz',
+            'md5': '4dd42cfce9aaa54db0ec698da6c48ec5',
+            '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',
+            'onnx_model': 'onnx/model.onnx',
+            'lm_url':'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
+            'lm_md5':'29e02312deb2e59b3c8686c7966d4fe3'
         },
     },
     "deepspeech2offline_librispeech-en-16k": {
@@ -224,6 +219,55 @@ asr_static_pretrained_models = {
             '29e02312deb2e59b3c8686c7966d4fe3'
         }
     },
+    "deepspeech2online_aishell-zh-16k": {
+        '1.0.1': {
+            'url':
+            'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_1.0.1.model.tar.gz',
+            'md5':
+            'df5ddeac8b679a470176649ac4b78726',
+            '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'
+        },
+        '1.0.2': {
+            'url':
+            'http://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_1.0.2.model.tar.gz',
+            'md5': '4dd42cfce9aaa54db0ec698da6c48ec5',
+            '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',
+            'onnx_model': 'onnx/model.onnx',
+            'lm_url':'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
+            'lm_md5':'29e02312deb2e59b3c8686c7966d4fe3'
+        },
+    },
+}
+
+asr_onnx_pretrained_models = {
+    "deepspeech2online_aishell-zh-16k": {
+        '1.0.2': {
+            'url':
+            'http://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_1.0.2.model.tar.gz',
+            'md5': '4dd42cfce9aaa54db0ec698da6c48ec5',
+            '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',
+            'onnx_model': 'onnx/model.onnx',
+            'lm_url':'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
+            'lm_md5':'29e02312deb2e59b3c8686c7966d4fe3'
+        },
+    },
 }
 
 # ---------------------------------

paddlespeech/resource/resource.py

@@ -164,9 +164,10 @@ class CommonTaskResource:
         try:
             import_models = '{}_{}_pretrained_models'.format(self.task,
                                                              self.model_format)
+            print(f"from .pretrained_models import {import_models}")
             exec('from .pretrained_models import {}'.format(import_models))
             models = OrderedDict(locals()[import_models])
-        except ImportError:
+        except Exception as e:
             models = OrderedDict({})  # no models.
         finally:
             return models

paddlespeech/s2t/frontend/featurizer/audio_featurizer.py

@@ -14,10 +14,11 @@
 """Contains the audio featurizer class."""
 import numpy as np
 import paddle
-import paddleaudio.compliance.kaldi as kaldi
 from python_speech_features import delta
 from python_speech_features import mfcc
 
+import paddlespeech.audio.compliance.kaldi as kaldi
+
 
 class AudioFeaturizer():
     """Audio featurizer, for extracting features from audio contents of

paddlespeech/s2t/transform/spectrogram.py

@@ -15,9 +15,10 @@
 import librosa
 import numpy as np
 import paddle
-import paddleaudio.compliance.kaldi as kaldi
 from python_speech_features import logfbank
 
+import paddlespeech.audio.compliance.kaldi as kaldi
+
 
 def stft(x,
          n_fft,

paddlespeech/server/conf/ws_application.yaml → paddlespeech/server/conf/ws_ds2_application.yaml

@@ -7,11 +7,11 @@ host: 0.0.0.0
 port: 8090
 
 # The task format in the engin_list is: <speech task>_<engine type>
-# task choices = ['asr_online', 'tts_online']
-# protocol = ['websocket', 'http'] (only one can be selected).
+# task choices = ['asr_online-inference', 'asr_online-onnx']
+# protocol = ['websocket'] (only one can be selected).
 # websocket only support online engine type.
 protocol: 'websocket'
-engine_list: ['asr_online']
+engine_list: ['asr_online-inference']
 
 
 #################################################################################
@@ -19,8 +19,8 @@ engine_list: ['asr_online']
 #################################################################################
 
 ################################### ASR #########################################
-################### speech task: asr; engine_type: online #######################
-asr_online:
+################### speech task: asr; engine_type: online-inference #######################
+asr_online-inference:
     model_type: 'deepspeech2online_aishell'
     am_model:    # the pdmodel file of am static model [optional]
     am_params:   # the pdiparams file of am static model [optional]
@@ -30,7 +30,7 @@ asr_online:
     decode_method: 
     num_decoding_left_chunks: 
     force_yes: True
-    device:  # cpu or gpu:id
+    device: 'cpu' # cpu or gpu:id
 
     am_predictor_conf:
         device:  # set 'gpu:id' or 'cpu'
@@ -38,6 +38,41 @@ asr_online:
         glog_info: False  # True -> print glog
         summary: True  # False -> do not show predictor config
 
+    chunk_buffer_conf:
+        frame_duration_ms: 80
+        shift_ms: 40
+        sample_rate: 16000
+        sample_width: 2
+        window_n: 7     # frame
+        shift_n: 4      # frame
+        window_ms: 20   # ms
+        shift_ms: 10    # ms
+
+
+
+################################### ASR #########################################
+################### speech task: asr; engine_type: online-onnx #######################
+asr_online-onnx:
+    model_type: 'deepspeech2online_aishell'
+    am_model:  # the pdmodel file of onnx am static model [optional]
+    am_params:  # the pdiparams file of am static model [optional]
+    lang: 'zh'
+    sample_rate: 16000
+    cfg_path: 
+    decode_method: 
+    num_decoding_left_chunks: 
+    force_yes: True
+    device: 'cpu' # cpu or gpu:id
+
+    # https://onnxruntime.ai/docs/api/python/api_summary.html#inferencesession
+    am_predictor_conf:
+        device: 'cpu' # set 'gpu:id' or 'cpu'
+        graph_optimization_level: 0 
+        intra_op_num_threads: 0 # Sets the number of threads used to parallelize the execution within nodes.
+        inter_op_num_threads: 0 # Sets the number of threads used to parallelize the execution of the graph (across nodes).
+        log_severity_level: 2   # Log severity level. Applies to session load, initialization, etc. 0:Verbose, 1:Info, 2:Warning. 3:Error, 4:Fatal. Default is 2.
+        log_verbosity_level: 0  # VLOG level if DEBUG build and session_log_severity_level is 0. Applies to session load, initialization, etc. Default is 0.
+
     chunk_buffer_conf:
         frame_duration_ms: 85
         shift_ms: 40

paddlespeech/server/engine/asr/online/onnx/__init__.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.
\ No newline at end of file

paddlespeech/server/engine/asr/online/onnx/asr_engine.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
+import sys
+from typing import ByteString
+from typing import Optional
+
+import numpy as np
+import paddle
+from numpy import float32
+from yacs.config import CfgNode
+
+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.transform.transformation import Transformation
+from paddlespeech.s2t.utils.utility import UpdateConfig
+from paddlespeech.server.engine.base_engine import BaseEngine
+from paddlespeech.server.utils import onnx_infer
+
+__all__ = ['PaddleASRConnectionHanddler', 'ASRServerExecutor', 'ASREngine']
+
+
+# ASR server connection process class
+class PaddleASRConnectionHanddler:
+    def __init__(self, asr_engine):
+        """Init a Paddle ASR Connection Handler instance
+
+        Args:
+            asr_engine (ASREngine): the global asr engine
+        """
+        super().__init__()
+        logger.info(
+            "create an paddle asr connection handler to process the websocket connection"
+        )
+        self.config = asr_engine.config  # server config
+        self.model_config = asr_engine.executor.config
+        self.asr_engine = asr_engine
+
+        # model_type, sample_rate and text_feature is shared for deepspeech2 and conformer
+        self.model_type = self.asr_engine.executor.model_type
+        self.sample_rate = self.asr_engine.executor.sample_rate
+        # tokens to text
+        self.text_feature = self.asr_engine.executor.text_feature
+
+        # extract feat, new only fbank in conformer model
+        self.preprocess_conf = self.model_config.preprocess_config
+        self.preprocess_args = {"train": False}
+        self.preprocessing = Transformation(self.preprocess_conf)
+
+        # frame window and frame shift, in samples unit
+        self.win_length = self.preprocess_conf.process[0]['win_length']
+        self.n_shift = self.preprocess_conf.process[0]['n_shift']
+
+        assert self.preprocess_conf.process[0]['fs'] == self.sample_rate, (
+            self.sample_rate, self.preprocess_conf.process[0]['fs'])
+        self.frame_shift_in_ms = int(
+            self.n_shift / self.preprocess_conf.process[0]['fs'] * 1000)
+
+        self.continuous_decoding = self.config.get("continuous_decoding", False)
+        self.init_decoder()
+        self.reset()
+
+    def init_decoder(self):
+        if "deepspeech2" in self.model_type:
+            assert self.continuous_decoding is False, "ds2 model not support endpoint"
+            self.am_predictor = self.asr_engine.executor.am_predictor
+
+            self.decoder = CTCDecoder(
+                odim=self.model_config.output_dim,  # <blank> is in  vocab
+                enc_n_units=self.model_config.rnn_layer_size * 2,
+                blank_id=self.model_config.blank_id,
+                dropout_rate=0.0,
+                reduction=True,  # sum
+                batch_average=True,  # sum / batch_size
+                grad_norm_type=self.model_config.get('ctc_grad_norm_type',
+                                                     None))
+
+            cfg = self.model_config.decode
+            decode_batch_size = 1  # for online
+            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)
+        else:
+            raise ValueError(f"Not supported: {self.model_type}")
+
+    def model_reset(self):
+        # cache for audio and feat
+        self.remained_wav = None
+        self.cached_feat = None
+
+    def output_reset(self):
+        ## outputs
+        # partial/ending decoding results
+        self.result_transcripts = ['']
+
+    def reset_continuous_decoding(self):
+        """
+        when in continous decoding, reset for next utterance.
+        """
+        self.global_frame_offset = self.num_frames
+        self.model_reset()
+
+    def reset(self):
+        if "deepspeech2" in self.model_type:
+            # for deepspeech2
+            # init state
+            self.chunk_state_h_box = np.zeros(
+                (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),
+                dtype=float32)
+            self.decoder.reset_decoder(batch_size=1)
+        else:
+            raise NotImplementedError(f"{self.model_type} not support.")
+
+        self.device = None
+
+        ## common
+        # global sample and frame step
+        self.num_samples = 0
+        self.global_frame_offset = 0
+        # frame step of cur utterance
+        self.num_frames = 0
+
+        ## endpoint
+        self.endpoint_state = False  # True for detect endpoint
+
+        ## conformer
+        self.model_reset()
+
+        ## outputs
+        self.output_reset()
+
+    def extract_feat(self, samples: ByteString):
+        logger.info("Online ASR extract the feat")
+        samples = np.frombuffer(samples, dtype=np.int16)
+        assert samples.ndim == 1
+
+        self.num_samples += samples.shape[0]
+        logger.info(
+            f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
+        )
+
+        # self.reamined_wav stores all the samples,
+        # include the original remained_wav and this package samples
+        if self.remained_wav is None:
+            self.remained_wav = samples
+        else:
+            assert self.remained_wav.ndim == 1  # (T,)
+            self.remained_wav = np.concatenate([self.remained_wav, samples])
+        logger.info(
+            f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
+        )
+
+        if len(self.remained_wav) < self.win_length:
+            # samples not enough for feature window
+            return 0
+
+        # fbank
+        x_chunk = self.preprocessing(self.remained_wav, **self.preprocess_args)
+        x_chunk = paddle.to_tensor(x_chunk, dtype="float32").unsqueeze(axis=0)
+
+        # feature cache
+        if self.cached_feat is None:
+            self.cached_feat = x_chunk
+        else:
+            assert (len(x_chunk.shape) == 3)  # (B,T,D)
+            assert (len(self.cached_feat.shape) == 3)  # (B,T,D)
+            self.cached_feat = paddle.concat(
+                [self.cached_feat, x_chunk], axis=1)
+
+        # set the feat device
+        if self.device is None:
+            self.device = self.cached_feat.place
+
+        # cur frame step
+        num_frames = x_chunk.shape[1]
+
+        # global frame step
+        self.num_frames += num_frames
+
+        # update remained wav
+        self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
+
+        logger.info(
+            f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
+        )
+        logger.info(
+            f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
+        )
+        logger.info(f"global samples: {self.num_samples}")
+        logger.info(f"global frames: {self.num_frames}")
+
+    def decode(self, is_finished=False):
+        """advance decoding
+
+        Args:
+            is_finished (bool, optional): Is last frame or not. Defaults to False.
+
+        Returns:
+            None: 
+        """
+        if "deepspeech2" in self.model_type:
+            decoding_chunk_size = 1  # decoding chunk size = 1. int decoding frame unit
+
+            context = 7  # context=7, in audio frame unit
+            subsampling = 4  # subsampling=4, in audio frame unit
+
+            cached_feature_num = context - subsampling
+            # decoding window for model, in audio frame unit
+            decoding_window = (decoding_chunk_size - 1) * subsampling + context
+            # decoding stride for model, in audio frame unit
+            stride = subsampling * decoding_chunk_size
+
+            if self.cached_feat is None:
+                logger.info("no audio feat, please input more pcm data")
+                return
+
+            num_frames = self.cached_feat.shape[1]
+            logger.info(
+                f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
+            )
+
+            # the cached feat must be larger decoding_window
+            if num_frames < decoding_window and not is_finished:
+                logger.info(
+                    f"frame feat num is less than {decoding_window}, please input more pcm data"
+                )
+                return None, None
+
+            # if is_finished=True, we need at least context frames
+            if num_frames < context:
+                logger.info(
+                    "flast {num_frames} is less than context {context} frames, and we cannot do model forward"
+                )
+                return None, None
+
+            logger.info("start to do model forward")
+            # num_frames - context + 1 ensure that current frame can get context window
+            if is_finished:
+                # if get the finished chunk, we need process the last context
+                left_frames = context
+            else:
+                # we only process decoding_window frames for one chunk
+                left_frames = decoding_window
+
+            end = None
+            for cur in range(0, num_frames - left_frames + 1, stride):
+                end = min(cur + decoding_window, num_frames)
+
+                # extract the audio
+                x_chunk = self.cached_feat[:, cur:end, :].numpy()
+                x_chunk_lens = np.array([x_chunk.shape[1]])
+
+                trans_best = self.decode_one_chunk(x_chunk, x_chunk_lens)
+
+            self.result_transcripts = [trans_best]
+
+            # update feat cache
+            self.cached_feat = self.cached_feat[:, end - cached_feature_num:, :]
+
+            # return trans_best[0]
+        else:
+            raise Exception(f"{self.model_type} not support paddleinference.")
+
+    @paddle.no_grad()
+    def decode_one_chunk(self, x_chunk, x_chunk_lens):
+        """forward one chunk frames
+
+        Args:
+            x_chunk (np.ndarray): (B,T,D), audio frames.
+            x_chunk_lens ([type]): (B,), audio frame lens
+
+        Returns:
+            logprob: poster probability.
+        """
+        logger.info("start to decoce one chunk for deepspeech2")
+        # state_c, state_h, audio_lens, audio
+        # 'chunk_state_c_box', 'chunk_state_h_box', 'audio_chunk_lens', 'audio_chunk'
+        input_names = [n.name for n in self.am_predictor.get_inputs()]
+        logger.info(f"ort inputs: {input_names}")
+        # 'softmax_0.tmp_0', 'tmp_5', 'concat_0.tmp_0', 'concat_1.tmp_0'
+        # audio, audio_lens, state_h, state_c
+        output_names = [n.name for n in self.am_predictor.get_outputs()]
+        logger.info(f"ort outpus: {output_names}")
+        assert (len(input_names) == len(output_names))
+        assert isinstance(input_names[0], str)
+
+        input_datas = [
+            self.chunk_state_c_box, self.chunk_state_h_box, x_chunk_lens,
+            x_chunk
+        ]
+        feeds = dict(zip(input_names, input_datas))
+
+        outputs = self.am_predictor.run([*output_names], {**feeds})
+
+        output_chunk_probs, output_chunk_lens, self.chunk_state_h_box, self.chunk_state_c_box = outputs
+        self.decoder.next(output_chunk_probs, output_chunk_lens)
+        trans_best, trans_beam = self.decoder.decode()
+        logger.info(f"decode one best result for deepspeech2: {trans_best[0]}")
+        return trans_best[0]
+
+    def get_result(self):
+        """return partial/ending asr result.
+
+        Returns:
+            str: one best result of partial/ending.
+        """
+        if len(self.result_transcripts) > 0:
+            return self.result_transcripts[0]
+        else:
+            return ''
+
+    def get_word_time_stamp(self):
+        return []
+
+    @paddle.no_grad()
+    def rescoring(self):
+        ...
+
+
+class ASRServerExecutor(ASRExecutor):
+    def __init__(self):
+        super().__init__()
+        self.task_resource = CommonTaskResource(
+            task='asr', model_format='onnx', inference_mode='online')
+
+    def update_config(self) -> None:
+        if "deepspeech2" in self.model_type:
+            with UpdateConfig(self.config):
+                # download lm
+                self.config.decode.lang_model_path = os.path.join(
+                    MODEL_HOME, 'language_model',
+                    self.config.decode.lang_model_path)
+
+            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,
+                os.path.dirname(self.config.decode.lang_model_path), lm_md5)
+        else:
+            raise NotImplementedError(
+                f"{self.model_type} not support paddleinference.")
+
+    def init_model(self) -> None:
+
+        if "deepspeech2" in self.model_type:
+            # AM predictor
+            logger.info("ASR engine start to init the am predictor")
+            self.am_predictor = onnx_infer.get_sess(
+                model_path=self.am_model, sess_conf=self.am_predictor_conf)
+        else:
+            raise NotImplementedError(
+                f"{self.model_type} not support paddleinference.")
+
+    def _init_from_path(self,
+                        model_type: str=None,
+                        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',
+                        num_decoding_left_chunks: int=-1,
+                        am_predictor_conf: dict=None):
+        """
+        Init model and other resources from a specific path.
+        """
+        if not model_type or not lang or not sample_rate:
+            logger.error(
+                "The model type or lang or sample rate is None, please input an valid server parameter yaml"
+            )
+            return False
+        assert am_params is None, "am_params not used in onnx engine"
+
+        self.model_type = model_type
+        self.sample_rate = sample_rate
+        self.decode_method = decode_method
+        self.num_decoding_left_chunks = num_decoding_left_chunks
+        # conf for paddleinference predictor or onnx
+        self.am_predictor_conf = am_predictor_conf
+        logger.info(f"model_type: {self.model_type}")
+
+        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:
+            self.res_path = self.task_resource.res_dir
+            self.cfg_path = os.path.join(
+                self.res_path, self.task_resource.res_dict['cfg_path'])
+        else:
+            self.cfg_path = os.path.abspath(cfg_path)
+            self.res_path = os.path.dirname(
+                os.path.dirname(os.path.abspath(self.cfg_path)))
+
+        self.am_model = os.path.join(self.res_path, self.task_resource.res_dict[
+            'onnx_model']) if am_model is None else os.path.abspath(am_model)
+
+        # self.am_params = os.path.join(
+        #     self.res_path, self.task_resource.res_dict[
+        #         'params']) if am_params is None else os.path.abspath(am_params)
+
+        logger.info("Load the pretrained model:")
+        logger.info(f"  tag = {tag}")
+        logger.info(f"  res_path: {self.res_path}")
+        logger.info(f"  cfg path: {self.cfg_path}")
+        logger.info(f"  am_model path: {self.am_model}")
+        # logger.info(f"  am_params path: {self.am_params}")
+
+        #Init body.
+        self.config = CfgNode(new_allowed=True)
+        self.config.merge_from_file(self.cfg_path)
+
+        if self.config.spm_model_prefix:
+            self.config.spm_model_prefix = os.path.join(
+                self.res_path, self.config.spm_model_prefix)
+            logger.info(f"spm model path: {self.config.spm_model_prefix}")
+
+        self.vocab = self.config.vocab_filepath
+
+        self.text_feature = TextFeaturizer(
+            unit_type=self.config.unit_type,
+            vocab=self.config.vocab_filepath,
+            spm_model_prefix=self.config.spm_model_prefix)
+
+        self.update_config()
+
+        # AM predictor
+        self.init_model()
+
+        logger.info(f"create the {model_type} model success")
+        return True
+
+
+class ASREngine(BaseEngine):
+    """ASR model resource
+
+    Args:
+        metaclass: Defaults to Singleton.
+    """
+
+    def __init__(self):
+        super(ASREngine, self).__init__()
+
+    def init_model(self) -> bool:
+        if not 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,
+                num_decoding_left_chunks=self.config.num_decoding_left_chunks,
+                am_predictor_conf=self.config.am_predictor_conf):
+            return False
+        return True
+
+    def init(self, config: dict) -> bool:
+        """init engine resource
+
+        Args:
+            config_file (str): config file
+
+        Returns:
+            bool: init failed or success
+        """
+        self.config = config
+        self.executor = ASRServerExecutor()
+
+        try:
+            self.device = self.config.get("device", paddle.get_device())
+            paddle.set_device(self.device)
+        except BaseException as e:
+            logger.error(
+                f"Set device failed, please check if device '{self.device}' is already used and the parameter 'device' in the yaml file"
+            )
+            logger.error(
+                "If all GPU or XPU is used, you can set the server to 'cpu'")
+            sys.exit(-1)
+
+        logger.info(f"paddlespeech_server set the device: {self.device}")
+
+        if not self.init_model():
+            logger.error(
+                "Init the ASR server occurs error, please check the server configuration yaml"
+            )
+            return False
+
+        logger.info("Initialize ASR server engine successfully.")
+        return True
+
+    def new_handler(self):
+        """New handler from model.
+
+        Returns:
+            PaddleASRConnectionHanddler: asr handler instance
+        """
+        return PaddleASRConnectionHanddler(self)
+
+    def preprocess(self, *args, **kwargs):
+        raise NotImplementedError("Online not using this.")
+
+    def run(self, *args, **kwargs):
+        raise NotImplementedError("Online not using this.")
+
+    def postprocess(self):
+        raise NotImplementedError("Online not using this.")

paddlespeech/server/engine/asr/online/paddleinference/__init__.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.
\ No newline at end of file

paddlespeech/server/engine/asr/online/python/__init__.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.
\ No newline at end of file

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

@@ -121,13 +121,13 @@ class PaddleASRConnectionHanddler:
             raise ValueError(f"Not supported: {self.model_type}")
 
     def model_reset(self):
-        if "deepspeech2" in self.model_type:
-            return
-
         # cache for audio and feat
         self.remained_wav = None
         self.cached_feat = None
 
+        if "deepspeech2" in self.model_type:
+            return
+
         ## conformer
         # cache for conformer online
         self.subsampling_cache = None
@@ -161,7 +161,9 @@ class PaddleASRConnectionHanddler:
         self.model_reset()
         self.searcher.reset()
         self.endpointer.reset()
-        self.output_reset()
+
+        # reset hys will trancate history transcripts.
+        # self.output_reset()
 
     def reset(self):
         if "deepspeech2" in self.model_type:
@@ -695,6 +697,66 @@ class ASRServerExecutor(ASRExecutor):
         self.task_resource = CommonTaskResource(
             task='asr', model_format='dynamic', inference_mode='online')
 
+    def update_config(self) -> None:
+        if "deepspeech2" in self.model_type:
+            with UpdateConfig(self.config):
+                # download lm
+                self.config.decode.lang_model_path = os.path.join(
+                    MODEL_HOME, 'language_model',
+                    self.config.decode.lang_model_path)
+
+            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,
+                os.path.dirname(self.config.decode.lang_model_path), lm_md5)
+        elif "conformer" in self.model_type or "transformer" in self.model_type:
+            with UpdateConfig(self.config):
+                logger.info("start to create the stream conformer asr engine")
+                # update the decoding method
+                if self.decode_method:
+                    self.config.decode.decoding_method = self.decode_method
+                # update num_decoding_left_chunks
+                if self.num_decoding_left_chunks:
+                    assert self.num_decoding_left_chunks == -1 or self.num_decoding_left_chunks >= 0, "num_decoding_left_chunks should be -1 or >=0"
+                    self.config.decode.num_decoding_left_chunks = self.num_decoding_left_chunks
+                # we only support ctc_prefix_beam_search and attention_rescoring dedoding method
+                # Generally we set the decoding_method to attention_rescoring
+                if self.config.decode.decoding_method not in [
+                        "ctc_prefix_beam_search", "attention_rescoring"
+                ]:
+                    logger.info(
+                        "we set the decoding_method to attention_rescoring")
+                    self.config.decode.decoding_method = "attention_rescoring"
+
+                assert self.config.decode.decoding_method in [
+                    "ctc_prefix_beam_search", "attention_rescoring"
+                ], f"we only support ctc_prefix_beam_search and attention_rescoring dedoding method, current decoding method is {self.config.decode.decoding_method}"
+        else:
+            raise Exception(f"not support: {self.model_type}")
+
+    def init_model(self) -> None:
+        if "deepspeech2" in self.model_type:
+            # AM predictor
+            logger.info("ASR engine start to init the am predictor")
+            self.am_predictor = init_predictor(
+                model_file=self.am_model,
+                params_file=self.am_params,
+                predictor_conf=self.am_predictor_conf)
+        elif "conformer" in self.model_type or "transformer" in self.model_type:
+            # load model
+            # model_type: {model_name}_{dataset}
+            model_name = self.model_type[:self.model_type.rindex('_')]
+            logger.info(f"model name: {model_name}")
+            model_class = self.task_resource.get_model_class(model_name)
+            model = model_class.from_config(self.config)
+            self.model = model
+            self.model.set_state_dict(paddle.load(self.am_model))
+            self.model.eval()
+        else:
+            raise Exception(f"not support: {self.model_type}")
+
     def _init_from_path(self,
                         model_type: str=None,
                         am_model: Optional[os.PathLike]=None,
@@ -716,6 +778,10 @@ class ASRServerExecutor(ASRExecutor):
 
         self.model_type = model_type
         self.sample_rate = sample_rate
+        self.decode_method = decode_method
+        self.num_decoding_left_chunks = num_decoding_left_chunks
+        # conf for paddleinference predictor or onnx
+        self.am_predictor_conf = am_predictor_conf
         logger.info(f"model_type: {self.model_type}")
 
         sample_rate_str = '16k' if sample_rate == 16000 else '8k'
@@ -761,62 +827,10 @@ class ASRServerExecutor(ASRExecutor):
             vocab=self.config.vocab_filepath,
             spm_model_prefix=self.config.spm_model_prefix)
 
-        if "deepspeech2" in model_type:
-            with UpdateConfig(self.config):
-                # download lm
-                self.config.decode.lang_model_path = os.path.join(
-                    MODEL_HOME, 'language_model',
-                    self.config.decode.lang_model_path)
-
-            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,
-                os.path.dirname(self.config.decode.lang_model_path), lm_md5)
+        self.update_config()
 
         # AM predictor
-            logger.info("ASR engine start to init the 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)
-
-        elif "conformer" in model_type or "transformer" in model_type:
-            with UpdateConfig(self.config):
-                logger.info("start to create the stream conformer asr engine")
-                # update the decoding method
-                if decode_method:
-                    self.config.decode.decoding_method = decode_method
-                # update num_decoding_left_chunks
-                if num_decoding_left_chunks:
-                    assert num_decoding_left_chunks == -1 or num_decoding_left_chunks >= 0, f"num_decoding_left_chunks should be -1 or >=0"
-                    self.config.decode.num_decoding_left_chunks = num_decoding_left_chunks
-                # we only support ctc_prefix_beam_search and attention_rescoring dedoding method
-                # Generally we set the decoding_method to attention_rescoring
-                if self.config.decode.decoding_method not in [
-                        "ctc_prefix_beam_search", "attention_rescoring"
-                ]:
-                    logger.info(
-                        "we set the decoding_method to attention_rescoring")
-                    self.config.decode.decoding_method = "attention_rescoring"
-
-                assert self.config.decode.decoding_method in [
-                    "ctc_prefix_beam_search", "attention_rescoring"
-                ], f"we only support ctc_prefix_beam_search and attention_rescoring dedoding method, current decoding method is {self.config.decode.decoding_method}"
-
-            # load model
-            model_name = model_type[:model_type.rindex(
-                '_')]  # model_type: {model_name}_{dataset}
-            logger.info(f"model name: {model_name}")
-            model_class = self.task_resource.get_model_class(model_name)
-            model = model_class.from_config(self.config)
-            self.model = model
-            self.model.set_state_dict(paddle.load(self.am_model))
-            self.model.eval()
-        else:
-            raise Exception(f"not support: {model_type}")
+        self.init_model()
 
         logger.info(f"create the {model_type} model success")
         return True
@@ -831,7 +845,20 @@ class ASREngine(BaseEngine):
 
     def __init__(self):
         super(ASREngine, self).__init__()
-        logger.info("create the online asr engine resource instance")
+
+    def init_model(self) -> bool:
+        if not 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,
+                num_decoding_left_chunks=self.config.num_decoding_left_chunks,
+                am_predictor_conf=self.config.am_predictor_conf):
+            return False
+        return True
 
     def init(self, config: dict) -> bool:
         """init engine resource
@@ -858,16 +885,7 @@ class ASREngine(BaseEngine):
 
         logger.info(f"paddlespeech_server set the device: {self.device}")
 
-        if not 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,
-                num_decoding_left_chunks=self.config.num_decoding_left_chunks,
-                am_predictor_conf=self.config.am_predictor_conf):
+        if not self.init_model():
             logger.error(
                 "Init the ASR server occurs error, please check the server configuration yaml"
             )

paddlespeech/server/engine/engine_factory.py

@@ -13,12 +13,16 @@
 # limitations under the License.
 from typing import Text
 
+from ..utils.log import logger
+
 __all__ = ['EngineFactory']
 
 
 class EngineFactory(object):
     @staticmethod
     def get_engine(engine_name: Text, engine_type: Text):
+        logger.info(f"{engine_name} : {engine_type} engine.")
+
         if engine_name == 'asr' and engine_type == 'inference':
             from paddlespeech.server.engine.asr.paddleinference.asr_engine import ASREngine
             return ASREngine()
@@ -26,7 +30,13 @@ class EngineFactory(object):
             from paddlespeech.server.engine.asr.python.asr_engine import ASREngine
             return ASREngine()
         elif engine_name == 'asr' and engine_type == 'online':
-            from paddlespeech.server.engine.asr.online.asr_engine import ASREngine
+            from paddlespeech.server.engine.asr.online.python.asr_engine import ASREngine
+            return ASREngine()
+        elif engine_name == 'asr' and engine_type == 'online-inference':
+            from paddlespeech.server.engine.asr.online.paddleinference.asr_engine import ASREngine
+            return ASREngine()
+        elif engine_name == 'asr' and engine_type == 'online-onnx':
+            from paddlespeech.server.engine.asr.online.onnx.asr_engine import ASREngine
             return ASREngine()
         elif engine_name == 'tts' and engine_type == 'inference':
             from paddlespeech.server.engine.tts.paddleinference.tts_engine import TTSEngine

paddlespeech/server/engine/vector/python/vector_engine.py

@@ -16,9 +16,9 @@ from collections import OrderedDict
 
 import numpy as np
 import paddle
-from paddleaudio.backends import load as load_audio
-from paddleaudio.compliance.librosa import melspectrogram
 
+from paddlespeech.audio.backends import load as load_audio
+from paddlespeech.audio.compliance.librosa import melspectrogram
 from paddlespeech.cli.log import logger
 from paddlespeech.cli.vector.infer import VectorExecutor
 from paddlespeech.server.engine.base_engine import BaseEngine

paddlespeech/server/util.py

@@ -24,11 +24,11 @@ from typing import Any
 from typing import Dict
 
 import paddle
-import paddleaudio
 import requests
 import yaml
 from paddle.framework import load
 
+import paddlespeech.audio
 from .entry import client_commands
 from .entry import server_commands
 from paddlespeech.cli import download
@@ -289,7 +289,7 @@ def _note_one_stat(cls_name, params={}):
 
     if 'audio_file' in params:
         try:
-            _, sr = paddleaudio.load(params['audio_file'])
+            _, sr = paddlespeech.audio.load(params['audio_file'])
         except Exception:
             sr = -1
 

paddlespeech/server/utils/onnx_infer.py

@@ -16,21 +16,34 @@ from typing import Optional
 
 import onnxruntime as ort
 
+from .log import logger
+
 
 def get_sess(model_path: Optional[os.PathLike]=None, sess_conf: dict=None):
+    logger.info(f"ort sessconf: {sess_conf}")
     sess_options = ort.SessionOptions()
     sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+    if sess_conf.get('graph_optimization_level', 99) == 0:
+        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
     sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
 
-    if "gpu" in sess_conf["device"]:
+    # "gpu:0"
+    providers = ['CPUExecutionProvider']
+    if "gpu" in sess_conf.get("device", ""):
+        providers = ['CUDAExecutionProvider']
         # fastspeech2/mb_melgan can't use trt now!
-        if sess_conf["use_trt"]:
+        if sess_conf.get("use_trt", 0):
             providers = ['TensorrtExecutionProvider']
+    logger.info(f"ort providers: {providers}")
+
+    if 'cpu_threads' in sess_conf:
+        sess_options.intra_op_num_threads = sess_conf.get("cpu_threads", 0)
     else:
-            providers = ['CUDAExecutionProvider']
-    elif sess_conf["device"] == "cpu":
-        providers = ['CPUExecutionProvider']
-    sess_options.intra_op_num_threads = sess_conf["cpu_threads"]
+        sess_options.intra_op_num_threads = sess_conf.get(
+            "intra_op_num_threads", 0)
+
+    sess_options.inter_op_num_threads = sess_conf.get("inter_op_num_threads", 0)
+
     sess = ort.InferenceSession(
         model_path, providers=providers, sess_options=sess_options)
     return sess

paddlespeech/server/ws/asr_api.py

@@ -92,6 +92,7 @@ async def websocket_endpoint(websocket: WebSocket):
                 else:
                     resp = {"status": "ok", "message": "no valid json data"}
                     await websocket.send_json(resp)
+
             elif "bytes" in message:
                 # bytes for the pcm data
                 message = message["bytes"]

paddlespeech/vector/exps/ecapa_tdnn/extract_emb.py

@@ -16,10 +16,10 @@ import os
 import time
 
 import paddle
-from paddleaudio.backends import load as load_audio
-from paddleaudio.compliance.librosa import melspectrogram
 from yacs.config import CfgNode
 
+from paddlespeech.audio.backends import load as load_audio
+from paddlespeech.audio.compliance.librosa import melspectrogram
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.vector.io.batch import feature_normalize
 from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn

paddlespeech/vector/exps/ecapa_tdnn/test.py

@@ -18,10 +18,10 @@ import numpy as np
 import paddle
 from paddle.io import BatchSampler
 from paddle.io import DataLoader
-from paddleaudio.metric import compute_eer
 from tqdm import tqdm
 from yacs.config import CfgNode
 
+from paddlespeech.audio.metric import compute_eer
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.vector.io.batch import batch_feature_normalize
 from paddlespeech.vector.io.dataset import CSVDataset

paddlespeech/vector/exps/ecapa_tdnn/train.py

@@ -20,9 +20,9 @@ import paddle
 from paddle.io import BatchSampler
 from paddle.io import DataLoader
 from paddle.io import DistributedBatchSampler
-from paddleaudio.compliance.librosa import melspectrogram
 from yacs.config import CfgNode
 
+from paddlespeech.audio.compliance.librosa import melspectrogram
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.vector.io.augment import build_augment_pipeline
 from paddlespeech.vector.io.augment import waveform_augment

paddlespeech/vector/io/dataset.py

@@ -15,9 +15,9 @@ from dataclasses import dataclass
 from dataclasses import fields
 
 from paddle.io import Dataset
-from paddleaudio import load as load_audio
-from paddleaudio.compliance.librosa import melspectrogram
 
+from paddlespeech.audio import load as load_audio
+from paddlespeech.audio.compliance.librosa import melspectrogram
 from paddlespeech.s2t.utils.log import Log
 logger = Log(__name__).getlog()
 

paddlespeech/vector/io/dataset_from_json.py

@@ -16,9 +16,10 @@ from dataclasses import dataclass
 from dataclasses import fields
 
 from paddle.io import Dataset
-from paddleaudio import load as load_audio
-from paddleaudio.compliance.librosa import melspectrogram
-from paddleaudio.compliance.librosa import mfcc
+
+from paddlespeech.audio import load as load_audio
+from paddlespeech.audio.compliance.librosa import melspectrogram
+from paddlespeech.audio.compliance.librosa import mfcc
 
 
 @dataclass

setup.py

@@ -24,6 +24,7 @@ from setuptools import find_packages
 from setuptools import setup
 from setuptools.command.develop import develop
 from setuptools.command.install import install
+from setuptools.command.test import test
 
 HERE = Path(os.path.abspath(os.path.dirname(__file__)))
 
@@ -31,42 +32,13 @@ VERSION = '0.0.0'
 COMMITID = 'none'
 
 base = [
-    "editdistance",
-    "g2p_en",
-    "g2pM",
-    "h5py",
-    "inflect",
-    "jieba",
-    "jsonlines",
-    "kaldiio",
-    "librosa==0.8.1",
-    "loguru",
-    "matplotlib",
-    "nara_wpe",
-    "onnxruntime",
-    "pandas",
-    "paddleaudio",
-    "paddlenlp",
-    "paddlespeech_feat",
-    "praatio==5.0.0",
-    "pypinyin",
-    "pypinyin-dict",
-    "python-dateutil",
-    "pyworld",
-    "resampy==0.2.2",
-    "sacrebleu",
-    "scipy",
-    "sentencepiece~=0.1.96",
-    "soundfile~=0.10",
-    "textgrid",
-    "timer",
-    "tqdm",
-    "typeguard",
-    "visualdl",
-    "webrtcvad",
-    "yacs~=0.1.8",
-    "prettytable",
-    "zhon",
+    "editdistance", "g2p_en", "g2pM", "h5py", "inflect", "jieba", "jsonlines",
+    "kaldiio", "librosa==0.8.1", "loguru", "matplotlib", "nara_wpe",
+    "onnxruntime", "pandas", "paddlenlp", "paddlespeech_feat", "praatio==5.0.0",
+    "pypinyin", "pypinyin-dict", "python-dateutil", "pyworld", "resampy==0.2.2",
+    "sacrebleu", "scipy", "sentencepiece~=0.1.96", "soundfile~=0.10",
+    "textgrid", "timer", "tqdm", "typeguard", "visualdl", "webrtcvad",
+    "yacs~=0.1.8", "prettytable", "zhon", 'colorlog', 'pathos == 0.2.8'
 ]
 
 server = [
@@ -177,7 +149,19 @@ class InstallCommand(install):
         install.run(self)
 
 
-    # cmd: python setup.py upload
+class TestCommand(test):
+    def finalize_options(self):
+        test.finalize_options(self)
+        self.test_args = []
+        self.test_suite = True
+
+    def run_tests(self):
+        # Run nose ensuring that argv simulates running nosetests directly
+        import nose
+        nose.run_exit(argv=['nosetests', '-w', 'tests'])
+
+
+# cmd: python setup.py upload
 class UploadCommand(Command):
     description = "Build and publish the package."
     user_options = []
@@ -279,11 +263,13 @@ setup_info = dict(
             "sphinx", "sphinx-rtd-theme", "numpydoc", "myst_parser",
             "recommonmark>=0.5.0", "sphinx-markdown-tables", "sphinx-autobuild"
         ],
+        'test': ['nose', 'torchaudio==0.10.2'],
     },
     cmdclass={
         'develop': DevelopCommand,
         'install': InstallCommand,
         'upload': UploadCommand,
+        'test': TestCommand,
     },
 
     # Package info

speechx/examples/ds2_ol/onnx/.gitignore

+data
+log
+exp

speechx/examples/ds2_ol/onnx/README.md

+# DeepSpeech2 ONNX model
+
+1. convert deepspeech2 model to ONNX, using Paddle2ONNX.
+2. check paddleinference and onnxruntime output equal.
+3. optimize onnx model
+4. check paddleinference and optimized onnxruntime output equal.
+
+Please make sure [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) and [onnx-simplifier](https://github.com/zh794390558/onnx-simplifier/tree/dyn_time_shape) version is correct.
+
+The example test with these packages installed:
+```
+paddle2onnx              0.9.8rc0    # develop af4354b4e9a61a93be6490640059a02a4499bc7a
+paddleaudio              0.2.1
+paddlefsl                1.1.0
+paddlenlp                2.2.6
+paddlepaddle-gpu         2.2.2
+paddlespeech             0.0.0       # develop
+paddlespeech-ctcdecoders 0.2.0
+paddlespeech-feat        0.1.0
+onnx                     1.11.0
+onnx-simplifier          0.0.0       # https://github.com/zh794390558/onnx-simplifier/tree/dyn_time_shape
+onnxoptimizer            0.2.7
+onnxruntime              1.11.0
+```
+
+## Using
+
+```
+bash run.sh
+```
+
+For more details please see `run.sh`.
+
+## Outputs
+The optimized onnx model is `exp/model.opt.onnx`.
+
+To show the graph, please using `local/netron.sh`.

speechx/examples/ds2_ol/onnx/local/infer_check.py

+#!/usr/bin/env python3
+# 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 argparse
+import os
+import pickle
+
+import numpy as np
+import onnxruntime
+import paddle
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--input_file',
+        type=str,
+        default="static_ds2online_inputs.pickle",
+        help="aishell ds2 input data file. For wenetspeech, we only feed for infer model",
+    )
+    parser.add_argument(
+        '--model_type',
+        type=str,
+        default="aishell",
+        help="aishell(1024) or wenetspeech(2048)", )
+    parser.add_argument(
+        '--model_dir', type=str, default=".", help="paddle model dir.")
+    parser.add_argument(
+        '--model_prefix',
+        type=str,
+        default="avg_1.jit",
+        help="paddle model prefix.")
+    parser.add_argument(
+        '--onnx_model',
+        type=str,
+        default='./model.old.onnx',
+        help="onnx model.")
+
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    FLAGS = parse_args()
+
+    # input and output
+    with open(FLAGS.input_file, 'rb') as f:
+        iodict = pickle.load(f)
+        print(iodict.keys())
+
+    audio_chunk = iodict['audio_chunk']
+    audio_chunk_lens = iodict['audio_chunk_lens']
+    chunk_state_h_box = iodict['chunk_state_h_box']
+    chunk_state_c_box = iodict['chunk_state_c_bos']
+    print("raw state shape: ", chunk_state_c_box.shape)
+
+    if FLAGS.model_type == 'wenetspeech':
+        chunk_state_h_box = np.repeat(chunk_state_h_box, 2, axis=-1)
+        chunk_state_c_box = np.repeat(chunk_state_c_box, 2, axis=-1)
+    print("state shape: ", chunk_state_c_box.shape)
+
+    # paddle
+    model = paddle.jit.load(os.path.join(FLAGS.model_dir, FLAGS.model_prefix))
+    res_chunk, res_lens, chunk_state_h, chunk_state_c = model(
+        paddle.to_tensor(audio_chunk),
+        paddle.to_tensor(audio_chunk_lens),
+        paddle.to_tensor(chunk_state_h_box),
+        paddle.to_tensor(chunk_state_c_box), )
+
+    # onnxruntime
+    options = onnxruntime.SessionOptions()
+    options.enable_profiling = True
+    sess = onnxruntime.InferenceSession(FLAGS.onnx_model, sess_options=options)
+    ort_res_chunk, ort_res_lens, ort_chunk_state_h, ort_chunk_state_c = sess.run(
+        ['softmax_0.tmp_0', 'tmp_5', 'concat_0.tmp_0', 'concat_1.tmp_0'], {
+            "audio_chunk": audio_chunk,
+            "audio_chunk_lens": audio_chunk_lens,
+            "chunk_state_h_box": chunk_state_h_box,
+            "chunk_state_c_box": chunk_state_c_box
+        })
+
+    print(sess.end_profiling())
+
+    # assert paddle equal ort
+    print(np.allclose(ort_res_chunk, res_chunk, atol=1e-6))
+    print(np.allclose(ort_res_lens, res_lens, atol=1e-6))
+
+    if FLAGS.model_type == 'aishell':
+        print(np.allclose(ort_chunk_state_h, chunk_state_h, atol=1e-6))
+        print(np.allclose(ort_chunk_state_c, chunk_state_c, atol=1e-6))

speechx/examples/ds2_ol/onnx/local/netron.sh

+#!/bin/bash
+
+# show model 
+
+if [ $# != 1 ];then
+    echo "usage: $0 model_path"
+    exit 1
+fi
+
+
+file=$1
+
+pip install netron
+netron -p 8082 --host $(hostname -i) $file
\ No newline at end of file

speechx/examples/ds2_ol/onnx/local/onnx_clone.sh

+
+#!/bin/bash
+
+# clone onnx repos
+git clone https://github.com/onnx/onnx.git
+git clone https://github.com/microsoft/onnxruntime.git
+git clone https://github.com/PaddlePaddle/Paddle2ONNX.git
\ No newline at end of file