Merge branch 'develop' into v0.3

ff7dbcc2 · Honei_X · GitHub · f7af037c · 2a661fcd · ff7dbcc2
10 changed file
--- a/demos/streaming_tts_server/README.md
+++ b/demos/streaming_tts_server/README.md
@@ -16,7 +16,7 @@ You can choose one way from meduim and hard to install paddlespeech.

 ### 2. Prepare config File
 The configuration file can be found in `conf/tts_online_application.yaml`.
- `protocol` indicates the network protocol used by the streaming TTS service. Currently, both http and websocket are supported.
+- `protocol` indicates the network protocol used by the streaming TTS service. Currently, both **http and websocket** are supported.
 - `engine_list` indicates the speech engine that will be included in the service to be started, in the format of `<speech task>_<engine type>`.
    - This demo mainly introduces the streaming speech synthesis service, so the speech task should be set to `tts`.
    - the engine type supports two forms: **online**  and **online-onnx**. `online` indicates an engine that uses python for dynamic graph inference; `online-onnx` indicates an engine that uses onnxruntime for inference. The inference speed of online-onnx is faster.
@@ -31,12 +31,12 @@ The configuration file can be found in `conf/tts_online_application.yaml`.
 - Inference speed: mb_melgan > hifigan; Audio quality: mb_melgan < hifigan


-
-### 3. Server Usage
+### 3. Streaming speech synthesis server and client using http protocol
+#### 3.1 Server Usage
 - Command Line (Recommended)

+  Start the service (the configuration file uses http by default):
  ```bash
-  # start the service
  paddlespeech_server start --config_file ./conf/tts_online_application.yaml
  ```

@@ -76,7 +76,7 @@ The configuration file can be found in `conf/tts_online_application.yaml`.
      log_file="./log/paddlespeech.log")
  ```

-  Output:
+ Output:
  ```bash
  [2022-04-24 21:00:16,934] [    INFO] - The first response time of the 0 warm up: 1.268730878829956 s
  [2022-04-24 21:00:17,046] [    INFO] - The first response time of the 1 warm up: 0.11168622970581055 s
@@ -94,17 +94,15 @@ The configuration file can be found in `conf/tts_online_application.yaml`.

  ```

- 
-### 4. Streaming TTS client Usage
+#### 3.2 Streaming TTS client Usage
 - Command Line (Recommended)

-    ```bash
-    # Access http streaming TTS service
-    paddlespeech_client tts_online --server_ip 127.0.0.1 --port 8092 --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
+    Access http streaming TTS service:

-    # Access websocket streaming TTS service
-    paddlespeech_client tts_online --server_ip 127.0.0.1 --port 8092 --protocol websocket --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
+    ```bash
+    paddlespeech_client tts_online --server_ip 127.0.0.1 --port 8092 --protocol http --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
    ```
+
    Usage:
  
    ```bash
@@ -122,7 +120,6 @@ The configuration file can be found in `conf/tts_online_application.yaml`.
    - `sample_rate`: Sampling rate, choices: [0, 8000, 16000], the default is the same as the model. Default: 0
    - `output`: Output wave filepath. Default: None, which means not to save the audio to the local.
    - `play`: Whether to play audio, play while synthesizing, default value: False, which means not playing. **Playing audio needs to rely on the pyaudio library**.
-
    
    Output:
    ```bash
@@ -165,8 +162,144 @@ The configuration file can be found in `conf/tts_online_application.yaml`.
  [2022-04-24 21:11:16,802] [    INFO] - 音频时长：3.825 s
  [2022-04-24 21:11:16,802] [    INFO] - RTF: 0.7846773683635238
  [2022-04-24 21:11:16,837] [    INFO] - 音频保存至：./output.wav
+  ```
+
+ 
+### 4. Streaming speech synthesis server and client using websocket protocol
+#### 4.1 Server Usage
+- Command Line (Recommended)
+  First modify the configuration file `conf/tts_online_application.yaml`, **set `protocol` to `websocket`**.
+  Start the service:
+  ```bash
+  paddlespeech_server start --config_file ./conf/tts_online_application.yaml
+  ```
+
+  Usage:
+  
+  ```bash
+  paddlespeech_server start --help
+  ```
+  Arguments:
+  - `config_file`: yaml file of the app, defalut: ./conf/tts_online_application.yaml
+  - `log_file`: log file. Default: ./log/paddlespeech.log
+
+  Output:
+  ```bash
+    [2022-04-27 10:18:09,107] [    INFO] - The first response time of the 0 warm up: 1.1551103591918945 s
+    [2022-04-27 10:18:09,219] [    INFO] - The first response time of the 1 warm up: 0.11204338073730469 s
+    [2022-04-27 10:18:09,324] [    INFO] - The first response time of the 2 warm up: 0.1051797866821289 s
+    [2022-04-27 10:18:09,325] [    INFO] - **********************************************************************
+    INFO:     Started server process [17600]
+    [2022-04-27 10:18:09] [INFO] [server.py:75] Started server process [17600]
+    INFO:     Waiting for application startup.
+    [2022-04-27 10:18:09] [INFO] [on.py:45] Waiting for application startup.
+    INFO:     Application startup complete.
+    [2022-04-27 10:18:09] [INFO] [on.py:59] Application startup complete.
+    INFO:     Uvicorn running on http://127.0.0.1:8092 (Press CTRL+C to quit)
+    [2022-04-27 10:18:09] [INFO] [server.py:211] Uvicorn running on http://127.0.0.1:8092 (Press CTRL+C to quit)


  ```

+- Python API
+  ```python
+  from paddlespeech.server.bin.paddlespeech_server import ServerExecutor
+
+  server_executor = ServerExecutor()
+  server_executor(
+      config_file="./conf/tts_online_application.yaml", 
+      log_file="./log/paddlespeech.log")
+  ```
+
+  Output:
+  ```bash
+    [2022-04-27 10:20:16,660] [    INFO] - The first response time of the 0 warm up: 1.0945196151733398 s
+    [2022-04-27 10:20:16,773] [    INFO] - The first response time of the 1 warm up: 0.11222052574157715 s
+    [2022-04-27 10:20:16,878] [    INFO] - The first response time of the 2 warm up: 0.10494542121887207 s
+    [2022-04-27 10:20:16,878] [    INFO] - **********************************************************************
+    INFO:     Started server process [23466]
+    [2022-04-27 10:20:16] [INFO] [server.py:75] Started server process [23466]
+    INFO:     Waiting for application startup.
+    [2022-04-27 10:20:16] [INFO] [on.py:45] Waiting for application startup.
+    INFO:     Application startup complete.
+    [2022-04-27 10:20:16] [INFO] [on.py:59] Application startup complete.
+    INFO:     Uvicorn running on http://127.0.0.1:8092 (Press CTRL+C to quit)
+    [2022-04-27 10:20:16] [INFO] [server.py:211] Uvicorn running on http://127.0.0.1:8092 (Press CTRL+C to quit)
+
+  ```
+
+#### 4.2 Streaming TTS client Usage
+- Command Line (Recommended)
+
+    Access websocket streaming TTS service:
+
+    ```bash
+    paddlespeech_client tts_online --server_ip 127.0.0.1 --port 8092 --protocol websocket --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
+    ```
+
+    Usage:
+  
+    ```bash
+    paddlespeech_client tts_online --help
+    ```
+
+    Arguments:
+    - `server_ip`: erver ip. Default: 127.0.0.1
+    - `port`: server port. Default: 8092
+    - `protocol`: Service protocol, choices: [http, websocket], default: http.
+    - `input`: (required): Input text to generate.
+    - `spk_id`: Speaker id for multi-speaker text to speech. Default: 0
+    - `speed`: Audio speed, the value should be set between 0 and 3. Default: 1.0
+    - `volume`: Audio volume, the value should be set between 0 and 3. Default: 1.0
+    - `sample_rate`: Sampling rate, choices: [0, 8000, 16000], the default is the same as the model. Default: 0
+    - `output`: Output wave filepath. Default: None, which means not to save the audio to the local.
+    - `play`: Whether to play audio, play while synthesizing, default value: False, which means not playing. **Playing audio needs to rely on the pyaudio library**.
+
+    
+    Output:
+    ```bash
+    [2022-04-27 10:21:04,262] [    INFO] - tts websocket client start
+    [2022-04-27 10:21:04,496] [    INFO] - 句子：您好，欢迎使用百度飞桨语音合成服务。
+    [2022-04-27 10:21:04,496] [    INFO] - 首包响应：0.2124948501586914 s
+    [2022-04-27 10:21:07,483] [    INFO] - 尾包响应：3.199106454849243 s
+    [2022-04-27 10:21:07,484] [    INFO] - 音频时长：3.825 s
+    [2022-04-27 10:21:07,484] [    INFO] - RTF: 0.8363677006141812
+    [2022-04-27 10:21:07,516] [    INFO] - 音频保存至：output.wav
+
+    ```
+
+- Python API
+  ```python
+  from paddlespeech.server.bin.paddlespeech_client import TTSOnlineClientExecutor
+  import json
+
+  executor = TTSOnlineClientExecutor()
+  executor(
+      input="您好，欢迎使用百度飞桨语音合成服务。",
+      server_ip="127.0.0.1",
+      port=8092,
+      protocol="websocket",
+      spk_id=0,
+      speed=1.0,
+      volume=1.0,
+      sample_rate=0,
+      output="./output.wav",
+      play=False)
+
+  ```
+
+  Output:
+  ```bash
+    [2022-04-27 10:22:48,852] [    INFO] - tts websocket client start
+    [2022-04-27 10:22:49,080] [    INFO] - 句子：您好，欢迎使用百度飞桨语音合成服务。
+    [2022-04-27 10:22:49,080] [    INFO] - 首包响应：0.21017956733703613 s
+    [2022-04-27 10:22:52,100] [    INFO] - 尾包响应：3.2304444313049316 s
+    [2022-04-27 10:22:52,101] [    INFO] - 音频时长：3.825 s
+    [2022-04-27 10:22:52,101] [    INFO] - RTF: 0.8445606356352762
+    [2022-04-27 10:22:52,134] [    INFO] - 音频保存至：./output.wav
+
+  ```
+
+
+
  
--- a/demos/streaming_tts_server/README_cn.md
+++ b/demos/streaming_tts_server/README_cn.md
-([简体中文](./README_cn.md)|English)
+(简体中文|[English](./README.md))

 # 流式语音合成服务

@@ -16,11 +16,11 @@

 ### 2. 准备配置文件
 配置文件可参见 `conf/tts_online_application.yaml` 。
- `protocol`表示该流式TTS服务使用的网络协议，目前支持 http 和 websocket 两种。
+- `protocol`表示该流式TTS服务使用的网络协议，目前支持 **http 和 websocket** 两种。
 - `engine_list`表示即将启动的服务将会包含的语音引擎，格式为 <语音任务>_<引擎类型>。
    - 该demo主要介绍流式语音合成服务，因此语音任务应设置为tts。
    - 目前引擎类型支持两种形式：**online** 表示使用python进行动态图推理的引擎；**online-onnx** 表示使用onnxruntime进行推理的引擎。其中，online-onnx的推理速度更快。
- 流式TTS引擎的AM模型支持：fastspeech2 以及fastspeech2_cnndecoder; Voc 模型支持：hifigan, mb_melgan
+- 流式TTS引擎的AM模型支持：**fastspeech2 以及fastspeech2_cnndecoder**; Voc 模型支持：**hifigan, mb_melgan**
 - 流式am推理中，每次会对一个chunk的数据进行推理以达到流式的效果。其中`am_block`表示chunk中的有效帧数，`am_pad` 表示一个chunk中am_block前后各加的帧数。am_pad的存在用于消除流式推理产生的误差，避免由流式推理对合成音频质量的影响。
    - fastspeech2不支持流式am推理，因此am_pad与am_block对它无效
    - fastspeech2_cnndecoder 支持流式推理，当am_pad=12时，流式推理合成音频与非流式合成音频一致
@@ -30,11 +30,12 @@
    - 当voc模型为hifigan，当voc_pad=20时，流式推理合成音频与非流式合成音频一致；当voc_pad=14时，合成音频听感上没有异常。
 - 推理速度：mb_melgan > hifigan; 音频质量：mb_melgan < hifigan

-### 3. 服务端使用方法
+### 3. 使用http协议的流式语音合成服务端及客户端使用方法
+#### 3.1 服务端使用方法
 - 命令行 (推荐使用)

+  启动服务（配置文件默认使用http）：
  ```bash
-  # 启动服务
  paddlespeech_server start --config_file ./conf/tts_online_application.yaml
  ```

@@ -44,7 +45,7 @@
  paddlespeech_server start --help
  ```
  参数:
-  - `config_file`: 服务的配置文件，默认： ./conf/application.yaml
+  - `config_file`: 服务的配置文件，默认： ./conf/tts_online_application.yaml
  - `log_file`: log 文件. 默认：./log/paddlespeech.log

  输出:
@@ -92,17 +93,15 @@

  ```

- 
-### 4. 流式TTS 客户端使用方法
+#### 3.2 客户端使用方法
 - 命令行 (推荐使用)

-    ```bash
-    # 访问 http 流式TTS服务
-    paddlespeech_client tts_online --server_ip 127.0.0.1 --port 8092 --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
+    访问 http 流式TTS服务：

-    # 访问 websocket 流式TTS服务
-    paddlespeech_client tts_online --server_ip 127.0.0.1 --port 8092 --protocol websocket --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
+    ```bash
+    paddlespeech_client tts_online --server_ip 127.0.0.1 --port 8092 --protocol http --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
    ```
+
    使用帮助:
  
    ```bash
@@ -163,8 +162,143 @@
  [2022-04-24 21:11:16,802] [    INFO] - 音频时长：3.825 s
  [2022-04-24 21:11:16,802] [    INFO] - RTF: 0.7846773683635238
  [2022-04-24 21:11:16,837] [    INFO] - 音频保存至：./output.wav
+  ```
+
+ 
+### 4. 使用websocket协议的流式语音合成服务端及客户端使用方法
+#### 4.1 服务端使用方法
+- 命令行 (推荐使用)
+  首先修改配置文件 `conf/tts_online_application.yaml`， **将 `protocol` 设置为 `websocket`**。
+  启动服务：
+  ```bash
+  paddlespeech_server start --config_file ./conf/tts_online_application.yaml
+  ```
+
+  使用方法：
+  
+  ```bash
+  paddlespeech_server start --help
+  ```
+  参数:
+  - `config_file`: 服务的配置文件，默认： ./conf/tts_online_application.yaml
+  - `log_file`: log 文件. 默认：./log/paddlespeech.log
+
+  输出:
+  ```bash
+    [2022-04-27 10:18:09,107] [    INFO] - The first response time of the 0 warm up: 1.1551103591918945 s
+    [2022-04-27 10:18:09,219] [    INFO] - The first response time of the 1 warm up: 0.11204338073730469 s
+    [2022-04-27 10:18:09,324] [    INFO] - The first response time of the 2 warm up: 0.1051797866821289 s
+    [2022-04-27 10:18:09,325] [    INFO] - **********************************************************************
+    INFO:     Started server process [17600]
+    [2022-04-27 10:18:09] [INFO] [server.py:75] Started server process [17600]
+    INFO:     Waiting for application startup.
+    [2022-04-27 10:18:09] [INFO] [on.py:45] Waiting for application startup.
+    INFO:     Application startup complete.
+    [2022-04-27 10:18:09] [INFO] [on.py:59] Application startup complete.
+    INFO:     Uvicorn running on http://127.0.0.1:8092 (Press CTRL+C to quit)
+    [2022-04-27 10:18:09] [INFO] [server.py:211] Uvicorn running on http://127.0.0.1:8092 (Press CTRL+C to quit)
+
+
+  ```
+
+- Python API
+  ```python
+  from paddlespeech.server.bin.paddlespeech_server import ServerExecutor
+
+  server_executor = ServerExecutor()
+  server_executor(
+      config_file="./conf/tts_online_application.yaml", 
+      log_file="./log/paddlespeech.log")
+  ```
+
+  输出：
+  ```bash
+    [2022-04-27 10:20:16,660] [    INFO] - The first response time of the 0 warm up: 1.0945196151733398 s
+    [2022-04-27 10:20:16,773] [    INFO] - The first response time of the 1 warm up: 0.11222052574157715 s
+    [2022-04-27 10:20:16,878] [    INFO] - The first response time of the 2 warm up: 0.10494542121887207 s
+    [2022-04-27 10:20:16,878] [    INFO] - **********************************************************************
+    INFO:     Started server process [23466]
+    [2022-04-27 10:20:16] [INFO] [server.py:75] Started server process [23466]
+    INFO:     Waiting for application startup.
+    [2022-04-27 10:20:16] [INFO] [on.py:45] Waiting for application startup.
+    INFO:     Application startup complete.
+    [2022-04-27 10:20:16] [INFO] [on.py:59] Application startup complete.
+    INFO:     Uvicorn running on http://127.0.0.1:8092 (Press CTRL+C to quit)
+    [2022-04-27 10:20:16] [INFO] [server.py:211] Uvicorn running on http://127.0.0.1:8092 (Press CTRL+C to quit)
+
+  ```
+
+#### 4.2 客户端使用方法
+- 命令行 (推荐使用)
+
+    访问 websocket 流式TTS服务：
+
+    ```bash
+    paddlespeech_client tts_online --server_ip 127.0.0.1 --port 8092 --protocol websocket --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
+    ```
+
+    使用帮助:
+  
+    ```bash
+    paddlespeech_client tts_online --help
+    ```
+
+    参数:
+    - `server_ip`: 服务端ip地址，默认: 127.0.0.1。
+    - `port`: 服务端口，默认: 8092。
+    - `protocol`: 服务协议，可选 [http, websocket], 默认: http。
+    - `input`: (必须输入): 待合成的文本。
+    - `spk_id`: 说话人 id，用于多说话人语音合成，默认值： 0。
+    - `speed`: 音频速度，该值应设置在 0 到 3 之间。 默认值：1.0
+    - `volume`: 音频音量，该值应设置在 0 到 3 之间。 默认值： 1.0
+    - `sample_rate`: 采样率，可选 [0, 8000, 16000]，默认值：0，表示与模型采样率相同
+    - `output`: 输出音频的路径， 默认值：None，表示不保存音频到本地。
+    - `play`: 是否播放音频，边合成边播放， 默认值：False，表示不播放。**播放音频需要依赖pyaudio库**。
+
+    
+    输出:
+    ```bash
+    [2022-04-27 10:21:04,262] [    INFO] - tts websocket client start
+    [2022-04-27 10:21:04,496] [    INFO] - 句子：您好，欢迎使用百度飞桨语音合成服务。
+    [2022-04-27 10:21:04,496] [    INFO] - 首包响应：0.2124948501586914 s
+    [2022-04-27 10:21:07,483] [    INFO] - 尾包响应：3.199106454849243 s
+    [2022-04-27 10:21:07,484] [    INFO] - 音频时长：3.825 s
+    [2022-04-27 10:21:07,484] [    INFO] - RTF: 0.8363677006141812
+    [2022-04-27 10:21:07,516] [    INFO] - 音频保存至：output.wav

+    ```
+
+- Python API
+  ```python
+  from paddlespeech.server.bin.paddlespeech_client import TTSOnlineClientExecutor
+  import json
+
+  executor = TTSOnlineClientExecutor()
+  executor(
+      input="您好，欢迎使用百度飞桨语音合成服务。",
+      server_ip="127.0.0.1",
+      port=8092,
+      protocol="websocket",
+      spk_id=0,
+      speed=1.0,
+      volume=1.0,
+      sample_rate=0,
+      output="./output.wav",
+      play=False)

  ```

+  输出:
+  ```bash
+    [2022-04-27 10:22:48,852] [    INFO] - tts websocket client start
+    [2022-04-27 10:22:49,080] [    INFO] - 句子：您好，欢迎使用百度飞桨语音合成服务。
+    [2022-04-27 10:22:49,080] [    INFO] - 首包响应：0.21017956733703613 s
+    [2022-04-27 10:22:52,100] [    INFO] - 尾包响应：3.2304444313049316 s
+    [2022-04-27 10:22:52,101] [    INFO] - 音频时长：3.825 s
+    [2022-04-27 10:22:52,101] [    INFO] - RTF: 0.8445606356352762
+    [2022-04-27 10:22:52,134] [    INFO] - 音频保存至：./output.wav
+
+  ```
+
+
  
--- a/docs/source/released_model.md
+++ b/docs/source/released_model.md
@@ -6,7 +6,7 @@
 ### Speech Recognition Model
 Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech | Example Link 
 :-------------:| :------------:| :-----: | -----: | :-----: |:-----:| :-----:  | :-----:  | :-----: 
-[Ds2 Online Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.0.model.tar.gz) | Aishell Dataset | Char-based | 479 MB  | 2 Conv + 5 LSTM layers with only forward direction | 0.0718 |-| 151 h | [D2 Online Aishell ASR0](../../examples/aishell/asr0) 
+[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 with only forward direction | 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) 
 [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) 

--- a/examples/aishell/asr0/RESULTS.md
+++ b/examples/aishell/asr0/RESULTS.md
@@ -4,6 +4,7 @@

 | Model | Number of Params | Release | Config | Test set | Valid Loss | CER | 
 | --- | --- | --- | --- | --- | --- | --- | 
+| DeepSpeech2 | 45.18M | r0.2.0 | conf/deepspeech2_online.yaml + U2 Data pipline and spec aug + fbank161 | test | 6.876979827880859 | 0.0666 |
 | DeepSpeech2 | 45.18M | r0.2.0 | conf/deepspeech2_online.yaml + spec aug + fbank161 | test | 7.679287910461426 | 0.0718 |
 | DeepSpeech2 | 45.18M | r0.2.0 | conf/deepspeech2_online.yaml + spec aug | test | 7.708217620849609| 0.078 |
 | DeepSpeech2 | 45.18M | v2.2.0 | conf/deepspeech2_online.yaml + spec aug | test | 7.994938373565674 | 0.080 |  

--- a/examples/ami/README.md
+++ b/examples/ami/README.md
 # Speaker Diarization on AMI corpus

-* sd0 - speaker diarization by AHC,SC base on x-vectors
+* sd0 - speaker diarization by AHC,SC base on embeddings
--- a/examples/ami/sd0/README.md
+++ b/examples/ami/sd0/README.md
@@ -7,7 +7,23 @@
 The script performs diarization using x-vectors(TDNN,ECAPA-TDNN) on the AMI mix-headset data. We demonstrate the use of different clustering methods: AHC, spectral.

 ## How to Run
+### prepare annotations and audios
+Download AMI corpus, You need around 10GB of free space to get whole data
+The signals are too large to package in this way, so you need to use the chooser to indicate which ones you wish to download
+
+```bash
+## download  annotations
+wget http://groups.inf.ed.ac.uk/ami/AMICorpusAnnotations/ami_public_manual_1.6.2.zip && unzip ami_public_manual_1.6.2.zip
+```
+
+then please follow https://groups.inf.ed.ac.uk/ami/download/ to download the Signals:
+1) Select one or more AMI meetings: the IDs please follow ./ami_split.py
+2) Select media streams: Just select Headset mix
+
+### start running
 Use the following command to run diarization on AMI corpus.
-`bash ./run.sh` 
+```bash
+./run.sh  --data_folder ./amicorpus  --manual_annot_folder ./ami_public_manual_1.6.2
+```

 ## Results (DER) coming soon! :)
--- a/examples/ami/sd0/run.sh
+++ b/examples/ami/sd0/run.sh
@@ -17,18 +17,6 @@ device=gpu

 . ${MAIN_ROOT}/utils/parse_options.sh || exit 1;

-if [ $stage -le 0 ]; then
-    # Prepare data
-    # Download AMI corpus, You need around 10GB of free space to get whole data
-    # The signals are too large to package in this way,
-    # so you need to use the chooser to indicate which ones you wish to download
-    echo "Please follow https://groups.inf.ed.ac.uk/ami/download/ to download the data."
-    echo "Annotations: AMI manual annotations v1.6.2 "
-    echo "Signals: "
-    echo "1) Select one or more AMI meetings: the IDs please follow ./ami_split.py"
-    echo "2) Select media streams: Just select Headset mix"
-fi
-
 if [ $stage -le 1 ]; then
    # Download the pretrained model
    wget https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz

--- a/paddlespeech/cli/asr/pretrained_models.py
+++ b/paddlespeech/cli/asr/pretrained_models.py
@@ -27,6 +27,16 @@ pretrained_models = {
        'ckpt_path':
        'exp/conformer/checkpoints/wenetspeech',
    },
+    "conformer_online_multicn-zh-16k": {
+        'url':
+        'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.0.model.tar.gz',
+        'md5':
+        '7989b3248c898070904cf042fd656003',
+        'cfg_path':
+        'model.yaml',
+        'ckpt_path':
+        'exp/chunk_conformer/checkpoints/multi_cn',
+    },
    "conformer_aishell-zh-16k": {
        'url':
        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_0.1.2.model.tar.gz',
@@ -57,6 +67,20 @@ pretrained_models = {
        'ckpt_path':
        'exp/transformer/checkpoints/avg_10',
    },
+    "deepspeech2online_wenetspeech-zh-16k": {
+        'url':
+        'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr0/WIP_asr0_deepspeech2_online_wenetspeech_ckpt_1.0.0a.model.tar.gz',
+        'md5':
+        'b3ef6fcae8c0058c3c53375341ccb209',
+        'cfg_path':
+        'model.yaml',
+        'ckpt_path':
+        'exp/deepspeech2_online/checkpoints/avg_3',
+        'lm_url':
+        'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
+        'lm_md5':
+        '29e02312deb2e59b3c8686c7966d4fe3'
+    },
    "deepspeech2offline_aishell-zh-16k": {
        'url':
        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
@@ -73,9 +97,9 @@ pretrained_models = {
    },
    "deepspeech2online_aishell-zh-16k": {
        'url':
-        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.0.model.tar.gz',
+        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz',
        'md5':
-        'd314960e83cc10dcfa6b04269f3054d4',
+        '98b87b171b7240b7cae6e07d8d0bc9be',
        'cfg_path':
        'model.yaml',
        'ckpt_path':

--- a/paddlespeech/server/engine/asr/online/asr_engine.py
+++ b/paddlespeech/server/engine/asr/online/asr_engine.py
@@ -43,9 +43,9 @@ __all__ = ['ASREngine']
 pretrained_models = {
    "deepspeech2online_aishell-zh-16k": {
        'url':
-        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.0.model.tar.gz',
+        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz',
        'md5':
-        'd314960e83cc10dcfa6b04269f3054d4',
+        '98b87b171b7240b7cae6e07d8d0bc9be',
        'cfg_path':
        'model.yaml',
        'ckpt_path':

--- a/tests/unit/cli/test_cli.sh
+++ b/tests/unit/cli/test_cli.sh
@@ -14,14 +14,16 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
 paddlespeech asr --input ./zh.wav
 paddlespeech asr --model conformer_aishell --input ./zh.wav
 paddlespeech asr --model conformer_online_aishell --input ./zh.wav
+paddlespeech asr --model conformer_online_multicn --input ./zh.wav
 paddlespeech asr --model transformer_librispeech --lang en --input ./en.wav
 paddlespeech asr --model deepspeech2offline_aishell --input ./zh.wav
+paddlespeech asr --model deepspeech2online_wenetspeech --input ./zh.wav
 paddlespeech asr --model deepspeech2online_aishell --input ./zh.wav
 paddlespeech asr --model deepspeech2offline_librispeech --lang en --input ./en.wav

 # long audio restriction
 {
-wget -c wget https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/test_long_audio_01.wav
+wget -c https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/test_long_audio_01.wav
 paddlespeech asr --input test_long_audio_01.wav
 if [ $? -ne 255 ]; then
   echo -e "\e[1;31mTime restriction not passed\e[0m"