# Advanced Development Guide (File transcription service)
 
FunASR provides a Chinese offline file transcription service that can be deployed locally or on a cloud server with just one click. The core of the service is the FunASR runtime SDK, which has been open-sourced. FunASR-runtime combines various capabilities such as speech endpoint detection (VAD), large-scale speech recognition (ASR) using Paraformer-large, and punctuation detection (PUNC), which have all been open-sourced by the speech laboratory of DAMO Academy on the Modelscope community. This enables accurate and efficient high-concurrency transcription of audio files.

This document serves as a development guide for the FunASR offline file transcription service. If you wish to quickly experience the offline file transcription service, please refer to the one-click deployment example for the FunASR offline file transcription service ([docs](./SDK_tutorial.md)).

## Installation of Docker

The following steps are for manually installing Docker and Docker images. If your Docker image has already been launched, you can ignore this step.

### Installation of Docker environment

```shell
# Ubuntu：
curl -fsSL https://test.docker.com -o test-docker.sh 
sudo sh test-docker.sh 
# Debian：
curl -fsSL https://get.docker.com -o get-docker.sh 
sudo sh get-docker.sh 
# CentOS：
curl -fsSL https://get.docker.com | bash -s docker --mirror Aliyun 
# MacOS：
brew install --cask --appdir=/Applications docker
```

More details could ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/installation/docker.html)

### Starting Docker

```shell
sudo systemctl start docker
```

### Pulling and launching images

Use the following command to pull and launch the Docker image for the FunASR runtime-SDK:

```shell
sudo docker pull registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.2.1

sudo docker run -p 10095:10095 -it --privileged=true -v /root:/workspace/models registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.2.1
```

Introduction to command parameters: 
```text
-p <host port>:<mapped docker port>: In the example, host machine (ECS) port 10095 is mapped to port 10095 in the Docker container. Make sure that port 10095 is open in the ECS security rules.

-v <host path>:<mounted Docker path>: In the example, the host machine path /root is mounted to the Docker path /workspace/models.

```


## Starting the server

Use the flollowing script to start the server ：
```shell
nohup bash run_server.sh \
  --download-model-dir /workspace/models \
  --vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
  --model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx  \
  --punc-dir damo/punc_ct-transformer_zh-cn-common-vocab272727-onnx > log.out 2>&1 &

# If you want to close ssl，please add：--certfile 0
# If you want to deploy the timestamp or hotword model, please set --model-dir to the corresponding model:
# speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-onnx（timestamp）
# damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404-onnx（hotword）
```

More details about the script run_server.sh:

The FunASR-wss-server supports downloading models from Modelscope. You can set the model download address (--download-model-dir, default is /workspace/models) and the model ID (--model-dir, --vad-dir, --punc-dir). Here is an example:

```shell
cd /workspace/FunASR/funasr/runtime/websocket/build/bin
./funasr-wss-server  \
  --download-model-dir /workspace/models \
  --model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
  --vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
  --punc-dir damo/punc_ct-transformer_zh-cn-common-vocab272727-onnx \
  --decoder-thread-num 32 \
  --io-thread-num  8 \
  --port 10095 \
  --certfile  ../../../ssl_key/server.crt \
  --keyfile ../../../ssl_key/server.key
 ```

Introduction to command parameters: 

```text
--download-model-dir: Model download address, download models from Modelscope by setting the model ID.
--model-dir: Modelscope model ID.
--quantize: True for quantized ASR model, False for non-quantized ASR model. Default is True.
--vad-dir: Modelscope model ID.
--vad-quant: True for quantized VAD model, False for non-quantized VAD model. Default is True.
--punc-dir: Modelscope model ID.
--punc-quant: True for quantized PUNC model, False for non-quantized PUNC model. Default is True.
--port: Port number that the server listens on. Default is 10095.
--decoder-thread-num: Number of inference threads that the server starts. Default is 8.
--io-thread-num: Number of IO threads that the server starts. Default is 1.
--certfile <string>: SSL certificate file. Default is ../../../ssl_key/server.crt. If you want to close ssl，set ""
--keyfile <string>: SSL key file. Default is ../../../ssl_key/server.key. If you want to close ssl，set ""
```

The FunASR-wss-server also supports loading models from a local path (see Preparing Model Resources for detailed instructions on preparing local model resources). Here is an example:

```shell
cd /workspace/FunASR/funasr/runtime/websocket/build/bin
./funasr-wss-server  \
  --model-dir /workspace/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
  --vad-dir /workspace/models/damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
  --punc-dir /workspace/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-onnx \
  --decoder-thread-num 32 \
  --io-thread-num  8 \
  --port 10095 \
  --certfile  ../../../ssl_key/server.crt \
  --keyfile ../../../ssl_key/server.key
 ```

After executing the above command, the real-time speech transcription service will be started. If the model is specified as a ModelScope model id, the following models will be automatically downloaded from ModelScope:
[FSMN-VAD](https://www.modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-onnx/summary)
[Paraformer-lagre](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx/summary)
[CT-Transformer](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-onnx/summary)

If you wish to deploy your fine-tuned model (e.g., 10epoch.pb), you need to manually rename the model to model.pb and replace the original model.pb in ModelScope. Then, specify the path as `model_dir`.



## Starting the client

After completing the deployment of FunASR offline file transcription service on the server, you can test and use the service by following these steps. Currently, FunASR-bin supports multiple ways to start the client. The following are command-line examples based on python-client, c++-client, and custom client Websocket communication protocol: 

### python-client
```shell
python funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode offline --audio_in "./data/wav.scp" --send_without_sleep --output_dir "./results"
```

Introduction to command parameters:

```text
--host: the IP address of the server. It can be set to 127.0.0.1 for local testing.
--port: the port number of the server listener.
--audio_in: the audio input. Input can be a path to a wav file or a wav.scp file (a Kaldi-formatted wav list in which each line includes a wav_id followed by a tab and a wav_path).
--output_dir: the path to the recognition result output.
--ssl: whether to use SSL encryption. The default is to use SSL.
--mode: offline mode.
--hotword If am is hotword model, setting hotword: *.txt(one hotword perline) or hotwords seperate by space (could be: 阿里巴巴 达摩院)
```

### c++-client
```shell
. /funasr-wss-client --server-ip 127.0.0.1 --port 10095 --wav-path test.wav --thread-num 1 --is-ssl 1
```

Introduction to command parameters:

```text
--host: the IP address of the server. It can be set to 127.0.0.1 for local testing.
--port: the port number of the server listener.
--audio_in: the audio input. Input can be a path to a wav file or a wav.scp file (a Kaldi-formatted wav list in which each line includes a wav_id followed by a tab and a wav_path).
--output_dir: the path to the recognition result output.
--ssl: whether to use SSL encryption. The default is to use SSL.
--mode: offline mode.
--hotword If am is hotword model, setting hotword: *.txt(one hotword perline) or hotwords seperate by space (could be: 阿里巴巴 达摩院)
```

### Custom client

If you want to define your own client, see the [Websocket communication protocol](./websocket_protocol.md)

## How to customize service deployment

The code for FunASR-runtime is open source. If the server and client cannot fully meet your needs, you can further develop them based on your own requirements:

### C++ client

https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/websocket

### Python client

https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/websocket

### C++ server

#### VAD
```c++
// The use of the VAD model consists of two steps: FsmnVadInit and FsmnVadInfer:
FUNASR_HANDLE vad_hanlde=FsmnVadInit(model_path, thread_num);
// Where: model_path contains "model-dir" and "quantize", thread_num is the ONNX thread count;
FUNASR_RESULT result=FsmnVadInfer(vad_hanlde, wav_file.c_str(), NULL, 16000);
// Where: vad_hanlde is the return value of FunOfflineInit, wav_file is the path to the audio file, and sampling_rate is the sampling rate (default 16k).
```

See the usage example for details [docs](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/onnxruntime/bin/funasr-onnx-offline-vad.cpp)

#### ASR
```text
// The use of the ASR model consists of two steps: FunOfflineInit and FunOfflineInfer:
FUNASR_HANDLE asr_hanlde=FunOfflineInit(model_path, thread_num);
// Where: model_path contains "model-dir" and "quantize", thread_num is the ONNX thread count;
FUNASR_RESULT result=FunOfflineInfer(asr_hanlde, wav_file.c_str(), RASR_NONE, NULL, 16000);
// Where: asr_hanlde is the return value of FunOfflineInit, wav_file is the path to the audio file, and sampling_rate is the sampling rate (default 16k).
```

See the usage example for details, [docs](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/onnxruntime/bin/funasr-onnx-offline.cpp)

#### PUNC
```text
// The use of the PUNC model consists of two steps: CTTransformerInit and CTTransformerInfer:
FUNASR_HANDLE punc_hanlde=CTTransformerInit(model_path, thread_num);
// Where: model_path contains "model-dir" and "quantize", thread_num is the ONNX thread count;
FUNASR_RESULT result=CTTransformerInfer(punc_hanlde, txt_str.c_str(), RASR_NONE, NULL);
// Where: punc_hanlde is the return value of CTTransformerInit, txt_str is the text
```
See the usage example for details, [docs](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/onnxruntime/bin/funasr-onnx-offline-punc.cpp)