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@@ -3,38 +3,19 @@
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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.
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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)).
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+<img src="docs/images/offline_structure.jpg" width="900"/>
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-## Installation of Docker
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-
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-The following steps are for manually installing Docker and Docker images. If your Docker image has already been launched, you can ignore this step.
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-
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-### Installation of Docker environment
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-
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+## Quick start
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+### Docker install
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+If you have already installed Docker, ignore this step!
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```shell
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-# Ubuntu：
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-curl -fsSL https://test.docker.com -o test-docker.sh
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-sudo sh test-docker.sh
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-# Debian：
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-curl -fsSL https://get.docker.com -o get-docker.sh
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-sudo sh get-docker.sh
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-# CentOS：
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-curl -fsSL https://get.docker.com | bash -s docker --mirror Aliyun
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-# MacOS：
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-brew install --cask --appdir=/Applications docker
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-```
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-
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-More details could ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/installation/docker.html)
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-
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-### Starting Docker
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-
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-```shell
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-sudo systemctl start docker
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+curl -O https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/shell/install_docker.sh;
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+sudo bash install_docker.sh
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```
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+If you do not have Docker installed, please refer to [Docker Installation](https://alibaba-damo-academy.github.io/FunASR/en/installation/docker.html)
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### Pulling and launching images
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-
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Use the following command to pull and launch the Docker image for the FunASR runtime-SDK:
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-
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```shell
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sudo docker pull registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.3.0
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@@ -46,11 +27,9 @@ Introduction to command parameters:
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-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.
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-v <host path>:<mounted Docker path>: In the example, the host machine path /root is mounted to the Docker path /workspace/models.
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-
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```
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-## Starting the server
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-
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+### Starting the server
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Use the flollowing script to start the server ：
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```shell
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nohup bash run_server.sh \
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@@ -59,7 +38,8 @@ nohup bash run_server.sh \
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--model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
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--punc-dir damo/punc_ct-transformer_cn-en-common-vocab471067-large-onnx \
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--lm-dir damo/speech_ngram_lm_zh-cn-ai-wesp-fst \
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- --itn-dir thuduj12/fst_itn_zh > log.out 2>&1 &
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+ --itn-dir thuduj12/fst_itn_zh \
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+ --hotword /workspace/models/hotwords.txt > log.out 2>&1 &
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# If you want to close ssl，please add：--certfile 0
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# If you want to deploy the timestamp or nn hotword model, please set --model-dir to the corresponding model:
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@@ -67,7 +47,6 @@ nohup bash run_server.sh \
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# damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404-onnx（hotword）
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# If you want to load hotwords on the server side, please configure the hotwords in the host machine file ./funasr-runtime-resources/models/hotwords.txt (docker mapping address: /workspace/models/hotwords.txt):
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# One hotword per line, format (hotword weight): 阿里巴巴 20"
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-
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```
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### More details about the script run_server.sh:
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@@ -92,7 +71,6 @@ nohup bash run_server.sh \
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```
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Introduction to run_server.sh parameters:
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-
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```text
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--download-model-dir: Model download address, download models from Modelscope by setting the model ID.
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--model-dir: Modelscope model ID.
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@@ -141,19 +119,14 @@ After executing the above command, the real-time speech transcription service wi
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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`.
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-
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-
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## Starting the client
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-
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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:
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### python-client
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```shell
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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"
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```
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-
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Introduction to command parameters:
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-
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```text
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--host: the IP address of the server. It can be set to 127.0.0.1 for local testing.
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--port: the port number of the server listener.
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@@ -171,7 +144,6 @@ Introduction to command parameters:
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```
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Introduction to command parameters:
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-
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```text
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--server-ip: the IP address of the server. It can be set to 127.0.0.1 for local testing.
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--port: the port number of the server listener.
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@@ -182,19 +154,15 @@ Introduction to command parameters:
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```
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### Custom client
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-
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If you want to define your own client, see the [Websocket communication protocol](./websocket_protocol.md)
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## How to customize service deployment
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-
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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:
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### C++ client
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-
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https://github.com/alibaba-damo-academy/FunASR/tree/main/runtime/websocket
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### Python client
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-
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https://github.com/alibaba-damo-academy/FunASR/tree/main/runtime/python/websocket
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### C++ server
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@@ -218,7 +186,6 @@ FUNASR_HANDLE asr_hanlde=FunOfflineInit(model_path, thread_num);
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FUNASR_RESULT result=FunOfflineInfer(asr_hanlde, wav_file.c_str(), RASR_NONE, NULL, 16000);
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// 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).
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```
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-
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See the usage example for details, [docs](https://github.com/alibaba-damo-academy/FunASR/blob/main/runtime/onnxruntime/bin/funasr-onnx-offline.cpp)
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#### PUNC
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lyblsgo
