Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main

README.md

@@ -12,7 +12,7 @@
 [**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new) 
 | [**Highlights**](#highlights)
 | [**Installation**](#installation)
-| [**Docs**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
+| [**Usage**](#usage)
 | [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations)
 | [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
 | [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
@@ -44,22 +44,68 @@ Or install from source code
 
 ``` sh
 git clone https://github.com/alibaba/FunASR.git && cd FunASR
-pip install -e ./
+pip3 install -e ./
 # For the users in China, you could install with the command:
-# pip install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple
+# pip3 install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple
 
 ```
 If you want to use the pretrained models in ModelScope, you should install the modelscope:
 
 ```shell
-pip install -U modelscope
+pip3 install -U modelscope
 # For the users in China, you could install with the command:
-# pip install -U modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html -i https://mirror.sjtu.edu.cn/pypi/web/simple
+# pip3 install -U modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html -i https://mirror.sjtu.edu.cn/pypi/web/simple
 ```
 
 For more details, please ref to [installation](https://alibaba-damo-academy.github.io/FunASR/en/installation/installation.html)
 
+## Usage
 
+You could use FunASR by:
+
+- egs
+- egs_modelscope
+- runtime
+
+### egs
+If you want to train the model from scratch, you could use funasr directly by recipe, as the following:
+```shell
+cd egs/aishell/paraformer
+. ./run.sh --CUDA_VISIBLE_DEVICES="0,1" --gpu_num=2
+```
+More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)
+
+### egs_modelscope
+If you want to infer or finetune pretraining models from modelscope, you could use funasr by modelscope pipeline, as the following:
+
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+)
+
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
+print(rec_result)
+# {'text': '欢迎大家来体验达摩院推出的语音识别模型'}
+```
+More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)
+
+### runtime
+
+An example with websocket:
+For the server:
+```shell
+python wss_srv_asr.py --port 10095
+```
+For the client:
+```shell
+python wss_client_asr.py --host "0.0.0.0" --port 10095 --mode 2pass --chunk_size "5,10,5"
+#python wss_client_asr.py --host "0.0.0.0" --port 10095 --mode 2pass --chunk_size "8,8,4" --audio_in "./data/wav.scp" --output_dir "./results"
+```
+More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/websocket_python.html#id2)
 ## Contact
 
 If you have any questions about FunASR, please contact us by

docs/benchmark/benchmark_pipeline_cer.md

@@ -1,4 +1,4 @@
-# Benchmark (ModeScope Pipeline)
+# Leaderboard IO
 
 
 ## Configuration

docs/index.rst

@@ -77,11 +77,12 @@ Overview
 
 .. toctree::
    :maxdepth: 1
-   :caption: Benchmark and Leadboard
+   :caption: Benchmark and Leaderboard
 
    ./benchmark/benchmark_onnx.md
    ./benchmark/benchmark_onnx_cpp.md
    ./benchmark/benchmark_libtorch.md
+   ./benchmark/benchmark_pipeline_cer.md
 
 
 .. toctree::

funasr/bin/asr_infer.py

@@ -609,7 +609,7 @@ class Speech2TextParaformer:
             hotword_str_list = []
             for hw in hotword_list_or_file.strip().split():
                 hotword_str_list.append(hw)
-                hw_list = hw
+                hw_list = hw.strip().split()
                 if seg_dict is not None:
                     hw_list = seg_tokenize(hw_list, seg_dict)
                 hotword_list.append(self.converter.tokens2ids(hw_list))

funasr/version.txt

@@ -1 +1 @@
-0.6.2
+0.6.3