fix bug for h5 hotwords (#1067)

zhaomingwork

2023-11-08 2e36e738ca39afc8d02f3d11013bd12f937cc874

 egs_modelscope/asr/TEMPLATE/README_zh.md

@@ -21,6 +21,33 @@
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)
```
#### [Paraformer长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
```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-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
    vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
    #punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
    punc_model='damo/punc_ct-transformer_cn-en-common-vocab471067-large',
)

rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav', 
                                batch_size_token=5000, batch_size_token_threshold_s=40, max_single_segment_time=6000)
print(rec_result)
```
其中： 
- `batch_size_token` 表示采用动态batch，batch中总token数为 `batch_size_token`，1 token = 60 ms. 
- `batch_size_token_threshold_s`: 表示音频时长超过 `batch_size_token_threshold_s`阈值是，batch数设置为1, 单位为s.
- `max_single_segment_time`: 表示VAD最大切割音频时长, 单位是ms.

建议：当您输入为长音频，遇到OOM问题时，因为显存占用与音频时长呈平方关系增加，分为3种情况：
- a)推理起始阶段，显存主要取决于`batch_size_token`，适当减小该值，可以减少显存占用；
- b)推理中间阶段，遇到VAD切割的长音频片段，总token数小于`batch_size_token`，仍然出现OOM，可以适当减小`batch_size_token_threshold_s`，超过阈值，强制batch为1; 
- c)推理快结束阶段，遇到VAD切割的长音频片段，总token数小于`batch_size_token`，且超过阈值`batch_size_token_threshold_s`，强制batch为1，仍然出现OOM，可以适当减小`max_single_segment_time`，使得VAD切割音频时长变短。

#### [Paraformer-实时模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary)
##### 实时推理
```python
@@ -68,7 +95,7 @@
```
演示代码完整版本，请参考[demo](https://github.com/alibaba-damo-academy/FunASR/discussions/241)

#### [Paraformer-contextual Model](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary)
#### [Paraformer-contextual 热词模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary)
```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
@@ -100,6 +127,29 @@
fast 和 normal 的解码模式是假流式解码，可用于评估识别准确性。
演示的完整代码，请参见 [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151)

#### [Paraformer-Spk model](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary)
返回识别结果的同时返回每个子句的说话人分类结果。关于说话人日志模型的详情请见[CAM++](https://modelscope.cn/models/damo/speech_campplus_speaker-diarization_common/summary)。

```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

if __name__ == '__main__':
    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_speaker_demo.wav'
    output_dir = "./results"
    inference_pipeline = pipeline(
        task=Tasks.auto_speech_recognition,
        model='damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn',
        model_revision='v0.0.2',
        vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
        punc_model='damo/punc_ct-transformer_cn-en-common-vocab471067-large',
        output_dir=output_dir,
    )
    rec_result = inference_pipeline(audio_in=audio_in, batch_size_token=5000, batch_size_token_threshold_s=40, max_single_segment_time=6000)
    print(rec_result)
```


#### [RNN-T-online 模型]()
Undo