From cfe22850e2a62071fca0fbdc15bb6e95ca555490 Mon Sep 17 00:00:00 2001
From: yufan-aslp <379840315@qq.com>
Date: 星期二, 25 四月 2023 15:42:03 +0800
Subject: [PATCH] update mfcc infer.sh

---
 egs_modelscope/asr/TEMPLATE/README.md |   32 ++++++++++++++++++++++++--------
 1 files changed, 24 insertions(+), 8 deletions(-)

diff --git a/egs_modelscope/asr/TEMPLATE/README.md b/egs_modelscope/asr/TEMPLATE/README.md
index 3daff1f..94b47ec 100644
--- a/egs_modelscope/asr/TEMPLATE/README.md
+++ b/egs_modelscope/asr/TEMPLATE/README.md
@@ -1,7 +1,7 @@
 # Speech Recognition
 
 > **Note**: 
-> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take typic model as example to demonstrate the usage.
+> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take the typic models as examples to demonstrate the usage.
 
 ## Inference
 
@@ -58,14 +58,30 @@
 #### [RNN-T-online model]()
 Undo
 
+#### [MFCCA Model](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary)
+For more model detailes, please refer to [docs](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary)
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950',
+    model_revision='v3.0.0'
+)
+
+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)
+```
+
 #### API-reference
 ##### Define pipeline
 - `task`: `Tasks.auto_speech_recognition`
 - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
-- `ngpu`: `1` (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
-- `ncpu`: `1` (Defalut), sets the number of threads used for intraop parallelism on CPU 
-- `output_dir`: `None` (Defalut), the output path of results if set
-- `batch_size`: `1` (Defalut), batch size when decoding
+- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
+- `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU 
+- `output_dir`: `None` (Default), the output path of results if set
+- `batch_size`: `1` (Default), batch size when decoding
 ##### Infer pipeline
 - `audio_in`: the input to decode, which could be: 
   - wav_path, `e.g.`: asr_example.wav,
@@ -79,7 +95,7 @@
   ```
   In this case of `wav.scp` input, `output_dir` must be set to save the output results
 - `audio_fs`: audio sampling rate, only set when audio_in is pcm audio
-- `output_dir`: None (Defalut), the output path of results if set
+- `output_dir`: None (Default), the output path of results if set
 
 ### Inference with multi-thread CPUs or multi GPUs
 FunASR also offer recipes [infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
@@ -186,7 +202,7 @@
 ```
 ## Inference with your finetuned model
 
-- Setting parameters in [infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) is the same with [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/egs_modelscope/asr/TEMPLATE#inference-with-multi-thread-cpus-or-multi-gpus) 
+- Setting parameters in [infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) is the same with [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/egs_modelscope/asr/TEMPLATE#inference-with-multi-thread-cpus-or-multi-gpus), `model` is the model name from modelscope, which you finetuned.
 
 - Decode with multi GPUs:
 ```shell
@@ -210,4 +226,4 @@
     --njob 64 \
     --checkpoint_dir "./checkpoint" \
     --checkpoint_name "valid.cer_ctc.ave.pb"
-```
\ No newline at end of file
+```

--
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