From 1fadb21eb61dc0e4df987eaf0f9c59bdacec6ca4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 05 五月 2023 18:49:33 +0800
Subject: [PATCH] docs

---
 egs_modelscope/speaker_diarization/TEMPLATE/README.md  |    4 ++--
 egs_modelscope/speaker_verification/TEMPLATE/README.md |    4 ++--
 egs_modelscope/tp/TEMPLATE/README.md                   |    4 ++--
 egs_modelscope/vad/TEMPLATE/README.md                  |    6 +++---
 egs_modelscope/asr/TEMPLATE/README.md                  |    6 +++---
 egs_modelscope/punctuation/TEMPLATE/README.md          |    6 +++---
 docs/modelscope_pipeline/quick_start.md                |    2 +-
 7 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/docs/modelscope_pipeline/quick_start.md b/docs/modelscope_pipeline/quick_start.md
index 436fb1d..7e35e91 100644
--- a/docs/modelscope_pipeline/quick_start.md
+++ b/docs/modelscope_pipeline/quick_start.md
@@ -1,7 +1,7 @@
 # Quick Start
 
 > **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/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take typic model as example to demonstrate the usage.
 
 
 ## Inference with pipeline
diff --git a/egs_modelscope/asr/TEMPLATE/README.md b/egs_modelscope/asr/TEMPLATE/README.md
index 30ae8c9..06daf26 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 the typic models as examples to demonstrate the usage.
+> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take the typic models as examples to demonstrate the usage.
 
 ## Inference
 
@@ -79,7 +79,7 @@
 ### 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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `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
@@ -103,7 +103,7 @@
 FunASR also offer recipes [egs_modelscope/asr/TEMPLATE/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.
 
 #### Settings of `infer.sh`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `data_dir`: the dataset dir needs to include `wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
 - `output_dir`: output dir of the recognition results
 - `batch_size`: `64` (Default), batch size of inference on gpu
diff --git a/egs_modelscope/punctuation/TEMPLATE/README.md b/egs_modelscope/punctuation/TEMPLATE/README.md
index dfbe044..08814ea 100644
--- a/egs_modelscope/punctuation/TEMPLATE/README.md
+++ b/egs_modelscope/punctuation/TEMPLATE/README.md
@@ -1,7 +1,7 @@
 # Punctuation Restoration
 
 > **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 finetune. Here we take the model of the punctuation model of CT-Transformer as example to demonstrate the usage.
+> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetune. Here we take the model of the punctuation model of CT-Transformer as example to demonstrate the usage.
 
 ## Inference
 
@@ -55,7 +55,7 @@
 ### API-reference
 #### Define pipeline
 - `task`: `Tasks.punctuation`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
 - `output_dir`: `None` (Default), the output path of results if set
 - `model_revision`: `None` (Default), setting the model version
@@ -71,7 +71,7 @@
 FunASR also offer recipes [egs_modelscope/punctuation/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/punctuation/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. It is an offline recipe and only support offline model.
 
 #### Settings of `infer.sh`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `data_dir`: the dataset dir needs to include `punc.txt`
 - `output_dir`: output dir of the recognition results
 - `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference
diff --git a/egs_modelscope/speaker_diarization/TEMPLATE/README.md b/egs_modelscope/speaker_diarization/TEMPLATE/README.md
index 99c9b59..ba179ed 100644
--- a/egs_modelscope/speaker_diarization/TEMPLATE/README.md
+++ b/egs_modelscope/speaker_diarization/TEMPLATE/README.md
@@ -2,7 +2,7 @@
 
 > **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) 
+[model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) 
 to inference and finetine. Here we take the model of xvector_sv as example to demonstrate the usage.
 
 ## Inference with pipeline
@@ -40,7 +40,7 @@
 ### API-reference
 #### Define pipeline
 - `task`: `Tasks.speaker_diarization`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
 - `output_dir`: `None` (Default), the output path of results if set
 - `batch_size`: `1` (Default), batch size when decoding
diff --git a/egs_modelscope/speaker_verification/TEMPLATE/README.md b/egs_modelscope/speaker_verification/TEMPLATE/README.md
index f7b64ce..d6736e3 100644
--- a/egs_modelscope/speaker_verification/TEMPLATE/README.md
+++ b/egs_modelscope/speaker_verification/TEMPLATE/README.md
@@ -2,7 +2,7 @@
 
 > **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) 
+[model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) 
 to inference and finetine. Here we take the model of xvector_sv as example to demonstrate the usage.
 
 ## Inference with pipeline
@@ -50,7 +50,7 @@
 ### API-reference
 #### Define pipeline
 - `task`: `Tasks.speaker_verification`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
 - `output_dir`: `None` (Default), the output path of results if set
 - `batch_size`: `1` (Default), batch size when decoding
diff --git a/egs_modelscope/tp/TEMPLATE/README.md b/egs_modelscope/tp/TEMPLATE/README.md
index 62c35d8..7cc8508 100644
--- a/egs_modelscope/tp/TEMPLATE/README.md
+++ b/egs_modelscope/tp/TEMPLATE/README.md
@@ -26,7 +26,7 @@
 ### API-reference
 #### Define pipeline
 - `task`: `Tasks.speech_timestamp`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `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
@@ -62,7 +62,7 @@
 FunASR also offer recipes [egs_modelscope/tp/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/tp/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
 
 #### Settings of `infer.sh`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `data_dir`: the dataset dir **must** include `wav.scp` and `text.txt`
 - `output_dir`: output dir of the recognition results
 - `batch_size`: `64` (Default), batch size of inference on gpu
diff --git a/egs_modelscope/vad/TEMPLATE/README.md b/egs_modelscope/vad/TEMPLATE/README.md
index 503b9bf..4c6f8c2 100644
--- a/egs_modelscope/vad/TEMPLATE/README.md
+++ b/egs_modelscope/vad/TEMPLATE/README.md
@@ -1,7 +1,7 @@
 # Voice Activity Detection
 
 > **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 finetune. Here we take the model of FSMN-VAD as example to demonstrate the usage.
+> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetune. Here we take the model of FSMN-VAD as example to demonstrate the usage.
 
 ## Inference
 
@@ -46,7 +46,7 @@
 ### API-reference
 #### Define pipeline
 - `task`: `Tasks.voice_activity_detection`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `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
@@ -70,7 +70,7 @@
 FunASR also offer recipes [egs_modelscope/vad/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/vad/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
 
 #### Settings of `infer.sh`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `data_dir`: the dataset dir needs to include `wav.scp`
 - `output_dir`: output dir of the recognition results
 - `batch_size`: `64` (Default), batch size of inference on gpu

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