docs

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

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 @@ 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
+- `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 @@ print(rec_result)
 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

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 @@ Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/
 ### 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 @@ Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/
 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

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 @@ print(results)
 ### 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

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 @@ Full code of demo, please ref to [infer.py](https://github.com/alibaba-damo-acad
 ### 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

egs_modelscope/tp/TEMPLATE/README.md

@@ -26,7 +26,7 @@ Timestamp pipeline can also be used after ASR pipeline to compose complete ASR f
 ### 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 @@ Timestamp pipeline can also be used after ASR pipeline to compose complete ASR f
 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

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 @@ Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/
 ### 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 @@ Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/
 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