install requirements automatically

2025-09-15 14:48:36 +08:00 · 2024-03-25 12:55:20 +08:00 · 2024-03-25 12:55:20 +08:00 · 817ff41fbc
commit 817ff41fbc
parent 447222c00e
55 changed files with 889 additions and 134 deletions
--- a/docs/tutorial/README_zh.md
+++ b/docs/tutorial/README_zh.md
@ -214,7 +214,6 @@ bash finetune.sh
 ```shell
 funasr/bin/train.py \
 ++model="${model_name_or_model_dir}" \
-++model_revision="${model_revision}" \
 ++train_data_set_list="${train_data}" \
 ++valid_data_set_list="${val_data}" \
 ++dataset_conf.batch_size=20000 \
@ -232,7 +231,6 @@ funasr/bin/train.py \
 ```

 - `model`（str）：模型名字（模型仓库中的ID），此时脚本会自动下载模型到本读；或者本地已经下载好的模型路径。
- `model_revision`（str）：当 `model` 为模型名字时，下载指定版本的模型。
 - `train_data_set_list`（str）：训练数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
 - `valid_data_set_list`（str）：验证数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
 - `dataset_conf.batch_type`（str）：`example`（默认），batch的类型。`example`表示按照固定数目batch_size个样本组batch；`length` or `token` 表示动态组batch，batch总长度或者token数为batch_size。
--- a/examples/README_zh.md
+++ b/examples/README_zh.md
@ -214,7 +214,6 @@ bash finetune.sh
 ```shell
 funasr/bin/train.py \
 ++model="${model_name_or_model_dir}" \
-++model_revision="${model_revision}" \
 ++train_data_set_list="${train_data}" \
 ++valid_data_set_list="${val_data}" \
 ++dataset_conf.batch_size=20000 \
@ -232,7 +231,6 @@ funasr/bin/train.py \
 ```

 - `model`（str）：模型名字（模型仓库中的ID），此时脚本会自动下载模型到本读；或者本地已经下载好的模型路径。
- `model_revision`（str）：当 `model` 为模型名字时，下载指定版本的模型。
 - `train_data_set_list`（str）：训练数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
 - `valid_data_set_list`（str）：验证数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
 - `dataset_conf.batch_type`（str）：`example`（默认），batch的类型。`example`表示按照固定数目batch_size个样本组batch；`length` or `token` 表示动态组batch，batch总长度或者token数为batch_size。
--- a/examples/common_voice/whisper_lid/demo_funasr.py
+++ b/examples/common_voice/whisper_lid/demo_funasr.py
@ -12,7 +12,7 @@ multilingual_wavs = [
    "example_ko.mp3",
 ]

-model = AutoModel(model="iic/speech_whisper-large_lid_multilingual_pytorch", model_revision="master")
+model = AutoModel(model="iic/speech_whisper-large_lid_multilingual_pytorch")
 for wav_id in multilingual_wavs:
    wav_file = f"{model.model_path}/examples/{wav_id}"
    res = model.generate(input=wav_file, data_type="sound", inference_clip_length=250)
--- a/examples/common_voice/whisper_lid/demo_modelscope.py
+++ b/examples/common_voice/whisper_lid/demo_modelscope.py
@ -15,7 +15,7 @@ multilingual_wavs=[

 inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
-    model='iic/speech_whisper-large_lid_multilingual_pytorch', model_revision="master")
+    model='iic/speech_whisper-large_lid_multilingual_pytorch')

 for wav in multilingual_wavs:
    rec_result = inference_pipeline(input=wav, inference_clip_length=250)
--- a/examples/industrial_data_pretraining/bicif_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/bicif_paraformer/demo.py
@ -6,13 +6,9 @@
 from funasr import AutoModel

 model = AutoModel(model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
-                  model_revision="master",
                  vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
-                  vad_model_revision="master",
                  punc_model="iic/punc_ct-transformer_cn-en-common-vocab471067-large",
-                  punc_model_revision="master",
                  # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
-                  # spk_model_revision="v2.0.2",
                  )

 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
--- a/examples/industrial_data_pretraining/bicif_paraformer/demo.sh
+++ b/examples/industrial_data_pretraining/bicif_paraformer/demo.sh
@ -1,23 +1,15 @@

 model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="master"
 vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-vad_model_revision="master"
 #punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
 punc_model="iic/punc_ct-transformer_cn-en-common-vocab471067-large"
-punc_model_revision="master"
 spk_model="iic/speech_campplus_sv_zh-cn_16k-common"
-spk_model_revision="v2.0.2"

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +vad_model=${vad_model} \
-+vad_model_revision=${vad_model_revision} \
 +punc_model=${punc_model} \
-+punc_model_revision=${punc_model_revision} \
 +spk_model=${spk_model} \
-+spk_model_revision=${spk_model_revision} \
 +input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav" \
 +output_dir="./outputs/debug" \
 +device="cpu" \
--- a/examples/industrial_data_pretraining/bicif_paraformer/export.py
+++ b/examples/industrial_data_pretraining/bicif_paraformer/export.py
@ -7,8 +7,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
-                  model_revision="master", device="cpu")
+model = AutoModel(model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch", device="cpu")

 res = model.export(type="onnx", quantize=False)
 print(res)
--- a/examples/industrial_data_pretraining/bicif_paraformer/export.sh
+++ b/examples/industrial_data_pretraining/bicif_paraformer/export.sh
@ -6,11 +6,9 @@ export HYDRA_FULL_ERROR=1


 model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="master"

 python -m funasr.bin.export \
 ++model=${model} \
-++model_revision=${model_revision} \
 ++type="onnx" \
 ++quantize=false \
 ++device="cpu"
--- a/examples/industrial_data_pretraining/bicif_paraformer/finetune.sh
+++ b/examples/industrial_data_pretraining/bicif_paraformer/finetune.sh
@ -11,7 +11,6 @@ gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

 ## option 1, download model automatically
 model_name_or_model_dir="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="master"

 ## option 2, download model by git
 #local_path_root=${workspace}/modelscope_models
@ -51,7 +50,6 @@ torchrun \
 --nproc_per_node ${gpu_num} \
 ../../../funasr/bin/train.py \
 ++model="${model_name_or_model_dir}" \
-++model_revision="${model_revision}" \
 ++train_data_set_list="${train_data}" \
 ++valid_data_set_list="${val_data}" \
 ++dataset_conf.batch_size=20000 \
--- a/examples/industrial_data_pretraining/campplus_sv/demo.py
+++ b/examples/industrial_data_pretraining/campplus_sv/demo.py
@ -5,8 +5,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/speech_campplus_sv_zh-cn_16k-common",
-                  model_revision="v2.0.2",
+model = AutoModel(model="iic/speech_campplus_sv_zh-cn_16k-common"
                  )

 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
--- a/examples/industrial_data_pretraining/conformer/demo.py
+++ b/examples/industrial_data_pretraining/conformer/demo.py
@ -5,8 +5,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch", model_revision="master",
-                  )
+model = AutoModel(model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch")

 res = model.generate(input="https://modelscope.oss-cn-beijing.aliyuncs.com/test/audios/asr_example.wav")
 print(res)
--- a/examples/industrial_data_pretraining/conformer/demo.sh
+++ b/examples/industrial_data_pretraining/conformer/demo.sh
@ -1,10 +1,8 @@

 model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch"
-model_revision="master"

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
 +output_dir="./outputs/debug" \
 +device="cpu" \
--- a/examples/industrial_data_pretraining/contextual_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/contextual_paraformer/demo.py
@ -5,7 +5,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", model_revision="master")
+model = AutoModel(model="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404")

 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
            hotword='达摩院 魔搭')
--- a/examples/industrial_data_pretraining/contextual_paraformer/demo.sh
+++ b/examples/industrial_data_pretraining/contextual_paraformer/demo.sh
@ -1,10 +1,9 @@

 model="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404"
-model_revision="master"
+

 python ../../../funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
 +output_dir="./outputs/debug" \
 +device="cpu" \
--- a/examples/industrial_data_pretraining/contextual_paraformer/finetune.sh
+++ b/examples/industrial_data_pretraining/contextual_paraformer/finetune.sh
@ -11,7 +11,7 @@ gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

 ## option 1, download model automatically
 model_name_or_model_dir="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404"
-model_revision="master"
+

 ## option 2, download model by git
 #local_path_root=${workspace}/modelscope_models
--- a/examples/industrial_data_pretraining/ct_transformer/demo.py
+++ b/examples/industrial_data_pretraining/ct_transformer/demo.py
@ -5,7 +5,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="master")
+model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch")

 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
 print(res)
@ -13,7 +13,7 @@ print(res)

 from funasr import AutoModel

-model = AutoModel(model="iic/punc_ct-transformer_cn-en-common-vocab471067-large", model_revision="master")
+model = AutoModel(model="iic/punc_ct-transformer_cn-en-common-vocab471067-large")

 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
 print(res)
--- a/examples/industrial_data_pretraining/ct_transformer/demo.sh
+++ b/examples/industrial_data_pretraining/ct_transformer/demo.sh
@ -1,13 +1,12 @@

 #model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
-#model_revision="master"
+#

 model="iic/punc_ct-transformer_cn-en-common-vocab471067-large"
-model_revision="master"
+

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt" \
 +output_dir="./outputs/debug" \
 +device="cpu"
--- a/examples/industrial_data_pretraining/ct_transformer/export.py
+++ b/examples/industrial_data_pretraining/ct_transformer/export.py
@ -8,7 +8,7 @@
 from funasr import AutoModel

 model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
-                  model_revision="master")
+                  )

 res = model.export(type="onnx", quantize=False)
 print(res)
--- a/examples/industrial_data_pretraining/ct_transformer/export.sh
+++ b/examples/industrial_data_pretraining/ct_transformer/export.sh
@ -6,7 +6,7 @@ export HYDRA_FULL_ERROR=1


 model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
-model_revision="master"
+

 python -m funasr.bin.export \
 ++model=${model} \
--- a/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py
+++ b/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py
@ -5,7 +5,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727", model_revision="master")
+model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727")

 inputs = "跨境河流是养育沿岸|人民的生命之源长期以来为帮助下游地区防灾减灾中方技术人员|在上游地区极为恶劣的自然条件下克服巨大困难甚至冒着生命危险|向印方提供汛期水文资料处理紧急事件中方重视印方在跨境河流问题上的关切|愿意进一步完善双方联合工作机制|凡是|中方能做的我们|都会去做而且会做得更好我请印度朋友们放心中国在上游的|任何开发利用都会经过科学|规划和论证兼顾上下游的利益"
 vads = inputs.split("|")
--- a/examples/industrial_data_pretraining/ct_transformer_streaming/demo.sh
+++ b/examples/industrial_data_pretraining/ct_transformer_streaming/demo.sh
@ -1,10 +1,9 @@

 model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727"
-model_revision="master"
+

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt" \
 +output_dir="./outputs/debug" \
 +device="cpu"
--- a/examples/industrial_data_pretraining/ct_transformer_streaming/export.py
+++ b/examples/industrial_data_pretraining/ct_transformer_streaming/export.py
@ -8,7 +8,7 @@
 from funasr import AutoModel

 model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
-                  model_revision="master")
+                  )

 res = model.export(type="onnx", quantize=False)
 print(res)
--- a/examples/industrial_data_pretraining/ct_transformer_streaming/export.sh
+++ b/examples/industrial_data_pretraining/ct_transformer_streaming/export.sh
@ -6,7 +6,7 @@ export HYDRA_FULL_ERROR=1


 model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727"
-model_revision="master"
+

 python -m funasr.bin.export \
 ++model=${model} \
--- a/examples/industrial_data_pretraining/emotion2vec/demo.py
+++ b/examples/industrial_data_pretraining/emotion2vec/demo.py
@ -6,7 +6,7 @@
 from funasr import AutoModel

 # model="iic/emotion2vec_base"
-model = AutoModel(model="iic/emotion2vec_base_finetuned", model_revision="master",
+model = AutoModel(model="iic/emotion2vec_base_finetuned",
                  # vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                  # vad_model_revision="master",
                  # vad_kwargs={"max_single_segment_time": 2000},
--- a/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
+++ b/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
@ -6,7 +6,7 @@
 from funasr import AutoModel
 wav_file = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav"

-model = AutoModel(model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="master")
+model = AutoModel(model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch")

 res = model.generate(input=wav_file)
 print(res)
--- a/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.sh
+++ b/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.sh
@ -1,11 +1,10 @@


 model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-model_revision="master"
+

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav" \
 +output_dir="./outputs/debug" \
 +device="cpu" \
--- a/examples/industrial_data_pretraining/fsmn_vad_streaming/export.py
+++ b/examples/industrial_data_pretraining/fsmn_vad_streaming/export.py
@ -8,7 +8,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="master")
+model = AutoModel(model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch")

 res = model.export(type="onnx", quantize=False)
 print(res)
--- a/examples/industrial_data_pretraining/fsmn_vad_streaming/export.sh
+++ b/examples/industrial_data_pretraining/fsmn_vad_streaming/export.sh
@ -7,7 +7,7 @@ export HYDRA_FULL_ERROR=1


 model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-model_revision="master"
+

 python -m funasr.bin.export \
 ++model=${model} \
--- a/examples/industrial_data_pretraining/monotonic_aligner/demo.py
+++ b/examples/industrial_data_pretraining/monotonic_aligner/demo.py
@ -5,7 +5,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/speech_timestamp_prediction-v1-16k-offline", model_revision="master")
+model = AutoModel(model="iic/speech_timestamp_prediction-v1-16k-offline")

 res = model.generate(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
                   "欢迎大家来到魔搭社区进行体验"),
--- a/examples/industrial_data_pretraining/monotonic_aligner/demo.sh
+++ b/examples/industrial_data_pretraining/monotonic_aligner/demo.sh
@ -1,10 +1,9 @@

 model="iic/speech_timestamp_prediction-v1-16k-offline"
-model_revision="master"
+

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +input='["https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", "欢迎大家来到魔搭社区进行体验"]' \
 +data_type='["sound", "text"]' \
 +output_dir="../outputs/debug" \
--- a/examples/industrial_data_pretraining/paraformer-zh-spk/README_zh.md
+++ b/examples/industrial_data_pretraining/paraformer-zh-spk/README_zh.md
@ -0,0 +1,432 @@
+(简体中文|[English](./README.md))
+
+FunASR开源了大量在工业数据上预训练模型，您可以在 [模型许可协议](https://github.com/alibaba-damo-academy/FunASR/blob/main/MODEL_LICENSE)下自由使用、复制、修改和分享FunASR模型，下面列举代表性的模型，更多模型请参考 [模型仓库](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo)。
+
+<div align="center">  
+<h4>
+ <a href="#模型推理"> 模型推理 </a>   
+｜<a href="#模型训练与测试"> 模型训练与测试 </a>
+｜<a href="#模型导出与测试"> 模型导出与测试 </a>
+</h4>
+</div>
+
+<a name="模型推理"></a>
+## 模型推理
+
+### 快速使用
+
+命令行方式调用：
+```shell
+funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wav
+```
+
+python代码调用（推荐）
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="paraformer-zh")
+
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav")
+print(res)
+```
+
+### 接口说明
+
+#### AutoModel 定义
+```python
+model = AutoModel(model=[str], device=[str], ncpu=[int], output_dir=[str], batch_size=[int], hub=[str], **kwargs)
+```
+- `model`(str): [模型仓库](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo) 中的模型名称，或本地磁盘中的模型路径
+- `device`(str): `cuda:0`（默认gpu0），使用 GPU 进行推理，指定。如果为`cpu`，则使用 CPU 进行推理
+- `ncpu`(int): `4` （默认），设置用于 CPU 内部操作并行性的线程数
+- `output_dir`(str): `None` （默认），如果设置，输出结果的输出路径
+- `batch_size`(int): `1` （默认），解码时的批处理，样本个数
+- `hub`(str)：`ms`（默认），从modelscope下载模型。如果为`hf`，从huggingface下载模型。
+- `**kwargs`(dict): 所有在`config.yaml`中参数，均可以直接在此处指定，例如，vad模型中最大切割长度 `max_single_segment_time=6000` （毫秒）。
+
+#### AutoModel 推理
+```python
+res = model.generate(input=[str], output_dir=[str])
+```
+- `input`: 要解码的输入，可以是：
+  - wav文件路径, 例如: asr_example.wav
+  - pcm文件路径, 例如: asr_example.pcm，此时需要指定音频采样率fs（默认为16000）
+  - 音频字节数流，例如：麦克风的字节数数据
+  - wav.scp，kaldi 样式的 wav 列表 (`wav_id \t wav_path`), 例如:
+  ```text
+  asr_example1  ./audios/asr_example1.wav
+  asr_example2  ./audios/asr_example2.wav
+  ```
+  在这种输入 `wav.scp` 的情况下，必须设置 `output_dir` 以保存输出结果
+  - 音频采样点，例如：`audio, rate = soundfile.read("asr_example_zh.wav")`, 数据类型为 numpy.ndarray。支持batch输入，类型为list：
+  ```[audio_sample1, audio_sample2, ..., audio_sampleN]```
+  - fbank输入，支持组batch。shape为[batch, frames, dim]，类型为torch.Tensor，例如
+- `output_dir`: None （默认），如果设置，输出结果的输出路径
+- `**kwargs`(dict): 与模型相关的推理参数，例如，`beam_size=10`，`decoding_ctc_weight=0.1`。
+
+
+### 更多用法介绍
+
+
+#### 非实时语音识别
+```python
+from funasr import AutoModel
+# paraformer-zh is a multi-functional asr model
+# use vad, punc, spk or not as you need
+model = AutoModel(model="paraformer-zh",  
+                  vad_model="fsmn-vad", 
+                  vad_kwargs={"max_single_segment_time": 60000},
+                  punc_model="ct-punc", 
+                  # spk_model="cam++"
+                  )
+wav_file = f"{model.model_path}/example/asr_example.wav"
+res = model.generate(input=wav_file, batch_size_s=300, batch_size_threshold_s=60, hotword='魔搭')
+print(res)
+```
+注意：
+- 通常模型输入限制时长30s以下，组合`vad_model`后，支持任意时长音频输入，不局限于paraformer模型，所有音频输入模型均可以。
+- `model`相关的参数可以直接在`AutoModel`定义中直接指定；与`vad_model`相关参数可以通过`vad_kwargs`来指定，类型为dict；类似的有`punc_kwargs`，`spk_kwargs`；
+- `max_single_segment_time`: 表示`vad_model`最大切割音频时长, 单位是毫秒ms.
+- `batch_size_s` 表示采用动态batch，batch中总音频时长，单位为秒s。
+- `batch_size_threshold_s`: 表示`vad_model`切割后音频片段时长超过 `batch_size_threshold_s`阈值时，将batch_size数设置为1, 单位为秒s.
+
+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_s`，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_s`，仍然出现OOM，可以适当减小`batch_size_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_s`，且超过阈值`batch_size_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。
+
+#### 实时语音识别
+
+```python
+from funasr import AutoModel
+
+chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
+encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
+decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
+
+model = AutoModel(model="paraformer-zh-streaming")
+
+import soundfile
+import os
+
+wav_file = os.path.join(model.model_path, "example/asr_example.wav")
+speech, sample_rate = soundfile.read(wav_file)
+chunk_stride = chunk_size[1] * 960 # 600ms
+
+cache = {}
+total_chunk_num = int(len((speech)-1)/chunk_stride+1)
+for i in range(total_chunk_num):
+    speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
+    is_final = i == total_chunk_num - 1
+    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
+    print(res)
+```
+
+注：`chunk_size`为流式延时配置，`[0,10,5]`表示上屏实时出字粒度为`10*60=600ms`，未来信息为`5*60=300ms`。每次推理输入为`600ms`（采样点数为`16000*0.6=960`），输出为对应文字，最后一个语音片段输入需要设置`is_final=True`来强制输出最后一个字。
+
+#### 语音端点检测（非实时）
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fsmn-vad")
+
+wav_file = f"{model.model_path}/example/asr_example.wav"
+res = model.generate(input=wav_file)
+print(res)
+```
+注：VAD模型输出格式为：`[[beg1, end1], [beg2, end2], .., [begN, endN]]`，其中`begN/endN`表示第`N`个有效音频片段的起始点/结束点，
+单位为毫秒。
+
+#### 语音端点检测（实时）
+```python
+from funasr import AutoModel
+
+chunk_size = 200 # ms
+model = AutoModel(model="fsmn-vad")
+
+import soundfile
+
+wav_file = f"{model.model_path}/example/vad_example.wav"
+speech, sample_rate = soundfile.read(wav_file)
+chunk_stride = int(chunk_size * sample_rate / 1000)
+
+cache = {}
+total_chunk_num = int(len((speech)-1)/chunk_stride+1)
+for i in range(total_chunk_num):
+    speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
+    is_final = i == total_chunk_num - 1
+    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
+    if len(res[0]["value"]):
+        print(res)
+```
+注：流式VAD模型输出格式为4种情况：
+- `[[beg1, end1], [beg2, end2], .., [begN, endN]]`：同上离线VAD输出结果。
+- `[[beg, -1]]`：表示只检测到起始点。
+- `[[-1, end]]`：表示只检测到结束点。
+- `[]`：表示既没有检测到起始点，也没有检测到结束点
+输出结果单位为毫秒，从起始点开始的绝对时间。
+
+#### 标点恢复
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="ct-punc")
+
+res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
+print(res)
+```
+
+#### 时间戳预测
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fa-zh")
+
+wav_file = f"{model.model_path}/example/asr_example.wav"
+text_file = f"{model.model_path}/example/text.txt"
+res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
+print(res)
+```
+更多（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
+
+<a name="核心功能"></a>
+## 模型训练与测试
+
+### 快速开始
+
+命令行执行（用于快速测试，不推荐）：
+```shell
+funasr-train ++model=paraformer-zh ++train_data_set_list=data/list/train.jsonl ++valid_data_set_list=data/list/val.jsonl ++output_dir="./outputs" &> log.txt &
+```
+
+python代码执行（可以多机多卡，推荐）
+
+```shell
+cd examples/industrial_data_pretraining/paraformer
+bash finetune.sh
+# "log_file: ./outputs/log.txt"
+```
+详细完整的脚本参考 [finetune.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/industrial_data_pretraining/paraformer/finetune.sh)
+
+### 详细参数介绍
+
+```shell
+funasr/bin/train.py \
++model="${model_name_or_model_dir}" \
++train_data_set_list="${train_data}" \
++valid_data_set_list="${val_data}" \
++dataset_conf.batch_size=20000 \
++dataset_conf.batch_type="token" \
++dataset_conf.num_workers=4 \
++train_conf.max_epoch=50 \
++train_conf.log_interval=1 \
++train_conf.resume=false \
++train_conf.validate_interval=2000 \
++train_conf.save_checkpoint_interval=2000 \
++train_conf.keep_nbest_models=20 \
++train_conf.avg_nbest_model=5 \
++optim_conf.lr=0.0002 \
++output_dir="${output_dir}" &> ${log_file}
+```
+
+- `model`（str）：模型名字（模型仓库中的ID），此时脚本会自动下载模型到本读；或者本地已经下载好的模型路径。
+- `train_data_set_list`（str）：训练数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+- `valid_data_set_list`（str）：验证数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+- `dataset_conf.batch_type`（str）：`example`（默认），batch的类型。`example`表示按照固定数目batch_size个样本组batch；`length` or `token` 表示动态组batch，batch总长度或者token数为batch_size。
+- `dataset_conf.batch_size`（int）：与 `batch_type` 搭配使用，当 `batch_type=example` 时，表示样本个数；当 `batch_type=length` 时，表示样本中长度，单位为fbank帧数（1帧10ms）或者文字token个数。
+- `train_conf.max_epoch`（int）：训练总epoch数。
+- `train_conf.log_interval`（int）：打印日志间隔step数。
+- `train_conf.resume`（int）：是否开启断点重训。
+- `train_conf.validate_interval`（int）：训练中做验证测试的间隔step数。
+- `train_conf.save_checkpoint_interval`（int）：训练中模型保存间隔step数。
+- `train_conf.keep_nbest_models`（int）：保留最大多少个模型参数，按照验证集acc排序，从高到底保留。
+- `train_conf.avg_nbest_model`（int）：对acc最高的n个模型取平均。
+- `optim_conf.lr`（float）：学习率。
+- `output_dir`（str）：模型保存路径。
+- `**kwargs`(dict): 所有在`config.yaml`中参数，均可以直接在此处指定，例如，过滤20s以上长音频：`dataset_conf.max_token_length=2000`，单位为音频fbank帧数（1帧10ms）或者文字token个数。
+
+#### 多gpu训练
+##### 单机多gpu训练
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
+../../../funasr/bin/train.py ${train_args}
+```
+--nnodes 表示参与的节点总数，--nproc_per_node 表示每个节点上运行的进程数
+
+##### 多机多gpu训练
+
+在主节点上，假设IP为192.168.1.1，端口为12345，使用的是2个GPU，则运行如下命令：
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 2 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+在从节点上（假设IP为192.168.1.2），你需要确保MASTER_ADDR和MASTER_PORT环境变量与主节点设置的一致，并运行同样的命令：
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 2 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+
+--nnodes 表示参与的节点总数，--nproc_per_node 表示每个节点上运行的进程数
+
+#### 准备数据
+
+`jsonl`格式可以参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+可以用指令 `scp2jsonl` 从wav.scp与text.txt生成。wav.scp与text.txt准备过程如下：
+
+`train_text.txt`
+
+左边为数据唯一ID，需与`train_wav.scp`中的`ID`一一对应
+右边为音频文件标注文本，格式如下：
+
+```bash
+ID0012W0013 当客户风险承受能力评估依据发生变化时
+ID0012W0014 所有只要处理 data 不管你是做 machine learning 做 deep learning
+ID0012W0015 he tried to think how it could be
+```
+
+
+`train_wav.scp`
+
+左边为数据唯一ID，需与`train_text.txt`中的`ID`一一对应
+右边为音频文件的路径，格式如下
+
+```bash
+BAC009S0764W0121 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav
+BAC009S0916W0489 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0916W0489.wav
+ID0012W0015 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_cn_en.wav
+```
+
+`生成指令`
+
+```shell
+# generate train.jsonl and val.jsonl from wav.scp and text.txt
+scp2jsonl \
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
++data_type_list='["source", "target"]' \
++jsonl_file_out="../../../data/list/train.jsonl"
+```
+
+（可选，非必需）如果需要从jsonl解析成wav.scp与text.txt，可以使用指令：
+
+```shell
+# generate wav.scp and text.txt from train.jsonl and val.jsonl
+jsonl2scp \
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
++data_type_list='["source", "target"]' \
++jsonl_file_in="../../../data/list/train.jsonl"
+```
+
+#### 查看训练日志
+
+##### 查看实验log
+```shell
+tail log.txt
+[2024-03-21 15:55:52,137][root][INFO] - train, rank: 3, epoch: 0/50, step: 6990/1, total step: 6990, (loss_avg_rank: 0.327), (loss_avg_epoch: 0.409), (ppl_avg_epoch: 1.506), (acc_avg_epoch: 0.795), (lr: 1.165e-04), [('loss_att', 0.259), ('acc', 0.825), ('loss_pre', 0.04), ('loss', 0.299), ('batch_size', 40)], {'data_load': '0.000', 'forward_time': '0.315', 'backward_time': '0.555', 'optim_time': '0.076', 'total_time': '0.947'}, GPU, memory: usage: 3.830 GB, peak: 18.357 GB, cache: 20.910 GB, cache_peak: 20.910 GB
+[2024-03-21 15:55:52,139][root][INFO] - train, rank: 1, epoch: 0/50, step: 6990/1, total step: 6990, (loss_avg_rank: 0.334), (loss_avg_epoch: 0.409), (ppl_avg_epoch: 1.506), (acc_avg_epoch: 0.795), (lr: 1.165e-04), [('loss_att', 0.285), ('acc', 0.823), ('loss_pre', 0.046), ('loss', 0.331), ('batch_size', 36)], {'data_load': '0.000', 'forward_time': '0.334', 'backward_time': '0.536', 'optim_time': '0.077', 'total_time': '0.948'}, GPU, memory: usage: 3.943 GB, peak: 18.291 GB, cache: 19.619 GB, cache_peak: 19.619 GB
+```
+指标解释：
+- `rank`：表示gpu id。
+- `epoch`,`step`,`total step`：表示当前epoch，step，总step。
+- `loss_avg_rank`：表示当前step，所有gpu平均loss。
+- `loss/ppl/acc_avg_epoch`：表示当前epoch周期，截止当前step数时，总平均loss/ppl/acc。epoch结束时的最后一个step表示epoch总平均loss/ppl/acc，推荐使用acc指标。
+- `lr`：当前step的学习率。
+- `[('loss_att', 0.259), ('acc', 0.825), ('loss_pre', 0.04), ('loss', 0.299), ('batch_size', 40)]`：表示当前gpu id的具体数据。
+- `total_time`：表示单个step总耗时。
+- `GPU, memory`：分别表示，模型使用/峰值显存，模型+缓存使用/峰值显存。
+
+##### tensorboard可视化
+```bash
+tensorboard --logdir /xxxx/FunASR/examples/industrial_data_pretraining/paraformer/outputs/log/tensorboard
+```
+浏览器中打开：http://localhost:6006/
+
+### 训练后模型测试
+
+
+#### 有configuration.json
+
+假定，训练模型路径为：./model_dir，如果改目录下有生成configuration.json，只需要将 [上述模型推理方法](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/README_zh.md#%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86) 中模型名字修改为模型路径即可
+
+例如：
+
+从shell推理
+```shell
+python -m funasr.bin.inference ++model="./model_dir" ++input=="${input}" ++output_dir="${output_dir}"
+```
+从python推理
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="./model_dir")
+
+res = model.generate(input=wav_file)
+print(res)
+```
+
+#### 无configuration.json时
+
+如果模型路径中无configuration.json时，需要手动指定具体配置文件路径与模型路径
+
+```shell
+python -m funasr.bin.inference \
+--config-path "${local_path}" \
+--config-name "${config}" \
++init_param="${init_param}" \
++tokenizer_conf.token_list="${tokens}" \
++frontend_conf.cmvn_file="${cmvn_file}" \
++input="${input}" \
++output_dir="${output_dir}" \
++device="${device}"
+```
+
+参数介绍
+- `config-path`：为实验中保存的 `config.yaml`，可以从实验输出目录中查找。
+- `config-name`：配置文件名，一般为 `config.yaml`，支持yaml格式与json格式，例如 `config.json`
+- `init_param`：需要测试的模型参数，一般为`model.pt`，可以自己选择具体的模型文件
+- `tokenizer_conf.token_list`：词表文件路径，一般在 `config.yaml` 有指定，无需再手动指定，当 `config.yaml` 中路径不正确时，需要在此处手动指定。
+- `frontend_conf.cmvn_file`：wav提取fbank中用到的cmvn文件，一般在 `config.yaml` 有指定，无需再手动指定，当 `config.yaml` 中路径不正确时，需要在此处手动指定。
+
+其他参数同上，完整 [示例](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/industrial_data_pretraining/paraformer/infer_from_local.sh)
+
+
+<a name="模型导出与测试"></a>
+## 模型导出与测试
+### 从命令行导出
+```shell
+funasr-export ++model=paraformer ++quantize=false
+```
+
+### 从Python导出
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="paraformer")
+
+res = model.export(quantize=False)
+```
+
+### 测试ONNX
+```python
+# pip3 install -U funasr-onnx
+from funasr_onnx import Paraformer
+model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+model = Paraformer(model_dir, batch_size=1, quantize=True)
+
+wav_path = ['~/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
+
+result = model(wav_path)
+print(result)
+```
+
+更多例子请参考 [样例](runtime/python/onnxruntime)
--- a/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
+++ b/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
@ -6,13 +6,9 @@
 from funasr import AutoModel

 model = AutoModel(model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
-                  model_revision="master",
                  vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
-                  vad_model_revision="master",
                  punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
-                  punc_model_revision="master",
                  # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
-                  # spk_model_revision="v2.0.2"
                  )

 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
--- a/examples/industrial_data_pretraining/paraformer-zh-spk/demo.sh
+++ b/examples/industrial_data_pretraining/paraformer-zh-spk/demo.sh
@ -1,23 +1,16 @@

 model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="master"
 vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-vad_model_revision="master"
-punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
-punc_model_revision="master"
+#punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
+punc_model="iic/punc_ct-transformer_cn-en-common-vocab471067-large"
 spk_model="iic/speech_campplus_sv_zh-cn_16k-common"
-spk_model_revision="v2.0.2"

 python funasr/bin/inference.py \
-+model=${model} \
-+model_revision=${model_revision} \
-+vad_model=${vad_model} \
-+vad_model_revision=${vad_model_revision} \
-+punc_model=${punc_model} \
-+punc_model_revision=${punc_model_revision} \
-+spk_model=${spk_model} \
-+spk_model_revision=${spk_model_revision} \
-+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
-+output_dir="./outputs/debug" \
-+device="cpu" \
-+"hotword='达摩院 魔搭'"
++model=${model} \
++vad_model=${vad_model} \
++punc_model=${punc_model} \
++spk_model=${spk_model} \
++input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
++output_dir="./outputs/debug" \
++device="cpu" \
++"hotword='达摩院 魔搭'"
--- a/examples/industrial_data_pretraining/paraformer/README_zh.md
+++ b/examples/industrial_data_pretraining/paraformer/README_zh.md
@ -214,7 +214,6 @@ bash finetune.sh
 ```shell
 funasr/bin/train.py \
 ++model="${model_name_or_model_dir}" \
-++model_revision="${model_revision}" \
 ++train_data_set_list="${train_data}" \
 ++valid_data_set_list="${val_data}" \
 ++dataset_conf.batch_size=20000 \
@ -232,7 +231,6 @@ funasr/bin/train.py \
 ```

 - `model`（str）：模型名字（模型仓库中的ID），此时脚本会自动下载模型到本读；或者本地已经下载好的模型路径。
- `model_revision`（str）：当 `model` 为模型名字时，下载指定版本的模型。
 - `train_data_set_list`（str）：训练数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
 - `valid_data_set_list`（str）：验证数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
 - `dataset_conf.batch_type`（str）：`example`（默认），batch的类型。`example`表示按照固定数目batch_size个样本组batch；`length` or `token` 表示动态组batch，batch总长度或者token数为batch_size。
--- a/examples/industrial_data_pretraining/paraformer/demo.py
+++ b/examples/industrial_data_pretraining/paraformer/demo.py
@ -5,15 +5,11 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch", 
-                  model_revision="master",
+model = AutoModel(model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
                  vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
-                  vad_model_revision="master",
                  vad_kwargs={"max_single_segment_time": 60000},
                  punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
-                  punc_model_revision="master",
                  # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
-                  # spk_model_revision="v2.0.2",
                  )

 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
@ -23,7 +19,7 @@ print(res)
 ''' can not use currently
 from funasr import AutoFrontend

-frontend = AutoFrontend(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="master")
+frontend = AutoFrontend(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")

 fbanks = frontend(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", batch_size=2)

--- a/examples/industrial_data_pretraining/paraformer/export.py
+++ b/examples/industrial_data_pretraining/paraformer/export.py
@ -9,8 +9,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
-                  model_revision="master")
+model = AutoModel(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",)

 res = model.export(type="onnx", quantize=False)
 print(res)
--- a/examples/industrial_data_pretraining/paraformer/export.sh
+++ b/examples/industrial_data_pretraining/paraformer/export.sh
@ -6,12 +6,11 @@ export HYDRA_FULL_ERROR=1


 model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="master"
+


 python -m funasr.bin.export \
 ++model=${model} \
-++model_revision=${model_revision} \
 ++type="onnx" \
 ++quantize=false

--- a/examples/industrial_data_pretraining/paraformer/finetune.sh
+++ b/examples/industrial_data_pretraining/paraformer/finetune.sh
@ -10,7 +10,7 @@ gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

 ## option 1, download model automatically
 model_name_or_model_dir="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="master"
+

 ## option 2, download model by git
 #local_path_root=${workspace}/modelscope_models
@ -50,7 +50,6 @@ torchrun \
 --nproc_per_node ${gpu_num} \
 ../../../funasr/bin/train.py \
 ++model="${model_name_or_model_dir}" \
-++model_revision="${model_revision}" \
 ++train_data_set_list="${train_data}" \
 ++valid_data_set_list="${val_data}" \
 ++dataset_conf.batch_size=20000 \
--- a/examples/industrial_data_pretraining/paraformer/infer.sh
+++ b/examples/industrial_data_pretraining/paraformer/infer.sh
@ -9,13 +9,12 @@ input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr
 output_dir="./outputs/debug"

 model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="master"
+

 device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"

 python -m funasr.bin.inference \
 ++model=${model} \
-++model_revision=${model_revision} \
 ++input="${input}" \
 ++output_dir="${output_dir}" \
 ++device="${device}" \
--- a/examples/industrial_data_pretraining/paraformer_streaming/README_zh.md
+++ b/examples/industrial_data_pretraining/paraformer_streaming/README_zh.md
@ -1,31 +1,54 @@
 (简体中文|[English](./README.md))

-# 语音识别
+FunASR开源了大量在工业数据上预训练模型，您可以在 [模型许可协议](https://github.com/alibaba-damo-academy/FunASR/blob/main/MODEL_LICENSE)下自由使用、复制、修改和分享FunASR模型，下面列举代表性的模型，更多模型请参考 [模型仓库](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo)。

-> **注意**:
-> pipeline 支持 [modelscope模型仓库](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) 中的所有模型进行推理和微调。这里我们以典型模型作为示例来演示使用方法。
+<div align="center">  
+<h4>
+ <a href="#模型推理"> 模型推理 </a>   
+｜<a href="#模型训练与测试"> 模型训练与测试 </a>
+｜<a href="#模型导出与测试"> 模型导出与测试 </a>
+</h4>
+</div>

-## 推理
+<a name="模型推理"></a>
+## 模型推理

 ### 快速使用
-#### [Paraformer 模型](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
+
+命令行方式调用：
+```shell
+funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wav
+```
+
+python代码调用（推荐）
+
 ```python
 from funasr import AutoModel

-model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
+model = AutoModel(model="paraformer-zh")

-res = model(input="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav")
 print(res)
 ```

-### API接口说明
+### 接口说明
+
 #### AutoModel 定义
- `model`: [模型仓库](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) 中的模型名称，或本地磁盘中的模型路径
- `device`: `cuda`（默认），使用 GPU 进行推理。如果为`cpu`，则使用 CPU 进行推理
- `ncpu`: `None` （默认），设置用于 CPU 内部操作并行性的线程数
- `output_dir`: `None` （默认），如果设置，输出结果的输出路径
- `batch_size`: `1` （默认），解码时的批处理大小
+```python
+model = AutoModel(model=[str], device=[str], ncpu=[int], output_dir=[str], batch_size=[int], hub=[str], **kwargs)
+```
+- `model`(str): [模型仓库](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo) 中的模型名称，或本地磁盘中的模型路径
+- `device`(str): `cuda:0`（默认gpu0），使用 GPU 进行推理，指定。如果为`cpu`，则使用 CPU 进行推理
+- `ncpu`(int): `4` （默认），设置用于 CPU 内部操作并行性的线程数
+- `output_dir`(str): `None` （默认），如果设置，输出结果的输出路径
+- `batch_size`(int): `1` （默认），解码时的批处理，样本个数
+- `hub`(str)：`ms`（默认），从modelscope下载模型。如果为`hf`，从huggingface下载模型。
+- `**kwargs`(dict): 所有在`config.yaml`中参数，均可以直接在此处指定，例如，vad模型中最大切割长度 `max_single_segment_time=6000` （毫秒）。
+
 #### AutoModel 推理
+```python
+res = model.generate(input=[str], output_dir=[str])
+```
 - `input`: 要解码的输入，可以是：
  - wav文件路径, 例如: asr_example.wav
  - pcm文件路径, 例如: asr_example.pcm，此时需要指定音频采样率fs（默认为16000）
@ -40,3 +63,370 @@ print(res)
  ```[audio_sample1, audio_sample2, ..., audio_sampleN]```
  - fbank输入，支持组batch。shape为[batch, frames, dim]，类型为torch.Tensor，例如
 - `output_dir`: None （默认），如果设置，输出结果的输出路径
+- `**kwargs`(dict): 与模型相关的推理参数，例如，`beam_size=10`，`decoding_ctc_weight=0.1`。
+
+
+### 更多用法介绍
+
+
+#### 非实时语音识别
+```python
+from funasr import AutoModel
+# paraformer-zh is a multi-functional asr model
+# use vad, punc, spk or not as you need
+model = AutoModel(model="paraformer-zh",  
+                  vad_model="fsmn-vad", 
+                  vad_kwargs={"max_single_segment_time": 60000},
+                  punc_model="ct-punc", 
+                  # spk_model="cam++"
+                  )
+wav_file = f"{model.model_path}/example/asr_example.wav"
+res = model.generate(input=wav_file, batch_size_s=300, batch_size_threshold_s=60, hotword='魔搭')
+print(res)
+```
+注意：
+- 通常模型输入限制时长30s以下，组合`vad_model`后，支持任意时长音频输入，不局限于paraformer模型，所有音频输入模型均可以。
+- `model`相关的参数可以直接在`AutoModel`定义中直接指定；与`vad_model`相关参数可以通过`vad_kwargs`来指定，类型为dict；类似的有`punc_kwargs`，`spk_kwargs`；
+- `max_single_segment_time`: 表示`vad_model`最大切割音频时长, 单位是毫秒ms.
+- `batch_size_s` 表示采用动态batch，batch中总音频时长，单位为秒s。
+- `batch_size_threshold_s`: 表示`vad_model`切割后音频片段时长超过 `batch_size_threshold_s`阈值时，将batch_size数设置为1, 单位为秒s.
+
+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_s`，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_s`，仍然出现OOM，可以适当减小`batch_size_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_s`，且超过阈值`batch_size_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。
+
+#### 实时语音识别
+
+```python
+from funasr import AutoModel
+
+chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
+encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
+decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
+
+model = AutoModel(model="paraformer-zh-streaming")
+
+import soundfile
+import os
+
+wav_file = os.path.join(model.model_path, "example/asr_example.wav")
+speech, sample_rate = soundfile.read(wav_file)
+chunk_stride = chunk_size[1] * 960 # 600ms
+
+cache = {}
+total_chunk_num = int(len((speech)-1)/chunk_stride+1)
+for i in range(total_chunk_num):
+    speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
+    is_final = i == total_chunk_num - 1
+    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
+    print(res)
+```
+
+注：`chunk_size`为流式延时配置，`[0,10,5]`表示上屏实时出字粒度为`10*60=600ms`，未来信息为`5*60=300ms`。每次推理输入为`600ms`（采样点数为`16000*0.6=960`），输出为对应文字，最后一个语音片段输入需要设置`is_final=True`来强制输出最后一个字。
+
+#### 语音端点检测（非实时）
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fsmn-vad")
+
+wav_file = f"{model.model_path}/example/asr_example.wav"
+res = model.generate(input=wav_file)
+print(res)
+```
+注：VAD模型输出格式为：`[[beg1, end1], [beg2, end2], .., [begN, endN]]`，其中`begN/endN`表示第`N`个有效音频片段的起始点/结束点，
+单位为毫秒。
+
+#### 语音端点检测（实时）
+```python
+from funasr import AutoModel
+
+chunk_size = 200 # ms
+model = AutoModel(model="fsmn-vad")
+
+import soundfile
+
+wav_file = f"{model.model_path}/example/vad_example.wav"
+speech, sample_rate = soundfile.read(wav_file)
+chunk_stride = int(chunk_size * sample_rate / 1000)
+
+cache = {}
+total_chunk_num = int(len((speech)-1)/chunk_stride+1)
+for i in range(total_chunk_num):
+    speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
+    is_final = i == total_chunk_num - 1
+    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
+    if len(res[0]["value"]):
+        print(res)
+```
+注：流式VAD模型输出格式为4种情况：
+- `[[beg1, end1], [beg2, end2], .., [begN, endN]]`：同上离线VAD输出结果。
+- `[[beg, -1]]`：表示只检测到起始点。
+- `[[-1, end]]`：表示只检测到结束点。
+- `[]`：表示既没有检测到起始点，也没有检测到结束点
+输出结果单位为毫秒，从起始点开始的绝对时间。
+
+#### 标点恢复
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="ct-punc")
+
+res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
+print(res)
+```
+
+#### 时间戳预测
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fa-zh")
+
+wav_file = f"{model.model_path}/example/asr_example.wav"
+text_file = f"{model.model_path}/example/text.txt"
+res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
+print(res)
+```
+更多（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
+
+<a name="核心功能"></a>
+## 模型训练与测试
+
+### 快速开始
+
+命令行执行（用于快速测试，不推荐）：
+```shell
+funasr-train ++model=paraformer-zh ++train_data_set_list=data/list/train.jsonl ++valid_data_set_list=data/list/val.jsonl ++output_dir="./outputs" &> log.txt &
+```
+
+python代码执行（可以多机多卡，推荐）
+
+```shell
+cd examples/industrial_data_pretraining/paraformer
+bash finetune.sh
+# "log_file: ./outputs/log.txt"
+```
+详细完整的脚本参考 [finetune.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/industrial_data_pretraining/paraformer/finetune.sh)
+
+### 详细参数介绍
+
+```shell
+funasr/bin/train.py \
++model="${model_name_or_model_dir}" \
++train_data_set_list="${train_data}" \
++valid_data_set_list="${val_data}" \
++dataset_conf.batch_size=20000 \
++dataset_conf.batch_type="token" \
++dataset_conf.num_workers=4 \
++train_conf.max_epoch=50 \
++train_conf.log_interval=1 \
++train_conf.resume=false \
++train_conf.validate_interval=2000 \
++train_conf.save_checkpoint_interval=2000 \
++train_conf.keep_nbest_models=20 \
++train_conf.avg_nbest_model=5 \
++optim_conf.lr=0.0002 \
++output_dir="${output_dir}" &> ${log_file}
+```
+
+- `model`（str）：模型名字（模型仓库中的ID），此时脚本会自动下载模型到本读；或者本地已经下载好的模型路径。
+- `train_data_set_list`（str）：训练数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+- `valid_data_set_list`（str）：验证数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+- `dataset_conf.batch_type`（str）：`example`（默认），batch的类型。`example`表示按照固定数目batch_size个样本组batch；`length` or `token` 表示动态组batch，batch总长度或者token数为batch_size。
+- `dataset_conf.batch_size`（int）：与 `batch_type` 搭配使用，当 `batch_type=example` 时，表示样本个数；当 `batch_type=length` 时，表示样本中长度，单位为fbank帧数（1帧10ms）或者文字token个数。
+- `train_conf.max_epoch`（int）：训练总epoch数。
+- `train_conf.log_interval`（int）：打印日志间隔step数。
+- `train_conf.resume`（int）：是否开启断点重训。
+- `train_conf.validate_interval`（int）：训练中做验证测试的间隔step数。
+- `train_conf.save_checkpoint_interval`（int）：训练中模型保存间隔step数。
+- `train_conf.keep_nbest_models`（int）：保留最大多少个模型参数，按照验证集acc排序，从高到底保留。
+- `train_conf.avg_nbest_model`（int）：对acc最高的n个模型取平均。
+- `optim_conf.lr`（float）：学习率。
+- `output_dir`（str）：模型保存路径。
+- `**kwargs`(dict): 所有在`config.yaml`中参数，均可以直接在此处指定，例如，过滤20s以上长音频：`dataset_conf.max_token_length=2000`，单位为音频fbank帧数（1帧10ms）或者文字token个数。
+
+#### 多gpu训练
+##### 单机多gpu训练
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
+../../../funasr/bin/train.py ${train_args}
+```
+--nnodes 表示参与的节点总数，--nproc_per_node 表示每个节点上运行的进程数
+
+##### 多机多gpu训练
+
+在主节点上，假设IP为192.168.1.1，端口为12345，使用的是2个GPU，则运行如下命令：
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 2 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+在从节点上（假设IP为192.168.1.2），你需要确保MASTER_ADDR和MASTER_PORT环境变量与主节点设置的一致，并运行同样的命令：
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 2 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+
+--nnodes 表示参与的节点总数，--nproc_per_node 表示每个节点上运行的进程数
+
+#### 准备数据
+
+`jsonl`格式可以参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+可以用指令 `scp2jsonl` 从wav.scp与text.txt生成。wav.scp与text.txt准备过程如下：
+
+`train_text.txt`
+
+左边为数据唯一ID，需与`train_wav.scp`中的`ID`一一对应
+右边为音频文件标注文本，格式如下：
+
+```bash
+ID0012W0013 当客户风险承受能力评估依据发生变化时
+ID0012W0014 所有只要处理 data 不管你是做 machine learning 做 deep learning
+ID0012W0015 he tried to think how it could be
+```
+
+
+`train_wav.scp`
+
+左边为数据唯一ID，需与`train_text.txt`中的`ID`一一对应
+右边为音频文件的路径，格式如下
+
+```bash
+BAC009S0764W0121 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav
+BAC009S0916W0489 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0916W0489.wav
+ID0012W0015 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_cn_en.wav
+```
+
+`生成指令`
+
+```shell
+# generate train.jsonl and val.jsonl from wav.scp and text.txt
+scp2jsonl \
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
++data_type_list='["source", "target"]' \
++jsonl_file_out="../../../data/list/train.jsonl"
+```
+
+（可选，非必需）如果需要从jsonl解析成wav.scp与text.txt，可以使用指令：
+
+```shell
+# generate wav.scp and text.txt from train.jsonl and val.jsonl
+jsonl2scp \
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
++data_type_list='["source", "target"]' \
++jsonl_file_in="../../../data/list/train.jsonl"
+```
+
+#### 查看训练日志
+
+##### 查看实验log
+```shell
+tail log.txt
+[2024-03-21 15:55:52,137][root][INFO] - train, rank: 3, epoch: 0/50, step: 6990/1, total step: 6990, (loss_avg_rank: 0.327), (loss_avg_epoch: 0.409), (ppl_avg_epoch: 1.506), (acc_avg_epoch: 0.795), (lr: 1.165e-04), [('loss_att', 0.259), ('acc', 0.825), ('loss_pre', 0.04), ('loss', 0.299), ('batch_size', 40)], {'data_load': '0.000', 'forward_time': '0.315', 'backward_time': '0.555', 'optim_time': '0.076', 'total_time': '0.947'}, GPU, memory: usage: 3.830 GB, peak: 18.357 GB, cache: 20.910 GB, cache_peak: 20.910 GB
+[2024-03-21 15:55:52,139][root][INFO] - train, rank: 1, epoch: 0/50, step: 6990/1, total step: 6990, (loss_avg_rank: 0.334), (loss_avg_epoch: 0.409), (ppl_avg_epoch: 1.506), (acc_avg_epoch: 0.795), (lr: 1.165e-04), [('loss_att', 0.285), ('acc', 0.823), ('loss_pre', 0.046), ('loss', 0.331), ('batch_size', 36)], {'data_load': '0.000', 'forward_time': '0.334', 'backward_time': '0.536', 'optim_time': '0.077', 'total_time': '0.948'}, GPU, memory: usage: 3.943 GB, peak: 18.291 GB, cache: 19.619 GB, cache_peak: 19.619 GB
+```
+指标解释：
+- `rank`：表示gpu id。
+- `epoch`,`step`,`total step`：表示当前epoch，step，总step。
+- `loss_avg_rank`：表示当前step，所有gpu平均loss。
+- `loss/ppl/acc_avg_epoch`：表示当前epoch周期，截止当前step数时，总平均loss/ppl/acc。epoch结束时的最后一个step表示epoch总平均loss/ppl/acc，推荐使用acc指标。
+- `lr`：当前step的学习率。
+- `[('loss_att', 0.259), ('acc', 0.825), ('loss_pre', 0.04), ('loss', 0.299), ('batch_size', 40)]`：表示当前gpu id的具体数据。
+- `total_time`：表示单个step总耗时。
+- `GPU, memory`：分别表示，模型使用/峰值显存，模型+缓存使用/峰值显存。
+
+##### tensorboard可视化
+```bash
+tensorboard --logdir /xxxx/FunASR/examples/industrial_data_pretraining/paraformer/outputs/log/tensorboard
+```
+浏览器中打开：http://localhost:6006/
+
+### 训练后模型测试
+
+
+#### 有configuration.json
+
+假定，训练模型路径为：./model_dir，如果改目录下有生成configuration.json，只需要将 [上述模型推理方法](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/README_zh.md#%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86) 中模型名字修改为模型路径即可
+
+例如：
+
+从shell推理
+```shell
+python -m funasr.bin.inference ++model="./model_dir" ++input=="${input}" ++output_dir="${output_dir}"
+```
+从python推理
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="./model_dir")
+
+res = model.generate(input=wav_file)
+print(res)
+```
+
+#### 无configuration.json时
+
+如果模型路径中无configuration.json时，需要手动指定具体配置文件路径与模型路径
+
+```shell
+python -m funasr.bin.inference \
+--config-path "${local_path}" \
+--config-name "${config}" \
++init_param="${init_param}" \
++tokenizer_conf.token_list="${tokens}" \
++frontend_conf.cmvn_file="${cmvn_file}" \
++input="${input}" \
++output_dir="${output_dir}" \
++device="${device}"
+```
+
+参数介绍
+- `config-path`：为实验中保存的 `config.yaml`，可以从实验输出目录中查找。
+- `config-name`：配置文件名，一般为 `config.yaml`，支持yaml格式与json格式，例如 `config.json`
+- `init_param`：需要测试的模型参数，一般为`model.pt`，可以自己选择具体的模型文件
+- `tokenizer_conf.token_list`：词表文件路径，一般在 `config.yaml` 有指定，无需再手动指定，当 `config.yaml` 中路径不正确时，需要在此处手动指定。
+- `frontend_conf.cmvn_file`：wav提取fbank中用到的cmvn文件，一般在 `config.yaml` 有指定，无需再手动指定，当 `config.yaml` 中路径不正确时，需要在此处手动指定。
+
+其他参数同上，完整 [示例](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/industrial_data_pretraining/paraformer/infer_from_local.sh)
+
+
+<a name="模型导出与测试"></a>
+## 模型导出与测试
+### 从命令行导出
+```shell
+funasr-export ++model=paraformer ++quantize=false
+```
+
+### 从Python导出
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="paraformer")
+
+res = model.export(quantize=False)
+```
+
+### 测试ONNX
+```python
+# pip3 install -U funasr-onnx
+from funasr_onnx import Paraformer
+model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+model = Paraformer(model_dir, batch_size=1, quantize=True)
+
+wav_path = ['~/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
+
+result = model(wav_path)
+print(result)
+```
+
+更多例子请参考 [样例](runtime/python/onnxruntime)
--- a/examples/industrial_data_pretraining/paraformer_streaming/demo.py
+++ b/examples/industrial_data_pretraining/paraformer_streaming/demo.py
@ -10,7 +10,7 @@ from funasr import AutoModel
 chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
 encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
 decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
-model = AutoModel(model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online", model_revision="master")
+model = AutoModel(model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online")

 wav_file = os.path.join(model.model_path, "example/asr_example.wav")
 res = model.generate(input=wav_file,
--- a/examples/industrial_data_pretraining/paraformer_streaming/demo.sh
+++ b/examples/industrial_data_pretraining/paraformer_streaming/demo.sh
@ -1,10 +1,9 @@

 model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
-model_revision="master"
+

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
 +output_dir="./outputs/debug" \
 +device="cpu" \
--- a/examples/industrial_data_pretraining/paraformer_streaming/export.py
+++ b/examples/industrial_data_pretraining/paraformer_streaming/export.py
@ -9,8 +9,7 @@

 from funasr import AutoModel

-model = AutoModel(model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online",
-                  model_revision="master")
+model = AutoModel(model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online", )

 res = model.export(type="onnx", quantize=False)
 print(res)
--- a/examples/industrial_data_pretraining/paraformer_streaming/export.sh
+++ b/examples/industrial_data_pretraining/paraformer_streaming/export.sh
@ -6,12 +6,11 @@ export HYDRA_FULL_ERROR=1


 model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
-model_revision="master"
+


 python -m funasr.bin.export \
 ++model=${model} \
-++model_revision=${model_revision} \
 ++type="onnx" \
 ++quantize=false \
 ++device="cpu"
--- a/examples/industrial_data_pretraining/paraformer_streaming/finetune.sh
+++ b/examples/industrial_data_pretraining/paraformer_streaming/finetune.sh
@ -10,7 +10,7 @@ gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

 ## option 1, download model automatically
 model_name_or_model_dir="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
-model_revision="master"
+

 ## option 2, download model by git
 #local_path_root=${workspace}/modelscope_models
@ -50,7 +50,6 @@ torchrun \
 --nproc_per_node ${gpu_num} \
 ../../../funasr/bin/train.py \
 ++model="${model_name_or_model_dir}" \
-++model_revision="${model_revision}" \
 ++train_data_set_list="${train_data}" \
 ++valid_data_set_list="${val_data}" \
 ++dataset_conf.batch_size=20000 \
--- a/examples/industrial_data_pretraining/scama/demo.py
+++ b/examples/industrial_data_pretraining/scama/demo.py
@ -9,7 +9,7 @@ chunk_size = [5, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
 encoder_chunk_look_back = 0 #number of chunks to lookback for encoder self-attention
 decoder_chunk_look_back = 0 #number of encoder chunks to lookback for decoder cross-attention

-model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_SCAMA_asr-zh-cn-16k-common-vocab8358-streaming", model_revision="master")
+model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_SCAMA_asr-zh-cn-16k-common-vocab8358-streaming")
 cache = {}
 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
            chunk_size=chunk_size,
--- a/examples/industrial_data_pretraining/scama/demo.sh
+++ b/examples/industrial_data_pretraining/scama/demo.sh
@ -1,10 +1,9 @@

 model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
-model_revision="master"
+

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
 +output_dir="./outputs/debug" \
 +device="cpu" \
--- a/examples/industrial_data_pretraining/seaco_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/seaco_paraformer/demo.py
@ -6,13 +6,9 @@
 from funasr import AutoModel

 model = AutoModel(model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
-                  model_revision="master",
                  # vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
-                  # vad_model_revision="master",
                  # punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
-                  # punc_model_revision="master",
                  # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
-                  # spk_model_revision="v2.0.2",
                  )


--- a/examples/industrial_data_pretraining/seaco_paraformer/demo.sh
+++ b/examples/industrial_data_pretraining/seaco_paraformer/demo.sh
@ -1,6 +1,6 @@

 model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="master"
+
 vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
 vad_model_revision="master"
 punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
@ -8,7 +8,6 @@ punc_model_revision="master"

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +vad_model=${vad_model} \
 +vad_model_revision=${vad_model_revision} \
 +punc_model=${punc_model} \
--- a/examples/industrial_data_pretraining/seaco_paraformer/finetune.sh
+++ b/examples/industrial_data_pretraining/seaco_paraformer/finetune.sh
@ -10,7 +10,6 @@ gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

 ## option 1, download model automatically
 model_name_or_model_dir="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="v2.0.7"

 ## option 2, download model by git
 #local_path_root=${workspace}/modelscope_models
@ -50,7 +49,6 @@ torchrun \
 --nproc_per_node ${gpu_num} \
 ../../../funasr/bin/train.py \
 ++model="${model_name_or_model_dir}" \
-++model_revision="${model_revision}" \
 ++train_data_set_list="${train_data}" \
 ++valid_data_set_list="${val_data}" \
 ++dataset_conf.batch_size=20000 \
--- a/examples/industrial_data_pretraining/transducer/demo.py
+++ b/examples/industrial_data_pretraining/transducer/demo.py
@ -7,8 +7,7 @@ from funasr import AutoModel

 # Transducer, BAT and RWKV_BAT models are just same to use, use the correct model_revision
 # https://modelscope.cn/models?name=transducer&page=1&tasks=auto-speech-recognition&type=audio
-model = AutoModel(model="iic/speech_bat_asr-zh-cn-16k-aishell1-vocab4234-pytorch", 
-                  model_revision="v2.0.2",
+model = AutoModel(model="iic/speech_bat_asr-zh-cn-16k-aishell1-vocab4234-pytorch",
                 )

 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
--- a/examples/industrial_data_pretraining/uniasr/demo.py
+++ b/examples/industrial_data_pretraining/uniasr/demo.py
@ -6,7 +6,7 @@
 from funasr import AutoModel


-model = AutoModel(model="iic/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline", model_revision="master",)
+model = AutoModel(model="iic/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline",)


 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
@ -16,7 +16,7 @@ print(res)
 ''' can not use currently
 from funasr import AutoFrontend

-frontend = AutoFrontend(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="master")
+frontend = AutoFrontend(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")

 fbanks = frontend(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", batch_size=2)

--- a/examples/industrial_data_pretraining/uniasr/demo.sh
+++ b/examples/industrial_data_pretraining/uniasr/demo.sh
@ -1,10 +1,9 @@

 model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_revision="master"
+

 python funasr/bin/inference.py \
 +model=${model} \
-+model_revision=${model_revision} \
 +input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
 +output_dir="./outputs/debug" \
 +device="cpu" \
--- a/examples/industrial_data_pretraining/whisper/demo.py
+++ b/examples/industrial_data_pretraining/whisper/demo.py
@ -8,7 +8,6 @@
 from funasr import AutoModel

 model = AutoModel(model="iic/Whisper-large-v3",
-                  model_revision="v2.0.5",
                  vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
 				  vad_kwargs={"max_single_segment_time": 30000},
                  )
--- a/examples/industrial_data_pretraining/whisper/infer.sh
+++ b/examples/industrial_data_pretraining/whisper/infer.sh
@ -11,13 +11,12 @@ input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr
 output_dir="./outputs/debug"

 model="iic/speech_whisper-large_asr_multilingual"
-model_revision="master"
+

 device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"

 python -m funasr.bin.inference \
 ++model=${model} \
-++model_revision=${model_revision} \
 ++input="${input}" \
 ++output_dir="${output_dir}" \
 ++device="${device}" \