diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,14 +1,318 @@
---
-frameworks:
-- Pytorch
-license: Apache License 2.0
tasks:
- auto-speech-recognition
+domain:
+- audio
+model-type:
+- autoregressive
+frameworks:
+- pytorch
+backbone:
+- transformer/conformer
+metrics:
+- CER
+license: Apache License 2.0
+language:
+- multilingual
+tags:
+- FunASR
+- Whisper
+datasets:
+ train:
+ - 680,000 hour
+ test:
+ - test
+indexing:
+ results:
+ - task:
+ name: Automatic Speech Recognition
+ dataset:
+ name: 680,000 hour
+ metrics:
+ - type: CER
+ value: 8.53% # float
+ description: greedy search, withou lm, avg.
+ args: default
+ - type: RTF
+ value: 0.0251 # float
+ description: GPU inference on V100
+ args: batch_size=1
+widgets:
+ - task: auto-speech-recognition
+ model_revision: v1.0.0
+ inputs:
+ - type: audio
+ name: input
+ title: 音频
+ examples:
+ - name: 1
+ title: 示例1
+ inputs:
+ - name: input
+ data: git://example/asr_example.wav
+ inferencespec:
+ cpu: 8 #CPU数量
+ memory: 4096
---
-###### 模型文件和权重，请浏览“模型文件”页面获取。
-###### 当前模型的贡献者未提供更加详细的模型介绍，但是您可以通过如下git clone命令，或者ModelScope SDK来下载模型。
-###### Clone with HTTP
+
+
+# Whisper模型介绍
+
+## [ModelScope-FunASR](https://github.com/alibaba-damo-academy/FunASR)
+[FunASR](https://github.com/alibaba-damo-academy/FunASR)希望在语音识别方面建立学术研究和工业应用之间的桥梁。通过支持在ModelScope上发布的工业级语音识别模型的训练和微调，研究人员和开发人员可以更方便地进行语音识别模型的研究和生产，并促进语音识别生态系统的发展。
+
+[**最新动态**](https://github.com/alibaba-damo-academy/FunASR#whats-new)
+| [**环境安装**](https://github.com/alibaba-damo-academy/FunASR#installation)
+| [**介绍文档**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
+| [**中文教程**](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C)
+| [**服务部署**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
+| [**模型库**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
+| [**联系我们**](https://github.com/alibaba-damo-academy/FunASR#contact)
+
+
+## 基于ModelScope进行推理
+
+- 推理支持音频格式如下：
+ - wav文件路径，例如：data/test/audios/asr_example.wav
+ - pcm文件路径，例如：data/test/audios/asr_example.pcm
+ - wav文件url，例如：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav
+ - wav二进制数据，格式bytes，例如：用户直接从文件里读出bytes数据或者是麦克风录出bytes数据。
+ - 已解析的audio音频，例如：audio, rate = soundfile.read("asr_example_zh.wav")，类型为numpy.ndarray或者torch.Tensor。
+ - wav.scp文件，需符合如下要求：
+
+```sh
+cat wav.scp
+asr_example1 data/test/audios/asr_example1.wav
+asr_example2 data/test/audios/asr_example2.wav
+...
```
-git clone https://www.modelscope.cn/iic/Whisper-large-v3.git
+
+- 若输入格式wav文件url，api调用方式可参考如下范例：
+
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='iic/Whisper-large-v3')
+
+rec_result = inference_pipeline(input='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav', language=None)
+print(rec_result)
```
-###### 如果您是本模型的贡献者，我们邀请您根据[模型贡献文档说明](https://www.modelscope.cn/docs/%E5%A6%82%E4%BD%95%E6%92%B0%E5%86%99%E5%A5%BD%E7%94%A8%E7%9A%84%E6%A8%A1%E5%9E%8B%E5%8D%A1%E7%89%87)，及时完善模型卡片内容。
\ No newline at end of file
+
+- 输入音频为pcm格式，调用api时需要传入音频采样率参数fs，例如：
+
+```python
+rec_result = inference_pipeline(input='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.pcm', fs=16000)
+```
+
+- 输入音频为wav格式，api调用方式可参考如下范例:
+
+```python
+rec_result = inference_pipeline(input'asr_example_zh.wav')
+```
+
+- 若输入格式为文件wav.scp(注：文件名需要以.scp结尾)，可添加 output_dir 参数将识别结果写入文件中，api调用方式可参考如下范例:
+
+```python
+inference_pipeline(input="wav.scp", output_dir='./output_dir')
+```
+识别结果输出路径结构如下：
+
+```sh
+tree output_dir/
+output_dir/
+└── 1best_recog
+ ├── score
+ └── text
+
+1 directory, 3 files
+```
+score：识别路径得分
+
+text：语音识别结果文件
+
+
+- 若输入音频为已解析的audio音频，api调用方式可参考如下范例：
+
+```python
+import soundfile
+
+waveform, sample_rate = soundfile.read("asr_example_zh.wav")
+rec_result = inference_pipeline(input=waveform)
+```
+
+- ASR、VAD、PUNC模型自由组合
+
+可根据使用需求对VAD和PUNC标点模型进行自由组合，使用方式如下：
+```python
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch', model_revision="v2.0.4",
+ vad_model='iic/speech_fsmn_vad_zh-cn-16k-common-pytorch', vad_model_revision="v2.0.4",
+ punc_model='iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch', punc_model_revision="v2.0.4",
+ # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
+ # spk_model_revision="v2.0.2",
+)
+```
+若不使用PUNC模型，可配置punc_model=""，或不传入punc_model参数，如需加入LM模型，可增加配置lm_model='damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch'，并设置lm_weight和beam_size参数。
+
+## 基于FunASR进行推理
+
+下面为快速上手教程，测试音频（[中文](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav)，[英文](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav)）
+
+### 可执行命令行
+在命令行终端执行：
+
+```shell
+funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=vad_example.wav
+```
+
+注：支持单条音频文件识别，也支持文件列表，列表为kaldi风格wav.scp：`wav_id wav_path`
+
+### python示例
+#### 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", model_revision="v2.0.4",
+ vad_model="fsmn-vad", vad_model_revision="v2.0.4",
+ punc_model="ct-punc-c", punc_model_revision="v2.0.4",
+ # spk_model="cam++", spk_model_revision="v2.0.2",
+ )
+res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
+ batch_size_s=300,
+ hotword='魔搭')
+print(res)
+```
+注：`model_hub`：表示模型仓库，`ms`为选择modelscope下载，`hf`为选择huggingface下载。
+
+#### 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", model_revision="v2.0.4")
+
+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", model_revision="v2.0.4")
+
+wav_file = f"{model.model_path}/example/asr_example.wav"
+res = model.generate(input=wav_file)
+print(res)
+```
+
+#### 语音端点检测（实时）
+```python
+from funasr import AutoModel
+
+chunk_size = 200 # ms
+model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
+
+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)
+```
+
+#### 标点恢复
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="ct-punc", model_revision="v2.0.4")
+
+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", model_revision="v2.0.4")
+
+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)
+```
+
+更多详细用法（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
+
+
+## 微调
+
+详细用法（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
+
+## 使用方式以及适用范围
+
+运行范围
+- 支持Linux-x86_64、Mac和Windows运行。
+
+使用方式
+- 直接推理：可以直接对输入音频进行解码，输出目标文字。
+
+使用范围与目标场景
+- 适合于离线语音识别场景
+
+
+## 模型局限性以及可能的偏差
+
+考虑到特征提取流程和工具以及训练工具差异，会对CER的数据带来一定的差异（<0.1%），推理GPU环境差异导致的RTF数值差异。
+
+
+
+## 相关论文以及引用信息
+
+```BibTeX
+@inproceedings{radford2023robust,
+ title={Robust speech recognition via large-scale weak supervision},
+ author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
+ booktitle={International Conference on Machine Learning},
+ pages={28492--28518},
+ year={2023},
+ organization={PMLR}
+}
+```
+
+
+
+
+
diff --git a/config.yaml b/config.yaml
new file mode 100644
index 0000000..887a298
--- /dev/null
+++ b/config.yaml
@@ -0,0 +1,38 @@
+# network architecture
+model: WhisperWarp
+model_conf:
+ lsm_weight: 0.1
+ length_normalized_loss: true
+ hub: funasr # openai
+
+
+
+# only use for hub == funasr,
+# if hub == openai, dims is automaticall download
+dims:
+ n_mels: 128
+ n_vocab: 51866
+ n_audio_ctx: 1500
+ n_audio_state: 1280
+ n_audio_head: 20
+ n_audio_layer: 32
+ n_text_ctx: 448
+ n_text_state: 1280
+ n_text_head: 20
+ n_text_layer: 32
+
+# frontend related
+frontend: WhisperFrontend
+frontend_conf:
+ fs: 16000
+ n_mels: ${dims.n_mels}
+ do_pad_trim: true
+
+tokenizer: WhisperTokenizer
+tokenizer_conf:
+ language: null
+ task: transcribe
+ is_multilingual: true
+ num_languages: 100
+
+scope_map: [none, "model."]
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