Initial commit

docs/m2met2/index.rst

+.. m2met2 documentation master file, created by
+   sphinx-quickstart on Tue Apr 11 14:18:55 2023.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+ASRU 2023 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION CHALLENGE 2.0 (M2MeT2.0)
+==================================================================================
+Building on the success of the M2MeT challenge, we are delighted to propose the M2MeT2.0 challenge as a special session at ASRU2023.
+To advance the current state-of-the-art in multi-talker automatic speech recognition, the M2MeT2.0 challenge proposes a speaker-attributed ASR task, comprising two sub-tracks: fixed and open training conditions.
+To facilitate reproducible research, we provide a comprehensive overview of the dataset, challenge rules, evaluation metrics, and baseline systems. 
+
+Now the new test set contains about 10 hours audio is available. You can download from `here <https://speech-lab-share-data.oss-cn-shanghai.aliyuncs.com/AliMeeting/openlr/Test_2023_Ali.tar.gz>`_
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+   
+   ./Introduction
+   ./Dataset
+   ./Track_setting_and_evaluation
+   ./Baseline
+   ./Rules
+   ./Challenge_result
+   ./Organizers
+   ./Contact
+

docs/m2met2/make.bat

+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/make.bat

+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/reference/FQA.md

+# FQA
+
+## How to use VAD model by modelscope pipeline
+Ref to [docs](https://github.com/alibaba-damo-academy/FunASR/discussions/236)
+
+## How to use Punctuation model by modelscope pipeline
+Ref to [docs](https://github.com/alibaba-damo-academy/FunASR/discussions/238)
+
+## How to use Parafomrer model for streaming by modelscope pipeline
+Ref to [docs](https://github.com/alibaba-damo-academy/FunASR/discussions/241)
+
+## How to use vad, asr and punc model by modelscope pipeline
+Ref to [docs](https://github.com/alibaba-damo-academy/FunASR/discussions/278)
+
+## How to combine vad, asr, punc and nnlm models inside modelscope pipeline
+Ref to [docs](https://github.com/alibaba-damo-academy/FunASR/discussions/134)
+
+## How to combine timestamp prediction model by modelscope pipeline
+Ref to [docs](https://github.com/alibaba-damo-academy/FunASR/discussions/246)
+
+## How to switch decoding mode between online and offline for UniASR model
+Ref to [docs](https://github.com/alibaba-damo-academy/FunASR/discussions/151)
\ No newline at end of file

docs/reference/application.md

+## Audio Cut
+
+## Realtime Speech Recognition
+
+## Audio Chat
\ No newline at end of file

docs/reference/build_task.md

+# Build custom tasks
+FunASR is similar to ESPNet, which applies `Task`  as the general interface ti achieve the training and inference of models. Each `Task` is a class inherited from `AbsTask` and its corresponding code can be seen in `funasr/tasks/abs_task.py`. The main functions of `AbsTask` are shown as follows:
+```python
+class AbsTask(ABC):
+    @classmethod
+    def add_task_arguments(cls, parser: argparse.ArgumentParser):
+        pass
+    
+    @classmethod
+    def build_preprocess_fn(cls, args, train):
+        (...)
+    
+    @classmethod
+    def build_collate_fn(cls, args: argparse.Namespace):
+        (...)
+
+    @classmethod
+    def build_model(cls, args):
+        (...)
+    
+    @classmethod
+    def main(cls, args):
+        (...)
+```
+- add_task_arguments：Add parameters required by a specified `Task`
+- build_preprocess_fn：定义如何处理对样本进行预处理 define how to preprocess samples
+- build_collate_fn：define how to combine multiple samples into a `batch`
+- build_model：define the model
+- main：training interface, starting training through `Task.main()`
+
+Next, we take the speech recognition as an example to introduce how to define a new `Task`. For the corresponding code, please see `ASRTask` in `funasr/tasks/asr.py`. The procedure of defining a new `Task` is actually the procedure of redefining the above functions according to the requirements of the specified `Task`.
+
+- add_task_arguments
+```python
+@classmethod
+def add_task_arguments(cls, parser: argparse.ArgumentParser):
+    group = parser.add_argument_group(description="Task related")
+    group.add_argument(
+        "--token_list",
+        type=str_or_none,
+        default=None,
+        help="A text mapping int-id to token",
+    )
+    (...)
+```
+For speech recognition tasks, specific parameters required include `token_list`, etc. According to the specific requirements of different tasks, users can define corresponding parameters in this function.
+
+- build_preprocess_fn
+```python
+@classmethod
+def build_preprocess_fn(cls, args, train):
+    if args.use_preprocessor:
+        retval = CommonPreprocessor(
+                    train=train,
+                    token_type=args.token_type,
+                    token_list=args.token_list,
+                    bpemodel=args.bpemodel,
+                    non_linguistic_symbols=args.non_linguistic_symbols,
+                    text_cleaner=args.cleaner,
+                    ...
+                )
+    else:
+        retval = None
+    return retval
+```
+This function defines how to preprocess samples. Specifically, the input of speech recognition tasks includes speech and text. For speech, functions such as (optional) adding noise and reverberation to the speech are supported. For text, functions such as (optional) processing text according to bpe and mapping text to `tokenid` are supported. Users can choose the preprocessing operation that needs to be performed on the sample. For the detail implementation, please refer to `CommonPreprocessor`.
+
+- build_collate_fn
+```python
+@classmethod
+def build_collate_fn(cls, args, train):
+    return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)
+```
+This function defines how to combine multiple samples into a `batch`. For speech recognition tasks, `padding` is employed to obtain equal-length data from different speech and text. Specifically, we set `0.0` as the default padding value for speech and `-1` as the default padding value for text. Users can define different `batch` operations here. For the detail implementation, please refer to `CommonCollateFn`.
+
+- build_model
+```python
+@classmethod
+def build_model(cls, args, train):
+    with open(args.token_list, encoding="utf-8") as f:
+        token_list = [line.rstrip() for line in f]
+        vocab_size = len(token_list)
+        frontend = frontend_class(**args.frontend_conf)
+        specaug = specaug_class(**args.specaug_conf)
+        normalize = normalize_class(**args.normalize_conf)
+        preencoder = preencoder_class(**args.preencoder_conf)
+        encoder = encoder_class(input_size=input_size, **args.encoder_conf)
+        postencoder = postencoder_class(input_size=encoder_output_size, **args.postencoder_conf)
+        decoder = decoder_class(vocab_size=vocab_size, encoder_output_size=encoder_output_size,  **args.decoder_conf)
+        ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **args.ctc_conf)
+        model = model_class(
+            vocab_size=vocab_size,
+            frontend=frontend,
+            specaug=specaug,
+            normalize=normalize,
+            preencoder=preencoder,
+            encoder=encoder,
+            postencoder=postencoder,
+            decoder=decoder,
+            ctc=ctc,
+            token_list=token_list,
+            **args.model_conf,
+        )
+    return model
+```
+This function defines the detail of the model. For different speech recognition models, the same speech recognition `Task` can usually be shared and the remaining thing needed to be done is to define a specific model in this function. For example, a speech recognition model with a standard encoder-decoder structure has been shown above. Specifically, it first defines each module of the model, including encoder, decoder, etc. and then combine these modules together to generate a complete model. In FunASR, the model needs to inherit `FunASRModel` and the corresponding code can be seen in `funasr/train/abs_espnet_model.py`. The main function needed to be implemented is the `forward` function.
+
+Next, we take `SANMEncoder` as an example to introduce how to use a custom encoder as a part of the model when defining the specified model and the corresponding code can be seen in `funasr/models/encoder/sanm_encoder.py`. For a custom encoder, in addition to inheriting the common encoder class `AbsEncoder`, it is also necessary to define the `forward` function to achieve the forward computation of the `encoder`. After defining the `encoder`, it should also be registered in the `Task`. The corresponding code example can be seen as below:
+```python
+encoder_choices = ClassChoices(
+    "encoder",
+    classes=dict(
+        conformer=ConformerEncoder,
+        transformer=TransformerEncoder,
+        rnn=RNNEncoder,
+        sanm=SANMEncoder,
+        sanm_chunk_opt=SANMEncoderChunkOpt,
+        data2vec_encoder=Data2VecEncoder,
+        mfcca_enc=MFCCAEncoder,
+    ),
+    type_check=AbsEncoder,
+    default="rnn",
+)
+```
+In this code, `sanm=SANMEncoder` takes the newly defined `SANMEncoder` as an optional choice of the `encoder`. Once the user specifies the `encoder` as `sanm` in the configuration file, the `SANMEncoder` will be correspondingly employed as the `encoder` module of the model.
\ No newline at end of file

docs/reference/papers.md

+# Papers
+
+FunASR have implemented the following paper code
+
+### Speech Recognition
+- [FunASR: A Fundamental End-to-End Speech Recognition Toolkit](https://arxiv.org/abs/2305.11013), INTERSPEECH 2023
+- [BAT: Boundary aware transducer for memory-efficient and low-latency ASR](https://arxiv.org/abs/2305.11571), INTERSPEECH 2023
+- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://arxiv.org/abs/2206.08317), INTERSPEECH 2022
+- [E-branchformer: Branchformer with enhanced merging for speech recognition](https://arxiv.org/abs/2210.00077), SLT 2022
+- [Branchformer: Parallel mlp-attention architectures to capture local and global context for speech recognition and understanding](https://proceedings.mlr.press/v162/peng22a.html?ref=https://githubhelp.com), ICML 2022
+- [Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model](https://arxiv.org/abs/2010.14099), arXiv preprint arXiv:2010.14099, 2020
+- [San-m: Memory equipped self-attention for end-to-end speech recognition](https://arxiv.org/pdf/2006.01713), INTERSPEECH 2020
+- [Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition](https://arxiv.org/abs/2006.01712), INTERSPEECH 2020
+- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100),  INTERSPEECH 2020
+- [Sequence-to-sequence learning with Transducers](https://arxiv.org/pdf/1211.3711.pdf), NIPS 2016
+
+
+### Multi-talker Speech Recognition
+- [MFCCA:Multi-Frame Cross-Channel attention for multi-speaker ASR in Multi-party meeting scenario](https://arxiv.org/abs/2210.05265), ICASSP 2022
+
+### Voice Activity Detection
+- [Deep-FSMN for Large Vocabulary Continuous Speech Recognition](https://arxiv.org/abs/1803.05030), ICASSP 2018
+
+### Punctuation Restoration
+- [CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection](https://arxiv.org/pdf/2003.01309.pdf), ICASSP 2018
+
+### Language Models
+- [Attention Is All You Need](https://arxiv.org/abs/1706.03762), NEURIPS 2017
+
+### Speaker Verification
+- [X-VECTORS: ROBUST DNN EMBEDDINGS FOR SPEAKER RECOGNITION](https://www.danielpovey.com/files/2018_icassp_xvectors.pdf), ICASSP 2018
+
+### Speaker diarization
+- [Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis](https://arxiv.org/abs/2211.10243), EMNLP 2022
+- [TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization](https://arxiv.org/abs/2303.05397), ICASSP 2023
+
+### Timestamp Prediction
+- [Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model](https://arxiv.org/abs/2301.12343), arXiv:2301.12343

docs/tutorial/Tables.md

+# FunASR-1.x.x Registration  New Model Tutorial
+
+([简体中文](./Tables_zh.md)|English)
+
+The original intention of the funasr-1.x.x version is to make model integration easier. The core feature is the registry and AutoModel:
+
+*   The introduction of the registry enables the development of building blocks to access the model, compatible with a variety of tasks;
+    
+*   The newly designed AutoModel interface unifies modelscope, huggingface, and funasr inference and training interfaces, and supports free download of repositories;
+    
+*   Support model export, demo-level service deployment, and industrial-level multi-concurrent service deployment;
+    
+*   Unify academic and industrial model inference training scripts;
+    
+
+# Quick to get started
+
+## AutoModel usage
+
+### SenseVoiceSmall 模型
+
+Input any length of voice, the output as the voice content corresponding to the text, the text has punctuation broken sentences, support Chinese, English, Japanese, Guangdong, Korean and 5 Chinese languages. \[Word-level timestamp and speaker identity\] will be supported later.
+
+```python
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(
+    model="iic/SenseVoiceSmall",
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0",
+)
+
+res = model.generate(
+    input=f"{model.model_path}/example/en.mp3",
+    language="auto",  # "zn", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=True,
+    batch_size_s=60,
+)
+text = rich_transcription_postprocess(res[0]["text"])
+print(text) #👏Senior staff, Priipal Doris Jackson, Wakefield faculty, and, of course, my fellow classmates.I am honored to have been chosen to speak before my classmates, as well as the students across America today.
+```
+
+## API documentation
+
+#### Definition of AutoModel
+
+```plaintext
+Model = AutoModel(model=[str], device=[str], ncpu=[int], output_dir=[str], batch_size= [int], hub=[str], **quargs)
+```
+
+*   `model`(str): [Model Warehouse](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo)The model name in, or the model path in the local disk
+    
+*   `device`(str): `cuda:0`(Default gpu0), using GPU for inference, specified. If`cpu`Then the CPU is used for inference
+    
+*   `ncpu`(int): `4`(Default), set the number of threads used for CPU internal operation parallelism
+    
+*   `output_dir`(str): `None`(Default) If set, the output path of the output result
+    
+*   `batch_size`(int): `1`(Default), batch processing during decoding, number of samples
+    
+*   `hub`(str)：`ms`(Default) to download the model from modelscope. If`hf`To download the model from huggingface.
+    
+*   `**kwargs`(dict): All in`config.yaml`Parameters, which can be specified directly here, for example, the maximum cut length in the vad model.`max_single_segment_time=6000`(Milliseconds).
+    
+
+#### AutoModel reasoning
+
+```plaintext
+Res = model.generate(input=[str], output_dir=[str])
+```
+
+*   *   wav file path, for example: asr\_example.wav
+        
+    *   pcm file path, for example: asr\_example.pcm, you need to specify the audio sampling rate fs (default is 16000)
+        
+    *   Audio byte stream, for example: microphone byte data
+        
+    *   wav.scp,kaldi-style wav list (`wav_id \t wav_path`), for example:
+        
+
+```plaintext
+Asr_example1./audios/asr_example1.wav
+Asr_example2./audios/asr_example2.wav
+
+```
+
+In this input
+
+*   Audio sampling points, for example:`audio, rate = soundfile.read("asr_example_zh.wav")`Is numpy.ndarray. batch input is supported. The type is list:`[audio_sample1, audio_sample2, ..., audio_sampleN]`
+    
+*   fbank input, support group batch. shape is \[batch, frames, dim\], type is torch.Tensor, for example
+    
+*   `output_dir`: None (default), if set, the output path of the output result
+    
+*   `**kwargs`(dict): Model-related inference parameters, e.g,`beam_size=10`,`decoding_ctc_weight=0.1`.
+    
+
+Detailed documentation link:[https://github.com/modelscope/FunASR/blob/main/examples/README\_zh.md](https://github.com/modelscope/FunASR/blob/main/examples/README_zh.md)
+
+# Registry Details
+
+Take the SenseVoiceSmall model as an example, explain how to register a new model, model link:
+
+**modelscope：**[https://www.modelscope.cn/models/iic/SenseVoiceSmall/files](https://www.modelscope.cn/models/iic/SenseVoiceSmall/files)
+
+**huggingface：**[https://huggingface.co/FunAudioLLM/SenseVoiceSmall](https://huggingface.co/FunAudioLLM/SenseVoiceSmall)
+
+## Model Resource Catalog
+
+![image.png](https://alidocs.oss-cn-zhangjiakou.aliyuncs.com/res/8oLl9y628rBNlapY/img/cab7f215-787f-4407-885a-14dc89ae9e02.png)
+
+Configuration File: config.yaml
+
+```yaml
+encoder: SenseVoiceEncoderSmall
+encoder_conf:
+    output_size: 512
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 50
+    tp_blocks: 20
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: pe
+    pos_enc_class: SinusoidalPositionEncoder
+    normalize_before: true
+    kernel_size: 11
+    sanm_shfit: 0
+    selfattention_layer_type: sanm
+
+
+model: SenseVoiceSmall
+model_conf:
+    length_normalized_loss: true
+    sos: 1
+    eos: 2
+    ignore_id: -1
+
+tokenizer: SentencepiecesTokenizer
+tokenizer_conf:
+  bpemodel: null
+  unk_symbol: <unk>
+  split_with_space: true
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+    cmvn_file: null
+
+
+dataset: SenseVoiceCTCDataset
+dataset_conf:
+  index_ds: IndexDSJsonl
+  batch_sampler: EspnetStyleBatchSampler
+  data_split_num: 32
+  batch_type: token
+  batch_size: 14000
+  max_token_length: 2000
+  min_token_length: 60
+  max_source_length: 2000
+  min_source_length: 60
+  max_target_length: 200
+  min_target_length: 0
+  shuffle: true
+  num_workers: 4
+  sos: ${model_conf.sos}
+  eos: ${model_conf.eos}
+  IndexDSJsonl: IndexDSJsonl
+  retry: 20
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 20
+  keep_nbest_models: 10
+  avg_nbest_model: 10
+  log_interval: 100
+  resume: true
+  validate_interval: 10000
+  save_checkpoint_interval: 10000
+
+optim: adamw
+optim_conf:
+  lr: 0.00002
+Scheduler: warmuplr
+Scheduler_conf:
+Warmup_steps: 25000
+
+```
+
+Model parameters: model.pt
+
+Path resolution: configuration.json (not required)
+
+```json
+{
+  "framework": "pytorch",
+  "task" : "auto-speech-recognition",
+  "model": {"type" : "funasr"},
+  "pipeline": {"type":"funasr-pipeline"},
+  "model_name_in_hub": {
+    "ms":"", 
+    "hf":""},
+  "file_path_metas": {
+    "init_param":"model.pt", 
+    "config":"config.yaml",
+    "tokenizer_conf": {"bpemodel": "chn_jpn_yue_eng_ko_spectok.bpe.model"},
+    "frontend_conf":{"cmvn_file": "am.mvn"}}
+}
+```
+
+The function of configuration.json is to add the model root directory to the item in file\_path\_metas, so that the path can be correctly parsed. For example, assume that the model root directory is:/home/zhifu.gzf/init\_model/SenseVoiceSmall,The relevant path in config.yaml in the directory is replaced with the correct path (ignoring irrelevant configuration):
+
+```yaml
+init_param: /home/zhifu.gz F/init_model/sensevoicemail Mall/model.pt
+
+tokenizer_conf:
+  bpemodel: /home/Zhifu.gzf/init_model/SenseVoiceSmall/chn_jpn_yue_eng_ko_spectok.bpe.model
+
+frontend_conf:
+    cmvn_file: /home/zhifu.Gzf/init_model/SenseVoiceSmall/am.mvn
+```
+
+## Registry
+
+![image](https://alidocs.oss-cn-zhangjiakou.aliyuncs.com/a/pDaAnLxn5IX2w9Y1/73da157edae94d78b68c8d30c8e085eb0521.png)
+
+### View Registry
+
+```plaintext
+from funasr.register import tables
+
+tables.print()
+```
+
+Support to view the specified type of Registry: 'tables.print("model")', currently funasr has registered model as shown in the figure above. The following categories are currently predefined:
+
+```python
+    model_classes = {}
+    frontend_classes = {}
+    specaug_classes = {}
+    normalize_classes = {}
+    encoder_classes = {}
+    decoder_classes = {}
+    joint_network_classes = {}
+    predictor_classes = {}
+    stride_conv_classes = {}
+    tokenizer_classes = {}
+    dataloader_classes = {}
+    batch_sampler_classes = {}
+    dataset_classes = {}
+    index_ds_classes = {}
+```
+
+### Registration Model
+
+```python
+from funasr.register import tables
+
+@tables.register("model_classes", "SenseVoiceSmall")
+class SenseVoiceSmall(nn.Module):
+  def __init__(*args, **kwargs):
+    ...
+
+  def forward(
+      self,
+      **kwargs,
+  ):  
+
+  def inference(
+      self,
+      data_in,
+      data_lengths=None,
+      key: list = None,
+      tokenizer=None,
+      frontend=None,
+      **kwargs,
+  ):
+    ...
+
+```
+
+Add @ tables.register("model\_classes", "SenseVoiceSmall") before the name of the class to be registered. The class needs to implement the following methods:\_\_init \_\_, forward, and inference.
+
+register Usage:
+
+```python
+@ tables.register("registration classification", "registration name")
+```
+
+Among them, "registration classification" can be a predefined classification (see the figure above). If it is a new classification defined by oneself, the new classification will be automatically written into the registry classification. "registration name" means the name you want to register and can be used directly in the future.
+
+Full code:[https://github.com/modelscope/FunASR/blob/main/funasr/models/sense\_voice/model.py#L443](https://github.com/modelscope/FunASR/blob/main/funasr/models/sense_voice/model.py#L443)
+
+After the registration is complete, specify the new registration model in config.yaml to define the model.
+
+```python
+model: SenseVoiceSmall
+model_conf:
+  ...
+```
+
+### Registration failed
+
+If the registration model or method is not found, assert model\_class is not None, f'{kwargs\["model"\]} is not registered '. The principle of model registration is to import the model file,You can view the specific reason for the registration failure through import. For example, the preceding model file is funasr/models/sense\_voice/model.py:
+
+```python
+from funasr.models.sense_voice.model import *
+```
+
+## Principles of Registration
+
+*   Model: models are independent of each other. Each Model needs to create a new Model directory under funasr/models/. Do not use class inheritance method!!! Do not import from other model directories, and put everything you need into your own model directory!!! Do not modify the existing model code!!!
+    
+*   dataset,frontend,tokenizer, if you can reuse the existing one, reuse it directly, if you cannot reuse it, please register a new one, modify it again, and do not modify the original one!!!
+    
+
+# Independent warehouse
+
+It can exist as a stand-alone repository for code secrecy, or as a stand-alone open source. Based on the registration mechanism, you do not need to integrate it into funasr. You can also use funasr for inference, and you can also directly perform inference. finetune is also supported.
+
+**Using AutoModel for inference**
+
+```python
+from funasr import AutoModel
+
+# trust_remote_code:'True' means that the model code implementation is loaded from 'remote_code', 'remote_code' specifies the location of the 'model' specific code (for example,'model.py') in the current directory, supports absolute and relative paths, and network url.
+model = AutoModel (
+model="iic/SenseVoiceSmall ",
+trust_remote_code=True
+remote_code = "./model.py", 
+)
+```
+
+**Direct inference**
+
+```python
+from model import SenseVoiceSmall
+
+m, kwargs = SenseVoiceSmall.from_pretrained(model="iic/SenseVoiceSmall")
+m.eval()
+
+res = m.inference(
+    data_in=f"{kwargs ['model_path']}/example/en.mp3",
+    language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=False,
+    ban_emo_unk=False,
+    **kwargs,
+)
+
+print(text)
+```
+
+Trim reference:[https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh](https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh)
\ No newline at end of file

docs/tutorial/Tables_zh.md

+# FunASR-1.x.x 注册新模型教程
+
+(简体中文|[English](./Tables.md))
+
+funasr-1.x.x 版本的设计初衷是【**让模型集成更简单**】，核心feature为注册表与AutoModel：
+
+*   注册表的引入，使得开发中可以用搭积木的方式接入模型，兼容多种task；
+    
+*   新设计的AutoModel接口，统一modelscope、huggingface与funasr推理与训练接口，支持自由选择下载仓库；
+    
+*   支持模型导出，demo级别服务部署，以及工业级别多并发服务部署；
+    
+*   统一学术与工业模型推理训练脚本；
+    
+
+# 快速上手
+
+## 基于automodel用法
+
+### SenseVoiceSmall模型
+
+输入任意时长语音，输出为语音内容对应文字，文字具有标点断句，支持中英日粤韩5中语言。【字级别时间戳，以及说话人身份】后续会支持。
+
+```python
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(
+    model="iic/SenseVoiceSmall",
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0",
+)
+
+res = model.generate(
+    input=f"{model.model_path}/example/en.mp3",
+    language="auto",  # "zn", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=True,
+    batch_size_s=60,
+)
+text = rich_transcription_postprocess(res[0]["text"])
+print(text) #👏Senior staff, Priipal Doris Jackson, Wakefield faculty, and, of course, my fellow classmates.I am honored to have been chosen to speak before my classmates, as well as the students across America today.
+```
+
+## API文档
+
+#### AutoModel 定义
+
+```plaintext
+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
+
+```plaintext
+res = model.generate(input=[str], output_dir=[str])
+```
+
+*   *   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:
+        
+
+```plaintext
+asr_example1  ./audios/asr_example1.wav
+asr_example2  ./audios/asr_example2.wav
+
+```
+
+在这种输入 
+
+*   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`。
+    
+
+详细文档链接：[https://github.com/modelscope/FunASR/blob/main/examples/README\_zh.md](https://github.com/modelscope/FunASR/blob/main/examples/README_zh.md)
+
+# 注册表详解
+
+以SenseVoiceSmall模型为例，讲解如何注册新模型，模型链接：
+
+**modelscope：**[https://www.modelscope.cn/models/iic/SenseVoiceSmall/files](https://www.modelscope.cn/models/iic/SenseVoiceSmall/files)
+
+**huggingface：**[https://huggingface.co/FunAudioLLM/SenseVoiceSmall](https://huggingface.co/FunAudioLLM/SenseVoiceSmall)
+
+## 模型资源目录
+
+![image.png](https://alidocs.oss-cn-zhangjiakou.aliyuncs.com/res/8oLl9y628rBNlapY/img/cab7f215-787f-4407-885a-14dc89ae9e02.png)
+
+**配置文件**：config.yaml
+
+```yaml
+encoder: SenseVoiceEncoderSmall
+encoder_conf:
+    output_size: 512
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 50
+    tp_blocks: 20
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: pe
+    pos_enc_class: SinusoidalPositionEncoder
+    normalize_before: true
+    kernel_size: 11
+    sanm_shfit: 0
+    selfattention_layer_type: sanm
+
+
+model: SenseVoiceSmall
+model_conf:
+    length_normalized_loss: true
+    sos: 1
+    eos: 2
+    ignore_id: -1
+
+tokenizer: SentencepiecesTokenizer
+tokenizer_conf:
+  bpemodel: null
+  unk_symbol: <unk>
+  split_with_space: true
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+    cmvn_file: null
+
+
+dataset: SenseVoiceCTCDataset
+dataset_conf:
+  index_ds: IndexDSJsonl
+  batch_sampler: EspnetStyleBatchSampler
+  data_split_num: 32
+  batch_type: token
+  batch_size: 14000
+  max_token_length: 2000
+  min_token_length: 60
+  max_source_length: 2000
+  min_source_length: 60
+  max_target_length: 200
+  min_target_length: 0
+  shuffle: true
+  num_workers: 4
+  sos: ${model_conf.sos}
+  eos: ${model_conf.eos}
+  IndexDSJsonl: IndexDSJsonl
+  retry: 20
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 20
+  keep_nbest_models: 10
+  avg_nbest_model: 10
+  log_interval: 100
+  resume: true
+  validate_interval: 10000
+  save_checkpoint_interval: 10000
+
+optim: adamw
+optim_conf:
+  lr: 0.00002
+scheduler: warmuplr
+scheduler_conf:
+  warmup_steps: 25000
+
+```
+
+**模型参数**：model.pt
+
+**路径解析**：configuration.json（非必需）
+
+```json
+{
+  "framework": "pytorch",
+  "task" : "auto-speech-recognition",
+  "model": {"type" : "funasr"},
+  "pipeline": {"type":"funasr-pipeline"},
+  "model_name_in_hub": {
+    "ms":"", 
+    "hf":""},
+  "file_path_metas": {
+    "init_param":"model.pt", 
+    "config":"config.yaml",
+    "tokenizer_conf": {"bpemodel": "chn_jpn_yue_eng_ko_spectok.bpe.model"},
+    "frontend_conf":{"cmvn_file": "am.mvn"}}
+}
+```
+
+configuration.json的作用是给file\_path\_metas中的item拼接上模型根目录，以便于路径能够被正确的解析，以上为例，假设模型根目录为：/home/zhifu.gzf/init\_model/SenseVoiceSmall，目录中config.yaml中的相关路径被替换成了正确的路径（忽略无关配置）：
+
+```yaml
+init_param: /home/zhifu.gzf/init_model/SenseVoiceSmall/model.pt
+
+tokenizer_conf:
+  bpemodel: /home/zhifu.gzf/init_model/SenseVoiceSmall/chn_jpn_yue_eng_ko_spectok.bpe.model
+
+frontend_conf:
+    cmvn_file: /home/zhifu.gzf/init_model/SenseVoiceSmall/am.mvn
+```
+
+## 注册表
+
+![image](https://alidocs.oss-cn-zhangjiakou.aliyuncs.com/a/6Ea1DxkZVte8y0g2/c92059e82c38493988fbc8c032d3f5380521.png)
+
+### 查看注册表
+
+```plaintext
+from funasr.register import tables
+
+tables.print()
+```
+
+支持查看指定类型的注册表：\`tables.print("model")\`，目前funasr已经注册模型如上图所示。目前预先定义了如下几个分类：
+
+```python
+    model_classes = {}
+    frontend_classes = {}
+    specaug_classes = {}
+    normalize_classes = {}
+    encoder_classes = {}
+    decoder_classes = {}
+    joint_network_classes = {}
+    predictor_classes = {}
+    stride_conv_classes = {}
+    tokenizer_classes = {}
+    dataloader_classes = {}
+    batch_sampler_classes = {}
+    dataset_classes = {}
+    index_ds_classes = {}
+```
+
+### 注册模型
+
+```python
+from funasr.register import tables
+
+@tables.register("model_classes", "SenseVoiceSmall")
+class SenseVoiceSmall(nn.Module):
+  def __init__(*args, **kwargs):
+    ...
+
+  def forward(
+      self,
+      **kwargs,
+  ):  
+
+  def inference(
+      self,
+      data_in,
+      data_lengths=None,
+      key: list = None,
+      tokenizer=None,
+      frontend=None,
+      **kwargs,
+  ):
+    ...
+
+```
+
+在需要注册的类名前加上 @tables.register("model\_classes", "SenseVoiceSmall")，即可完成注册，类需要实现有：\_\_init\_\_，forward，inference方法。
+
+register用法：
+
+```python
+@tables.register("注册分类", "注册名")
+```
+
+其中，"注册分类"可以是预先定义好的分类（见上面图），如果是自己定义的新分类，会自动将新分类写进注册表分类中，"注册名"即希望注册名字，后续可以直接来使用。
+
+完整代码：[https://github.com/modelscope/FunASR/blob/main/funasr/models/sense\_voice/model.py#L443](https://github.com/modelscope/FunASR/blob/main/funasr/models/sense_voice/model.py#L443)
+
+注册完成后，在config.yaml中指定新注册模型，即可实现对模型的定义
+
+```python
+model: SenseVoiceSmall
+model_conf:
+  ...
+```
+
+### 注册失败
+
+如果出现找不到注册模型或发方法，assert model\_class is not None, f'{kwargs\["model"\]} is not registered'。模型注册的原理是，import 模型文件，可以通过import来查看具体注册失败原因，例如，上述模型文件为，funasr/models/sense\_voice/model.py：
+
+```python
+from funasr.models.sense_voice.model import *
+```
+
+## 注册原则
+
+*   Model：模型之间互相独立，每一个模型，都需要在funasr/models/下面新建一个模型目录，不要采用类的继承方法！！！不要从其他模型目录中import，所有需要用到的都单独放到自己的模型目录中！！！不要修改现有的模型代码！！！
+    
+*   dataset，frontend，tokenizer，如果能复用现有的，直接复用，如果不能复用，请注册一个新的，再修改，不要修改原来的！！！
+    
+
+# 独立仓库
+
+可以作为独立仓库存在，用于代码保密，或者独立开源。基于注册机制，无需集成到funasr中，使用funasr进行推理，也可以直接进行推理，同样支持finetune
+
+**使用AutoModel进行推理**
+
+```python
+from funasr import AutoModel
+
+# trust_remote_code：`True` 表示 model 代码实现从 `remote_code` 处加载，`remote_code` 指定 `model` 具体代码的位置（例如，当前目录下的 `model.py`），支持绝对路径与相对路径，以及网络 url。
+model = AutoModel(
+    model="iic/SenseVoiceSmall",
+    trust_remote_code=True,  
+    remote_code="./model.py", 
+)
+```
+
+**直接进行推理**
+
+```python
+from model import SenseVoiceSmall
+
+m, kwargs = SenseVoiceSmall.from_pretrained(model="iic/SenseVoiceSmall")
+m.eval()
+
+res = m.inference(
+    data_in=f"{kwargs ['model_path']}/example/en.mp3",
+    language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=False,
+    ban_emo_unk=False,
+    **kwargs,
+)
+
+print(text)
+```
+
+微调参考：[https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh](https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh)
\ No newline at end of file

examples/aishell/branchformer/README.md

+# Branchformer Result
+
+## Training Config
+- Feature info: using raw speech, extracting 80 dims fbank online, global cmvn, speed perturb(0.9, 1.0, 1.1), specaugment
+- Train info: lr 0.001, batch_size 10000, 4 gpu(Tesla V100), acc_grad 1, 180 epochs
+- Train config: conf/train_asr_branchformer.yaml
+- LM config: LM was not used
+
+## Results (CER)
+
+|   testset   | CER(%)  |
+|:-----------:|:-------:|
+|     dev     |  4.15   |
+|    test     |  4.51   |
\ No newline at end of file

examples/aishell/branchformer/conf/branchformer_12e_6d_2048_256.yaml

+# This is an example that demonstrates how to configure a model file.
+# You can modify the configuration according to your own requirements.
+
+# to print the register_table:
+# from funasr.register import tables
+# tables.print()
+
+# network architecture
+model: Branchformer
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
+
+# encoder
+encoder: BranchformerEncoder
+encoder_conf:
+    output_size: 256
+    use_attn: true
+    attention_heads: 4
+    attention_layer_type: rel_selfattn
+    pos_enc_layer_type: rel_pos
+    rel_pos_type: latest
+    use_cgmlp: true
+    cgmlp_linear_units: 2048
+    cgmlp_conv_kernel: 31
+    use_linear_after_conv: false
+    gate_activation: identity
+    merge_method: concat
+    cgmlp_weight: 0.5               # used only if merge_method is "fixed_ave"
+    attn_branch_drop_rate: 0.0      # used only if merge_method is "learned_ave"
+    num_blocks: 24
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d
+    stochastic_depth_rate: 0.0
+
+# decoder
+decoder: TransformerDecoder
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.
+    src_attention_dropout_rate: 0.
+
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    dither: 0.0
+    lfr_m: 1
+    lfr_n: 1
+
+specaug: SpecAug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 40
+    num_time_mask: 2
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 180
+  keep_nbest_models: 10
+  avg_keep_nbest_models_type: acc
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.001
+   weight_decay: 0.000001
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 35000
+
+dataset: AudioDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: EspnetStyleBatchSampler
+    batch_type: length # example or length
+    batch_size: 10000 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 1024
+    shuffle: True
+    num_workers: 4
+    preprocessor_speech: SpeechPreprocessSpeedPerturb
+    preprocessor_speech_conf:
+      speed_perturb: [0.9, 1.0, 1.1]
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+  unk_symbol: <unk>
+
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+normalize: null
+
+beam_size: 10
+decoding_ctc_weight: 0.4
\ No newline at end of file

examples/aishell/branchformer/demo_infer.sh

+../paraformer/demo_infer.sh
\ No newline at end of file

examples/aishell/branchformer/demo_train_or_finetune.sh

+../paraformer/demo_train_or_finetune.sh
\ No newline at end of file