initial commit

FunASR/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
+

FunASR/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

FunASR/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

FunASR/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

FunASR/docs/reference/application.md

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

FunASR/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

FunASR/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

FunASR/docs/tutorial/README.md

+([简体中文](./README_zh.md)|English)
+
+FunASR has open-sourced a large number of pre-trained models on industrial data. You are free to use, copy, modify, and share FunASR models under the [Model License Agreement](https://github.com/alibaba-damo-academy/FunASR/blob/main/MODEL_LICENSE). Below, we list some representative models. For a comprehensive list, please refer to our [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo).
+
+<div align="center">  
+<h4>
+ <a href="#Inference"> Model Inference </a>   
+｜<a href="#Training"> Model Training and Testing </a>
+｜<a href="#Export"> Model Export and Testing </a>
+</h4>
+</div>
+
+<a name="Inference"></a>
+## Model Inference
+
+### Quick Start
+
+For command-line invocation:
+```shell
+funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wav
+```
+
+For python code invocation (recommended): 
+
+```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)
+```
+
+### API Description 
+#### AutoModel Definition
+```python
+model = AutoModel(model=[str], device=[str], ncpu=[int], output_dir=[str], batch_size=[int], hub=[str], **kwargs)
+```
+- `model`(str): model name in the [Model Repository](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo), or a model path on local disk.
+- `device`(str): `cuda:0` (default gpu0) for using GPU for inference, specify `cpu` for using CPU.
+- `ncpu`(int): `4` (default), sets the number of threads for CPU internal operations.
+- `output_dir`(str): `None` (default), set this to specify the output path for the results.
+- `batch_size`(int): `1` (default), the number of samples per batch during decoding.
+- `hub`(str)：`ms` (default) to download models from ModelScope. Use `hf` to download models from Hugging Face.
+- `**kwargs`(dict): Any parameters found in config.yaml can be directly specified here, for instance, the maximum segmentation length in the vad model max_single_segment_time=6000 (milliseconds).
+
+#### AutoModel Inference
+```python
+res = model.generate(input=[str], output_dir=[str])
+```
+- `input`: The input to be decoded, which could be:
+  - A wav file path, e.g., asr_example.wav
+  - A pcm file path, e.g., asr_example.pcm, in this case, specify the audio sampling rate fs (default is 16000)
+  - An audio byte stream, e.g., byte data from a microphone
+  - A wav.scp, a Kaldi-style wav list (wav_id \t wav_path), for example:
+  ```text
+  asr_example1  ./audios/asr_example1.wav
+  asr_example2  ./audios/asr_example2.wav
+  ```
+  When using wav.scp as input, you must set output_dir to save the output results.
+  - Audio samples, `e.g.`: `audio, rate = soundfile.read("asr_example_zh.wav")`, data type is numpy.ndarray. Supports batch inputs, type is list：
+  ```[audio_sample1, audio_sample2, ..., audio_sampleN]```
+  - fbank input, supports batch grouping. Shape is [batch, frames, dim], type is torch.Tensor.
+- `output_dir`: None (default), if set, specifies the output path for the results.
+- `**kwargs`(dict): Inference parameters related to the model, for example,`beam_size=10`，`decoding_ctc_weight=0.1`.
+
+
+### More Usage Introduction
+
+
+#### Speech Recognition (Non-streaming)
+```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)
+```
+Notes:
+- Typically, the input duration for models is limited to under 30 seconds. However, when combined with `vad_model`, support for audio input of any length is enabled, not limited to the paraformer model—any audio input model can be used.
+- Parameters related to model can be directly specified in the definition of AutoModel; parameters related to `vad_model` can be set through `vad_kwargs`, which is a dict; similar parameters include `punc_kwargs` and `spk_kwargs`.
+- `max_single_segment_time`: Denotes the maximum audio segmentation length for `vad_model`, measured in milliseconds (ms).
+- `batch_size_s` represents the use of dynamic batching, where the total audio duration within a batch is measured in seconds (s).
+- `batch_size_threshold_s`: Indicates that when the duration of an audio segment post-VAD segmentation exceeds the batch_size_threshold_s threshold, the batch size is set to 1, measured in seconds (s).
+
+Recommendations: 
+
+When you input long audio and encounter Out Of Memory (OOM) issues, since memory usage tends to increase quadratically with audio length, consider the following three scenarios:
+
+a) At the beginning of inference, memory usage primarily depends on `batch_size_s`. Appropriately reducing this value can decrease memory usage.
+b) During the middle of inference, when encountering long audio segments cut by VAD and the total token count is less than `batch_size_s`, yet still facing OOM, you can appropriately reduce `batch_size_threshold_s`. If the threshold is exceeded, the batch size is forced to 1.
+c) Towards the end of inference, if long audio segments cut by VAD have a total token count less than `batch_size_s` and exceed the `threshold` batch_size_threshold_s, forcing the batch size to 1 and still facing OOM, you may reduce `max_single_segment_time` to shorten the VAD audio segment length.
+
+#### Speech Recognition (Streaming)
+```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)
+```
+Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.
+
+#### Voice Activity Detection (Non-Streaming)
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fsmn-vad")
+wav_file = f"{model.model_path}/example/vad_example.wav"
+res = model.generate(input=wav_file)
+print(res)
+```
+Note: The output format of the VAD model is: `[[beg1, end1], [beg2, end2], ..., [begN, endN]]`, where `begN/endN` indicates the starting/ending point of the `N-th` valid audio segment, measured in milliseconds.
+
+#### Voice Activity Detection (Streaming)
+```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)
+```
+Note: The output format for the streaming VAD model can be one of four scenarios:
+- `[[beg1, end1], [beg2, end2], .., [begN, endN]]`：The same as the offline VAD output result mentioned above.
+- `[[beg, -1]]`：Indicates that only a starting point has been detected.
+- `[[-1, end]]`：Indicates that only an ending point has been detected.
+- `[]`：Indicates that neither a starting point nor an ending point has been detected. 
+
+The output is measured in milliseconds and represents the absolute time from the starting point.
+#### Punctuation Restoration
+```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)
+```
+#### Timestamp Prediction
+```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)
+```
+
+More examples ref to [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)
+
+<a name="Training"></a>
+## Model Training and Testing
+
+### Quick Start
+
+Execute via command line (for quick testing, not recommended):
+```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 &
+```
+
+Execute with Python code (supports multi-node and multi-GPU, recommended):
+
+```shell
+cd examples/industrial_data_pretraining/paraformer
+bash finetune.sh
+# "log_file: ./outputs/log.txt"
+```
+Full code ref to [finetune.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/industrial_data_pretraining/paraformer/finetune.sh)
+
+### Detailed Parameter Description:
+
+```shell
+funasr/bin/train.py \
+++model="${model_name_or_model_dir}" \
+++train_data_set_list="${train_data}" \
+++valid_data_set_list="${val_data}" \
+++dataset_conf.batch_size=20000 \
+++dataset_conf.batch_type="token" \
+++dataset_conf.num_workers=4 \
+++train_conf.max_epoch=50 \
+++train_conf.log_interval=1 \
+++train_conf.resume=false \
+++train_conf.validate_interval=2000 \
+++train_conf.save_checkpoint_interval=2000 \
+++train_conf.keep_nbest_models=20 \
+++train_conf.avg_nbest_model=10 \
+++optim_conf.lr=0.0002 \
+++output_dir="${output_dir}" &> ${log_file}
+```
+
+- `model`（str）: The name of the model (the ID in the model repository), at which point the script will automatically download the model to local storage; alternatively, the path to a model already downloaded locally.
+- `train_data_set_list`（str）: The path to the training data, typically in jsonl format, for specific details refer to [examples](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list).
+- `valid_data_set_list`（str）：The path to the validation data, also generally in jsonl format, for specific details refer to examples](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list).
+- `dataset_conf.batch_type`（str）：example (default), the type of batch. example means batches are formed with a fixed number of batch_size samples; length or token means dynamic batching, with total length or number of tokens of the batch equalling batch_size.
+- `dataset_conf.batch_size`（int）：Used in conjunction with batch_type. When batch_type=example, it represents the number of samples; when batch_type=length, it represents the length of the samples, measured in fbank frames (1 frame = 10 ms) or the number of text tokens.
+- `train_conf.max_epoch`（int）：The total number of epochs for training.
+- `train_conf.log_interval`（int）：The number of steps between logging.
+- `train_conf.resume`（int）：Whether to enable checkpoint resuming for training.
+- `train_conf.validate_interval`（int）：The interval in steps to run validation tests during training.
+- `train_conf.save_checkpoint_interval`（int）：The interval in steps for saving the model during training.
+- `train_conf.keep_nbest_models`（int）：The maximum number of model parameters to retain, sorted by validation set accuracy, from highest to lowest.
+- `train_conf.avg_nbest_model`（int）：Average over the top n models with the highest accuracy.
+- `optim_conf.lr`（float）：The learning rate.
+- `output_dir`（str）：The path for saving the model.
+- `**kwargs`(dict): Any parameters in config.yaml can be specified directly here, for example, to filter out audio longer than 20s: dataset_conf.max_token_length=2000, measured in fbank frames (1 frame = 10 ms) or the number of text tokens.
+
+#### Multi-GPU Training
+##### Single-Machine Multi-GPU Training
+```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 ${train_args}
+```
+--nnodes represents the total number of participating nodes, while --nproc_per_node indicates the number of processes running on each node.
+
+##### Multi-Machine Multi-GPU Training
+
+On the master node, assuming the IP is 192.168.1.1 and the port is 12345, and you're using 2 GPUs, you would run the following command:
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+On the worker node (assuming the IP is 192.168.1.2), you need to ensure that the MASTER_ADDR and MASTER_PORT environment variables are set to match those of the master node, and then run the same command:
+
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+
+--nnodes indicates the total number of nodes participating in the training, --node_rank represents the ID of the current node, and --nproc_per_node specifies the number of processes running on each node (usually corresponds to the number of GPUs).
+
+#### Data prepare
+
+`jsonl` ref to（[demo](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）.
+The instruction scp2jsonl can be used to generate from wav.scp and text.txt. The preparation process for wav.scp and text.txt is as follows:
+
+`train_text.txt`
+
+```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`
+
+
+```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
+```
+
+`Command`
+
+```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"
+```
+
+(Optional, not required) If you need to parse from jsonl back to wav.scp and text.txt, you can use the following command:
+
+```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"
+```
+
+#### Training log
+
+##### log.txt
+```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
+```
+
+
+- `rank`：gpu id。
+- `epoch`,`step`,`total step`：the current epoch, step, and total steps.
+- `loss_avg_rank`：the average loss across all GPUs for the current step.
+- `loss/ppl/acc_avg_epoch`：the overall average loss/perplexity/accuracy for the current epoch, up to the current step count. The last step of the epoch when it ends represents the total average loss/perplexity/accuracy for that epoch; it is recommended to use the accuracy metric.
+- `lr`：the learning rate for the current step.
+- `[('loss_att', 0.259), ('acc', 0.825), ('loss_pre', 0.04), ('loss', 0.299), ('batch_size', 40)]`：the specific data for the current GPU ID.
+- `total_time`：the total time taken for a single step.
+- `GPU, memory`：the model-used/peak memory and the model+cache-used/peak memory.
+
+##### tensorboard
+```bash
+tensorboard --logdir /xxxx/FunASR/examples/industrial_data_pretraining/paraformer/outputs/log/tensorboard
+```
+http://localhost:6006/
+
+### 训练后模型测试
+
+
+#### With `configuration.json` file
+
+Assuming the training model path is: ./model_dir, if a configuration.json file has been generated in this directory, you only need to change the model name to the model path in the above model inference method. 
+
+For example, for shell inference:
+```shell
+python -m funasr.bin.inference ++model="./model_dir" ++input=="${input}" ++output_dir="${output_dir}"
+```
+
+Python inference
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="./model_dir")
+
+res = model.generate(input=wav_file)
+print(res)
+```
+
+#### Without `configuration.json` file
+
+If there is no configuration.json in the model path, you need to manually specify the exact configuration file path and the model path.
+
+```shell
+python -m funasr.bin.inference \
+--config-path "${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}"
+```
+
+Parameter Introduction
+- `config-path`：This is the path to the config.yaml saved during the experiment, which can be found in the experiment's output directory.
+- `config-name`：The name of the configuration file, usually config.yaml. It supports both YAML and JSON formats, for example config.json.
+- `init_param`：The model parameters that need to be tested, usually model.pt. You can choose a specific model file as needed.
+- `tokenizer_conf.token_list`：The path to the vocabulary file, which is normally specified in config.yaml. There is no need to manually specify it again unless the path in config.yaml is incorrect, in which case the correct path must be manually specified here.
+- `frontend_conf.cmvn_file`：The CMVN (Cepstral Mean and Variance Normalization) file used when extracting fbank features from WAV files, which is usually specified in config.yaml. There is no need to manually specify it again unless the path in config.yaml is incorrect, in which case the correct path must be manually specified here.
+
+Other parameters are the same as mentioned above. A complete [example](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/industrial_data_pretraining/paraformer/infer_from_local.sh) can be found here.
+
+<a name="Export"></a>
+## Export ONNX
+
+### Command-line usage
+```shell
+funasr-export ++model=paraformer ++quantize=false ++device=cpu
+```
+
+### Python
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="paraformer", device="cpu")
+
+res = model.export(quantize=False)
+```
+
+### Test 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)
+```
+
+More examples ref to [demo](https://github.com/alibaba-damo-academy/FunASR/tree/main/runtime/python/onnxruntime)
\ No newline at end of file

FunASR/docs/tutorial/README_zh.md

+(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/vad_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="${val_data}" \
+++dataset_conf.batch_size=20000 \
+++dataset_conf.batch_type="token" \
+++dataset_conf.num_workers=4 \
+++train_conf.max_epoch=50 \
+++train_conf.log_interval=1 \
+++train_conf.resume=false \
+++train_conf.validate_interval=2000 \
+++train_conf.save_checkpoint_interval=2000 \
+++train_conf.keep_nbest_models=20 \
+++train_conf.avg_nbest_model=10 \
+++optim_conf.lr=0.0002 \
+++output_dir="${output_dir}" &> ${log_file}
+```
+
+- `model`（str）：模型名字（模型仓库中的ID），此时脚本会自动下载模型到本读；或者本地已经下载好的模型路径。
+- `train_data_set_list`（str）：训练数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+- `valid_data_set_list`（str）：验证数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+- `dataset_conf.batch_type`（str）：`example`（默认），batch的类型。`example`表示按照固定数目batch_size个样本组batch；`length` or `token` 表示动态组batch，batch总长度或者token数为batch_size。
+- `dataset_conf.batch_size`（int）：与 `batch_type` 搭配使用，当 `batch_type=example` 时，表示样本个数；当 `batch_type=length` 时，表示样本中长度，单位为fbank帧数（1帧10ms）或者文字token个数。
+- `train_conf.max_epoch`（int）：`100`（默认），训练总epoch数。
+- `train_conf.log_interval`（int）：`50`（默认），打印日志间隔step数。
+- `train_conf.resume`（int）：`True`（默认），是否开启断点重训。
+- `train_conf.validate_interval`（int）：`5000`（默认），训练中做验证测试的间隔step数。
+- `train_conf.save_checkpoint_interval`（int）：`5000`（默认），训练中模型保存间隔step数。
+- `train_conf.avg_keep_nbest_models_type`（str）：`acc`（默认），保留nbest的标准为acc（越大越好）。`loss`表示，保留nbest的标准为loss（越小越好）。
+- `train_conf.keep_nbest_models`（int）：`500`（默认），保留最大多少个模型参数，配合 `avg_keep_nbest_models_type` 按照验证集 acc/loss 保留最佳的n个模型，其他删除，节约存储空间。
+- `train_conf.avg_nbest_model`（int）：`10`（默认），保留最大多少个模型参数，配合 `avg_keep_nbest_models_type` 按照验证集 acc/loss 对最佳的n个模型平均。
+- `train_conf.accum_grad`（int）：`1`（默认），梯度累积功能。
+- `train_conf.grad_clip`（float）：`10.0`（默认），梯度截断功能。
+- `train_conf.use_fp16`（bool）：`False`（默认），开启fp16训练，加快训练速度。
+- `optim_conf.lr`（float）：学习率。
+- `output_dir`（str）：模型保存路径。
+- `**kwargs`(dict): 所有在`config.yaml`中参数，均可以直接在此处指定，例如，过滤20s以上长音频：`dataset_conf.max_token_length=2000`，单位为音频fbank帧数（1帧10ms）或者文字token个数。
+
+#### 多gpu训练
+##### 单机多gpu训练
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(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 --node_rank 0 --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 --node_rank 1 --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，--node_rank 表示当前节点id，--nproc_per_node 表示每个节点上运行的进程数（通常为gpu个数）
+
+#### 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，如果该目录下有生成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](https://github.com/alibaba-damo-academy/FunASR/tree/main/runtime/python/onnxruntime)

FunASR/examples/README.md

+([简体中文](./README_zh.md)|English)
+
+FunASR has open-sourced a large number of pre-trained models on industrial data. You are free to use, copy, modify, and share FunASR models under the [Model License Agreement](https://github.com/alibaba-damo-academy/FunASR/blob/main/MODEL_LICENSE). Below, we list some representative models. For a comprehensive list, please refer to our [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo).
+
+<div align="center">  
+<h4>
+ <a href="#Inference"> Model Inference </a>   
+｜<a href="#Training"> Model Training and Testing </a>
+｜<a href="#Export"> Model Export and Testing </a>
+</h4>
+</div>
+
+<a name="Inference"></a>
+## Model Inference
+
+### Quick Start
+
+For command-line invocation:
+```shell
+funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wav
+```
+
+For python code invocation (recommended): 
+
+```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)
+```
+
+### API Description 
+#### AutoModel Definition
+```python
+model = AutoModel(model=[str], device=[str], ncpu=[int], output_dir=[str], batch_size=[int], hub=[str], **kwargs)
+```
+- `model`(str): model name in the [Model Repository](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo), or a model path on local disk.
+- `device`(str): `cuda:0` (default gpu0) for using GPU for inference, specify `cpu` for using CPU.
+- `ncpu`(int): `4` (default), sets the number of threads for CPU internal operations.
+- `output_dir`(str): `None` (default), set this to specify the output path for the results.
+- `batch_size`(int): `1` (default), the number of samples per batch during decoding.
+- `hub`(str)：`ms` (default) to download models from ModelScope. Use `hf` to download models from Hugging Face.
+- `**kwargs`(dict): Any parameters found in config.yaml can be directly specified here, for instance, the maximum segmentation length in the vad model max_single_segment_time=6000 (milliseconds).
+
+#### AutoModel Inference
+```python
+res = model.generate(input=[str], output_dir=[str])
+```
+- `input`: The input to be decoded, which could be:
+  - A wav file path, e.g., asr_example.wav
+  - A pcm file path, e.g., asr_example.pcm, in this case, specify the audio sampling rate fs (default is 16000)
+  - An audio byte stream, e.g., byte data from a microphone
+  - A wav.scp, a Kaldi-style wav list (wav_id \t wav_path), for example:
+  ```text
+  asr_example1  ./audios/asr_example1.wav
+  asr_example2  ./audios/asr_example2.wav
+  ```
+  When using wav.scp as input, you must set output_dir to save the output results.
+  - Audio samples, `e.g.`: `audio, rate = soundfile.read("asr_example_zh.wav")`, data type is numpy.ndarray. Supports batch inputs, type is list：
+  ```[audio_sample1, audio_sample2, ..., audio_sampleN]```
+  - fbank input, supports batch grouping. Shape is [batch, frames, dim], type is torch.Tensor.
+- `output_dir`: None (default), if set, specifies the output path for the results.
+- `**kwargs`(dict): Inference parameters related to the model, for example,`beam_size=10`，`decoding_ctc_weight=0.1`.
+
+
+### More Usage Introduction
+
+
+#### Speech Recognition (Non-streaming)
+##### SenseVoice
+```python
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model_dir = "iic/SenseVoiceSmall"
+
+model = AutoModel(
+    model=model_dir,
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0",
+)
+
+# en
+res = model.generate(
+    input=f"{model.model_path}/example/en.mp3",
+    cache={},
+    language="auto",  # "zn", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=True,
+    batch_size_s=60,
+    merge_vad=True,  #
+    merge_length_s=15,
+)
+text = rich_transcription_postprocess(res[0]["text"])
+print(text)
+```
+Notes:
+- `model_dir`: The name of the model, or the path to the model on the local disk.
+- `vad_model`: This indicates the activation of VAD (Voice Activity Detection). The purpose of VAD is to split long audio into shorter clips. In this case, the inference time includes both VAD and SenseVoice total consumption, and represents the end-to-end latency. If you wish to test the SenseVoice model's inference time separately, the VAD model can be disabled.
+- `vad_kwargs`: Specifies the configurations for the VAD model. `max_single_segment_time`: denotes the maximum duration for audio segmentation by the `vad_model`, with the unit being milliseconds (ms).
+- `use_itn`: Whether the output result includes punctuation and inverse text normalization.
+- `batch_size_s`: Indicates the use of dynamic batching, where the total duration of audio in the batch is measured in seconds (s).
+- `merge_vad`: Whether to merge short audio fragments segmented by the VAD model, with the merged length being `merge_length_s`, in seconds (s).
+- `ban_emo_unk`: Whether to ban the output of the `emo_unk` token.
+
+##### Paraformer
+```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)
+```
+Notes:
+- Typically, the input duration for models is limited to under 30 seconds. However, when combined with `vad_model`, support for audio input of any length is enabled, not limited to the paraformer model—any audio input model can be used.
+- Parameters related to model can be directly specified in the definition of AutoModel; parameters related to `vad_model` can be set through `vad_kwargs`, which is a dict; similar parameters include `punc_kwargs` and `spk_kwargs`.
+- `max_single_segment_time`: Denotes the maximum audio segmentation length for `vad_model`, measured in milliseconds (ms).
+- `batch_size_s` represents the use of dynamic batching, where the total audio duration within a batch is measured in seconds (s).
+- `batch_size_threshold_s`: Indicates that when the duration of an audio segment post-VAD segmentation exceeds the batch_size_threshold_s threshold, the batch size is set to 1, measured in seconds (s).
+
+Recommendations: 
+
+When you input long audio and encounter Out Of Memory (OOM) issues, since memory usage tends to increase quadratically with audio length, consider the following three scenarios:
+
+a) At the beginning of inference, memory usage primarily depends on `batch_size_s`. Appropriately reducing this value can decrease memory usage.
+b) During the middle of inference, when encountering long audio segments cut by VAD and the total token count is less than `batch_size_s`, yet still facing OOM, you can appropriately reduce `batch_size_threshold_s`. If the threshold is exceeded, the batch size is forced to 1.
+c) Towards the end of inference, if long audio segments cut by VAD have a total token count less than `batch_size_s` and exceed the `threshold` batch_size_threshold_s, forcing the batch size to 1 and still facing OOM, you may reduce `max_single_segment_time` to shorten the VAD audio segment length.
+
+#### Speech Recognition (Streaming)
+```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)
+```
+Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.
+
+#### Voice Activity Detection (Non-Streaming)
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fsmn-vad")
+wav_file = f"{model.model_path}/example/vad_example.wav"
+res = model.generate(input=wav_file)
+print(res)
+```
+Note: The output format of the VAD model is: `[[beg1, end1], [beg2, end2], ..., [begN, endN]]`, where `begN/endN` indicates the starting/ending point of the `N-th` valid audio segment, measured in milliseconds.
+
+#### Voice Activity Detection (Streaming)
+```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)
+```
+Note: The output format for the streaming VAD model can be one of four scenarios:
+- `[[beg1, end1], [beg2, end2], .., [begN, endN]]`：The same as the offline VAD output result mentioned above.
+- `[[beg, -1]]`：Indicates that only a starting point has been detected.
+- `[[-1, end]]`：Indicates that only an ending point has been detected.
+- `[]`：Indicates that neither a starting point nor an ending point has been detected. 
+
+The output is measured in milliseconds and represents the absolute time from the starting point.
+#### Punctuation Restoration
+```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)
+```
+#### Timestamp Prediction
+```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)
+```
+
+More examples ref to [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)
+
+<a name="Training"></a>
+## Model Training and Testing
+
+### Quick Start
+
+Execute via command line (for quick testing, not recommended):
+```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 &
+```
+
+Execute with Python code (supports multi-node and multi-GPU, recommended):
+
+```shell
+cd examples/industrial_data_pretraining/paraformer
+bash finetune.sh
+# "log_file: ./outputs/log.txt"
+```
+Full code ref to [finetune.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/industrial_data_pretraining/paraformer/finetune.sh)
+
+### Detailed Parameter Description:
+
+```shell
+funasr/bin/train.py \
+++model="${model_name_or_model_dir}" \
+++train_data_set_list="${train_data}" \
+++valid_data_set_list="${val_data}" \
+++dataset_conf.batch_size=20000 \
+++dataset_conf.batch_type="token" \
+++dataset_conf.num_workers=4 \
+++train_conf.max_epoch=50 \
+++train_conf.log_interval=1 \
+++train_conf.resume=false \
+++train_conf.validate_interval=2000 \
+++train_conf.save_checkpoint_interval=2000 \
+++train_conf.keep_nbest_models=20 \
+++train_conf.avg_nbest_model=10 \
+++optim_conf.lr=0.0002 \
+++output_dir="${output_dir}" &> ${log_file}
+```
+
+- `model`（str）: The name of the model (the ID in the model repository), at which point the script will automatically download the model to local storage; alternatively, the path to a model already downloaded locally.
+- `train_data_set_list`（str）: The path to the training data, typically in jsonl format, for specific details refer to [examples](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list).
+- `valid_data_set_list`（str）：The path to the validation data, also generally in jsonl format, for specific details refer to examples](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list).
+- `dataset_conf.batch_type`（str）：example (default), the type of batch. example means batches are formed with a fixed number of batch_size samples; length or token means dynamic batching, with total length or number of tokens of the batch equalling batch_size.
+- `dataset_conf.batch_size`（int）：Used in conjunction with batch_type. When batch_type=example, it represents the number of samples; when batch_type=length, it represents the length of the samples, measured in fbank frames (1 frame = 10 ms) or the number of text tokens.
+- `train_conf.max_epoch`（int）：The total number of epochs for training.
+- `train_conf.log_interval`（int）：The number of steps between logging.
+- `train_conf.resume`（int）：Whether to enable checkpoint resuming for training.
+- `train_conf.validate_interval`（int）：The interval in steps to run validation tests during training.
+- `train_conf.save_checkpoint_interval`（int）：The interval in steps for saving the model during training.
+- `train_conf.keep_nbest_models`（int）：The maximum number of model parameters to retain, sorted by validation set accuracy, from highest to lowest.
+- `train_conf.avg_nbest_model`（int）：Average over the top n models with the highest accuracy.
+- `optim_conf.lr`（float）：The learning rate.
+- `output_dir`（str）：The path for saving the model.
+- `**kwargs`(dict): Any parameters in config.yaml can be specified directly here, for example, to filter out audio longer than 20s: dataset_conf.max_token_length=2000, measured in fbank frames (1 frame = 10 ms) or the number of text tokens.
+
+#### Multi-GPU Training
+##### Single-Machine Multi-GPU Training
+```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} --master_port 12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+--nnodes represents the total number of participating nodes, while --nproc_per_node indicates the number of processes running on each node. --master_port indicates the port is 12345
+
+##### Multi-Machine Multi-GPU Training
+
+On the master node, assuming the IP is 192.168.1.1 and the port is 12345, and you're using 2 GPUs, you would run the following command:
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+On the worker node (assuming the IP is 192.168.1.2), you need to ensure that the MASTER_ADDR and MASTER_PORT environment variables are set to match those of the master node, and then run the same command:
+
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+
+--nnodes indicates the total number of nodes participating in the training, --node_rank represents the ID of the current node, and --nproc_per_node specifies the number of processes running on each node (usually corresponds to the number of GPUs).
+
+#### Data prepare
+
+`jsonl` ref to（[demo](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）.
+The instruction scp2jsonl can be used to generate from wav.scp and text.txt. The preparation process for wav.scp and text.txt is as follows:
+
+`train_text.txt`
+
+```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`
+
+
+```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
+```
+
+`Command`
+
+```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"
+```
+
+(Optional, not required) If you need to parse from jsonl back to wav.scp and text.txt, you can use the following command:
+
+```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"
+```
+
+#### Training log
+
+##### log.txt
+```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
+```
+
+
+- `rank`：gpu id。
+- `epoch`,`step`,`total step`：the current epoch, step, and total steps.
+- `loss_avg_rank`：the average loss across all GPUs for the current step.
+- `loss/ppl/acc_avg_epoch`：the overall average loss/perplexity/accuracy for the current epoch, up to the current step count. The last step of the epoch when it ends represents the total average loss/perplexity/accuracy for that epoch; it is recommended to use the accuracy metric.
+- `lr`：the learning rate for the current step.
+- `[('loss_att', 0.259), ('acc', 0.825), ('loss_pre', 0.04), ('loss', 0.299), ('batch_size', 40)]`：the specific data for the current GPU ID.
+- `total_time`：the total time taken for a single step.
+- `GPU, memory`：the model-used/peak memory and the model+cache-used/peak memory.
+
+##### tensorboard
+```bash
+tensorboard --logdir /xxxx/FunASR/examples/industrial_data_pretraining/paraformer/outputs/log/tensorboard
+```
+http://localhost:6006/
+
+### 训练后模型测试
+
+
+#### With `configuration.json` file
+
+Assuming the training model path is: ./model_dir, if a configuration.json file has been generated in this directory, you only need to change the model name to the model path in the above model inference method. 
+
+For example, for shell inference:
+```shell
+python -m funasr.bin.inference ++model="./model_dir" ++input=="${input}" ++output_dir="${output_dir}"
+```
+
+Python inference
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="./model_dir")
+
+res = model.generate(input=wav_file)
+print(res)
+```
+
+#### Without `configuration.json` file
+
+If there is no configuration.json in the model path, you need to manually specify the exact configuration file path and the model path.
+
+```shell
+python -m funasr.bin.inference \
+--config-path "${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}"
+```
+
+Parameter Introduction
+- `config-path`：This is the path to the config.yaml saved during the experiment, which can be found in the experiment's output directory.
+- `config-name`：The name of the configuration file, usually config.yaml. It supports both YAML and JSON formats, for example config.json.
+- `init_param`：The model parameters that need to be tested, usually model.pt. You can choose a specific model file as needed.
+- `tokenizer_conf.token_list`：The path to the vocabulary file, which is normally specified in config.yaml. There is no need to manually specify it again unless the path in config.yaml is incorrect, in which case the correct path must be manually specified here.
+- `frontend_conf.cmvn_file`：The CMVN (Cepstral Mean and Variance Normalization) file used when extracting fbank features from WAV files, which is usually specified in config.yaml. There is no need to manually specify it again unless the path in config.yaml is incorrect, in which case the correct path must be manually specified here.
+
+Other parameters are the same as mentioned above. A complete [example](https://github.com/alibaba-damo-academy/FunASR/blob/main/examples/industrial_data_pretraining/paraformer/infer_from_local.sh) can be found here.
+
+<a name="Export"></a>
+## Export ONNX
+
+### Command-line usage
+```shell
+funasr-export ++model=paraformer ++quantize=false ++device=cpu
+```
+
+### Python
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="paraformer", device="cpu")
+
+res = model.export(quantize=False)
+```
+
+### Test 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)
+```
+
+More examples ref to [demo](https://github.com/alibaba-damo-academy/FunASR/tree/main/runtime/python/onnxruntime)
\ No newline at end of file

FunASR/examples/README_zh.md

+(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
+##### SenseVoice
+```python
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model_dir = "iic/SenseVoiceSmall"
+
+model = AutoModel(
+    model=model_dir,
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0",
+)
+
+# en
+res = model.generate(
+    input=f"{model.model_path}/example/en.mp3",
+    cache={},
+    language="auto",  # "zn", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=True,
+    batch_size_s=60,
+    merge_vad=True,  #
+    merge_length_s=15,
+)
+text = rich_transcription_postprocess(res[0]["text"])
+print(text)
+```
+参数说明：
+- `model_dir`：模型名称，或本地磁盘中的模型路径。
+- `vad_model`：表示开启VAD，VAD的作用是将长音频切割成短音频，此时推理耗时包括了VAD与SenseVoice总耗时，为链路耗时，如果需要单独测试SenseVoice模型耗时，可以关闭VAD模型。
+- `vad_kwargs`：表示VAD模型配置,`max_single_segment_time`: 表示`vad_model`最大切割音频时长, 单位是毫秒ms。
+- `use_itn`：输出结果中是否包含标点与逆文本正则化。
+- `batch_size_s` 表示采用动态batch，batch中总音频时长，单位为秒s。
+- `merge_vad`：是否将 vad 模型切割的短音频碎片合成，合并后长度为`merge_length_s`，单位为秒s。
+- `ban_emo_unk`：禁用emo_unk标签，禁用后所有的句子都会被赋与情感标签。
+
+##### Paraformer
+```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/vad_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="${val_data}" \
+++dataset_conf.batch_size=20000 \
+++dataset_conf.batch_type="token" \
+++dataset_conf.num_workers=4 \
+++train_conf.max_epoch=50 \
+++train_conf.log_interval=1 \
+++train_conf.resume=false \
+++train_conf.validate_interval=2000 \
+++train_conf.save_checkpoint_interval=2000 \
+++train_conf.keep_nbest_models=20 \
+++train_conf.avg_nbest_model=10 \
+++optim_conf.lr=0.0002 \
+++output_dir="${output_dir}" &> ${log_file}
+```
+
+- `model`（str）：模型名字（模型仓库中的ID），此时脚本会自动下载模型到本地；或者本地已经下载好的模型路径。
+- `train_data_set_list`（str）：训练数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+- `valid_data_set_list`（str）：验证数据路径，默认为jsonl格式，具体参考（[例子](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list)）。
+- `dataset_conf.batch_type`（str）：`example`（默认），batch的类型。`example`表示按照固定数目batch_size个样本组batch；`length` or `token` 表示动态组batch，batch总长度或者token数为batch_size。
+- `dataset_conf.batch_size`（int）：与 `batch_type` 搭配使用，当 `batch_type=example` 时，表示样本个数；当 `batch_type=length` 时，表示样本中长度，单位为fbank帧数（1帧10ms）或者文字token个数。
+- `train_conf.max_epoch`（int）：`100`（默认），训练总epoch数。
+- `train_conf.log_interval`（int）：`50`（默认），打印日志间隔step数。
+- `train_conf.resume`（int）：`True`（默认），是否开启断点重训。
+- `train_conf.validate_interval`（int）：`5000`（默认），训练中做验证测试的间隔step数。
+- `train_conf.save_checkpoint_interval`（int）：`5000`（默认），训练中模型保存间隔step数。
+- `train_conf.avg_keep_nbest_models_type`（str）：`acc`（默认），保留nbest的标准为acc（越大越好）。`loss`表示，保留nbest的标准为loss（越小越好）。
+- `train_conf.keep_nbest_models`（int）：`500`（默认），保留最大多少个模型参数，配合 `avg_keep_nbest_models_type` 按照验证集 acc/loss 保留最佳的n个模型，其他删除，节约存储空间。
+- `train_conf.avg_nbest_model`（int）：`10`（默认），保留最大多少个模型参数，配合 `avg_keep_nbest_models_type` 按照验证集 acc/loss 对最佳的n个模型平均。
+- `train_conf.accum_grad`（int）：`1`（默认），梯度累积功能。
+- `train_conf.grad_clip`（float）：`10.0`（默认），梯度截断功能。
+- `train_conf.use_fp16`（bool）：`False`（默认），开启fp16训练，加快训练速度。
+- `optim_conf.lr`（float）：学习率。
+- `output_dir`（str）：模型保存路径。
+- `**kwargs`(dict): 所有在`config.yaml`中参数，均可以直接在此处指定，例如，过滤20s以上长音频：`dataset_conf.max_token_length=2000`，单位为音频fbank帧数（1帧10ms）或者文字token个数。
+
+#### 多gpu训练
+##### 单机多gpu训练
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 1 --nproc_per_node ${gpu_num} --master_port 12345 \
+../../../funasr/bin/train.py ${train_args}
+```
+--nnodes 表示参与的节点总数，--nproc_per_node 表示每个节点上运行的进程数，--master_port 表示端口号
+
+##### 多机多gpu训练
+
+在主节点上，假设IP为192.168.1.1，端口为12345，使用的是2个GPU，则运行如下命令：
+```shell
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+torchrun --nnodes 2 --node_rank 0 --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 --node_rank 1 --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，--node_rank 表示当前节点id，--nproc_per_node 表示每个节点上运行的进程数（通常为gpu个数），--master_port 表示端口号
+
+#### 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](https://github.com/alibaba-damo-academy/FunASR/tree/main/runtime/python/onnxruntime)
\ No newline at end of file

FunASR/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

FunASR/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

FunASR/examples/aishell/branchformer/demo_infer.sh

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

FunASR/examples/aishell/branchformer/demo_train_or_finetune.sh

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

FunASR/examples/aishell/branchformer/infer.sh

+
+
+python -m funasr.bin.inference \
+--config-path="/mnt/workspace/FunASR/examples/aishell/paraformer/exp/baseline_paraformer_conformer_12e_6d_2048_256_zh_char_exp3" \
+--config-name="config.yaml" \
+++init_param="/mnt/workspace/FunASR/examples/aishell/paraformer/exp/baseline_paraformer_conformer_12e_6d_2048_256_zh_char_exp3/model.pt.ep38" \
+++tokenizer_conf.token_list="/mnt/nfs/zhifu.gzf/data/AISHELL-1-feats/DATA/data/zh_token_list/char/tokens.txt" \
+++frontend_conf.cmvn_file="/mnt/nfs/zhifu.gzf/data/AISHELL-1-feats/DATA/data/train/am.mvn" \
+++input="/mnt/nfs/zhifu.gzf/data/AISHELL-1/data_aishell/wav/train/S0002/BAC009S0002W0122.wav" \
+++output_dir="./outputs/debug" \
+++device="cuda:0" \
+

FunASR/examples/aishell/branchformer/local/aishell_data_prep.sh

+#!/bin/bash
+
+# Copyright 2017 Xingyu Na
+# Apache 2.0
+
+#. ./path.sh || exit 1;
+
+if [ $# != 3 ]; then
+  echo "Usage: $0 <audio-path> <text-path> <output-path>"
+  echo " $0 /export/a05/xna/data/data_aishell/wav /export/a05/xna/data/data_aishell/transcript data"
+  exit 1;
+fi
+
+aishell_audio_dir=$1
+aishell_text=$2/aishell_transcript_v0.8.txt
+output_dir=$3
+
+train_dir=$output_dir/data/local/train
+dev_dir=$output_dir/data/local/dev
+test_dir=$output_dir/data/local/test
+tmp_dir=$output_dir/data/local/tmp
+
+mkdir -p $train_dir
+mkdir -p $dev_dir
+mkdir -p $test_dir
+mkdir -p $tmp_dir
+
+# data directory check
+if [ ! -d $aishell_audio_dir ] || [ ! -f $aishell_text ]; then
+  echo "Error: $0 requires two directory arguments"
+  exit 1;
+fi
+
+# find wav audio file for train, dev and test resp.
+find $aishell_audio_dir -iname "*.wav" > $tmp_dir/wav.flist
+n=`cat $tmp_dir/wav.flist | wc -l`
+[ $n -ne 141925 ] && \
+  echo Warning: expected 141925 data data files, found $n
+
+grep -i "wav/train" $tmp_dir/wav.flist > $train_dir/wav.flist || exit 1;
+grep -i "wav/dev" $tmp_dir/wav.flist > $dev_dir/wav.flist || exit 1;
+grep -i "wav/test" $tmp_dir/wav.flist > $test_dir/wav.flist || exit 1;
+
+rm -r $tmp_dir
+
+# Transcriptions preparation
+for dir in $train_dir $dev_dir $test_dir; do
+  echo Preparing $dir transcriptions
+  sed -e 's/\.wav//' $dir/wav.flist | awk -F '/' '{print $NF}' > $dir/utt.list
+  paste -d' ' $dir/utt.list $dir/wav.flist > $dir/wav.scp_all
+  utils/filter_scp.pl -f 1 $dir/utt.list $aishell_text > $dir/transcripts.txt
+  awk '{print $1}' $dir/transcripts.txt > $dir/utt.list
+  utils/filter_scp.pl -f 1 $dir/utt.list $dir/wav.scp_all | sort -u > $dir/wav.scp
+  sort -u $dir/transcripts.txt > $dir/text
+done
+
+mkdir -p $output_dir/data/train $output_dir/data/dev $output_dir/data/test
+
+for f in wav.scp text; do
+  cp $train_dir/$f $output_dir/data/train/$f || exit 1;
+  cp $dev_dir/$f $output_dir/data/dev/$f || exit 1;
+  cp $test_dir/$f $output_dir/data/test/$f || exit 1;
+done
+
+echo "$0: AISHELL data preparation succeeded"
+exit 0;

FunASR/examples/aishell/branchformer/local/download_and_untar.sh

+#!/usr/bin/env bash
+
+# Copyright   2014  Johns Hopkins University (author: Daniel Povey)
+#             2017  Xingyu Na
+# Apache 2.0
+
+remove_archive=false
+
+if [ "$1" == --remove-archive ]; then
+  remove_archive=true
+  shift
+fi
+
+if [ $# -ne 3 ]; then
+  echo "Usage: $0 [--remove-archive] <data-base> <url-base> <corpus-part>"
+  echo "e.g.: $0 /export/a05/xna/data www.openslr.org/resources/33 data_aishell"
+  echo "With --remove-archive it will remove the archive after successfully un-tarring it."
+  echo "<corpus-part> can be one of: data_aishell, resource_aishell."
+fi
+
+data=$1
+url=$2
+part=$3
+
+if [ ! -d "$data" ]; then
+  echo "$0: no such directory $data"
+  exit 1;
+fi
+
+part_ok=false
+list="data_aishell resource_aishell"
+for x in $list; do
+  if [ "$part" == $x ]; then part_ok=true; fi
+done
+if ! $part_ok; then
+  echo "$0: expected <corpus-part> to be one of $list, but got '$part'"
+  exit 1;
+fi
+
+if [ -z "$url" ]; then
+  echo "$0: empty URL base."
+  exit 1;
+fi
+
+if [ -f $data/$part/.complete ]; then
+  echo "$0: data part $part was already successfully extracted, nothing to do."
+  exit 0;
+fi
+
+# sizes of the archive files in bytes.
+sizes="15582913665 1246920"
+
+if [ -f $data/$part.tgz ]; then
+  size=$(/bin/ls -l $data/$part.tgz | awk '{print $5}')
+  size_ok=false
+  for s in $sizes; do if [ $s == $size ]; then size_ok=true; fi; done
+  if ! $size_ok; then
+    echo "$0: removing existing file $data/$part.tgz because its size in bytes $size"
+    echo "does not equal the size of one of the archives."
+    rm $data/$part.tgz
+  else
+    echo "$data/$part.tgz exists and appears to be complete."
+  fi
+fi
+
+if [ ! -f $data/$part.tgz ]; then
+  if ! command -v wget >/dev/null; then
+    echo "$0: wget is not installed."
+    exit 1;
+  fi
+  full_url=$url/$part.tgz
+  echo "$0: downloading data from $full_url.  This may take some time, please be patient."
+
+  cd $data || exit 1
+  if ! wget --no-check-certificate $full_url; then
+    echo "$0: error executing wget $full_url"
+    exit 1;
+  fi
+fi
+
+cd $data || exit 1
+
+if ! tar -xvzf $part.tgz; then
+  echo "$0: error un-tarring archive $data/$part.tgz"
+  exit 1;
+fi
+
+touch $data/$part/.complete
+
+if [ $part == "data_aishell" ]; then
+  cd $data/$part/wav || exit 1
+  for wav in ./*.tar.gz; do
+    echo "Extracting wav from $wav"
+    tar -zxf $wav && rm $wav
+  done
+fi
+
+echo "$0: Successfully downloaded and un-tarred $data/$part.tgz"
+
+if $remove_archive; then
+  echo "$0: removing $data/$part.tgz file since --remove-archive option was supplied."
+  rm $data/$part.tgz
+fi
+
+exit 0;