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-# Speech Recognition
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-
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-> **Note**:
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-> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take the typic models as examples to demonstrate the usage.
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-
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-## Inference
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-
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-### Quick start
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-#### [Paraformer Model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/summary)
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-```python
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-from modelscope.pipelines import pipeline
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-from modelscope.utils.constant import Tasks
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-
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-inference_pipeline = pipeline(
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- task=Tasks.auto_speech_recognition,
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- model='damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch',
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-)
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-
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-rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_tr.wav')
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-print(rec_result)
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-```
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-#### [UniASR Turkish Model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/summary)
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-There are three decoding mode for UniASR model(`fast`、`normal`、`offline`), for more model details, please refer to [docs](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)
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-```python
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-decoding_model = "fast" # "fast"、"normal"、"offline"
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-inference_pipeline = pipeline(
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- task=Tasks.auto_speech_recognition,
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- model='damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch',
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- param_dict={"decoding_model": decoding_model})
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-
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-rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_tr.wav')
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-print(rec_result)
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-```
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-The decoding mode of `fast` and `normal` is fake streaming, which could be used for evaluating of recognition accuracy.
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-Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151)
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-
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-
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-### API-reference
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-#### Define pipeline
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-- `task`: `Tasks.auto_speech_recognition`
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-- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
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-- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
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-- `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU
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-- `output_dir`: `None` (Default), the output path of results if set
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-- `batch_size`: `1` (Default), batch size when decoding
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-#### Infer pipeline
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-- `audio_in`: the input to decode, which could be:
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- - wav_path, `e.g.`: asr_example.wav,
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- - pcm_path, `e.g.`: asr_example.pcm,
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- - audio bytes stream, `e.g.`: bytes data from a microphone
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- - audio sample point，`e.g.`: `audio, rate = soundfile.read("asr_example_tr.wav")`, the dtype is numpy.ndarray or torch.Tensor
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- - wav.scp, kaldi style wav list (`wav_id \t wav_path`), `e.g.`:
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- ```text
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- asr_example1 ./audios/asr_example1.wav
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- asr_example2 ./audios/asr_example2.wav
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- ```
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- In this case of `wav.scp` input, `output_dir` must be set to save the output results
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-- `audio_fs`: audio sampling rate, only set when audio_in is pcm audio
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-- `output_dir`: None (Default), the output path of results if set
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-
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-### Inference with multi-thread CPUs or multi GPUs
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-FunASR also offer recipes [egs_modelscope/asr/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
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-
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-#### Settings of `infer.sh`
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-- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
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-- `data_dir`: the dataset dir needs to include `wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
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-- `output_dir`: output dir of the recognition results
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-- `batch_size`: `64` (Default), batch size of inference on gpu
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-- `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference
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-- `gpuid_list`: `0,1` (Default), which gpu_ids are used to infer
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-- `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding
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-- `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models
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-- `checkpoint_name`: only used for infer finetuned models, `valid.cer_ctc.ave.pb` (Default), which checkpoint is used to infer
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-- `decoding_mode`: `normal` (Default), decoding mode for UniASR model(fast、normal、offline)
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-- `hotword_txt`: `None` (Default), hotword file for contextual paraformer model(the hotword file name ends with .txt")
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-
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-
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-#### Decode with multi-thread CPUs:
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-```shell
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- bash infer.sh \
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- --model "damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch" \
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- --data_dir "./data/test" \
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- --output_dir "./results" \
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- --gpu_inference false \
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- --njob 64
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-```
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-
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-#### Results
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-
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-The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
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-
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-If you decode the SpeechIO test sets, you can use textnorm with `stage`=3, and `DETAILS.txt`, `RESULTS.txt` record the results and CER after text normalization.
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-
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-
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-## Finetune with pipeline
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-
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-### Quick start
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-[finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/finetune.py)
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-```python
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-import os
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-from modelscope.metainfo import Trainers
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-from modelscope.trainers import build_trainer
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-from modelscope.msdatasets.audio.asr_dataset import ASRDataset
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-
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-def modelscope_finetune(params):
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- if not os.path.exists(params.output_dir):
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- os.makedirs(params.output_dir, exist_ok=True)
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- # dataset split ["train", "validation"]
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- ds_dict = ASRDataset.load(params.data_path, namespace='speech_asr')
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- kwargs = dict(
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- model=params.model,
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- data_dir=ds_dict,
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- dataset_type=params.dataset_type,
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- work_dir=params.output_dir,
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- batch_bins=params.batch_bins,
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- max_epoch=params.max_epoch,
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- lr=params.lr)
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- trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
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- trainer.train()
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-
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-
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-if __name__ == '__main__':
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- from funasr.utils.modelscope_param import modelscope_args
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- params = modelscope_args(model="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch")
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- params.output_dir = "./checkpoint" # 模型保存路径
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- params.data_path = "speech_asr_aishell1_trainsets" # 数据路径，可以为modelscope中已上传数据，也可以是本地数据
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- params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large
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- params.batch_bins = 2000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒，
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- params.max_epoch = 50 # 最大训练轮数
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- params.lr = 0.00005 # 设置学习率
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-
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- modelscope_finetune(params)
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-```
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-
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-```shell
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-python finetune.py &> log.txt &
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-```
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-
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-### Finetune with your data
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-
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-- Modify finetune training related parameters in [finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/finetune.py)
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- - `output_dir`: result dir
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- - `data_dir`: the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
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- - `dataset_type`: for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
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- - `batch_bins`: batch size. For dataset_type is `small`, `batch_bins` indicates the feature frames. For dataset_type is `large`, `batch_bins` indicates the duration in ms
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- - `max_epoch`: number of training epoch
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- - `lr`: learning rate
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-
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-- Training data formats：
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-```sh
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-cat ./example_data/text
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-BAC009S0002W0122 而 对 楼 市 成 交 抑 制 作 用 最 大 的 限 购
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-BAC009S0002W0123 也 成 为 地 方 政 府 的 眼 中 钉
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-english_example_1 hello world
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-english_example_2 go swim 去 游 泳
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-
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-cat ./example_data/wav.scp
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-BAC009S0002W0122 /mnt/data/wav/train/S0002/BAC009S0002W0122.wav
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-BAC009S0002W0123 /mnt/data/wav/train/S0002/BAC009S0002W0123.wav
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-english_example_1 /mnt/data/wav/train/S0002/english_example_1.wav
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-english_example_2 /mnt/data/wav/train/S0002/english_example_2.wav
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-```
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-
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-- Then you can run the pipeline to finetune with:
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-```shell
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-python finetune.py
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-```
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-If you want finetune with multi-GPUs, you could:
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-```shell
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-CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1
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-```
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-## Inference with your finetuned model
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-
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-- Setting parameters in [egs_modelscope/asr/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) is the same with [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/egs_modelscope/asr/TEMPLATE#inference-with-multi-thread-cpus-or-multi-gpus), `model` is the model name from modelscope, which you finetuned.
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-
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-
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-- Decode with multi-thread CPUs:
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-```shell
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- bash infer.sh \
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- --model "damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch" \
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- --data_dir "./data/test" \
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- --output_dir "./results" \
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- --gpu_inference false \
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- --njob 64 \
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- --checkpoint_dir "./checkpoint" \
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- --checkpoint_name "valid.cer_ctc.ave.pb"
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-```
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chong.zhang