docs

游雁

2023-04-20 100ea0304b956e55a9c2fe284b1ee1a26bdf2b7c

 egs_modelscope/asr/TEMPLATE/README.md

@@ -1,12 +1,12 @@
# Speech Recognition

> **Note**: 
> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take model of Paraformer and Paraformer-online as example to demonstrate the usage.
> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take typic model as example to demonstrate the usage.

## Inference

### Quick start
#### [Paraformer model](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
#### [Paraformer Model](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
@@ -19,7 +19,7 @@
rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
print(rec_result)
```
#### [Paraformer-online model](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary)
#### [Paraformer-online Model](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary)
```python
inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
@@ -41,7 +41,7 @@
```
Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/241)

#### [UniASR model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)
#### [UniASR Model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)
There are three decoding mode for UniASR model(`fast`、`normal`、`offline`), for more model detailes, please refer to [docs](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)
```python
decoding_model = "fast" # "fast"、"normal"、"offline"
@@ -59,21 +59,21 @@
Undo

#### API-reference
##### define pipeline
##### Define pipeline
- `task`: `Tasks.auto_speech_recognition`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: 1 (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
- `ncpu`: 1 (Defalut), sets the number of threads used for intraop parallelism on CPU 
- `output_dir`: None (Defalut), the output path of results if set
- `batch_size`: 1 (Defalut), batch size when decoding
##### infer pipeline
##### Infer pipeline
- `audio_in`: the input to decode, which could be: 
  - wav_path, `e.g.`: asr_example.wav,
  - pcm_path, `e.g.`: asr_example.pcm, 
  - audio bytes stream, `e.g.`: bytes data from a microphone
  - audio sample point，`e.g.`: `audio, rate = soundfile.read("asr_example_zh.wav")`, the dtype is numpy.ndarray or torch.Tensor
  - wav.scp, kaldi style wav list (`wav_id \t wav_path``), `e.g.`: 
  ```cat wav.scp
  ```text
  asr_example1  ./audios/asr_example1.wav
  asr_example2  ./audios/asr_example2.wav
  ```
@@ -85,13 +85,15 @@
FunASR also offer recipes [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.

- Setting parameters in `infer.sh`
    - <strong>model:</strong> # model name on ModelScope
    - <strong>data_dir:</strong> # the dataset dir needs to include `${data_dir}/wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
    - <strong>output_dir:</strong> # result dir
    - <strong>batch_size:</strong> # batchsize of inference
    - <strong>gpu_inference:</strong> # whether to perform gpu decoding, set false for cpu decoding
    - <strong>gpuid_list:</strong> # set gpus, e.g., gpuid_list="0,1"
    - <strong>njob:</strong> # the number of jobs for CPU decoding, if `gpu_inference`=false, use CPU decoding, please set `njob`
    - `model`: model name on ModelScope
    - `data_dir`: the dataset dir needs to include `${data_dir}/wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
    - `output_dir`: result dir
    - `batch_size`: batchsize of inference
    - `gpu_inference`: whether to perform gpu decoding, set false for cpu decoding
    - `gpuid_list`: set gpus, e.g., `gpuid_list`="0,1"
    - `njob`: the number of jobs for CPU decoding, if `gpu_inference`=false, use CPU decoding, please set `njob`
    - `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models
    - `checkpoint_name`: only used for infer finetuned models, `valid.cer_ctc.ave.pb` (Default), which checkpoint is used to infer

- Decode with multi GPUs:
```shell
@@ -167,12 +169,12 @@
### Finetune with your data

- Modify finetune training related parameters in [finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/finetune.py)
    - <strong>output_dir:</strong> # result dir
    - <strong>data_dir:</strong> # the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
    - <strong>dataset_type:</strong> # for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
    - <strong>batch_bins:</strong> # 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
    - <strong>max_epoch:</strong> # number of training epoch
    - <strong>lr:</strong> # learning rate
    - `output_dir`: result dir
    - `data_dir`: the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
    - `dataset_type`: for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
    - `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
    - `max_epoch`: number of training epoch
    - `lr`: learning rate

- Then you can run the pipeline to finetune with:
```shell
@@ -183,14 +185,29 @@
CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1
```
## Inference with your finetuned model
- Modify inference related parameters in [infer_after_finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer_after_finetune.py)
    - <strong>modelscope_model_name: </strong> # model name on ModelScope
    - <strong>output_dir:</strong> # result dir
    - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
    - <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
    - <strong>batch_size:</strong> # batchsize of inference  

- Then you can run the pipeline to finetune with:
```python
    python infer_after_finetune.py
- Setting parameters in [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) 

- Decode with multi GPUs:
```shell
    bash infer.sh \
    --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
    --data_dir "./data/test" \
    --output_dir "./results" \
    --batch_size 64 \
    --gpu_inference true \
    --gpuid_list "0,1" \
    --checkpoint_dir "./checkpoint" \
    --checkpoint_name "valid.cer_ctc.ave.pb"
```
- Decode with multi-thread CPUs:
```shell
    bash infer.sh \
    --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
    --data_dir "./data/test" \
    --output_dir "./results" \
    --gpu_inference false \
    --njob 64 \
    --checkpoint_dir "./checkpoint" \
    --checkpoint_name "valid.cer_ctc.ave.pb"
```