FunASR/docs/modescope_pipeline/asr_pipeline.md

Speech Recognition

Note

: The modelscope pipeline supports all the models in model zoo to inference and finetine. Here we take model of Paraformer and Paraformer-online as example to demonstrate the usage.

Inference

Quick start

Paraformer model

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
)

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

inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
    )
import soundfile
speech, sample_rate = soundfile.read("example/asr_example.wav")

param_dict = {"cache": dict(), "is_final": False}
chunk_stride = 7680# 480ms
# first chunk, 480ms
speech_chunk = speech[0:chunk_stride] 
rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
print(rec_result)
# next chunk, 480ms
speech_chunk = speech[chunk_stride:chunk_stride+chunk_stride]
rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
print(rec_result)

Full code of demo, please ref to demo

UniASR model

There are three decoding mode for UniASR model(fast、normal、offline), for more model detailes, please refer to docs

decoding_model = "fast" # "fast"、"normal"、"offline"
inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825',
    param_dict={"decoding_model": decoding_model})

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)

The decoding mode of fast and normal Full code of demo, please ref to demo

RNN-T-online model

Undo

API-reference

define pipeline

• task: Tasks.auto_speech_recognition
• model: model name in model zoo, 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

• 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.`:

  asr_example1  ./audios/asr_example1.wav
  asr_example2  ./audios/asr_example2.wav
  

  In this case of wav.scp input, output_dir must be set to save the output results
• audio_fs: audio sampling rate, only set when audio_in is pcm audio
• output_dir: None (Defalut), the output path of results if set

Inference with multi-thread CPUs or multi GPUs

FunASR also offer recipes infer.sh to decode with multi-thread CPUs, or multi GPUs.

• Setting parameters in infer.sh

  • 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

• Decode with multi GPUs:

    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"

• Decode with multi-thread CPUs:

    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

• Results

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.

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.

Finetune with pipeline

Quick start

finetune.py

import os
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from modelscope.msdatasets.audio.asr_dataset import ASRDataset

def modelscope_finetune(params):
    if not os.path.exists(params.output_dir):
        os.makedirs(params.output_dir, exist_ok=True)
    # dataset split ["train", "validation"]
    ds_dict = ASRDataset.load(params.data_path, namespace='speech_asr')
    kwargs = dict(
        model=params.model,
        data_dir=ds_dict,
        dataset_type=params.dataset_type,
        work_dir=params.output_dir,
        batch_bins=params.batch_bins,
        max_epoch=params.max_epoch,
        lr=params.lr)
    trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
    trainer.train()


if __name__ == '__main__':
    from funasr.utils.modelscope_param import modelscope_args
    params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
    params.output_dir = "./checkpoint"                      # 模型保存路径
    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
    params.dataset_type = "small"                           # 小数据量设置small，若数据量大于1000小时，请使用large
    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，
    params.max_epoch = 50                                   # 最大训练轮数
    params.lr = 0.00005                                     # 设置学习率
    
    modelscope_finetune(params)

python finetune.py &> log.txt &

Finetune with your data

• Modify finetune training related parameters in finetune.py

  • 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:

python finetune.py

If you want finetune with multi-GPUs, you could:

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

  • modelscope_model_name: # model name on ModelScope
  • output_dir: # result dir
  • data_dir: # the dataset dir needs to include test/wav.scp. If test/text is also exists, CER will be computed
  • decoding_model_name: # set the checkpoint name for decoding, e.g., valid.cer_ctc.ave.pb
  • batch_size: # batchsize of inference

• Then you can run the pipeline to finetune with:

    python infer_after_finetune.py