Quick Start

Inference with pipeline

Speech Recognition

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)

Voice Activity Detection

FSMN-VAD

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
import logging
logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)

inference_pipeline = pipeline(
    task=Tasks.voice_activity_detection,
    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
    )

segments_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav')
print(segments_result)

Punctuation Restoration

CT_Transformer

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

inference_pipeline = pipeline(
    task=Tasks.punctuation,
    model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
    )

rec_result = inference_pipeline(text_in='我们都是木头人不会讲话不会动')
print(rec_result)

Timestamp Prediction

TP-Aligner

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

inference_pipeline = pipeline(
    task=Tasks.speech_timestamp,
    model='damo/speech_timestamp_prediction-v1-16k-offline',)

rec_result = inference_pipeline(
    audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_timestamps.wav',
    text_in='一 个 东 太 平 洋 国 家 为 什 么 跑 到 西 太 平 洋 来 了 呢',)
print(rec_result)

Speaker Verification

X-vector

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
import numpy as np

inference_sv_pipline = pipeline(
    task=Tasks.speaker_verification,
    model='damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch'
)

# embedding extract
spk_embedding = inference_sv_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav')["spk_embedding"]

# speaker verification
rec_result = inference_sv_pipline(audio_in=('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav','https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_same.wav'))
print(rec_result["scores"][0])

FAQ

How to switch device from GPU to CPU with pipeline

The pipeline defaults to decoding with GPU (ngpu=1) when GPU is available. If you want to switch to CPU, you could set ngpu=0
python inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch', ngpu=0, )

How to infer from local model path

Download model to local dir, by modelscope-sdk

from modelscope.hub.snapshot_download import snapshot_download

local_dir_root = "./models_from_modelscope"
model_dir = snapshot_download('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch', cache_dir=local_dir_root)

Or download model to local dir, by git lfs
shell git lfs install # git clone https://www.modelscope.cn/<namespace>/<model-name>.git git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git

Infer with local model path
python local_dir_root = "./models_from_modelscope/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model=local_dir_root, )

Finetune with pipeline

Speech Recognition

Paraformer model

finetune.py
```python
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)

```shell
python finetune.py &> log.txt &

FAQ

Multi GPUs training and distributed training

If you want finetune with multi-GPUs, you could:
shell CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1