(简体中文|English)
快速使用
注意: modelscope pipeline支持model zoo中的所有模型进行推理和微调。这里我们以typic模型为例来演示用法。
使用pipeline进行推理
语音识别
Paraformer模型
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)
# {'text': '欢迎大家来体验达摩院推出的语音识别模型'}
语音端点检测
FSMN-VAD模型
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)
# {'text': [[70, 2340], [2620, 6200], [6480, 23670], [23950, 26250], [26780, 28990], [29950, 31430], [31750, 37600], [38210, 46900], [47310, 49630], [49910, 56460], [56740, 59540], [59820, 70450]]}
标点恢复
CT_Transformer模型
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)
# {'text': '我们都是木头人,不会讲话,不会动。'}
时间戳预测
TP-Aligner模型
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)
# {'text': '<sil> 0.000 0.380;一 0.380 0.560;个 0.560 0.800;东 0.800 0.980;太 0.980 1.140;平 1.140 1.260;洋 1.260 1.440;国 1.440 1.680;家 1.680 1.920;<sil> 1.920 2.040;为 2.040 2.200;什 2.200 2.320;么 2.320 2.500;跑 2.500 2.680;到 2.680 2.860;西 2.860 3.040;太 3.040 3.200;平 3.200 3.380;洋 3.380 3.500;来 3.500 3.640;了 3.640 3.800;呢 3.800 4.150;<sil> 4.150 4.440;', 'timestamp': [[380, 560], [560, 800], [800, 980], [980, 1140], [1140, 1260], [1260, 1440], [1440, 1680], [1680, 1920], [2040, 2200], [2200, 2320], [2320, 2500], [2500, 2680], [2680, 2860], [2860, 3040], [3040, 3200], [3200, 3380], [3380, 3500], [3500, 3640], [3640, 3800], [3800, 4150]]}
说话人确认
X-vector模型
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])
# 0.8540499500025098
说话人日志
SOND模型
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
inference_diar_pipline = pipeline(
mode="sond_demo",
num_workers=0,
task=Tasks.speaker_diarization,
diar_model_config="sond.yaml",
model='damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch',
model_revision="v1.0.3",
sv_model="damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch",
sv_model_revision="v1.0.0",
)
audio_list=[
"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/record.wav",
"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_A.wav",
"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_B.wav",
"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_B1.wav"
]
results = inference_diar_pipline(audio_in=audio_list)
print(results)
# {'text': 'spk1 [(0.8, 1.84), (2.8, 6.16), (7.04, 10.64), (12.08, 12.8), (14.24, 15.6)]\nspk2 [(0.0, 1.12), (1.68, 3.2), (4.48, 7.12), (8.48, 9.04), (10.56, 14.48), (15.44, 16.0)]'}
常见问题
使用pipeline进行推理,如何在CPU与GPU进行切换
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
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
ngpu=0,
)
如何从本地模型进行推理(不联网使用)
使用modelscope-sdk将模型下载到本地
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)
或者使用git将模型下载到本地
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
从下载的本地模型进行推理(可以不联网使用)
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,
)
使用pipeline进行微调
语音识别
Paraformer模型
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 &
tail log.txt
[bach-gpu011024008134] 2023-04-23 18:59:13,976 (e2e_asr_paraformer:467) INFO: enable sampler in paraformer, sampling_ratio: 0.75
[bach-gpu011024008134] 2023-04-23 18:59:48,924 (trainer:777) INFO: 2epoch:train:1-50batch:50num_updates: iter_time=0.008, forward_time=0.302, loss_att=0.186, acc=0.942, loss_pre=0.005, loss=0.192, backward_time=0.231, optim_step_time=0.117, optim0_lr0=7.484e-06, train_time=0.753
[bach-gpu011024008134] 2023-04-23 19:00:23,869 (trainer:777) INFO: 2epoch:train:51-100batch:100num_updates: iter_time=1.152e-04, forward_time=0.275, loss_att=0.184, acc=0.945, loss_pre=0.005, loss=0.189, backward_time=0.234, optim_step_time=0.117, optim0_lr0=7.567e-06, train_time=0.699
[bach-gpu011024008134] 2023-04-23 19:00:58,463 (trainer:777) INFO: 2epoch:train:101-150batch:150num_updates: iter_time=1.123e-04, forward_time=0.271, loss_att=0.204, acc=0.942, loss_pre=0.005, loss=0.210, backward_time=0.231, optim_step_time=0.116, optim0_lr0=7.651e-06, train_time=0.692
常见问题
多GPU训练
可以使用下面的指令进行多GPU训练
CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1