错误提示 'segmentation-clustering is not in the pipelines registry'。这个错误提示表明 ModelScope 没有找到名为 'segmentation-clustering' 的管道（pipeline）。

可能出现这个错误的原因是因为您使用的 ModelScope 版本过低，不支持 'segmentation-clustering' 管道。请确保您使用的是 ModelScope 1.6.1 或更高版本，以获得对 'segmentation-clustering' 管道的支持。

提供能在其他服务器ubuntu环境下跑通的脚本，亲测有效

环境如下：

Ubuntu 20.04 + Python3.8

+---------------------------------------------------------------------------------------+ | NVIDIA-SMI 530.30.02 Driver Version: 530.30.02 CUDA Version: 12.1 | |-----------------------------------------+----------------------+----------------------+

‘’‘ #!/bin/bash

设置显存分片大小，防止OOM爆显存

cat>/etc/profile.d/proxy.sh<<EOF export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:32 EOF

更新以及安装必须的软件

apt update apt upgrade -y apt list --upgradable -a

apt-get install libsndfile1 sox nano wget curl git zip -y

apt autoclean -y apt autoremove -y

登录时使能设置环境变量

source /etc/profile.d/proxy.sh

克隆官方基础库，魔法自备

git clone https://github.com/modelscope/modelscope.git

安装Audio所必须的包，亲测有效

cd modelscope python -m pip install --upgrade pip pip install -r requirements/tests.txt pip install -r requirements/framework.txt -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html pip install -r requirements/audio.txt -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html pip install -r requirements/nlp.txt -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html pip install . pip install tts-autolabel -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

pip install typeguard==2.13.3 pydantic==1.10.10 numpy==1.21.6 kantts==0.0.1 -y pip uninstall funasr -y

下载nltk包到根目录

cd ~ wget https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/download_files/nltk_data.zip unzip nltk_data.zip

接下来就可以按照描述中的步骤进行体验

‘’‘

‘’‘

接下来用这个python脚本运行测试一下环境是否ok，如果没报错就应该是ok了

from modelscope.tools import run_auto_label from modelscope.metainfo import Trainers from modelscope.trainers import build_trainer from modelscope.utils.audio.audio_utils import TtsTrainType import os from modelscope.models.audio.tts import SambertHifigan from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks import torch print(torch.version) print(torch.cuda.is_available())

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

text = '待合成文本' model_id = 'damo/speech_sambert-hifigan_tts_zh-cn_16k' sambert_hifigan_tts = pipeline(task=Tasks.text_to_speech, model=model_id) output = sambert_hifigan_tts(input=text, voice='zhitian_emo') wav = output[OutputKeys.OUTPUT_WAV] with open('output.wav', 'wb') as f: f.write(wav)

‘’‘

最后需要特别注意的是，预训练model_revision = "v1.0.4"才可以

kwargs = dict( model=pretrained_model_id, # 指定要finetune的模型 model_revision = "v1.0.4", # 就是这里，只有改成1.0.4才顺利通过 work_dir=pretrain_work_dir, # 指定临时工作目录 train_dataset=dataset_id, # 指定数据集id train_type=train_info # 指定要训练类型及参数 )