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亲测只有model_revision = 'v1.0.4'才能正常跑通，kantts必须安装0.0.1 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 )

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 # 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 )

测试只能用kantts==0.0.1 pip install tts-autolabel kantts==0.0.1 -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

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 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 )

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 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 )