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2023年07月

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  • 回答了问题 2023-07-02

    PTTS-basemodel微调报错

    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 )

    踩0 评论0
  • 回答了问题 2023-07-02

    使用SambertHifigan个性化语音合成-中文-预训练-16k报错，完全按照模型介绍中的操作

    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 kantts==0.0.1 -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 -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 )

    踩0 评论1
  • 回答了问题 2023-07-02

    kantts 这个模型是什么东西啊，运行会报错

    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

    踩0 评论0
  • 回答了问题 2023-07-02

    modelscope1.6.1的环境，本地报错似乎提示使用正确 modelscope版本

    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 )

    踩0 评论0
  • 回答了问题 2023-07-02

    基于PTTS-basemodel微调时报错InvalidProtobuf: [ONNXRuntime

    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 )

    踩0 评论0
  • 提交了问题 2023-07-02

    提供能在其他服务器ubuntu环境下跑通的脚本，亲测有效

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