kan-tts docker本地化
环境安装
下载docker镜像(python3.8的)
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.8.0-py38-torch2.0.1-tf2.13.0-1.9.2安装基础模型
pip install modelscope安装语音模型
pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html自动标注
安装最新版tts-autolabel
# 运行此代码块安装
tts-autolabel import sys !{sys.executable} -m pip install -U tts-autolabel -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
如果网不行，指定阿里镜像源
!{sys.executable} -m pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
自动标注
from modelscope.tools import run_auto_labelinput_wav = "./test_wavs/"
output_data = "./output_training_data/"ret, report = run_auto_label(input_wav=input_wav, work_dir=output_data, resource_revision="v1.0.7")微调
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from modelscope.utils.audio.audio_utils import TtsTrainTypepretrained_model_id = 'damo/speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k'dataset_id = "./output_training_data/"
pretrain_work_dir = "./pretrain_work_dir/"# 训练信息，用于指定需要训练哪个或哪些模型，这里展示AM和Vocoder模型皆进行训练
# 目前支持训练：TtsTrainType.TRAIN_TYPE_SAMBERT, TtsTrainType.TRAIN_TYPE_VOC
# 训练SAMBERT会以模型最新step作为基础进行finetune
train_info = {TtsTrainType.TRAIN_TYPE_SAMBERT: { # 配置训练AM（sambert）模型'train_steps': 202, # 训练多少个step 'save_interval_steps': 200, # 每训练多少个step保存一次checkpoint'log_interval': 10 # 每训练多少个step打印一次训练日志}
}# 配置训练参数，指定数据集，临时工作目录和train_info
kwargs = dict(model=pretrained_model_id, # 指定要finetune的模型model_revision = "v1.0.6",work_dir=pretrain_work_dir, # 指定临时工作目录train_dataset=dataset_id, # 指定数据集idtrain_type=train_info # 指定要训练类型及参数
)trainer = build_trainer(Trainers.speech_kantts_trainer,default_args=kwargs)trainer.train()其中
pretrained_model_id = 'damo/speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k'
要下载下来
最好提取下载，然后pretrained_model_id后面就等于下面下载的地址
# 克隆预训练模型
git clone https://www.modelscope.cn/damo/speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k.git
拉取下来，然后合成
合成模型
import os
from modelscope.models.audio.tts import SambertHifigan
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasksmodel_dir = os.path.abspath("./pretrain_work_dir")custom_infer_abs = {'voice_name':'F7','am_ckpt':os.path.join(model_dir, 'tmp_am', 'ckpt'),'am_config':os.path.join(model_dir, 'tmp_am', 'config.yaml'),'voc_ckpt':os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan', 'ckpt'),'voc_config':os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan','config.yaml'),'audio_config':os.path.join(model_dir, 'data', 'audio_config.yaml'),'se_file':os.path.join(model_dir, 'data', 'se', 'se.npy')
}
kwargs = {'custom_ckpt': custom_infer_abs}model_id = SambertHifigan(os.path.join(model_dir, "orig_model"), **kwargs)inference = pipeline(task=Tasks.text_to_speech, model=model_id)
output = inference(input="今天的天气真不错")import IPython.display as ipd
ipd.Audio(output["output_wav"], rate=16000)参考地址:
环境安装
SambertHifigan个性化语音合成-中文-预训练-16k
