babysor
保姆级别教程（持续更新各类社区/非官方教程----
播放都是杂音。。。。。。。。。。。。。
C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main>python synthesizer_train.py mandarin E:\datat\rain_set\train
Arguments:
run_id: mandarin
syn_dir: E:\datat\rain_set\train
models_dir: synthesizer/saved_models/
save_every: 1000
backup_every: 25000
force_restart: False
hparams:
Checkpoint path: synthesizer\saved_models\mandarin\mandarin.pt Loading training data from: E:\datat\rain_set\train\train.txt Using model: Tacotron Using device: cpu
Initialising Tacotron Model...
Trainable Parameters: 30.872M
Loading weights at synthesizer\saved_models\mandarin\mandarin.pt Tacotron weights loaded from step 0 Using inputs from: E:\datat\rain_set\train\train.txt E:\datat\rain_set\train\mels E:\datat\rain_set\train\embeds Traceback (most recent call last): File "synthesizer_train.py", line 35, in <module> train(**vars(args)) File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\train.py", line 111, in train dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams) File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\synthesizer_dataset.py", line 12, in init with metadata_fpath.open("r", encoding="utf-8") as metadata_file: File "C:\ProgramData\Anaconda3\lib\pathlib.py", line 1221, in open return io.open(self, mode, buffering, encoding, errors, newline, File "C:\ProgramData\Anaconda3\lib\pathlib.py", line 1077, in _opener return self._accessor.open(self, flags, mode) FileNotFoundError: [Errno 2] No such file or directory: 'E:\datat\rain_set\train\train.txt'
C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main>python demo_toolbox.py -d E:\data\train_set\train 2021-08-19 17:56:17.809226: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2021-08-19 17:56:17.809396: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. Arguments: datasets_root: E:\data\train_set\train enc_models_dir: encoder\saved_models syn_models_dir: synthesizer\saved_models voc_models_dir: vocoder\saved_models cpu: False seed: None no_mp3_support: False
Warning: you do not have any of the recognized datasets in E:\data\train_set\train. The recognized datasets are: LibriSpeech/dev-clean LibriSpeech/dev-other LibriSpeech/test-clean LibriSpeech/test-other LibriSpeech/train-clean-100 LibriSpeech/train-clean-360 LibriSpeech/train-other-500 LibriTTS/dev-clean LibriTTS/dev-other LibriTTS/test-clean LibriTTS/test-other LibriTTS/train-clean-100 LibriTTS/train-clean-360 LibriTTS/train-other-500 LJSpeech-1.1 VoxCeleb1/wav VoxCeleb1/test_wav VoxCeleb2/dev/aac VoxCeleb2/test/aac VCTK-Corpus/wav48 aidatatang_200zh/corpus/dev aidatatang_200zh/corpus/test Feel free to add your own. You can still use the toolbox by recording samples yourself. Loaded encoder "pretrained.pt" trained to step 1564501 Synthesizer using device: cpu Trainable Parameters: 30.872M Traceback (most recent call last): File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\toolbox_init_.py", line 122, in <lambda> func = lambda: self.synthesize() or self.vocode() File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\toolbox_init_.py", line 229, in synthesize specs = self.synthesizer.synthesize_spectrograms(texts, embeds) File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\inference.py", line 86, in synthesize_spectrograms self.load() File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\inference.py", line 64, in load self._model.load(self.model_fpath) File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\models\tacotron.py", line 497, in load self.load_state_dict(checkpoint["model_state"]) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1223, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for Tacotron: size mismatch for encoder.embedding.weight: copying a param with shape torch.Size([75, 512]) from checkpoint, the shape in current model is torch.Size([70, 512]).
| Generating 1/1
Done.
------------------ 原始邮件 ------------------ 发件人: "babysor/Realtime-Voice-Clone-Chinese" @.>; 发送时间: 2021年8月19日(星期四) 下午5:57 @.>; @.>;"State @.>; 主题: Re: [babysor/Realtime-Voice-Clone-Chinese] 来个保姆级别教程@@ (#20)
别害羞，快分享一下卡在哪里啦，我再优化优化
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出一个详细的教程吧，大佬👍
---原始邮件--- 发件人: @.> 发送时间: 2021年8月19日(周四) 下午5:57 收件人: @.>; 抄送: @.>;"State @.>; 主题: Re: [babysor/Realtime-Voice-Clone-Chinese] 来个保姆级别教程@@ (#20)
别害羞，快分享一下卡在哪里啦，我再优化优化
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E:\data\aidatatang_200zh\aidatatang_200zh\corpus\train 数据集解压路径 这一步synthesizer_preprocess_audio.py有问题吗 C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main> python synthesizer_preprocess_audio.py E:\data\aidatatang_200zh\aidatatang_200zh Arguments: datasets_root: E:\data\aidatatang_200zh\aidatatang_200zh out_dir: E:\data\aidatatang_200zh\aidatatang_200zh\SV2TTS\synthesizer n_processes: None skip_existing: False hparams: no_alignments: False dataset: aidatatang_200zh
Using data from:
E:\data\aidatatang_200zh\aidatatang_200zh\aidatatang_200zh\corpus\train
Traceback (most recent call last):
File "synthesizer_preprocess_audio.py", line 63, in
E:\data\aidatatang_200zh\aidatatang_200zh\corpus\train 数据集解压路径 这一步synthesizer_preprocess_audio.py有问题吗 C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main> python synthesizer_preprocess_audio.py E:\data\aidatatang_200zh\aidatatang_200zh Arguments: datasets_root: E:\data\aidatatang_200zh\aidatatang_200zh out_dir: E:\data\aidatatang_200zh\aidatatang_200zh\SV2TTS\synthesizer n_processes: None skip_existing: False hparams: no_alignments: False dataset: aidatatang_200zh
Using data from: E:\data\aidatatang_200zh\aidatatang_200zh\aidatatang_200zh\corpus\train Traceback (most recent call last): File "synthesizer_preprocess_audio.py", line 63, in preprocess_dataset(**vars(args)) File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\preprocess.py", line 32, in preprocess_dataset assert all(input_dir.exists() for input_dir in input_dirs) AssertionError
python synthesizer_preprocess_audio.py E:\data\aidatatang_200zh 不用多一层
python synthesizer_preprocess_audio.py E:\data\aidatatang_200zh 不用多一层 解决了
python synthesizer_preprocess_audio.py E:\data\aidatatang_200zh 不用多一层 @解决了大佬
这步也太慢了。。。 C:\Users\lxd\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main>python synthesizer_train.py mandarin D:\data\aidatatang_200zh\SV2TTS\synthesizer Arguments: run_id: mandarin syn_dir: D:\data\aidatatang_200zh\SV2TTS\synthesizer models_dir: synthesizer/saved_models/ save_every: 1000 backup_every: 25000 force_restart: False hparams:
Checkpoint path: synthesizer\saved_models\mandarin\mandarin.pt Loading training data from: D:\data\aidatatang_200zh\SV2TTS\synthesizer\train.txt Using model: Tacotron Using device: cpu
Initialising Tacotron Model...
Trainable Parameters: 30.872M
Loading weights at synthesizer\saved_models\mandarin\mandarin.pt Tacotron weights loaded from step 0 Using inputs from: D:\data\aidatatang_200zh\SV2TTS\synthesizer\train.txt D:\data\aidatatang_200zh\SV2TTS\synthesizer\mels D:\data\aidatatang_200zh\SV2TTS\synthesizer\embeds Found 122482 samples +----------------+------------+---------------+------------------+ | Steps with r=2 | Batch Size | Learning Rate | Outputs/Step (r) | +----------------+------------+---------------+------------------+ | 20k Steps | 12 | 0.001 | 2 | +----------------+------------+---------------+------------------+
{| Epoch: 1/2 (500/10207) | Loss: 0.9025 | 0.065 steps/s | Step: 0k | }Input at step 500: wo3 yao4 gei3 wang2 ming2 da3 dian4 hua4~__________________________________________________________ {| Epoch: 1/2 (1000/10207) | Loss: 0.8266 | 0.071 steps/s | Step: 1k | }Input at step 1000: na4 me wo3 jiu4 chong2 xin1 ren4 shi2 ni3~______________________________________________________________ {| Epoch: 1/2 (1500/10207) | Loss: 0.7602 | 0.074 steps/s | Step: 1k | }Input at step 1500: mei3 tian1 dou1 na4 me wan3 shui4 jiao4~___________________________________ {| Epoch: 1/2 (2000/10207) | Loss: 0.7415 | 0.075 steps/s | Step: 2k | }Input at step 2000: da3 dian4 hua4 gei3 deng4 han4 ling2~_________________________________________________________________________ {| Epoch: 1/2 (2500/10207) | Loss: 0.6921 | 0.068 steps/s | Step: 2k | }Input at step 2500: zhen1 xiang4 yong3 yuan3 zhi3 you3 yi2 ge4~___________________________________ {| Epoch: 1/2 (3000/10207) | Loss: 0.6741 | 0.072 steps/s | Step: 3k | }Input at step 3000: xia4 men2 wai4 guo2 yu3 xue2 xiao4 chu1 er4 nian2 ji2 chen2 xiao3 qi2 jia1 de zhu4 zhi3~ {| Epoch: 1/2 (3500/10207) | Loss: 0.6499 | 0.070 steps/s | Step: 3k | }Input at step 3500: ru2 guo3 wo3 he2 ni3 zai4 yi4 qi3~_______________________________________________________ {| Epoch: 1/2 (4000/10207) | Loss: 0.6679 | 0.073 steps/s | Step: 4k | }Input at step 4000: fu4 jin4 de ping2 an1 yin2 hang2~_________________________________ {| Epoch: 1/2 (4500/10207) | Loss: 0.6349 | 0.069 steps/s | Step: 4k | }Input at step 4500: ming2 zi4 shi4 hui3 guo4 cheng2 nuo4 shu1~_____________________________________________________ {| Epoch: 1/2 (5000/10207) | Loss: 0.6392 | 0.073 steps/s | Step: 5k | }Input at step 5000: wo3 shen2 me shi2 hou4 cai2 neng2 chong1 man3 dian4~_______________________ {| Epoch: 1/2 (5500/10207) | Loss: 0.6293 | 0.073 steps/s | Step: 5k | }Input at step 5500: wo3 da3 ni3 hao3 bu4 hao3 ma ge2 shi4 chong2 fu4~___________ {| Epoch: 1/2 (6000/10207) | Loss: 0.6715 | 0.077 steps/s | Step: 6k | }Input at step 6000: ci3 ji4 hao3 wu2 liao2 da3 yi1 dian4 ying3 ming2~___________________________________ {| Epoch: 1/2 (6500/10207) | Loss: 0.6446 | 0.075 steps/s | Step: 6k | }Input at step 6500: wo3 gei3 ni3 fa1 de ni3 shou1 dao4 le ma~___________________________________________________________ {| Epoch: 1/2 (7000/10207) | Loss: 0.6022 | 0.068 steps/s | Step: 7k | }Input at step 7000: ning4 que1 wu2 lan4 zhi3 wei4 yi3 hou4 de du2 yi1 wu2 er4~________________________ {| Epoch: 1/2 (7500/10207) | Loss: 0.6178 | 0.067 steps/s | Step: 7k | }Input at step 7500: mei2 you3 wang3 luo4 ni3 hai2 hui4 liao2 tian1 ma~______________________ {| Epoch: 1/2 (8000/10207) | Loss: 0.6041 | 0.068 steps/s | Step: 8k | }Input at step 8000: wo3 bu4 fa1 le wo3 yao4 shui4 jiao4 le~____________________________________________________________________________________________ {| Epoch: 1/2 (8500/10207) | Loss: 0.6078 | 0.072 steps/s | Step: 8k | }Input at step 8500: ni3 cai1 lai2 cai1 qu4 ye3 cai1 bu4 ming2 bai2~____________________________________________________ {| Epoch: 1/2 (9000/10207) | Loss: 0.6055 | 0.072 steps/s | Step: 9k | }Input at step 9000: ni3 wen4 le wo3 tou2 dou1 da4 le~_______________________________________ {| Epoch: 1/2 (9500/10207) | Loss: 0.5816 | 0.069 steps/s | Step: 9k | }Input at step 9500: xia4 ban1 mei2 you3 mei2 chu1 qu4 guang4~_______________________________________ {| Epoch: 1/2 (10000/10207) | Loss: 0.5664 | 0.068 steps/s | Step: 10k | }Input at step 10000: ni3 jin1 tian1 bu2 shi4 bu4 shang4 ban1 ma~__________________________________________________ {| Epoch: 1/2 (10207/10207) | Loss: 0.5879 | 0.071 steps/s | Step: 10k | } {| Epoch: 2/2 (293/10207) | Loss: 0.5840 | 0.070 steps/s | Step: 10k | }Input at step 10500: ai4 qing2 xiao3 shuo1 ma2 que4 gao3 ding4 hua1 mei3 nan2~_________________________________ {| Epoch: 2/2 (322/10207) | Loss: 0.5892 | 0.070 steps/s | Step: 10k | }
Arguments: datasets_root: D:\Data\aidatatang_200zh out_dir: D:\Data\aidatatang_200zh\SV2TTS\synthesizer n_processes: None skip_existing: False hparams: no_alignments: False dataset: aidatatang_200zh
Using data from:
D:\Data\aidatatang_200zh\aidatatang_200zh\corpus\train
Traceback (most recent call last):
File "synthesizer_preprocess_audio.py", line 63, in
trian里是长这样的嘛
7天有效
For non-trainer: https://github.com/babysor/MockingBird/wiki For trainer: https://vaj2fgg8yn.feishu.cn/docs/doccn7kAbr3SJz0KM0SIDJ0Xnhd#
D:\Downloads\MockingBird-main>python demo_toolbox.py -d "D:\Downloads\aidatatang_200zh" 2021-10-01 14:32:20.420728: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2021-10-01 14:32:20.420998: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. Arguments: datasets_root: D:\Downloads\aidatatang_200zh enc_models_dir: encoder\saved_models syn_models_dir: synthesizer\saved_models voc_models_dir: vocoder\saved_models cpu: False seed: None no_mp3_support: False
Traceback (most recent call last):
File "D:\Downloads\MockingBird-main\demo_toolbox.py", line 43, in
python demo_toolbox.py -d E:\Fun\datasets Arguments: datasets_root: E:\Fun\datasets enc_models_dir: encoder\saved_models syn_models_dir: synthesizer\saved_models voc_models_dir: vocoder\saved_models cpu: False seed: None no_mp3_support: False
Warning: you do not have any of the recognized datasets in E:\Fun\datasets. The recognized datasets are: LibriSpeech/dev-clean LibriSpeech/dev-other LibriSpeech/test-clean LibriSpeech/test-other LibriSpeech/train-clean-100 LibriSpeech/train-clean-360 LibriSpeech/train-other-500 LibriTTS/dev-clean LibriTTS/dev-other LibriTTS/test-clean LibriTTS/test-other LibriTTS/train-clean-100 LibriTTS/train-clean-360 LibriTTS/train-other-500 LJSpeech-1.1 VoxCeleb1/wav VoxCeleb1/test_wav VoxCeleb2/dev/aac VoxCeleb2/test/aac VCTK-Corpus/wav48 aidatatang_200zh/corpus/dev aidatatang_200zh/corpus/test aishell3/test/wav magicdata/train Feel free to add your own. You can still use the toolbox by recording samples yourself. 这里出了什么问题，进去之后数据那两行是灰色的。求教QAQ
@zhuzaileiting 你这是用cpu训练的，GPU速度大概在1.3-2 steps/s
您好，我根据您的文档修改了Batch size调整到36，现在显存已经使用80%，但是显卡使用率还是只有12%，我还需要调整哪个参数呢？
建了二群 方便交流
田渣渣 @.***> 于 2021年10月9日周六 下午1:23写道：
求群成员邀请入群，wx是Mr-Sandman___
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@zhuzaileiting 你这是用cpu训练的，GPU速度大概在1.3-2 steps/s
您好，我根据您的文档修改了Batch size调整到36，现在显存已经使用80%，但是显卡使用率还是只有12%，我还需要调整哪个参数呢？
我3090，设置为48，0.86 setps/s，达不到1.3-2
@zhuzaileiting 你这是用cpu训练的，GPU速度大概在1.3-2 steps/s
您好，我根据您的文档修改了Batch size调整到36，现在显存已经使用80%，但是显卡使用率还是只有12%，我还需要调整哪个参数呢？
我3090，设置为48，0.86 setps/s，达不到1.3-2
不同batch size，step速度没有可比较性。
@zhuzaileiting 你这是用cpu训练的，GPU速度大概在1.3-2 steps/s
您好，我根据您的文档修改了Batch size调整到36，现在显存已经使用80%，但是显卡使用率还是只有12%，我还需要调整哪个参数呢？
我3090，设置为48，0.86 setps/s，达不到1.3-2
不同batch size，step速度没有可比较性。
和数据集大小也有关吧
@zhuzaileiting 你这是用cpu训练的，GPU速度大概在1.3-2 steps/s
您好，我根据您的文档修改了Batch size调整到36，现在显存已经使用80%，但是显卡使用率还是只有12%，我还需要调整哪个参数呢？
我3090，设置为48，0.86 setps/s，达不到1.3-2
不同batch size，step速度没有可比较性。
和数据集大小也有关吧
是的
别害羞，快分享一下卡在哪里啦，我再优化优化
作者你好，有没有考虑出一个docker镜像，所有搭建问题都迎刃而解。。。
需要考虑显卡部分，现在确实有支持cuda的docker，你能帮我research一下吗？
别害羞，快分享一下卡在哪里啦，我再优化优化
作者你好，有没有考虑出一个docker镜像，所有搭建问题都迎刃而解。。。
需要考虑显卡部分，现在确实有支持cuda的docker，你能帮我research一下吗？
只用于预测的容器镜像我已经构建好，内置预训练模型，本次测试跑过了。
docker run -p 8080:8080 -it jiada/mocking_bird
别害羞，快分享一下卡在哪里啦，我再优化优化
作者你好，有没有考虑出一个docker镜像，所有搭建问题都迎刃而解。。。
需要考虑显卡部分，现在确实有支持cuda的docker，你能帮我research一下吗？
只用于预测的容器镜像我已经构建好，内置预训练模型，本次测试跑过了。
docker run -p 8080:8080 -it jiada/mocking_bird
有测试的相关信息吗？如果方便的话发起pr？
别害羞，快分享一下卡在哪里啦，我再优化优化
作者你好，有没有考虑出一个docker镜像，所有搭建问题都迎刃而解。。。
需要考虑显卡部分，现在确实有支持cuda的docker，你能帮我research一下吗？
只用于预测的容器镜像我已经构建好，内置预训练模型，本次测试跑过了。
docker run -p 8080:8080 -it jiada/mocking_bird
我试用了一下，有一些问题
- 基于浏览器的限制，不是https没法获取麦克风权限，本人不擅长python，只好另辟蹊径在不改动镜像的情况下，在宿主机上搭了个nginx随便起了个端口号666 配好ssl证书来代理8080
- 第二个问题那应该真的是一个问题，我进到容器里执行demo_toolbox报错，我有个疑问，是不是这个demo_tool只针对windows啊（我是mac）。报错如所示
# pwd
/workspace
# python demo_toolbox.py
Traceback (most recent call last):
File "demo_toolbox.py", line 2, in <module>
from toolbox import Toolbox
File "/workspace/toolbox/__init__.py", line 1, in <module>
from toolbox.ui import UI
File "/workspace/toolbox/ui.py", line 11, in <module>
import sounddevice as sd
File "/opt/conda/lib/python3.7/site-packages/sounddevice.py", line 71, in <module>
raise OSError('PortAudio library not found')
OSError: PortAudio library not found
别害羞，快分享一下位置啦，我再优化优化
作者你好，有没有考虑出一个码头工人假象，所有搭建的问题都迎刃而解。。。
需要考虑显瘦部分，现在确实有支持 cuda 的码头工人，你能帮我研究一下吗？
仅用于预测的假设我已经构建好，构建了预训练模型，本次测试跑过了。
docker run -p 8080:8080 -it jiada/mocking_bird我试用了一下，有一些问题
- 基于浏览器的限制，不是https无法获取获取权限，只有自己不擅长蟒蛇，好另一种方法可以在不伪装镜像的下，在智能机上机上了几个nginx随便找个端口号666情况配好ssl来代理8080
- 第二个问题那应该是一个问题，进入容器里执行demo_toolbox，我这个问题，是不是demo_tool只针对windows啊（我是mac）。报错如所示
#密码 /工作区 # python demo_toolbox.py 回溯（最近一次调用最后一次）： 文件“ demo_toolbox.py ”，第2行，在 <模块> 从工具箱导入工具箱 文件“ /workspace/toolbox/__init__.py ”，第1行，在 <模块> 从 toolbox.ui 导入 UI 文件“ /workspace/toolbox/ui.py ”，第11行，在 <模块> 将声音设备导入为 sd 文件“ /opt/conda/lib/python3.7/site-packages/sounddevice.py ”，第 71 行，在 < module > 中 引发 OSError( ' PortAudio library not found ' ) OSError：找不到 PortAudio 库
我也是这个问题 怎么解决啊
C:\mockingbird\MockingBird-main>python demo_toolbox.py
Traceback (most recent call last):
File "demo_toolbox.py", line 2, in
- [ ]
我想了下 这个docker里运行GUI的软件 也许还得参考
https://www.cloudsavvyit.com/10520/how-to-run-gui-applications-in-a-docker-container/
ERKE PLAY @.***> 于 2021年12月6日周一 下午2:45写道：
还有入群码吗
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dadayefeng @.***> 于 2021年12月13日周一 下午9:33写道：
大佬更新一下二维码
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做了一个视频 https://www.bilibili.com/video/BV15Z4y197Ra 还算详细，应该只漏了ffmpeg的安装
方便指定放到readme吗？
谢谢！
牛文龙 博源金融
联系我
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D:\M\MockingBird-main>python pre.py D:\数据集 -d aidatatang_200zh -n 7
Using data from:
D:\数据集\aidatatang_200zh\corpus\train
Traceback (most recent call last):
File "D:\M\MockingBird-main\pre.py", line 74, in
在输入音频后出现了这个报错……
Feel free to add your own. You can still use the toolbox by recording samples yourself.
Traceback (most recent call last):
File "D:\mokingbird\MockingBird-main\MockingBird-main\toolbox_init_.py", line 103, in
在输入音频后出现了这个问题……
随意添加您自己的。您仍然可以通过自己录制样本来使用工具箱。 Traceback（最近一次调用最后）： 文件“D:\mokingbird\MockingBird-main\MockingBird-main\toolbox_init _.py ”，第 103 行，在 func = lambda: self.load_from_browser(self.ui.browse_file()) 文件“D:\mokingbird\MockingBird-main\MockingBird-main\toolbox_init _.py ”，第 170 行，在 load_from_browser wav = Synthesizer.load_preprocess_wav(fpath) 文件“D:\mokingbird\MockingBird-main\MockingBird-main\ synthesizer\inference.py"，第 146 行，在 load_preprocess_wav wav = librosa.load(str(fpath), hparams.sample_rate)[0] TypeError: load() 采用 1 个位置参数，但给出了 2 个
已解决 在命令的命令输入pip install librosa==0.8.1
各位大佬！！帮我看看这是什么错误啊，，我已经把模板放入相应的文件夹里了可是仍然不行额
D:\迅雷下载\MockingBird-main>python demo_toolbox.py Arguments: datasets_root: None enc_models_dir: encoder\saved_models syn_models_dir: synthesizer\saved_models voc_models_dir: vocoder\saved_models cpu: False seed: None no_mp3_support: False
Warning: you did not pass a root directory for datasets as argument. The recognized datasets are: LibriSpeech/dev-clean LibriSpeech/dev-other LibriSpeech/test-clean LibriSpeech/test-other LibriSpeech/train-clean-100 LibriSpeech/train-clean-360 LibriSpeech/train-other-500 LibriTTS/dev-clean LibriTTS/dev-other LibriTTS/test-clean LibriTTS/test-other LibriTTS/train-clean-100 LibriTTS/train-clean-360 LibriTTS/train-other-500 LJSpeech-1.1 VoxCeleb1/wav VoxCeleb1/test_wav VoxCeleb2/dev/aac VoxCeleb2/test/aac VCTK-Corpus/wav48 aidatatang_200zh/corpus/dev aidatatang_200zh/corpus/test aishell3/test/wav magicdata/train Feel free to add your own. You can still use the toolbox by recording samples yourself.
大佬，我requirements安装没报错，但是运行程序时会报错:
Traceback (most recent call last):
File "E:\MockingBird\demo_toolbox.py", line 2, in
pip install torchcomplex
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大佬，我requirements安装没报错，但是运行程序时会报错: Traceback (most recent call last): File "E:\MockingBird\demo_toolbox.py", line 2, in from toolbox import Toolbox File "E:\MockingBird\toolbox_init_.py", line 6, in import ppg_extractor as extractor File "E:\MockingBird\ppg_extractor_init_.py", line 6, in from .frontend import DefaultFrontend File "E:\MockingBird\ppg_extractor\frontend.py", line 5, in from torch_complex.tensor import ComplexTensor ModuleNotFoundError: No module named 'torch_complex'
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大佬们 问个问题 我自己的数据太小了 中途更换别的数据集进行训练 但是出现了这样的错误代码
warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
Traceback (most recent call last):
File "E:\数据集制作\MockingBird-main\synthesizer_train.py", line 37, in
