保姆级别教程（持续更新各类社区/非官方教程----

[zhuzaileiting]

（作者借楼编辑ing
社区视频教程：
奶糖 https://www.bilibili.com/video/BV1dq4y137pH

[babysor]

再分享 #122

[zhuzaileiting]

播放都是杂音。。。。。。。。。。。。。 C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main&gt;python synthesizer_train.py mandarin E:\datat\rain_set\train\ Arguments: &nbsp; &nbsp; run_id:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; mandarin &nbsp; &nbsp; syn_dir:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;E:\datat\rain_set\train\ &nbsp; &nbsp; models_dir:&nbsp; &nbsp; &nbsp; synthesizer/saved_models/ &nbsp; &nbsp; save_every:&nbsp; &nbsp; &nbsp; 1000 &nbsp; &nbsp; backup_every:&nbsp; &nbsp; 25000 &nbsp; &nbsp; force_restart:&nbsp; &nbsp;False &nbsp; &nbsp; 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: &nbsp; &nbsp; &nbsp; &nbsp; E:\datat\rain_set\train\train.txt &nbsp; &nbsp; &nbsp; &nbsp; E:\datat\rain_set\train\mels &nbsp; &nbsp; &nbsp; &nbsp; E:\datat\rain_set\train\embeds Traceback (most recent call last): &nbsp; File "synthesizer_train.py", line 35, in <module&gt; &nbsp; &nbsp; train(**vars(args)) &nbsp; File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\train.py", line 111, in train &nbsp; &nbsp; dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams) &nbsp; File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\synthesizer_dataset.py", line 12, in __init__ &nbsp; &nbsp; with metadata_fpath.open("r", encoding="utf-8") as metadata_file: &nbsp; File "C:\ProgramData\Anaconda3\lib\pathlib.py", line 1221, in open &nbsp; &nbsp; return io.open(self, mode, buffering, encoding, errors, newline, &nbsp; File "C:\ProgramData\Anaconda3\lib\pathlib.py", line 1077, in _opener &nbsp; &nbsp; 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&gt;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: &nbsp; &nbsp; datasets_root:&nbsp; &nbsp; E:\data\train_set\train &nbsp; &nbsp; enc_models_dir:&nbsp; &nbsp;encoder\saved_models &nbsp; &nbsp; syn_models_dir:&nbsp; &nbsp;synthesizer\saved_models &nbsp; &nbsp; voc_models_dir:&nbsp; &nbsp;vocoder\saved_models &nbsp; &nbsp; cpu:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; False &nbsp; &nbsp; seed:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;None &nbsp; &nbsp; no_mp3_support:&nbsp; &nbsp;False Warning: you do not have any of the recognized datasets in E:\data\train_set\train. The recognized datasets are: &nbsp; &nbsp; &nbsp; &nbsp; LibriSpeech/dev-clean &nbsp; &nbsp; &nbsp; &nbsp; LibriSpeech/dev-other &nbsp; &nbsp; &nbsp; &nbsp; LibriSpeech/test-clean &nbsp; &nbsp; &nbsp; &nbsp; LibriSpeech/test-other &nbsp; &nbsp; &nbsp; &nbsp; LibriSpeech/train-clean-100 &nbsp; &nbsp; &nbsp; &nbsp; LibriSpeech/train-clean-360 &nbsp; &nbsp; &nbsp; &nbsp; LibriSpeech/train-other-500 &nbsp; &nbsp; &nbsp; &nbsp; LibriTTS/dev-clean &nbsp; &nbsp; &nbsp; &nbsp; LibriTTS/dev-other &nbsp; &nbsp; &nbsp; &nbsp; LibriTTS/test-clean &nbsp; &nbsp; &nbsp; &nbsp; LibriTTS/test-other &nbsp; &nbsp; &nbsp; &nbsp; LibriTTS/train-clean-100 &nbsp; &nbsp; &nbsp; &nbsp; LibriTTS/train-clean-360 &nbsp; &nbsp; &nbsp; &nbsp; LibriTTS/train-other-500 &nbsp; &nbsp; &nbsp; &nbsp; LJSpeech-1.1 &nbsp; &nbsp; &nbsp; &nbsp; VoxCeleb1/wav &nbsp; &nbsp; &nbsp; &nbsp; VoxCeleb1/test_wav &nbsp; &nbsp; &nbsp; &nbsp; VoxCeleb2/dev/aac &nbsp; &nbsp; &nbsp; &nbsp; VoxCeleb2/test/aac &nbsp; &nbsp; &nbsp; &nbsp; VCTK-Corpus/wav48 &nbsp; &nbsp; &nbsp; &nbsp; aidatatang_200zh/corpus/dev &nbsp; &nbsp; &nbsp; &nbsp; 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): &nbsp; File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\toolbox\__init__.py", line 122, in <lambda&gt; &nbsp; &nbsp; func = lambda: self.synthesize() or self.vocode() &nbsp; File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\toolbox\__init__.py", line 229, in synthesize &nbsp; &nbsp; specs = self.synthesizer.synthesize_spectrograms(texts, embeds) &nbsp; File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\inference.py", line 86, in synthesize_spectrograms &nbsp; &nbsp; self.load() &nbsp; File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\inference.py", line 64, in load &nbsp; &nbsp; self._model.load(self.model_fpath) &nbsp; File "C:\Users\Administrator\Desktop\Realtime-Voice-Clone-Chinese-main\Realtime-Voice-Clone-Chinese-main\synthesizer\models\tacotron.py", line 497, in load &nbsp; &nbsp; self.load_state_dict(checkpoint["model_state"]) &nbsp; File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1223, in load_state_dict &nbsp; &nbsp; raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for Tacotron: &nbsp; &nbsp; &nbsp; &nbsp; 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.

…

------------------&nbsp;原始邮件&nbsp;------------------ 发件人: "babysor/Realtime-Voice-Clone-Chinese" ***@***.***&gt;; 发送时间:&nbsp;2021年8月19日(星期四) 下午5:57 ***@***.***&gt;; ***@***.***&gt;;"State ***@***.***&gt;; 主题:&nbsp;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 — You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub, or unsubscribe. Triage notifications on the go with GitHub Mobile for iOS or Android.

[zhuzaileiting]

出一个详细的教程吧，大佬👍

…

---原始邮件--- 发件人: ***@***.***&gt; 发送时间: 2021年8月19日(周四) 下午5:57 收件人: ***@***.***&gt;; 抄送: ***@***.***&gt;;"State ***@***.***&gt;; 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 — You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub, or unsubscribe. Triage notifications on the go with GitHub Mobile for iOS or Android.

[babysor]

这。。看起来你都没train起来synthesizer啊

[zhangji261]

同求，比如数据集在哪里下载

[XiuChen-Liu]

同求，比如数据集在哪里下载

#14 closed裡面有同樣的問題，有放下載連結

[zhuzaileiting]

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

[babysor]

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

[zhuzaileiting]

python synthesizer_preprocess_audio.py E:\data\aidatatang_200zh 不用多一层
解决了

[zhuzaileiting]

python synthesizer_preprocess_audio.py E:\data\aidatatang_200zh 不用多一层
@解决了大佬

[zhuzaileiting]

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 | }

[miven]

@zhuzaileiting 你这是用cpu训练的，GPU速度大概在1.3-2 steps/s

[zhuzaileiting]

...GPU，怎么配置显卡全局配置了呀。。