chaiyujin / dctts-pytorch

The pytorch implementation of DC-TTS

DC-TTS

The pytorch implementation of papar Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention.

Thanks for Kyubyong/dc_tts, which helped me a lot to overcome some difficulties.

Dataset

• The LJ Speech Dataset. A public domain speech dataset consisting of 13,100 short audio clips of a single female speaker.

Train

I have tuned hyper parameters and trained a model with The LJ Speech Dataset. The hyper parameters may not be the best and are slightly different with those used in original paper.

To train a model yourself with The LJ Speech Dataset:

1. Download the dataset and extract into a directory, set the directory in pkg/hyper.py
2. Run preprocess

   python3 main.py --action preprocess
   

3. Train Text2Mel network, you can change the device to train text2mel in pkg/hyper.py

   python3 main.py --action train --module Text2Mel
   

4. Train SSRN network, also, it's possible to change the training device

   python3 main.py --action train --module SuperRes
   

Samples

Some synthesized samples are contained in directory synthesis. The according sentences are listed in sentences.txt. The pre-trained model for Text2Mel and SuperRes (auto-saved at logdir/text2mel/pkg/trained.pkg and logdir/superres/pkg/trained.pkg in training phase) will be loaded when synthesizing.

You can synthesis samples listed in sentences.txt with

python3 main.py --action synthesis

• Attention Matrix for the sentence: "Which came first... the chicken or the egg? Did the universe have a beginning... and if so, what happened before then? Where did the universe come from... and where is it going?"

Pre-trained model

The samples in directory synthesis is sampled with 410k batches trained Text2Mel and 190k batches trained SuperRes.

The current result is not very satisfying, specificly, some vowels are skipped. Hope someone can find better hyper parameters and train better models. Please tell me if you were able to get a great model.

You can download the current pre-trained model from my dropbox.

Dependancy

• scipy, librosa, num2words
• pytorch >= 0.4.0

Relative

TensorFlow implementation: Kyubyong/dc_tts

Please email me or open an issue, if you have any question or suggestion.

dctts-pytorch's Issues

overwrite the spectrogram

in current commit, overwrite the spectrogram because of all gs=0

https://github.com/chaiyujin/dctts-pytorch/blob/master/pkg/train.py#L196
https://github.com/chaiyujin/dctts-pytorch/blob/master/pkg/train.py#L293

dctts-pytorch/logdir/text2mel$ ls
atten_0.png  atten_guide_0.png	loss  mel_input_0.png  mel_pred_0.png  mel_true_0.png  pkg   query_0.png

dctts-pytorch/logdir/superres$ ls 
input_0.png  loss  pkg	pred_0.png  true_0.png

Multiple GPU

Hello,

how i can incorporate the DataParallel in this project?
model = nn.DataParallel(model)

thanks

Tuning required for other dataset

Hello,
thanks for the well written repo, it's very easy to use.

I'm currently trying to train on the UK English data set from http://www.m-ailabs.bayern/en/the-mailabs-speech-dataset/, what audio-preprocessing hyper-parameters would you recommend changing?

Currently I've only updated the data_max_text_length and data_max_mel_length to fit the new data, and everything else is left as is.

Thanks

Training with GPU vs Movidius

Hello,
I'm noob but i like try...
I use GPU Nvidia 1050 but in trining every loop ~39 min 👎 and need 4329. Is normal?

Is easy modified this code for Movidius NCS compatible?

Thanks!

Training type

Hi! One question,
Its obligatory train in Text2Mel and SSRN? Or if only train one of this works?

Thanks!

problem with my own small dataset : ValueError: negative step not yet supported

hi, i create my own dataset in Persian language with 229 rows ( i know its very few but its only for test ) .
at first i change this : ( vocab = "PE ابپتثجچ‌حخدذرز‌ژس‌شصضطظعغفقکگلمنوهیءآاًهٔة'.?")
and after runing (python3 main.py --action train --module Text2Mel) i get this error:

ule Text2Mel
train: Text to Mel
loop 0
▝[1/8] |gs: 0, mels: 0.321015, bd1: 0.693171, atten: 0.637633, scale: 99.999000| [0.0% eta ETA unknown]Traceback (most recent call last):
File "main.py", line 62, in
main()
File "main.py", line 47, in main
train(args.module, args.load)
File "/home/ai/dctts-pytorch/pkg/train.py", line 54, in train
train_text2mel(load_trained)
File "/home/ai/dctts-pytorch/pkg/train.py", line 197, in train_text2mel
plot_spectrum(mels[0].cpu().data, "mel_true", gs, dir=logdir)
File "/home/ai/dctts-pytorch/pkg/utils.py", line 110, in plot_spectrum
im = ax.imshow(np.flip(spectrum, 0), cmap="jet", aspect=0.2 * spectrum.shape[1] / spectrum.shape[0])
File "<array_function internals>", line 6, in flip
File "/home/ai/Downloads/con/lib/python3.7/site-packages/numpy/lib/function_base.py", line 254, in flip
return m[indexer]
ValueError: negative step not yet supported

GPU idle between iteration

Awesome work.

When I check the GPU utilization during training, I found a lot of GPU idle time between each iteration.

Did you see similar behavior? I'm using NFS storage. so one possible reason would be load and feed mel would be the bottleneck.

• Text2Mel 64batch on V100 16GB ( pytorch 1.0)

nvidia-smi -l 1 | grep MiB
| N/A   44C    P0    72W / 300W |   5484MiB / 16130MiB |      0%      Default |
| N/A   45C    P0   162W / 300W |   5484MiB / 16130MiB |     31%      Default |
| N/A   44C    P0    72W / 300W |   5484MiB / 16130MiB |      0%      Default |
| N/A   46C    P0   209W / 300W |   5484MiB / 16130MiB |     95%      Default |
| N/A   44C    P0    72W / 300W |   5484MiB / 16130MiB |      0%      Default |
| N/A   46C    P0   208W / 300W |   5484MiB / 16130MiB |     24%      Default |
| N/A   44C    P0    72W / 300W |   5484MiB / 16130MiB |      0%      Default |
| N/A   45C    P0    77W / 300W |   5484MiB / 16130MiB |     94%      Default |
| N/A   44C    P0    72W / 300W |   5484MiB / 16130MiB |      0%      Default |
| N/A   44C    P0    71W / 300W |   5484MiB / 16130MiB |      0%      Default |

• SSRN log with 32batch on V100 16GB ( pytorch 1.0)

nvidia-smi -l 1 | grep MiB
| N/A   43C    P0    60W / 300W |  10386MiB / 16130MiB |      0%      Default |
| N/A   47C    P0   207W / 300W |  10386MiB / 16130MiB |     29%      Default |
| N/A   44C    P0    69W / 300W |  10386MiB / 16130MiB |      0%      Default |
| N/A   44C    P0    69W / 300W |  10386MiB / 16130MiB |      0%      Default |
| N/A   49C    P0   263W / 300W |  10386MiB / 16130MiB |    100%      Default |
| N/A   44C    P0    68W / 300W |  10386MiB / 16130MiB |      0%      Default |
| N/A   44C    P0    68W / 300W |  10386MiB / 16130MiB |      0%      Default |
| N/A   44C    P0    68W / 300W |  10386MiB / 16130MiB |      0%      Default |
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| N/A   45C    P0    68W / 300W |  10386MiB / 16130MiB |      0%      Default |
| N/A   44C    P0    68W / 300W |  10386MiB / 16130MiB |      0%      Default |
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| N/A   46C    P0    61W / 300W |  10386MiB / 16130MiB |     34%      Default |
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| N/A   45C    P0    70W / 300W |  10386MiB / 16130MiB |      0%      Default |
| N/A   44C    P0    70W / 300W |  10386MiB / 16130MiB |      0%      Default |
| N/A   50C    P0   293W / 300W |  10386MiB / 16130MiB |    100%      Default |
| N/A   44C    P0    67W / 300W |  10386MiB / 16130MiB |      0%      Default |