Awesome Open Source
Awesome Open Source


A Chainer implementation of VQ-VAE( ).


Trained about 63 hours with one 1080Ti (150000 iterations) on VCTK-Corpus. You can download pretrained model from here.


loss1 loss2 loss3




I trained and generated with

  • python(3.5.2)
  • chainer(4.0.0b3)
  • librosa(0.5.1)

And now you can try it on Google Colaboratory. You don't need install chainer/librosa in your local or buy GPUs. Check this.


download dataset

You can download VCTK-Corpus(en) from here. And you can download CMU-ARCTIC(en)/voice-statistics-corpus(ja) very easily via my repository.

set parameters

parameters of training

  • batchsize
    • Batch size.
  • lr
    • Learning rate.
  • ema_mu
    • Rate of exponential moving average. If this is greater than 1 doesn't apply.
  • trigger
    • How many times you update the model. You can set this parameter like as (<int>, 'iteration') or (<int>, 'epoch')
  • evaluate_interval
    • The interval that you evaluate validation dataset. You can set this parameter like as trigger.
  • snapshot_interval
    • The interval that you save snapshot. You can set this parameter like as trigger.
  • report_interval
    • The interval that you write log of loss. You can set this parameter like as trigger.

parameters of dataset

  • root
    • The root directory of training dataset.
  • dataset
    • The architecture of the directory of training dataset. Now this parameter supports VCTK, ARCTIC and 'vs'.
  • split_seed
    • A seed for splitting dataset into train and validation.

parameters of preprocessing

  • sr
    • Sampling rate. If it's different from input file, be resampled by librosa.
  • res_type
    • The resampling algorithm used in librosa.
  • top_db
    • The threshold db for triming silence.
  • input_dim
    • The input channels of wave. If it is 1, mu-law is not applied. Else mu-law is applied.
  • quantize
    • The number for quantize.
  • length
    • How many samples used for training.
  • use_logistic
    • Use mixture of logistics or not.

parameters of VQ

  • d
    • The parameter d in the paper.
  • k
    • The parameter k in the paper.

parameters of Decoder(WaveNet)

  • n_loop
    • If you want to make network like dilations [1, 2, 4, 1, 2, 4] set n_loop as 2.
  • n_layer
    • If you want to make network like dilations [1, 2, 4, 1, 2, 4] set n_layer as 3.
  • filter_size
    • The filter size of each dilated convolution.
  • residual_channels
    • The number of input/output channels of residual blocks.
  • dilated_channels
    • The number of output channels of causal dilated convolution layers. This is splited into tanh and sigmoid so the number of hidden units is half of this number.
  • skip_channels
    • The number of channels of skip connections and last projection layer.
  • n_mixture
    • The number of logistic distribution. It is used only use_logistic is True.
  • log_scale_min
    • The number for stability. It is used only use_logistic is True.
  • global_condition_dim
    • The dimension of speaker embeded-vector.
  • local_condition_dim
    • The dimension of local contioning vectors.
  • dropout_zero_rate
    • The rate of 0 in dropout. If 0 doesn't apply dropout.

parameters of losses

  • beta
    • The parameter beta in the paper.

parameters of losses

  • use_ema
    • If True use the value of exponential moving average.
  • apply_dropout
    • If True apply dropout.


(without GPU)

(with GPU #n)
python -g n

If you want to use multi GPUs, you can add IDs like below.

python -g 0 1 2

You can resume snapshot and restart training like below.

python -r snapshot_iter_100000

Other arguments -f and -p are parameters for multiprocess in preprocessing. -f means the number of prefetch and -p means the number of processes.


python -i <input file> -o <output file> -m <trained model> -s <speaker>

If you don't set -o, default file name result.wav is used. If you don't set -s, the speaker is same as input file that got from filepath.


  • [x] upload generated sample
  • [x] using GPU fot generating
  • [x] descritized mixture of logistics
  • [ ] Parallel WaveNet

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