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nii-yamagishilab / project-CURRENNT-scripts

This repository contains the scripts to use CURRENNT
BSD 3-Clause "New" or "Revised" License
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prodject-CURRENNT-scrits -------------------------------------------

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Copyright (c) 2020 National Institute of Informatics

THE NATIONAL INSTITUTE OF INFORMATICS AND THE CONTRIBUTORS TO THIS

WORK DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING

ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT

SHALL THE NATIONAL INSTITUTE OF INFORMATICS NOR THE CONTRIBUTORS

BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY

DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,

WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS

ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE

OF THIS SOFTWARE.

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Author: Xin Wang

Date: 2016 - 2020

Contact: wangxin at nii.ac.jp

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This repository contains the scripts to use CURRENNT - waveform-modeling: scripts for waveform models - acoustic-modeling: scripts for acoustic models

For NSF waveform-models, pytorch re-implementation is available now: https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts

----------------- 0. before using ----------------------

Please install CURRENNT and pyTools before using this repository. https://github.com/nii-yamagishilab/project-CURRENNT-public

Please also install sox http://sox.sourceforge.net

Please modify the environment variables in ./init.sh

Please read the intruction below to use this repository

----------------- 1. waveform models -------------------- ./waveform-modeling

| - DATA Directory to store the data for model training (validation will be selected automatically from these data)

Usage:

  1. Install CURRENNT and pyTools, which can be downloaded from https://github.com/nii-yamagishilab/project-CURRENNT-public

  2. For a quick check 2.1 modify the path in ./init.sh

    2.2 run commands: $: source ./init.sh $: cd waveform-modeling/project-NSF-pretrained/ $: run 01_gen.sh

    Waveforms should be generated in ./waveform-modeling/project-NSF-pretrained/MODELS/NSF/output

  3. For model training using the provided sample data

    $: source ./init.sh $: cd waveform-modeling/project-NSF/ $: run 00_run.sh

    After which you can get a trained model in ./waveform-modeling/project-NSF/MODELS/h-sinc-NSF/trained_network.jsn

    $: run 01_gen.sh After which you can get some waveforms in ./waveform-modeling/project-NSF/MODELS/h-sinc-NSF/output

    00_run.sh and 01_gen.sh are only for demonstration. Please don't expect good output since the model is only trained using less than 10 utterances.

    To train a good model, you may need to use the whole data from one speaker of the CMU-arctic corpus.

  4. For training using your own data (or CMU-arctic data):

    1. Put waveforms and acoustic features in ./DATA, which stores the training data (validation data will be automatically selected from ./DATA)
    2. Read and configure config.py in project-NSF or project-Wavenet
    3. Run 00_run.sh
    4. Put test data in ./TESTDATA, configure config.py and Run 01_gen.sh

----------------- 2. acoustic models -------------------- ./acoustic-modeling

|- DATA Directory to store the data for model training (for demonstration)

Usage: please read README in project-***, and follow the steps to use

---------------------- Notes -----------------------------------

Data IO:

  1. All the feature files except waveforms should be saved as binary, float32, little-endian. You may check the data included in waveform-modeling/TESTDATA-for-pretrained/mfbsp/*. $: source ./init.sh $: python

    from ioTools import readwrite mel = readwrite.read_raw_mat('./waveform-modeling/TESTDATA-for-pretrained/mfbsp/arctic_a0001.mfbsp', 80) mel.shape (671, 80) mel[0] array([-0.5804557 , 0.64407444, 0.95468473, 0.9076864 , 0.7409921 , 0.5190042 , 0.2543492 , 0.07272982, -0.02690054, -0.08740515, -0.14761803, -0.24591875, -0.40614623, -0.49193513, -0.69396436, -0.6916913 , -0.67114395, -0.7134891 , -0.8428167 , -0.9381612 , -0.9604182 , -1.020919 , -1.0435845 , -1.077764 , -1.1341914 , -1.2459651 , -1.2637303 , -1.3517176 , -1.3730341 , -1.4908298 , -1.5803422 , -1.6830492 , -1.495914 , -1.3974593 , -1.4675162 , -1.5840678 , -1.6725454 , -1.584573 , -1.7740629 , -2.0816884 , -1.8013108 , -1.7561418 , -2.1837492 , -2.3368633 , -2.0934896 , -2.2621613 , -2.200103 , -2.3064234 , -1.9597349 , -2.2220097 , -2.296505 , -2.0561726 , -2.4601874 , -1.997487 , -2.0367327 , -2.2498186 , -2.8739617 , -2.859662 , -2.680149 , -3.1865113 , -3.172631 , -2.8548572 , -3.0105433 , -2.7395592 , -2.7438028 , -2.625576 , -2.8859007 , -2.8262408 , -2.6442852 , -2.847031 , -2.9952297 , -2.9672284 , -2.6831682 , -3.2138064 , -3.3470006 , -3.4002392 , -2.8704414 , -2.958755 , -3.2552214 , -3.5245233 ], dtype=float32) mel[0,0] -0.5804557 f0 = readwrite.read_raw_mat('./waveform-modeling/TESTDATA-for-pretrained/f0/arctic_a0001.f0', 1) f0.shape (671,)

    Here, the binary mel-spectrogram is a matrix with 671 frames and 80 dimenions/frame. The F0 is one-dimensional vector with 671 frames.

    Notice that in physical memory, one datum in a two dimensional data matrix (e.g., mel-spectrom) is accessed through DataArray[D * n + d], where D is the feature dimension, n is the frame index, and d is the dimension index within one frame.

  2. You can use numpy.tofile to write the data into binary,float32,litten-endian format. You can also use the write_raw_mat function in pyTools, which is a wrapper of numpy.tofile

    $: source ./init.sh $: python

    from ioTools import readwrite import numpy as np data = np.random.randn(10,2) data array([[-1.0792218 , -2.00836936], [-0.84080859, 1.81592092], [ 0.48318553, -0.76937456], [-1.90552536, -0.68052287], [-0.72223174, -2.97435219], [ 0.19932163, 0.29391472], [ 0.36049151, 0.64871376], [ 0.73407896, 0.6574951 ], [ 0.5137015 , -0.67185778], [-0.62208806, -0.35707845]])

    write data to './tmp_data.bin'

    readwrite.write_raw_mat(data, 'tmp_data.bin') True

    read data from './tmp_data.bin'

    data_read =readwrite.read_raw_mat('tmp_data.bin', 2)

    data_read should be the same to data

    (except for the nuemrical reason due to the conversion from

    float64 to float32)

    data_read array([[-1.0792218 , -2.0083694 ], [-0.8408086 , 1.815921 ], [ 0.48318553, -0.76937455], [-1.9055253 , -0.68052286], [-0.72223175, -2.9743521 ], [ 0.19932163, 0.2939147 ], [ 0.3604915 , 0.64871377], [ 0.73407894, 0.6574951 ], [ 0.5137015 , -0.6718578 ], [-0.6220881 , -0.35707846]], dtype=float32)

Useful CURRENNT commands

  1. Continue training using epoch.autosave $: currennt --continue epoch.autosave

    If autosave = true is configured in train_config.cfg, CURRENNT will save trained models after every training epoch. The name of the saved model will be epoch***.autosave.

    If the training is terminated for any reason, you can simply use the above command to resume the training from the lastes training epoch.

    No additional argument is needed because epoch***.autosave saves all the arguments, weights, intermediate gradients ...

  2. Convert .autosave to .jsn $: currennt --print_weight_to epoch.jsn --print_weight_opt 2 --cuda off --network epoch.autosave

    .autosave is the trained model after epochs. It can be used as a trained network to generate output. However, .autosave training arguments, gradients, etc, which makes .autosave very large.

    ***.jsn is the trained model, without data unnecessary for generation.

    To save space, use the above command to convert .auto to .jsn.

    Note that network.jsn usually denotes the initial network without any trained weight.

  3. Plot the network typology $: currennt --network_graph network.gv --network network.jsn --cuda off $: dot -Tpdf -o network.pdf network.gv

    Step1 converts network.jsn to network.gv, a file in dot-language Step2 uses "dot" to produce a picture given the network.gv You can check ./acoustic-modeling/project-DAR-continuous/MODELS/DAR_001/network.pdf

  4. For debugging, you can use $: gdb --args currennt where is the argument string to CURRENNT. To compile a version of currennt for debugging, please check README in https://github.com/nii-yamagishilab/project-CURRENNT-public/tree/master/CURRENNT_codes

Error messages

  1. Memory error when using CURRENNT

    'thrust::system::system_error' what(): device free failed: an illegal memory access was encountered Aborted (core dumped)

    or

    Could not create layer: __copy::trivial_device_copy H->D: failed: invalid argument

    The two errors above indicate insufficient GPU memory. Either reduce layer size, utterance length, or ask Xin Wang

  2. Scripts configuration error:

    resolution not found in --resolutionsFAILED: Resolution error

    This means that upsampling_rate in wavefor-modeling//config.py is not correctly configured. Note that this upsampling_rate denotes the up-sampling of acoustic features (from frame-level to waveform-sampling-level), its value should be equal to frame_shift sampling_rate_of_waveform. See config.py for example