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# ====== About run.pl, queue.pl, slurm.pl, and ssh.pl ====== | ||
# Usage: <cmd>.pl [options] JOB=1:<nj> <log> <command...> | ||
# e.g. | ||
# run.pl --mem 4G JOB=1:10 echo.JOB.log echo JOB | ||
# | ||
# Options: | ||
# --time <time>: Limit the maximum time to execute. | ||
# --mem <mem>: Limit the maximum memory usage. | ||
# -–max-jobs-run <njob>: Limit the number parallel jobs. This is ignored for non-array jobs. | ||
# --num-threads <ngpu>: Specify the number of CPU core. | ||
# --gpu <ngpu>: Specify the number of GPU devices. | ||
# --config: Change the configuration file from default. | ||
# | ||
# "JOB=1:10" is used for "array jobs" and it can control the number of parallel jobs. | ||
# The left string of "=", i.e. "JOB", is replaced by <N>(Nth job) in the command and the log file name, | ||
# e.g. "echo JOB" is changed to "echo 3" for the 3rd job and "echo 8" for 8th job respectively. | ||
# Note that the number must start with a positive number, so you can't use "JOB=0:10" for example. | ||
# | ||
# run.pl, queue.pl, slurm.pl, and ssh.pl have unified interface, not depending on its backend. | ||
# These options are mapping to specific options for each backend and | ||
# it is configured by "conf/queue.conf" and "conf/slurm.conf" by default. | ||
# If jobs failed, your configuration might be wrong for your environment. | ||
# | ||
# | ||
# The official documentaion for run.pl, queue.pl, slurm.pl, and ssh.pl: | ||
# "Parallelization in Kaldi": http://kaldi-asr.org/doc/queue.html | ||
# =========================================================~ | ||
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# Select the backend used by run.sh from "local", "stdout", "sge", "slurm", or "ssh" | ||
cmd_backend="local" | ||
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# Local machine, without any Job scheduling system | ||
if [ "${cmd_backend}" = local ]; then | ||
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# The other usage | ||
export train_cmd="utils/run.pl" | ||
# Used for "*_train.py": "--gpu" is appended optionally by run.sh | ||
export cuda_cmd="utils/run.pl" | ||
# Used for "*_recog.py" | ||
export decode_cmd="utils/run.pl" | ||
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# Local machine, without any Job scheduling system | ||
elif [ "${cmd_backend}" = stdout ]; then | ||
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# The other usage | ||
export train_cmd="utils/stdout.pl" | ||
# Used for "*_train.py": "--gpu" is appended optionally by run.sh | ||
export cuda_cmd="utils/stdout.pl" | ||
# Used for "*_recog.py" | ||
export decode_cmd="utils/stdout.pl" | ||
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# "qsub" (SGE, Torque, PBS, etc.) | ||
elif [ "${cmd_backend}" = sge ]; then | ||
# The default setting is written in conf/queue.conf. | ||
# You must change "-q g.q" for the "queue" for your environment. | ||
# To know the "queue" names, type "qhost -q" | ||
# Note that to use "--gpu *", you have to setup "complex_value" for the system scheduler. | ||
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export train_cmd="utils/queue.pl" | ||
export cuda_cmd="utils/queue.pl" | ||
export decode_cmd="utils/queue.pl" | ||
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# "sbatch" (Slurm) | ||
elif [ "${cmd_backend}" = slurm ]; then | ||
# The default setting is written in conf/slurm.conf. | ||
# You must change "-p cpu" and "-p gpu" for the "partion" for your environment. | ||
# To know the "partion" names, type "sinfo". | ||
# You can use "--gpu * " by defualt for slurm and it is interpreted as "--gres gpu:*" | ||
# The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}". | ||
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export train_cmd="utils/slurm.pl" | ||
export cuda_cmd="utils/slurm.pl" | ||
export decode_cmd="utils/slurm.pl" | ||
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elif [ "${cmd_backend}" = ssh ]; then | ||
# You have to create ".queue/machines" to specify the host to execute jobs. | ||
# e.g. .queue/machines | ||
# host1 | ||
# host2 | ||
# host3 | ||
# Assuming you can login them without any password, i.e. You have to set ssh keys. | ||
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export train_cmd="utils/ssh.pl" | ||
export cuda_cmd="utils/ssh.pl" | ||
export decode_cmd="utils/ssh.pl" | ||
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else | ||
echo "$0: Error: Unknown cmd_backend=${cmd_backend}" 1>&2 | ||
return 1 | ||
fi |
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# This is the hyperparameter configuration file for Parallel WaveGAN. | ||
# Please make sure this is adjusted for the JSSS dataset. If you want to | ||
# apply to the other dataset, you might need to carefully change some parameters. | ||
# This configuration requires 12 GB GPU memory and takes ~3 days on RTX TITAN. | ||
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########################################################### | ||
# FEATURE EXTRACTION SETTING # | ||
########################################################### | ||
sampling_rate: 24000 # Sampling rate. | ||
fft_size: 2048 # FFT size. | ||
hop_size: 300 # Hop size. | ||
win_length: 1200 # Window length. | ||
# If set to null, it will be the same as fft_size. | ||
window: "hann" # Window function. | ||
num_mels: 80 # Number of mel basis. | ||
fmin: 80 # Minimum freq in mel basis calculation. | ||
fmax: 7600 # Maximum frequency in mel basis calculation. | ||
global_gain_scale: 1.0 # Will be multiplied to all of waveform. | ||
trim_silence: false # Whether to trim the start and end of silence. | ||
trim_threshold_in_db: 40 # Need to tune carefully if the recording is not good. | ||
trim_frame_size: 2048 # Frame size in trimming. | ||
trim_hop_size: 512 # Hop size in trimming. | ||
format: "hdf5" # Feature file format. "npy" or "hdf5" is supported. | ||
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########################################################### | ||
# GENERATOR NETWORK ARCHITECTURE SETTING # | ||
########################################################### | ||
generator_params: | ||
in_channels: 1 # Number of input channels. | ||
out_channels: 1 # Number of output channels. | ||
kernel_size: 3 # Kernel size of dilated convolution. | ||
layers: 30 # Number of residual block layers. | ||
stacks: 3 # Number of stacks i.e., dilation cycles. | ||
residual_channels: 64 # Number of channels in residual conv. | ||
gate_channels: 128 # Number of channels in gated conv. | ||
skip_channels: 64 # Number of channels in skip conv. | ||
aux_channels: 80 # Number of channels for auxiliary feature conv. | ||
# Must be the same as num_mels. | ||
aux_context_window: 2 # Context window size for auxiliary feature. | ||
# If set to 2, previous 2 and future 2 frames will be considered. | ||
dropout: 0.0 # Dropout rate. 0.0 means no dropout applied. | ||
use_weight_norm: true # Whether to use weight norm. | ||
# If set to true, it will be applied to all of the conv layers. | ||
upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture. | ||
upsample_params: # Upsampling network parameters. | ||
upsample_scales: [4, 5, 3, 5] # Upsampling scales. Prodcut of these must be the same as hop size. | ||
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########################################################### | ||
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING # | ||
########################################################### | ||
discriminator_params: | ||
in_channels: 1 # Number of input channels. | ||
out_channels: 1 # Number of output channels. | ||
kernel_size: 3 # Number of output channels. | ||
layers: 10 # Number of conv layers. | ||
conv_channels: 64 # Number of chnn layers. | ||
bias: true # Whether to use bias parameter in conv. | ||
use_weight_norm: true # Whether to use weight norm. | ||
# If set to true, it will be applied to all of the conv layers. | ||
nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv. | ||
nonlinear_activation_params: # Nonlinear function parameters | ||
negative_slope: 0.2 # Alpha in LeakyReLU. | ||
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########################################################### | ||
# STFT LOSS SETTING # | ||
########################################################### | ||
stft_loss_params: | ||
fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss. | ||
hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss | ||
win_lengths: [600, 1200, 240] # List of window length for STFT-based loss. | ||
window: "hann_window" # Window function for STFT-based loss | ||
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########################################################### | ||
# ADVERSARIAL LOSS SETTING # | ||
########################################################### | ||
lambda_adv: 4.0 # Loss balancing coefficient. | ||
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########################################################### | ||
# DATA LOADER SETTING # | ||
########################################################### | ||
batch_size: 6 # Batch size. | ||
batch_max_steps: 25500 # Length of each audio in batch. Make sure dividable by hop_size. | ||
pin_memory: true # Whether to pin memory in Pytorch DataLoader. | ||
num_workers: 2 # Number of workers in Pytorch DataLoader. | ||
remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps. | ||
allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory. | ||
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########################################################### | ||
# OPTIMIZER & SCHEDULER SETTING # | ||
########################################################### | ||
generator_optimizer_params: | ||
lr: 0.0001 # Generator's learning rate. | ||
eps: 1.0e-6 # Generator's epsilon. | ||
weight_decay: 0.0 # Generator's weight decay coefficient. | ||
generator_scheduler_params: | ||
step_size: 200000 # Generator's scheduler step size. | ||
gamma: 0.5 # Generator's scheduler gamma. | ||
# At each step size, lr will be multiplied by this parameter. | ||
generator_grad_norm: 10 # Generator's gradient norm. | ||
discriminator_optimizer_params: | ||
lr: 0.00005 # Discriminator's learning rate. | ||
eps: 1.0e-6 # Discriminator's epsilon. | ||
weight_decay: 0.0 # Discriminator's weight decay coefficient. | ||
discriminator_scheduler_params: | ||
step_size: 200000 # Discriminator's scheduler step size. | ||
gamma: 0.5 # Discriminator's scheduler gamma. | ||
# At each step size, lr will be multiplied by this parameter. | ||
discriminator_grad_norm: 1 # Discriminator's gradient norm. | ||
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########################################################### | ||
# INTERVAL SETTING # | ||
########################################################### | ||
discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator. | ||
train_max_steps: 400000 # Number of training steps. | ||
save_interval_steps: 5000 # Interval steps to save checkpoint. | ||
eval_interval_steps: 1000 # Interval steps to evaluate the network. | ||
log_interval_steps: 100 # Interval steps to record the training log. | ||
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########################################################### | ||
# OTHER SETTING # | ||
########################################################### | ||
num_save_intermediate_results: 4 # Number of results to be saved as intermediate results. |
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# Default configuration | ||
command sbatch --export=PATH --ntasks-per-node=1 | ||
option time=* --time $0 | ||
option mem=* --mem-per-cpu $0 | ||
option mem=0 # Do not add anything to qsub_opts | ||
option num_threads=* --cpus-per-task $0 --ntasks-per-node=1 | ||
option num_threads=1 --cpus-per-task 1 --ntasks-per-node=1 # Do not add anything to qsub_opts | ||
default gpu=0 | ||
option gpu=0 -p cpu | ||
option gpu=* -p gpu --gres=gpu:$0 | ||
# note: the --max-jobs-run option is supported as a special case | ||
# by slurm.pl and you don't have to handle it in the config file. |
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#!/bin/bash | ||
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# Copyright 2020 Tomoki Hayashi | ||
# MIT License (https://opensource.org/licenses/MIT) | ||
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# Download JSSS Corpus | ||
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# shellcheck disable=SC1091 | ||
. ./path.sh || exit 1 | ||
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download_dir=$1 | ||
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# check arguments | ||
if [ $# != 1 ]; then | ||
echo "Usage: $0 <download_dir>" | ||
exit 1 | ||
fi | ||
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set -euo pipefail | ||
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url="https://drive.google.com/a/g.sp.m.is.nagoya-u.ac.jp/uc?id=1NyiZCXkYTdYBNtD1B-IMAYCVa-0SQsKX" | ||
if [ ! -e "${download_dir}/jsss_ver1" ]; then | ||
utils/download_from_google_drive.sh "${url}" "${download_dir}" zip | ||
echo "Successfully downloaded JSSS corpus." | ||
else | ||
echo "Already exists. Skipped." | ||
fi | ||
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cwd=$(pwd) | ||
if [ ! -e "${download_dir}/JSSSLabel" ]; then | ||
echo "Downloading full-context labels for jsut v1.1..." | ||
cd "${download_dir}" | ||
git clone https://github.com/kan-bayashi/JSSSLabel | ||
for name in long-form short-form simplification summarization; do | ||
cp -vr JSSSLabel/${name} jsss_ver1 | ||
done | ||
cd "${cwd}" | ||
echo "Successfully downloaded JSSS label." | ||
else | ||
echo "Already exists. Skipped." | ||
fi |
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