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| Original file line number | Diff line number | Diff line change |
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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 configuration file for LibriTTS dataset. | ||
| # This configuration is based on HiFiGAN V1, which is | ||
| # an official configuration. But I found that the optimizer | ||
| # setting does not work well with my implementation. | ||
| # So I changed optimizer settings as follows: | ||
| # - AdamW -> Adam | ||
| # - betas: [0.8, 0.99] -> betas: [0.5, 0.9] | ||
| # - Scheduler: ExponentialLR -> MultiStepLR | ||
| # To match the shift size difference, the upsample scales | ||
| # is also modified from the original 256 shift setting. | ||
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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: 20 # Need to tune carefully if the recording is not good. | ||
| trim_frame_size: 1024 # Frame size in trimming. | ||
| trim_hop_size: 256 # Hop size in trimming. | ||
| format: "hdf5" # Feature file format. "npy" or "hdf5" is supported. | ||
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| ########################################################### | ||
| # GENERATOR NETWORK ARCHITECTURE SETTING # | ||
| ########################################################### | ||
| generator_type: HiFiGANGenerator | ||
| generator_params: | ||
| in_channels: 80 # Number of input channels. | ||
| out_channels: 1 # Number of output channels. | ||
| channels: 512 # Number of initial channels. | ||
| kernel_size: 7 # Kernel size of initial and final conv layers. | ||
| upsample_scales: [5, 5, 4, 3] # Upsampling scales. | ||
| upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers. | ||
| resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks. | ||
| resblock_dilations: # Dilations for residual blocks. | ||
| - [1, 3, 5] | ||
| - [1, 3, 5] | ||
| - [1, 3, 5] | ||
| use_additional_convs: true # Whether to use additional conv layer in residual blocks. | ||
| bias: true # Whether to use bias parameter in conv. | ||
| nonlinear_activation: "LeakyReLU" # Nonlinear activation type. | ||
| nonlinear_activation_params: # Nonlinear activation paramters. | ||
| negative_slope: 0.1 | ||
| use_weight_norm: true # Whether to apply weight normalization. | ||
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| ########################################################### | ||
| # DISCRIMINATOR NETWORK ARCHITECTURE SETTING # | ||
| ########################################################### | ||
| discriminator_type: HiFiGANMultiScaleMultiPeriodDiscriminator | ||
| discriminator_params: | ||
| scales: 3 # Number of multi-scale discriminator. | ||
| scale_downsample_pooling: "AvgPool1d" # Pooling operation for scale discriminator. | ||
| scale_downsample_pooling_params: | ||
| kernel_size: 4 # Pooling kernel size. | ||
| stride: 2 # Pooling stride. | ||
| padding: 2 # Padding size. | ||
| scale_discriminator_params: | ||
| in_channels: 1 # Number of input channels. | ||
| out_channels: 1 # Number of output channels. | ||
| kernel_sizes: [15, 41, 5, 3] # List of kernel sizes. | ||
| channels: 128 # Initial number of channels. | ||
| max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers. | ||
| max_groups: 16 # Maximum number of groups in downsampling conv layers. | ||
| bias: true | ||
| downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales. | ||
| nonlinear_activation: "LeakyReLU" # Nonlinear activation. | ||
| nonlinear_activation_params: | ||
| negative_slope: 0.1 | ||
| follow_official_norm: true # Whether to follow the official norm setting. | ||
| periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator. | ||
| period_discriminator_params: | ||
| in_channels: 1 # Number of input channels. | ||
| out_channels: 1 # Number of output channels. | ||
| kernel_sizes: [5, 3] # List of kernel sizes. | ||
| channels: 32 # Initial number of channels. | ||
| downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales. | ||
| max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers. | ||
| bias: true # Whether to use bias parameter in conv layer." | ||
| nonlinear_activation: "LeakyReLU" # Nonlinear activation. | ||
| nonlinear_activation_params: # Nonlinear activation paramters. | ||
| negative_slope: 0.1 | ||
| use_weight_norm: true # Whether to apply weight normalization. | ||
| use_spectral_norm: false # Whether to apply spectral normalization. | ||
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| ########################################################### | ||
| # STFT LOSS SETTING # | ||
| ########################################################### | ||
| use_stft_loss: false # Whether to use multi-resolution STFT loss. | ||
| use_mel_loss: true # Whether to use Mel-spectrogram loss. | ||
| mel_loss_params: | ||
| fs: 24000 | ||
| fft_size: 2048 | ||
| hop_size: 300 | ||
| win_length: 1200 | ||
| window: "hann" | ||
| num_mels: 80 | ||
| fmin: 0 | ||
| fmax: 12000 | ||
| log_base: null | ||
| generator_adv_loss_params: | ||
| average_by_discriminators: false # Whether to average loss by #discriminators. | ||
| discriminator_adv_loss_params: | ||
| average_by_discriminators: false # Whether to average loss by #discriminators. | ||
| use_feat_match_loss: true | ||
| feat_match_loss_params: | ||
| average_by_discriminators: false # Whether to average loss by #discriminators. | ||
| average_by_layers: false # Whether to average loss by #layers in each discriminator. | ||
| include_final_outputs: false # Whether to include final outputs in feat match loss calculation. | ||
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| ########################################################### | ||
| # ADVERSARIAL LOSS SETTING # | ||
| ########################################################### | ||
| lambda_aux: 45.0 # Loss balancing coefficient for STFT loss. | ||
| lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss. | ||
| lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss.. | ||
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| ########################################################### | ||
| # DATA LOADER SETTING # | ||
| ########################################################### | ||
| batch_size: 16 # Batch size. | ||
| batch_max_steps: 8400 # 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: false # Whether to remove samples the length of which are less than batch_max_steps. | ||
| allow_cache: false # Whether to allow cache in dataset. If true, it requires cpu memory. | ||
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| ########################################################### | ||
| # OPTIMIZER & SCHEDULER SETTING # | ||
| ########################################################### | ||
| generator_optimizer_type: Adam | ||
| generator_optimizer_params: | ||
| lr: 2.0e-4 | ||
| betas: [0.5, 0.9] | ||
| weight_decay: 0.0 | ||
| generator_scheduler_type: MultiStepLR | ||
| generator_scheduler_params: | ||
| gamma: 0.5 | ||
| milestones: | ||
| - 200000 | ||
| - 400000 | ||
| - 600000 | ||
| - 800000 | ||
| generator_grad_norm: -1 | ||
| discriminator_optimizer_type: Adam | ||
| discriminator_optimizer_params: | ||
| lr: 2.0e-4 | ||
| betas: [0.5, 0.9] | ||
| weight_decay: 0.0 | ||
| discriminator_scheduler_type: MultiStepLR | ||
| discriminator_scheduler_params: | ||
| gamma: 0.5 | ||
| milestones: | ||
| - 200000 | ||
| - 400000 | ||
| - 600000 | ||
| - 800000 | ||
| discriminator_grad_norm: -1 | ||
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| ########################################################### | ||
| # INTERVAL SETTING # | ||
| ########################################################### | ||
| generator_train_start_steps: 1 # Number of steps to start to train discriminator. | ||
| discriminator_train_start_steps: 0 # Number of steps to start to train discriminator. | ||
| train_max_steps: 2500000 # Number of training steps. | ||
| save_interval_steps: 10000 # 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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