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Merge pull request #5158 from A-Quarter-Mile/tmp_muskit
[SVS] Add new recipes
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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 documentation 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 [ "${cmd_backend}" = local ]; then | ||
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| # The other usage | ||
| export train_cmd="run.pl" | ||
| # Used for "*_train.py": "--gpu" is appended optionally by run.sh | ||
| export cuda_cmd="run.pl" | ||
| # Used for "*_recog.py" | ||
| export decode_cmd="run.pl" | ||
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| # Local machine logging to stdout and log file, without any Job scheduling system | ||
| elif [ "${cmd_backend}" = stdout ]; then | ||
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| # The other usage | ||
| export train_cmd="stdout.pl" | ||
| # Used for "*_train.py": "--gpu" is appended optionally by run.sh | ||
| export cuda_cmd="stdout.pl" | ||
| # Used for "*_recog.py" | ||
| export decode_cmd="stdout.pl" | ||
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| # "qsub" (Sun Grid Engine, or derivation of it) | ||
| elif [ "${cmd_backend}" = sge ]; then | ||
| # The default setting is written in conf/queue.conf. | ||
| # You must change "-q g.q" for the "queue" for your environment. | ||
| # To know the "queue" names, type "qhost -q" | ||
| # Note that to use "--gpu *", you have to setup "complex_value" for the system scheduler. | ||
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| export train_cmd="queue.pl" | ||
| export cuda_cmd="queue.pl" | ||
| export decode_cmd="queue.pl" | ||
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| # "qsub" (Torque/PBS.) | ||
| elif [ "${cmd_backend}" = pbs ]; then | ||
| # The default setting is written in conf/pbs.conf. | ||
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| export train_cmd="pbs.pl" | ||
| export cuda_cmd="pbs.pl" | ||
| export decode_cmd="pbs.pl" | ||
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| # "sbatch" (Slurm) | ||
| elif [ "${cmd_backend}" = slurm ]; then | ||
| # The default setting is written in conf/slurm.conf. | ||
| # You must change "-p cpu" and "-p gpu" for the "partition" for your environment. | ||
| # To know the "partion" names, type "sinfo". | ||
| # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*" | ||
| # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}". | ||
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| export train_cmd="slurm.pl" | ||
| export cuda_cmd="slurm.pl" | ||
| export decode_cmd="slurm.pl" | ||
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| elif [ "${cmd_backend}" = ssh ]; then | ||
| # You have to create ".queue/machines" to specify the host to execute jobs. | ||
| # e.g. .queue/machines | ||
| # host1 | ||
| # host2 | ||
| # host3 | ||
| # Assuming you can login them without any password, i.e. You have to set ssh keys. | ||
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| export train_cmd="ssh.pl" | ||
| export cuda_cmd="ssh.pl" | ||
| export decode_cmd="ssh.pl" | ||
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| # This is an example of specifying several unique options in the JHU CLSP cluster setup. | ||
| # Users can modify/add their own command options according to their cluster environments. | ||
| elif [ "${cmd_backend}" = jhu ]; then | ||
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| export train_cmd="queue.pl --mem 2G" | ||
| export cuda_cmd="queue-freegpu.pl --mem 2G --gpu 1 --config conf/queue.conf" | ||
| export decode_cmd="queue.pl --mem 4G" | ||
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| else | ||
| echo "$0: Error: Unknown cmd_backend=${cmd_backend}" 1>&2 | ||
| return 1 | ||
| fi |
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| tuning/decode_rnn.yaml |
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| tuning/train_naive_rnn.yaml |
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| # This configuration is the decoding setting for FastSpeech or FastSpeech2. | ||
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| ########################################################## | ||
| # DECODING SETTING # | ||
| ########################################################## | ||
| # speed_control_alpha: 1 # alpha to control the speed of generated speech | ||
| # 1 < alpha makes slower and 1 > alpha makes faster | ||
| use_teacher_forcing: false # whether to use teacher forcing | ||
| # if true, we use groundtruth of durations | ||
| # (+ pitch & energy for FastSpeech2) |
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| ########################################################## | ||
| # SVS MODEL SETTING # | ||
| ########################################################## | ||
| svs: naive_rnn # model architecture | ||
| svs_conf: # keyword arguments for the selected model | ||
| midi_dim: 129 # midi dimension (note number + silence) | ||
| embed_dim: 512 # char or phn embedding dimension | ||
| eprenet_conv_layers: 0 # prenet (from bytesing) conv layers | ||
| eprenet_conv_chans: 256 # prenet (from bytesing) conv channels numbers | ||
| eprenet_conv_filts: 3 # prenet (from bytesing) conv filters size | ||
| elayers: 3 # number of lstm layers in encoder | ||
| eunits: 512 # number of lstm units | ||
| ebidirectional: True # if bidirectional in encoder | ||
| midi_embed_integration_type: add # how to integrate midi information | ||
| dlayers: 5 # number of lstm layers in decoder | ||
| dunits: 1024 # number of lstm units in decoder | ||
| dbidirectional: True # if bidirectional in decoder | ||
| postnet_layers: 5 # number of layers in postnet | ||
| postnet_chans: 512 # number of channels in postnet | ||
| postnet_filts: 5 # filter size of postnet layer | ||
| use_batch_norm: true # whether to use batch normalization in postnet | ||
| reduction_factor: 1 # reduction factor | ||
| eprenet_dropout_rate: 0.2 # prenet dropout rate | ||
| edropout_rate: 0.1 # encoder dropout rate | ||
| ddropout_rate: 0.1 # decoder dropout rate | ||
| postnet_dropout_rate: 0.5 # postnet dropout_rate | ||
| init_type: pytorch # parameter initialization | ||
| use_masking: true # whether to apply masking for padded part in loss calculation | ||
| loss_type: L1 | ||
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| # extra module for additional inputs | ||
| pitch_extract: dio # pitch extractor type | ||
| pitch_extract_conf: | ||
| use_token_averaged_f0: false | ||
| pitch_normalize: global_mvn # normalizer for the pitch feature | ||
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| ########################################################## | ||
| # OPTIMIZER SETTING # | ||
| ########################################################## | ||
| optim: adam # optimizer type | ||
| optim_conf: # keyword arguments for selected optimizer | ||
| lr: 1.0e-03 # learning rate | ||
| eps: 1.0e-06 # epsilon | ||
| weight_decay: 0.0 # weight decay coefficient | ||
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| ########################################################## | ||
| # OTHER TRAINING SETTING # | ||
| ########################################################## | ||
| # num_iters_per_epoch: 200 # number of iterations per epoch | ||
| max_epoch: 500 # number of epochs | ||
| grad_clip: 1.0 # gradient clipping norm | ||
| grad_noise: false # whether to use gradient noise injection | ||
| accum_grad: 1 # gradient accumulation | ||
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| batch_type: sorted | ||
| batch_size: 16 | ||
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| sort_in_batch: descending # how to sort data in making batch | ||
| sort_batch: descending # how to sort created batches | ||
| num_workers: 8 # number of workers of data loader | ||
| train_dtype: float32 # dtype in training | ||
| log_interval: null # log interval in iterations | ||
| keep_nbest_models: 2 # number of models to keep | ||
| num_att_plot: 3 # number of attention figures to be saved in every check | ||
| seed: 0 # random seed number | ||
| best_model_criterion: | ||
| - - valid | ||
| - loss | ||
| - min | ||
| - - train | ||
| - loss | ||
| - min |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
|
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| ########################################################## | ||
| # SVS MODEL SETTING # | ||
| ########################################################## | ||
| svs: naive_rnn_dp # model architecture | ||
| svs_conf: # keyword arguments for the selected model | ||
| midi_dim: 129 # midi dimension (note number + silence) | ||
| embed_dim: 512 # char or phn embedding dimension | ||
| tempo_dim: 500 | ||
| eprenet_conv_layers: 0 # prenet (from bytesing) conv layers | ||
| eprenet_conv_chans: 256 # prenet (from bytesing) conv channels numbers | ||
| eprenet_conv_filts: 3 # prenet (from bytesing) conv filters size | ||
| elayers: 3 # number of lstm layers in encoder | ||
| eunits: 256 # number of lstm units | ||
| ebidirectional: True # if bidirectional in encoder | ||
| midi_embed_integration_type: add # how to integrate midi information | ||
| dlayers: 2 # number of lstm layers in decoder | ||
| dunits: 256 # number of lstm units in decoder | ||
| dbidirectional: True # if bidirectional in decoder | ||
| postnet_layers: 5 # number of layers in postnet | ||
| postnet_chans: 512 # number of channels in postnet | ||
| postnet_filts: 5 # filter size of postnet layer | ||
| use_batch_norm: true # whether to use batch normalization in postnet | ||
| reduction_factor: 1 # reduction factor | ||
| eprenet_dropout_rate: 0.2 # prenet dropout rate | ||
| edropout_rate: 0.1 # encoder dropout rate | ||
| ddropout_rate: 0.1 # decoder dropout rate | ||
| postnet_dropout_rate: 0.5 # postnet dropout_rate | ||
| init_type: pytorch # parameter initialization | ||
| use_masking: true # whether to apply masking for padded part in loss calculation | ||
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| # extra module for additional inputs | ||
| pitch_extract: dio # pitch extractor type | ||
| pitch_extract_conf: | ||
| use_token_averaged_f0: false | ||
| pitch_normalize: global_mvn # normalizer for the pitch feature | ||
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| ########################################################## | ||
| # OPTIMIZER SETTING # | ||
| ########################################################## | ||
| optim: adam # optimizer type | ||
| optim_conf: # keyword arguments for selected optimizer | ||
| lr: 1.0e-03 # learning rate | ||
| eps: 1.0e-06 # epsilon | ||
| weight_decay: 0.0 # weight decay coefficient | ||
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| ########################################################## | ||
| # OTHER TRAINING SETTING # | ||
| ########################################################## | ||
| # num_iters_per_epoch: 200 # number of iterations per epoch | ||
| max_epoch: 500 # number of epochs | ||
| grad_clip: 1.0 # gradient clipping norm | ||
| grad_noise: false # whether to use gradient noise injection | ||
| accum_grad: 1 # gradient accumulation | ||
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| batch_type: sorted | ||
| batch_size: 16 | ||
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| sort_in_batch: descending # how to sort data in making batch | ||
| sort_batch: descending # how to sort created batches | ||
| num_workers: 8 # number of workers of data loader | ||
| train_dtype: float32 # dtype in training | ||
| log_interval: null # log interval in iterations | ||
| keep_nbest_models: 2 # number of models to keep | ||
| num_att_plot: 3 # number of attention figures to be saved in every check | ||
| seed: 0 # random seed number | ||
| best_model_criterion: | ||
| - - valid | ||
| - loss | ||
| - min | ||
| - - train | ||
| - loss | ||
| - min |
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