test fixes

TTS/bin/distribute_tts.py

@@ -1,65 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import os
-import sys
-import pathlib
-import time
-import subprocess
-import argparse
-import torch
-
-
-def main():
-    """
-    Call train.py as a new process and pass command arguments
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--continue_path',
-        type=str,
-        help='Training output folder to continue training. Use to continue a training. If it is used, "config_path" is ignored.',
-        default='',
-        required='--config_path' not in sys.argv)
-    parser.add_argument(
-        '--restore_path',
-        type=str,
-        help='Model file to be restored. Use to finetune a model.',
-        default='')
-    parser.add_argument(
-        '--config_path',
-        type=str,
-        help='Path to config file for training.',
-        required='--continue_path' not in sys.argv
-    )
-    args = parser.parse_args()
-
-    num_gpus = torch.cuda.device_count()
-    group_id = time.strftime("%Y_%m_%d-%H%M%S")
-
-    # set arguments for train.py
-    folder_path = pathlib.Path(__file__).parent.absolute()
-    command = [os.path.join(folder_path, 'train_tts.py')]
-    command.append('--continue_path={}'.format(args.continue_path))
-    command.append('--restore_path={}'.format(args.restore_path))
-    command.append('--config_path={}'.format(args.config_path))
-    command.append('--group_id=group_{}'.format(group_id))
-    command.append('')
-
-    # run processes
-    processes = []
-    for i in range(num_gpus):
-        my_env = os.environ.copy()
-        my_env["PYTHON_EGG_CACHE"] = "/tmp/tmp{}".format(i)
-        command[-1] = '--rank={}'.format(i)
-        stdout = None if i == 0 else open(os.devnull, 'w')
-        p = subprocess.Popen(['python3'] + command, stdout=stdout, env=my_env)
-        processes.append(p)
-        print(command)
-
-    for p in processes:
-        p.wait()
-
-
-if __name__ == '__main__':
-    main()

TTS/tts/utils/generic_utils.py

@@ -248,6 +248,7 @@ def check_config_tts(c):
     check_argument('batch_group_size', c, restricted=True, val_type=int, min_val=0)
     check_argument('min_seq_len', c, restricted=True, val_type=int, min_val=0)
     check_argument('max_seq_len', c, restricted=True, val_type=int, min_val=10)
+    check_argument('compute_input_seq_cache', c, restricted=True, val_type=bool)
 
     # paths
     check_argument('output_path', c, restricted=True, val_type=str)

tests/inputs/test_glow_tts.json

@@ -70,6 +70,7 @@
     "eval_batch_size":1,
     "r": 1,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
     "loss_masking": true,   // enable / disable loss masking against the sequence padding.
+    "data_dep_init_iter": 1,
 
     // VALIDATION
     "run_eval": true,
@@ -105,6 +106,7 @@
     "min_seq_len": 3,       // DATASET-RELATED: minimum text length to use in training
     "max_seq_len": 500,     // DATASET-RELATED: maximum text length
     "compute_f0": false,     // compute f0 values in data-loader
+    "compute_input_seq_cache": true,
 
     // PATHS
     "output_path": "tests/train_outputs/",

tests/inputs/test_train_config.json

@@ -132,6 +132,7 @@
     "batch_group_size": 0,  //Number of batches to shuffle after bucketing.
     "min_seq_len": 6,       // DATASET-RELATED: minimum text length to use in training
     "max_seq_len": 153,     // DATASET-RELATED: maximum text length
+    "compute_input_seq_cache": true,
 
     // PATHS
     "output_path": "tests/train_outputs/",

tests/test_tacotron_train.sh

@@ -1,14 +1,13 @@
 #!/usr/bin/env bash
-
 set -xe
 BASEDIR=$(dirname "$0")
 echo "$BASEDIR"
 # run training
-CUDA_VISIBLE_DEVICES="" python TTS/bin/train_tts.py --config_path $BASEDIR/inputs/test_train_config.json
+CUDA_VISIBLE_DEVICES="" python TTS/bin/train_tacotron.py --config_path $BASEDIR/inputs/test_train_config.json
 # find the training folder
 LATEST_FOLDER=$(ls $BASEDIR/train_outputs/| sort | tail -1)
 echo $LATEST_FOLDER
 # continue the previous training
-CUDA_VISIBLE_DEVICES=""  python TTS/bin/train_tts.py --continue_path $BASEDIR/train_outputs/$LATEST_FOLDER
+CUDA_VISIBLE_DEVICES=""  python TTS/bin/train_tacotron.py --continue_path $BASEDIR/train_outputs/$LATEST_FOLDER
 # remove all the outputs
 rm -rf $BASEDIR/train_outputs/

tests/test_tts_train.sh

@@ -1,13 +0,0 @@
-#!/usr/bin/env bash
-set -xe
-BASEDIR=$(dirname "$0")
-echo "$BASEDIR"
-# run training
-CUDA_VISIBLE_DEVICES="" python TTS/bin/train_tacotron.py --config_path $BASEDIR/inputs/test_train_config.json
-# find the training folder
-LATEST_FOLDER=$(ls $BASEDIR/train_outputs/| sort | tail -1)
-echo $LATEST_FOLDER
-# continue the previous training
-CUDA_VISIBLE_DEVICES=""  python TTS/bin/train_tacotron.py --continue_path $BASEDIR/train_outputs/$LATEST_FOLDER
-# remove all the outputs
-rm -rf $BASEDIR/train_outputs/