def train(args, log_dir, hparams): log('\n#############################################################\n') log('Tacotron Train\n') log('###########################################################\n') checkpoint = tacotron_train(args, log_dir, hparams) tf.reset_default_graph() if checkpoint is None: raise('Error occured while training Tacotron, Exiting!') log('\n#############################################################\n') log('Tacotron GTA Synthesis\n') log('###########################################################\n') input_path = tacotron_synthesize(args, hparams, checkpoint) log('\n#############################################################\n') log('Wavenet Train\n') log('###########################################################\n') wavenet_train(args, log_dir, hparams, input_path)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--base_dir', default='') parser.add_argument('--hparams', default='', help='Hyperparameter overrides as a comma-separated list of name=value pairs') parser.add_argument('--tacotron_input', default='training_data/train.txt') parser.add_argument('--wavenet_input', default='tacotron_output/gta/map.txt') parser.add_argument('--name', help='Name of logging directory.') parser.add_argument('--model', default='Tacotron-2') parser.add_argument('--input_dir', default='training_data/', help='folder to contain inputs sentences/targets') parser.add_argument('--output_dir', default='output/', help='folder to contain synthesized mel spectrograms') parser.add_argument('--mode', default='synthesis', help='mode for synthesis of tacotron after training') parser.add_argument('--GTA', default='True', help='Ground truth aligned synthesis, defaults to True, only considered in Tacotron synthesis mode') parser.add_argument('--restore', type=bool, default=True, help='Set this to False to do a fresh training') parser.add_argument('--summary_interval', type=int, default=250, help='Steps between running summary ops') parser.add_argument('--checkpoint_interval', type=int, default=5000, help='Steps between writing checkpoints') parser.add_argument('--eval_interval', type=int, default=10000, help='Steps between eval on test data') parser.add_argument('--tacotron_train_steps', type=int, default=160000, help='total number of tacotron training steps') parser.add_argument('--wavenet_train_steps', type=int, default=360000, help='total number of wavenet training steps') parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.') args = parser.parse_args() accepted_models = ['Tacotron', 'WaveNet', 'Both', 'Tacotron-2'] if args.model not in accepted_models: raise ValueError('please enter a valid model to train: {}'.format(accepted_models)) log_dir, hparams = prepare_run(args) if args.model == 'Tacotron': tacotron_train(args, log_dir, hparams) elif args.model == 'WaveNet': wavenet_train(args, log_dir, hparams, args.wavenet_input) elif args.model in ('Both', 'Tacotron-2'): train(args, log_dir, hparams) else: raise ValueError('Model provided {} unknown! {}'.format(args.model, accepted_models))
def main(): parser = argparse.ArgumentParser() parser.add_argument('--base_dir', default='') parser.add_argument( '--hparams', default='', help= 'Hyperparameter overrides as a comma-separated list of name=value pairs' ) parser.add_argument('--tacotron_input', default='training_data/train.txt') parser.add_argument('--wavenet_input', default='tacotron_output/gta/map.txt') parser.add_argument('--name', help='Name of logging directory.') parser.add_argument('--model', default='Tacotron') parser.add_argument('--input_dir', default='training_data', help='folder to contain inputs sentences/targets') parser.add_argument('--output_dir', default='output', help='folder to contain synthesized mel spectrograms') parser.add_argument('--mode', default='synthesis', help='mode for synthesis of tacotron after training') parser.add_argument( '--GTA', default='True', help= 'Ground truth aligned synthesis, defaults to True, only considered in Tacotron synthesis mode' ) parser.add_argument('--restore', type=bool, default=True, help='Set this to False to do a fresh training') parser.add_argument('--summary_interval', type=int, default=250, help='Steps between running summary ops') parser.add_argument( '--embedding_interval', type=int, default=10000, help='Steps between updating embeddings projection visualization') parser.add_argument('--checkpoint_interval', type=int, default=1000, help='Steps between writing checkpoints') parser.add_argument('--eval_interval', type=int, default=1000, help='Steps between eval on test data') parser.add_argument('--tacotron_train_steps', type=int, default=150000, help='total number of tacotron training steps') parser.add_argument('--wavenet_train_steps', type=int, default=750000, help='total number of wavenet training steps') parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.') parser.add_argument('--slack_url', default=None, help='slack webhook notification destination link') args = parser.parse_args() accepted_models = ['Tacotron', 'WaveNet', 'Tacotron-2'] if args.model not in accepted_models: raise ValueError( 'please enter a valid model to train: {}'.format(accepted_models)) log_dir, hparams = prepare_run(args) if args.model == 'Tacotron': tacotron_train(args, log_dir, hparams) elif args.model == 'WaveNet': wavenet_train(args, log_dir, hparams, args.wavenet_input) elif args.model == 'Tacotron-2': train(args, log_dir, hparams) else: raise ValueError('Model provided {} unknown! {}'.format( args.model, accepted_models))
def main(): parser = argparse.ArgumentParser() parser.add_argument('--base_dir', default='dataset/MultiSpeaker', required=True) parser.add_argument('--hparams', default='', help='Hyperparameter overrides from a json file') parser.add_argument('--tacotron_input', default='training_data/train.txt') parser.add_argument('--wavenet_input', default='tacotron_output/gta/map.txt') parser.add_argument('--name', help='Name of logging directory.') parser.add_argument('--model', default='MultiSpeaker', required=True) parser.add_argument('--input_dir', default='training_data', help='folder to contain inputs sentences/targets') parser.add_argument('--output_dir', default='output/', help='folder to contain synthesized mel spectrograms') parser.add_argument('--mode', default='synthesis', help='mode for synthesis of tacotron after training') parser.add_argument( '--GTA', default='True', help= 'Ground truth aligned synthesis, defaults to True, only considered in Tacotron synthesis mode' ) parser.add_argument('--restore', type=bool, default=True, help='Set this to False to do a fresh training') parser.add_argument('--summary_interval', type=int, default=200, help='Steps between running summary ops') parser.add_argument('--checkpoint_interval', type=int, default=500, help='Steps between writing checkpoints') parser.add_argument('--eval_interval', type=int, default=200, help='Steps between eval on test data') parser.add_argument('--tacotron_train_steps', type=int, default=500000, help='total number of tacotron training steps') parser.add_argument('--wavenet_train_steps', type=int, default=360000, help='total number of wavenet training steps') parser.add_argument('--tf_log_level', type=int, default=2, help='Tensorflow C++ log level.') args = parser.parse_args() accepted_models = [ 'Tacotron', 'WaveNet', 'Both', 'Tacotron-2', 'MultiSpeaker' ] if args.model not in accepted_models: raise ValueError( 'please enter a valid model to train: {}'.format(accepted_models)) log_dir, hparams = prepare_run(args) if args.model == 'Tacotron': tacotron_train(args, log_dir, hparams) elif args.model == 'WaveNet': wavenet_train(args, log_dir, hparams, args.wavenet_input) elif args.model == 'MultiSpeaker': multispeaker_train(args, log_dir, hparams) elif args.model in ('Both', 'Tacotron-2'): train(args, log_dir, hparams) else: raise ValueError('Model provided {} unknown! {}'.format( args.model, accepted_models))
parser.add_argument('--checkpoint_interval', type=int, default=500, >>>>>>> f33090dba9ba4bc52db8367abdc48841d13c48f8 help='Steps between writing checkpoints') parser.add_argument('--eval_interval', type=int, default=1000, help='Steps between eval on test data') parser.add_argument('--tacotron_train_steps', type=int, default=1000000, help='total number of tacotron training steps') parser.add_argument('--wavenet_train_steps', type=int, default=750000, help='total number of wavenet training steps') parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.') parser.add_argument('--slack_url', default=None, help='slack webhook notification destination link') args = parser.parse_args() accepted_models = ['Tacotron', 'WaveNet', 'Tacotron-2'] if args.model not in accepted_models: raise ValueError('please enter a valid model to train: {}'.format(accepted_models)) log_dir, hparams = prepare_run(args) if args.model == 'Tacotron': tacotron_train(args, log_dir, hparams) elif args.model == 'WaveNet': wavenet_train(args, log_dir, hparams, args.wavenet_input) elif args.model == 'Tacotron-2': train(args, log_dir, hparams) else: raise ValueError('Model provided {} unknown! {}'.format(args.model, accepted_models)) if __name__ == '__main__': main()
def main(): parser = argparse.ArgumentParser() parser.add_argument('--base_dir', default=os.path.expanduser('~/tacotron')) parser.add_argument('--log_dir', default=os.path.expanduser('~/tacotron')) parser.add_argument('--tacotron_input', default='training/train.txt') parser.add_argument('--wavenet_input', default='tacotron_output/gta/map.txt') parser.add_argument('--model', default='tacotron') parser.add_argument('--variant', default='tacotron') parser.add_argument( '--name', help='Name of the run. Used for logging. Defaults to model name.') parser.add_argument( '--hparams', default='', help= 'Hyperparameter overrides as a comma-separated list of name=value pairs' ) parser.add_argument('--restore_step', type=int, help='Global step to restore from checkpoint.') parser.add_argument('--summary_interval', type=int, default=100, help='Steps between running summary ops.') parser.add_argument('--checkpoint_interval', type=int, default=1000, help='Steps between writing checkpoints.') parser.add_argument('--slack_url', help='Slack webhook URL to get periodic reports.') parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.') parser.add_argument('--git', action='store_true', help='If set, verify that the client is clean.') parser.add_argument('--num_steps', type=int, default=100000, help='Maximum number of steps to run training for.') parser.add_argument( '--wavenet_num_steps', type=int, default=500000, help='Maximum number of steps to run wavenet training for') parser.add_argument('--eal_dir', default='') parser.add_argument('--eal_ckpt', default='') parser.add_argument('--eal_ft', default=False, action='store_true', help='load the weights, not Adam / BatchNorm history') parser.add_argument('--eal_trainAlign', default=False, action='store_true', help='train attention mechanism') parser.add_argument('--eal_trainJoint', default=False, action='store_true', help='train all weight in EAL mode with joint cost') parser.add_argument('--eal_alignScale', type=float, default=1.0, help='scaler for loss_align') parser.add_argument('--eal_only_start', type=int, default=500000, help='eal only from 500k iterations on') args = parser.parse_args() accepted_models = ['tacotron', 'wavenet'] if args.model not in accepted_models: raise ValueError( 'please enter a valid model to train: {}'.format(accepted_models)) os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level) run_name = args.name or args.variant log_dir = os.path.join(args.log_dir, 'logs-%s' % run_name) os.makedirs(log_dir, exist_ok=True) infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url) log('Initialised log file') hparams.parse(args.hparams) if args.model == 'tacotron': # if args.eal_dir=='online': # train_tacotron_online(log_dir, args, args.tacotron_input) # else: # train_tacotron(log_dir, args, args.tacotron_input) train_tacotron_online(log_dir, args, args.tacotron_input) elif args.model == 'wavenet': wavenet_train(log_dir, args, hparams, args.wavenet_input) else: raise ValueError('Model provided {} unknown! {}'.format( args.model, accepted_models))
def train(args, log_dir, hparams): state_file = os.path.join(log_dir, 'state_log') #Get training states (taco_state, GTA_state, wave_state), input_path = read_seq(state_file) if not taco_state: print( '\n#############################################################\n' ) log('\n#############################################################\n' ) print('Tacotron Train\n') log('Tacotron Train\n') print('###########################################################\n') log('###########################################################\n') checkpoint = tacotron_train(args, log_dir, hparams) tf.reset_default_graph() #Sleep 1/2 second to let previous graph close and avoid error messages while synthesis sleep(0.5) if checkpoint is None: raise ('Error occured while training Tacotron, Exiting!') taco_state = 1 save_seq(state_file, [taco_state, GTA_state, wave_state], input_path) else: checkpoint = os.path.join(log_dir, 'taco_pretrained/') if not GTA_state: print( '\n#############################################################\n' ) log('\n#############################################################\n' ) print('Tacotron GTA Synthesis\n') log('Tacotron GTA Synthesis\n') print('###########################################################\n') log('###########################################################\n') input_path = tacotron_synthesize(args, hparams, checkpoint) tf.reset_default_graph() #Sleep 1/2 second to let previous graph close and avoid error messages while Wavenet is training sleep(0.5) GTA_state = 1 save_seq(state_file, [taco_state, GTA_state, wave_state], input_path) else: input_path = os.path.join('tacotron_' + args.output_dir, 'gta', 'map.txt') if input_path == '' or input_path is None: raise RuntimeError( 'input_path has an unpleasant value -> {}'.format(input_path)) if not wave_state: print( '\n#############################################################\n' ) log('\n#############################################################\n' ) print('Wavenet Train\n') log('Wavenet Train\n') print('###########################################################\n') log('###########################################################\n') checkpoint = wavenet_train(args, log_dir, hparams, input_path) if checkpoint is None: raise ('Error occured while training Wavenet, Exiting!') wave_state = 1 save_seq(state_file, [taco_state, GTA_state, wave_state], input_path) if wave_state and GTA_state and taco_state: print('TRAINING IS ALREADY COMPLETE!!') log('TRAINING IS ALREADY COMPLETE!!')