help='experiment case name of train1') parser.add_argument('case2', type=str, help='experiment case name of train2') parser.add_argument('-ckpt', help='checkpoint to load model.') parser.add_argument('-gpu', help='comma separated list of GPU(s) to use.') parser.add_argument('-r', action='store_true', help='start training from the beginning.') arguments = parser.parse_args() return arguments if __name__ == '__main__': args = get_arguments() print(args.case2) hp.set_hparam_yaml(args.case2, default_file='hparams/{}.yaml'.format(args.case2)) logdir_train1 = '{}/{}/train1'.format(hp.logdir_path, args.case1) logdir_train2 = '{}/{}/train2'.format(hp.logdir_path, args.case2) if args.r: remove_all_files(logdir_train2) print('case1: {}, case2: {}, logdir1: {}, logdir2: {}'.format( args.case1, args.case2, logdir_train1, logdir_train2)) train(args, logdir1=logdir_train1, logdir2=logdir_train2) print("Done")
return not_converted def preprocessing(dataset_path, isConverting=False): s = datetime.datetime.now() wav_files = glob.glob(dataset_path) dataset_path = dataset_path.replace('WAV', 'npz') npz_files = glob.glob(dataset_path) if len(npz_files) is 0: generate_npz(wav_files) else: convert_list = matching_list(wav_files, npz_files) if len(convert_list) is 0: print('All WAV files in dataset directory are already converted!') else: generate_npz(convert_list) e = datetime.datetime.now() diff = e - s print("Done. elapsed time:{}s".format(diff.seconds)) if __name__ == '__main__': hp.set_hparam_yaml("TIMIT2") data_path = "/home/cocoonmola/datasets/TIMIT2/TRAIN/*/*/*.WAV" preprocessing(data_path)
type=str, help='experiment case name of train1') parser.add_argument('case2', type=str, help='experiment case name of train2') parser.add_argument('-ckpt', help='checkpoint to load model.') parser.add_argument('-gpu', help='comma separated list of GPU(s) to use.') parser.add_argument('-r', action='store_true', help='start training from the beginning.') arguments = parser.parse_args() return arguments if __name__ == '__main__': args = get_arguments() hp.set_hparam_yaml(args.case2) logdir_train1 = '{}/{}/train1'.format(hp.logdir_path, args.case1) logdir_train2 = '{}/{}/train2'.format(hp.logdir_path, args.case2) if args.r: remove_all_files(logdir_train2) print('case1: {}, case2: {}, logdir1: {}, logdir2: {}'.format( args.case1, args.case2, logdir_train1, logdir_train2)) print('dataset : {}'.format(hp.train2.data_path)) train(args, logdir1=logdir_train1, logdir2=logdir_train2) print("Done")
def ckpt2mel(predictor, ppgs_dir, mel_dir, save_dir): print("get into ckpt") for fi in os.listdir(ppgs_dir): print("fi",fi) #ppgs_name = os.path.join(ppgs_dir, fi) mel, ppgs = queue_input(fi, ppgs_dir, mel_dir) pred_mel = predictor(mel, ppgs) #print("pred_mel",pred_mel.size()) pred_mel = np.array(pred_mel) print("pred_mel",pred_mel.shape) length = pred_mel.shape[2] width = pred_mel.shape[3] pred_mel = pred_mel.reshape((length, width)) save_name = fi.split('.npy')[0] if hp.default.n_mels == 20: npy_dir = os.path.join(save_dir,'lpc20') if not os.path.exists(npy_dir): os.makedirs(npy_dir) npy_path = os.path.join(npy_dir, '%s_20.npy' %save_name) np.save(npy_path, pred_mel) print('saved',npy_dir)if hp.default.n_mels == 32: npy_dir = os.path.join(save_dir,'lpc32') if not os.path.exists(npy_dir): os.makedirs(npy_dir) npy_path = os.path.join(npy_dir, '%s_32.npy' %save_name) np.save(npy_path, pred_mel) print('saved',npy_dir)def do_convert(args, logdir2): # Load graph model = Net2() index = 0 ppgs_dir = hp.convert.ppgs_path mel_dir = hp.convert.mel_path #for fi in os.listdir(ppgs_dir): #print("fi",fi) #ppgs_path = os.path.join(ppgs_dir, fi) #df = Net2DataFlow(hp.convert.mel_path, ppgs_path, hp.convert.batch_size) #print("finish df") ckpt2 = '{}/{}'.format(logdir2, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2) print("ckpt2",ckpt2) session_inits = [] if ckpt2: session_inits.append(SaverRestore(ckpt2)) pred_conf = PredictConfig( model=model, input_names=get_eval_input_names(), output_names=get_eval_output_names(), session_init=ChainInit(session_inits)) predictor = OfflinePredictor(pred_conf) print("after predictor") #import pdb;pdb.set_trace() ckpt2mel(predictor, ppgs_dir, mel_dir, hp.convert.save_path) print("success") def get_arguments(): parser = argparse.ArgumentParser() parser.add_argument('case2', type=str, help='experiment case name of train2') parser.add_argument('-ckpt', help='checkpoint to load model.') arguments = parser.parse_args() return arguments if __name__ == '__main__': args = get_arguments() hp.set_hparam_yaml(args.case2) logdir_train2 = '{}/{}/train2'.format(hp.logdir_path, args.case2) print('case2: {},logdir2: {}'.format(args.case2, logdir_train2)) s = datetime.datetime.now() do_convert(args, logdir2=logdir_train2) e = datetime.datetime.now() diff = e - s print("Done. elapsed time:{}s".format(diff.seconds))