def version_compare(version1, version2): version1 = version1.split(".") version2 = version2.split(".") num = min(len(version1), len(version2)) for index in range(num): try: vn1 = int(version1[index]) except: vn1 = 0 try: vn2 = int(version2[index]) except: vn2 = 0 if vn1 > vn2: return True elif vn1 < vn2: return False return len(version1) > len(version2) if version_compare(paddle.__version__, "1.8.0"): print("verison greater than 1.8") import paddle.fluid as fluid else: print("verison is: %s" % paddle.__version__) import paddle.fluid.dygraph as dygraph dygraph.enable_dygraph()
help="path to save synthesized audio") args = parser.parse_args() with open(args.config, 'rt') as f: config = ruamel.yaml.safe_load(f) print("Command Line Args: ") for k, v in vars(args).items(): print("{}: {}".format(k, v)) if args.device == -1: place = fluid.CPUPlace() else: place = fluid.CUDAPlace(args.device) dg.enable_dygraph(place) model = make_model(config) checkpoint_dir = os.path.join(args.output, "checkpoints") if args.checkpoint is not None: iteration = io.load_parameters(model, checkpoint_path=args.checkpoint) else: iteration = io.load_parameters(model, checkpoint_dir=checkpoint_dir, iteration=args.iteration) # WARNING: don't forget to remove weight norm to re-compute each wrapped layer's weight # removing weight norm also speeds up computation for layer in model.sublayers(): if isinstance(layer, WeightNormWrapper): layer.remove_weight_norm()
parser.add_argument("--config", type=str, required=True, help="config file") parser.add_argument("--input", type=str, required=True, help="text file to synthesize") parser.add_argument("--output", type=str, required=True, help="path to save audio") parser.add_argument("--checkpoint", type=str, required=True, help="data path of the checkpoint") parser.add_argument("--monotonic_layers", type=str, required=True, help="monotonic decoder layers' indices(start from 1)") parser.add_argument("--vocoder", type=str, default="waveflow", choices=['griffin-lim', 'waveflow'], help="vocoder to use") args = parser.parse_args() with open(args.config, 'rt') as f: config = yaml.safe_load(f) dg.enable_dygraph(fluid.CUDAPlace(0)) main(args, config)
parser.add_argument("--input", type=str, required=True, help="data path of the original data") args = parser.parse_args() with open(args.config, 'rt') as f: config = yaml.safe_load(f) print("========= Command Line Arguments ========") for k, v in vars(args).items(): print("{}: {}".format(k, v)) print("=========== Configurations ==============") for k in ["p_pronunciation", "batch_size"]: print("{}: {}".format(k, config[k])) ljspeech = LJSpeech(args.input) collate_fn = DataCollector(config["p_pronunciation"]) dg.enable_dygraph(fluid.CPUPlace()) sampler = PartialyRandomizedSimilarTimeLengthSampler(ljspeech.num_frames()) cargo = DataCargo(ljspeech, collate_fn, batch_size=config["batch_size"], sampler=sampler) loader = DataLoader\ .from_generator(capacity=5, return_list=True)\ .set_batch_generator(cargo) for i, batch in tqdm.tqdm(enumerate(loader)): continue