221 lines
8.6 KiB
Python
221 lines
8.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import argparse
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import ruamel.yaml
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import random
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from tqdm import tqdm
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import pickle
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import numpy as np
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from tensorboardX import SummaryWriter
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import paddle.fluid.dygraph as dg
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from paddle import fluid
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from parakeet.models.wavenet import WaveNet, UpsampleNet
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from parakeet.models.clarinet import STFT, Clarinet, ParallelWaveNet
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from parakeet.data import TransformDataset, SliceDataset, RandomSampler, SequentialSampler, DataCargo
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from parakeet.utils.layer_tools import summary, freeze
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from utils import make_output_tree, valid_model, save_checkpoint, load_checkpoint, load_wavenet
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sys.path.append("../wavenet")
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from data import LJSpeechMetaData, Transform, DataCollector
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="train a clarinet model with LJspeech and a trained wavenet model."
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)
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parser.add_argument("--config", type=str, help="path of the config file.")
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parser.add_argument(
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"--device", type=int, default=-1, help="device to use.")
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parser.add_argument(
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"--output",
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type=str,
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default="experiment",
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help="path to save student.")
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parser.add_argument("--data", type=str, help="path of LJspeech dataset.")
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parser.add_argument("--resume", type=str, help="checkpoint to load from.")
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parser.add_argument(
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"--wavenet", type=str, help="wavenet checkpoint to use.")
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args = parser.parse_args()
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with open(args.config, 'rt') as f:
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config = ruamel.yaml.safe_load(f)
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ljspeech_meta = LJSpeechMetaData(args.data)
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data_config = config["data"]
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sample_rate = data_config["sample_rate"]
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n_fft = data_config["n_fft"]
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win_length = data_config["win_length"]
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hop_length = data_config["hop_length"]
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n_mels = data_config["n_mels"]
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train_clip_seconds = data_config["train_clip_seconds"]
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transform = Transform(sample_rate, n_fft, win_length, hop_length, n_mels)
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ljspeech = TransformDataset(ljspeech_meta, transform)
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valid_size = data_config["valid_size"]
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ljspeech_valid = SliceDataset(ljspeech, 0, valid_size)
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ljspeech_train = SliceDataset(ljspeech, valid_size, len(ljspeech))
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teacher_config = config["teacher"]
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n_loop = teacher_config["n_loop"]
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n_layer = teacher_config["n_layer"]
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filter_size = teacher_config["filter_size"]
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context_size = 1 + n_layer * sum([filter_size**i for i in range(n_loop)])
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print("context size is {} samples".format(context_size))
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train_batch_fn = DataCollector(context_size, sample_rate, hop_length,
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train_clip_seconds)
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valid_batch_fn = DataCollector(
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context_size, sample_rate, hop_length, train_clip_seconds, valid=True)
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batch_size = data_config["batch_size"]
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train_cargo = DataCargo(
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ljspeech_train,
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train_batch_fn,
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batch_size,
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sampler=RandomSampler(ljspeech_train))
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# only batch=1 for validation is enabled
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valid_cargo = DataCargo(
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ljspeech_valid,
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valid_batch_fn,
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batch_size=1,
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sampler=SequentialSampler(ljspeech_valid))
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make_output_tree(args.output)
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if args.device == -1:
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place = fluid.CPUPlace()
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else:
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place = fluid.CUDAPlace(args.device)
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with dg.guard(place):
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# conditioner(upsampling net)
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conditioner_config = config["conditioner"]
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upsampling_factors = conditioner_config["upsampling_factors"]
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upsample_net = UpsampleNet(upscale_factors=upsampling_factors)
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freeze(upsample_net)
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residual_channels = teacher_config["residual_channels"]
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loss_type = teacher_config["loss_type"]
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output_dim = teacher_config["output_dim"]
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log_scale_min = teacher_config["log_scale_min"]
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assert loss_type == "mog" and output_dim == 3, \
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"the teacher wavenet should be a wavenet with single gaussian output"
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teacher = WaveNet(n_loop, n_layer, residual_channels, output_dim,
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n_mels, filter_size, loss_type, log_scale_min)
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freeze(teacher)
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student_config = config["student"]
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n_loops = student_config["n_loops"]
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n_layers = student_config["n_layers"]
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student_residual_channels = student_config["residual_channels"]
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student_filter_size = student_config["filter_size"]
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student_log_scale_min = student_config["log_scale_min"]
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student = ParallelWaveNet(n_loops, n_layers, student_residual_channels,
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n_mels, student_filter_size)
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stft_config = config["stft"]
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stft = STFT(
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n_fft=stft_config["n_fft"],
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hop_length=stft_config["hop_length"],
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win_length=stft_config["win_length"])
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lmd = config["loss"]["lmd"]
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model = Clarinet(upsample_net, teacher, student, stft,
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student_log_scale_min, lmd)
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summary(model)
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# optim
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train_config = config["train"]
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learning_rate = train_config["learning_rate"]
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anneal_rate = train_config["anneal_rate"]
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anneal_interval = train_config["anneal_interval"]
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lr_scheduler = dg.ExponentialDecay(
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learning_rate, anneal_interval, anneal_rate, staircase=True)
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optim = fluid.optimizer.Adam(
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lr_scheduler, parameter_list=model.parameters())
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gradiant_max_norm = train_config["gradient_max_norm"]
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clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
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gradiant_max_norm)
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assert args.wavenet or args.resume, "you should load from a trained wavenet or resume training; training without a trained wavenet is not recommended."
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if args.wavenet:
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load_wavenet(model, args.wavenet)
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if args.resume:
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load_checkpoint(model, optim, args.resume)
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# loader
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train_loader = fluid.io.DataLoader.from_generator(
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capacity=10, return_list=True)
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train_loader.set_batch_generator(train_cargo, place)
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valid_loader = fluid.io.DataLoader.from_generator(
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capacity=10, return_list=True)
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valid_loader.set_batch_generator(valid_cargo, place)
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# train
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max_iterations = train_config["max_iterations"]
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checkpoint_interval = train_config["checkpoint_interval"]
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eval_interval = train_config["eval_interval"]
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checkpoint_dir = os.path.join(args.output, "checkpoints")
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state_dir = os.path.join(args.output, "states")
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log_dir = os.path.join(args.output, "log")
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writer = SummaryWriter(log_dir)
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# training loop
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global_step = 1
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global_epoch = 1
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while global_step < max_iterations:
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epoch_loss = 0.
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for j, batch in tqdm(enumerate(train_loader), desc="[train]"):
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audios, mels, audio_starts = batch
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model.train()
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loss_dict = model(
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audios, mels, audio_starts, clip_kl=global_step > 500)
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writer.add_scalar("learning_rate",
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optim._learning_rate.step().numpy()[0],
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global_step)
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for k, v in loss_dict.items():
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writer.add_scalar("loss/{}".format(k),
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v.numpy()[0], global_step)
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l = loss_dict["loss"]
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step_loss = l.numpy()[0]
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print("[train] loss: {:<8.6f}".format(step_loss))
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epoch_loss += step_loss
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l.backward()
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optim.minimize(l, grad_clip=clipper)
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optim.clear_gradients()
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if global_step % eval_interval == 0:
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# evaluate on valid dataset
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valid_model(model, valid_loader, state_dir, global_step,
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sample_rate)
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if global_step % checkpoint_interval == 0:
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save_checkpoint(model, optim, checkpoint_dir, global_step)
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global_step += 1
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# epoch loss
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average_loss = epoch_loss / j
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writer.add_scalar("average_loss", average_loss, global_epoch)
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global_epoch += 1
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