98 lines
3.4 KiB
Python
98 lines
3.4 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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from __future__ import division
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import os
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import soundfile as sf
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from tensorboardX import SummaryWriter
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from collections import OrderedDict
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from paddle import fluid
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import paddle.fluid.dygraph as dg
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def make_output_tree(output_dir):
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checkpoint_dir = os.path.join(output_dir, "checkpoints")
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if not os.path.exists(checkpoint_dir):
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os.makedirs(checkpoint_dir)
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state_dir = os.path.join(output_dir, "states")
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if not os.path.exists(state_dir):
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os.makedirs(state_dir)
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def valid_model(model, valid_loader, output_dir, global_step, sample_rate):
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model.eval()
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for i, batch in enumerate(valid_loader):
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# print("sentence {}".format(i))
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path = os.path.join(output_dir,
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"step_{}_sentence_{}.wav".format(global_step, i))
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audio_clips, mel_specs, audio_starts = batch
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wav_var = model.synthesis(mel_specs)
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wav_np = wav_var.numpy()[0]
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sf.write(path, wav_np, samplerate=sample_rate)
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print("generated {}".format(path))
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def eval_model(model, valid_loader, output_dir, sample_rate):
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model.eval()
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for i, batch in enumerate(valid_loader):
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# print("sentence {}".format(i))
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path = os.path.join(output_dir, "sentence_{}.wav".format(i))
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audio_clips, mel_specs, audio_starts = batch
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wav_var = model.synthesis(mel_specs)
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wav_np = wav_var.numpy()[0]
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sf.write(path, wav_np, samplerate=sample_rate)
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print("generated {}".format(path))
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def save_checkpoint(model, optim, checkpoint_dir, global_step):
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path = os.path.join(checkpoint_dir, "step_{}".format(global_step))
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dg.save_dygraph(model.state_dict(), path)
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print("saving model to {}".format(path + ".pdparams"))
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if optim:
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dg.save_dygraph(optim.state_dict(), path)
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print("saving optimizer to {}".format(path + ".pdopt"))
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def load_model(model, path):
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model_dict, _ = dg.load_dygraph(path)
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model.set_dict(model_dict)
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print("loaded model from {}.pdparams".format(path))
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def load_checkpoint(model, optim, path):
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model_dict, optim_dict = dg.load_dygraph(path)
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model.set_dict(model_dict)
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print("loaded model from {}.pdparams".format(path))
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if optim_dict:
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optim.set_dict(optim_dict)
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print("loaded optimizer from {}.pdparams".format(path))
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def load_wavenet(model, path):
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wavenet_dict, _ = dg.load_dygraph(path)
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encoder_dict = OrderedDict()
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teacher_dict = OrderedDict()
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for k, v in wavenet_dict.items():
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if k.startswith("encoder."):
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encoder_dict[k.split('.', 1)[1]] = v
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else:
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# k starts with "decoder."
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teacher_dict[k.split('.', 1)[1]] = v
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model.encoder.set_dict(encoder_dict)
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model.teacher.set_dict(teacher_dict)
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print("loaded the encoder part and teacher part from wavenet model.")
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