utils.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import division
import os
import soundfile as sf
from collections import OrderedDict

from paddle import fluid
import paddle.fluid.dygraph as dg


def make_output_tree(output_dir):
    checkpoint_dir = os.path.join(output_dir, "checkpoints")
    if not os.path.exists(checkpoint_dir):
        os.makedirs(checkpoint_dir)

    state_dir = os.path.join(output_dir, "states")
    if not os.path.exists(state_dir):
        os.makedirs(state_dir)


def eval_model(model, valid_loader, output_dir, iteration, sample_rate):
    model.eval()
    for i, batch in enumerate(valid_loader):
        # print("sentence {}".format(i))
        path = os.path.join(output_dir,
                            "sentence_{}_step_{}.wav".format(i, iteration))
        audio_clips, mel_specs, audio_starts = batch
        wav_var = model.synthesis(mel_specs)
        wav_np = wav_var.numpy()[0]
        sf.write(path, wav_np, samplerate=sample_rate)
        print("generated {}".format(path))


def load_wavenet(model, path):
    wavenet_dict, _ = dg.load_dygraph(path)
    encoder_dict = OrderedDict()
    teacher_dict = OrderedDict()
    for k, v in wavenet_dict.items():
        if k.startswith("encoder."):
            encoder_dict[k.split('.', 1)[1]] = v
        else:
            # k starts with "decoder."
            teacher_dict[k.split('.', 1)[1]] = v

    model.encoder.set_dict(encoder_dict)
    model.teacher.set_dict(teacher_dict)
    print("loaded the encoder part and teacher part from wavenet model.")