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# -*- coding: utf-8 -*-
# Copyright 2020 Minh Nguyen (@dathudeptrai)
#
# 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.
import tensorflow as tf
import logging
import os
import numpy as np
import pytest
from tensorflow_tts.configs import MultiBandMelGANGeneratorConfig
from tensorflow_tts.models import TFPQMF, TFMelGANGenerator
os.environ["CUDA_VISIBLE_DEVICES"] = ""
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
def make_multi_band_melgan_generator_args(**kwargs):
defaults = dict(
out_channels=1,
kernel_size=7,
filters=512,
use_bias=True,
upsample_scales=[8, 8, 2, 2],
stack_kernel_size=3,
stacks=3,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"alpha": 0.2},
padding_type="REFLECT",
subbands=4,
tabs=62,
cutoff_ratio=0.15,
beta=9.0,
)
defaults.update(kwargs)
return defaults
@pytest.mark.parametrize(
"dict_g",
[
{"subbands": 4, "upsample_scales": [2, 4, 8], "stacks": 4, "out_channels": 4},
{"subbands": 4, "upsample_scales": [4, 4, 4], "stacks": 5, "out_channels": 4},
],
)
def test_multi_band_melgan(dict_g):
args_g = make_multi_band_melgan_generator_args(**dict_g)
args_g = MultiBandMelGANGeneratorConfig(**args_g)
generator = TFMelGANGenerator(args_g, name="multi_band_melgan")
generator._build()
pqmf = TFPQMF(args_g, name="pqmf")
fake_mels = tf.random.uniform(shape=[1, 100, 80], dtype=tf.float32)
fake_y = tf.random.uniform(shape=[1, 100 * 256, 1], dtype=tf.float32)
y_hat_subbands = generator(fake_mels)
y_hat = pqmf.synthesis(y_hat_subbands)
y_subbands = pqmf.analysis(fake_y)
assert np.shape(y_subbands) == np.shape(y_hat_subbands)
assert np.shape(fake_y) == np.shape(y_hat)
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