🐜 CI for Tensorflow 2.3.1 and added pytest for hifigan.

Author: dathudeptrai

.github/workflows/ci.yaml

@@ -17,7 +17,7 @@ jobs:
       max-parallel: 10
       matrix:
         python-version: [3.7]
-        tensorflow-version: [2.3.0]
+        tensorflow-version: [2.3.1]
     steps:
       - uses: actions/checkout@master
       - uses: actions/setup-python@v1

test/test_hifigan.py

@@ -0,0 +1,125 @@
+# -*- coding: utf-8 -*-
+# Copyright 2020 TensorFlowTTS Team
+#
+# 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 logging
+import os
+
+import pytest
+import tensorflow as tf
+
+from tensorflow_tts.configs import (
+    HifiGANDiscriminatorConfig,
+    HifiGANGeneratorConfig,
+    MelGANDiscriminatorConfig,
+)
+from tensorflow_tts.models import (
+    TFHifiGANGenerator,
+    TFHifiGANMultiPeriodDiscriminator,
+    TFMelGANMultiScaleDiscriminator,
+)
+
+from examples.hifigan.train_hifigan import TFHifiGANDiscriminator
+
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+)
+
+
+def make_hifigan_generator_args(**kwargs):
+    defaults = dict(
+        out_channels=1,
+        kernel_size=7,
+        filters=128,
+        use_bias=True,
+        upsample_scales=[8, 8, 2, 2],
+        stacks=3,
+        stack_kernel_size=[3, 7, 11],
+        stack_dilation_rate=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+        nonlinear_activation="LeakyReLU",
+        nonlinear_activation_params={"alpha": 0.2},
+        padding_type="REFLECT",
+        use_final_nolinear_activation=True,
+        is_weight_norm=True,
+        initializer_seed=42,
+    )
+    defaults.update(kwargs)
+    return defaults
+
+
+def make_hifigan_discriminator_args(**kwargs):
+    defaults_multisperiod = dict(
+        out_channels=1,
+        period_scales=[2, 3, 5, 7, 11],
+        n_layers=5,
+        kernel_size=5,
+        strides=3,
+        filters=8,
+        filter_scales=4,
+        max_filters=1024,
+        nonlinear_activation="LeakyReLU",
+        nonlinear_activation_params={"alpha": 0.2},
+        is_weight_norm=True,
+        initializer_seed=42,
+    )
+    defaults_multisperiod.update(kwargs)
+    defaults_multiscale = dict(
+        out_channels=1,
+        scales=3,
+        downsample_pooling="AveragePooling1D",
+        downsample_pooling_params={"pool_size": 4, "strides": 2,},
+        kernel_sizes=[5, 3],
+        filters=16,
+        max_downsample_filters=1024,
+        use_bias=True,
+        downsample_scales=[4, 4, 4, 4],
+        nonlinear_activation="LeakyReLU",
+        nonlinear_activation_params={"alpha": 0.2},
+        padding_type="REFLECT",
+    )
+    defaults_multiscale.update(kwargs)
+    return [defaults_multisperiod, defaults_multiscale]
+
+
+@pytest.mark.parametrize(
+    "dict_g, dict_d, dict_loss",
+    [
+        ({}, {}, {}),
+        ({"kernel_size": 3}, {}, {}),
+        ({"filters": 1024}, {}, {}),
+        ({"stack_kernel_size": [1, 2, 3]}, {}, {}),
+        ({"stack_kernel_size": [3, 5, 7], "stacks": 3}, {}, {}),
+        ({"upsample_scales": [4, 4, 4, 4]}, {}, {}),
+        ({"upsample_scales": [8, 8, 2, 2]}, {}, {}),
+        ({"filters": 1024, "upsample_scales": [8, 8, 2, 2]}, {}, {}),
+    ],
+)
+def test_hifigan_trainable(dict_g, dict_d, dict_loss):
+    batch_size = 4
+    batch_length = 4096
+    args_g = make_hifigan_generator_args(**dict_g)
+    args_d_p, args_d_s = make_hifigan_discriminator_args(**dict_d)
+
+    args_g = HifiGANGeneratorConfig(**args_g)
+    args_d_p = HifiGANDiscriminatorConfig(**args_d_p)
+    args_d_s = MelGANDiscriminatorConfig(**args_d_s)
+
+    generator = TFHifiGANGenerator(args_g)
+
+    discriminator_p = TFHifiGANMultiPeriodDiscriminator(args_d_p)
+    discriminator_s = TFMelGANMultiScaleDiscriminator(args_d_s)
+    discriminator = TFHifiGANDiscriminator(discriminator_p, discriminator_s)