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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 logging
import os
import yaml
import pytest
import tensorflow as tf
from tensorflow_tts.configs import FastSpeech2Config
from tensorflow_tts.models import TFFastSpeech2
from tensorflow_tts.utils import return_strategy
from examples.fastspeech2.train_fastspeech2 import FastSpeech2Trainer
os.environ["CUDA_VISIBLE_DEVICES"] = ""
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
@pytest.mark.parametrize("new_size", [100, 200, 300])
def test_fastspeech_resize_positional_embeddings(new_size):
config = FastSpeech2Config()
fastspeech2 = TFFastSpeech2(config, name="fastspeech")
fastspeech2._build()
fastspeech2.save_weights("./test.h5")
fastspeech2.resize_positional_embeddings(new_size)
fastspeech2.load_weights("./test.h5", by_name=True, skip_mismatch=True)
@pytest.mark.parametrize(
"var_train_expr, config_path",
[
(None, "./examples/fastspeech2/conf/fastspeech2.v1.yaml"),
("embeddings|encoder", "./examples/fastspeech2/conf/fastspeech2.v1.yaml"),
("embeddings|encoder", "./examples/fastspeech2/conf/fastspeech2.v2.yaml"),
("embeddings|encoder", "./examples/fastspeech2/conf/fastspeech2.baker.v2.yaml"),
("embeddings|encoder", "./examples/fastspeech2/conf/fastspeech2.kss.v1.yaml"),
("embeddings|encoder", "./examples/fastspeech2/conf/fastspeech2.kss.v2.yaml"),
],
)
def test_fastspeech2_train_some_layers(var_train_expr, config_path):
config = FastSpeech2Config(n_speakers=5)
model = TFFastSpeech2(config)
model._build()
optimizer = tf.keras.optimizers.Adam(lr=0.001)
with open(config_path) as f:
config = yaml.load(f, Loader=yaml.Loader)
config.update({"outdir": "./"})
config.update({"var_train_expr": var_train_expr})
STRATEGY = return_strategy()
trainer = FastSpeech2Trainer(
config=config, strategy=STRATEGY, steps=0, epochs=0, is_mixed_precision=False,
)
trainer.compile(model, optimizer)
len_trainable_vars = len(trainer._trainable_variables)
all_trainable_vars = len(model.trainable_variables)
if var_train_expr is None:
tf.debugging.assert_equal(len_trainable_vars, all_trainable_vars)
else:
tf.debugging.assert_less(len_trainable_vars, all_trainable_vars)
@pytest.mark.parametrize("num_hidden_layers,n_speakers", [(2, 1), (3, 2), (4, 3)])
def test_fastspeech_trainable(num_hidden_layers, n_speakers):
config = FastSpeech2Config(
encoder_num_hidden_layers=num_hidden_layers,
decoder_num_hidden_layers=num_hidden_layers + 1,
n_speakers=n_speakers,
)
fastspeech2 = TFFastSpeech2(config, name="fastspeech")
optimizer = tf.keras.optimizers.Adam(lr=0.001)
# fake inputs
input_ids = tf.convert_to_tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], tf.int32)
attention_mask = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], tf.int32)
speaker_ids = tf.convert_to_tensor([0], tf.int32)
duration_gts = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], tf.int32)
f0_gts = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], tf.float32)
energy_gts = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], tf.float32)
mel_gts = tf.random.uniform(shape=[1, 10, 80], dtype=tf.float32)
@tf.function
def one_step_training():
with tf.GradientTape() as tape:
mel_outputs_before, _, duration_outputs, _, _ = fastspeech2(
input_ids, speaker_ids, duration_gts, f0_gts, energy_gts, training=True,
)
duration_loss = tf.keras.losses.MeanSquaredError()(
duration_gts, duration_outputs
)
mel_loss = tf.keras.losses.MeanSquaredError()(mel_gts, mel_outputs_before)
loss = duration_loss + mel_loss
gradients = tape.gradient(loss, fastspeech2.trainable_variables)
optimizer.apply_gradients(zip(gradients, fastspeech2.trainable_variables))
tf.print(loss)
import time
for i in range(2):
if i == 1:
start = time.time()
one_step_training()
print(time.time() - start)
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