File size: 2,995 Bytes
d5ee97c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
# -*- 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 pytest
import tensorflow as tf
from tensorflow_tts.configs import FastSpeechConfig
from tensorflow_tts.models import TFFastSpeech
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 = FastSpeechConfig()
fastspeech = TFFastSpeech(config, name="fastspeech")
fastspeech._build()
fastspeech.save_weights("./test.h5")
fastspeech.resize_positional_embeddings(new_size)
fastspeech.load_weights("./test.h5", by_name=True, skip_mismatch=True)
@pytest.mark.parametrize("num_hidden_layers,n_speakers", [(2, 1), (3, 2), (4, 3)])
def test_fastspeech_trainable(num_hidden_layers, n_speakers):
config = FastSpeechConfig(
encoder_num_hidden_layers=num_hidden_layers,
decoder_num_hidden_layers=num_hidden_layers + 1,
n_speakers=n_speakers,
)
fastspeech = TFFastSpeech(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)
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 = fastspeech(
input_ids, speaker_ids, duration_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, fastspeech.trainable_variables)
optimizer.apply_gradients(zip(gradients, fastspeech.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)
|