File size: 2,870 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 |
# -*- 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.
"""Tensorflow Savable Model modules."""
import numpy as np
import tensorflow as tf
from tensorflow_tts.models import (
TFFastSpeech,
TFFastSpeech2,
TFMelGANGenerator,
TFMBMelGANGenerator,
TFHifiGANGenerator,
TFTacotron2,
TFParallelWaveGANGenerator,
)
class SavableTFTacotron2(TFTacotron2):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
def call(self, inputs, training=False):
input_ids, input_lengths, speaker_ids = inputs
return super().inference(input_ids, input_lengths, speaker_ids)
def _build(self):
input_ids = tf.convert_to_tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]], dtype=tf.int32)
input_lengths = tf.convert_to_tensor([9], dtype=tf.int32)
speaker_ids = tf.convert_to_tensor([0], dtype=tf.int32)
self([input_ids, input_lengths, speaker_ids])
class SavableTFFastSpeech(TFFastSpeech):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
def call(self, inputs, training=False):
input_ids, speaker_ids, speed_ratios = inputs
return super()._inference(input_ids, speaker_ids, speed_ratios)
def _build(self):
input_ids = tf.convert_to_tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], tf.int32)
speaker_ids = tf.convert_to_tensor([0], tf.int32)
speed_ratios = tf.convert_to_tensor([1.0], tf.float32)
self([input_ids, speaker_ids, speed_ratios])
class SavableTFFastSpeech2(TFFastSpeech2):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
def call(self, inputs, training=False):
input_ids, speaker_ids, speed_ratios, f0_ratios, energy_ratios = inputs
return super()._inference(
input_ids, speaker_ids, speed_ratios, f0_ratios, energy_ratios
)
def _build(self):
input_ids = tf.convert_to_tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], tf.int32)
speaker_ids = tf.convert_to_tensor([0], tf.int32)
speed_ratios = tf.convert_to_tensor([1.0], tf.float32)
f0_ratios = tf.convert_to_tensor([1.0], tf.float32)
energy_ratios = tf.convert_to_tensor([1.0], tf.float32)
self([input_ids, speaker_ids, speed_ratios, f0_ratios, energy_ratios])
|