mirror of https://github.com/coqui-ai/TTS.git
138 lines
5.8 KiB
Python
138 lines
5.8 KiB
Python
import os
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import unittest
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import numpy as np
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import tensorflow as tf
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import torch
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from tests import get_tests_input_path
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from TTS.tts.tf.models.tacotron2 import Tacotron2
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from TTS.tts.tf.utils.tflite import (convert_tacotron2_to_tflite,
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load_tflite_model)
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from TTS.utils.io import load_config
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tf.get_logger().setLevel('INFO')
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#pylint: disable=unused-variable
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torch.manual_seed(1)
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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c = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
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class TacotronTFTrainTest(unittest.TestCase):
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@staticmethod
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def generate_dummy_inputs():
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chars_seq = torch.randint(0, 24, (8, 128)).long().to(device)
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chars_seq_lengths = torch.randint(100, 128, (8, )).long().to(device)
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chars_seq_lengths = torch.sort(chars_seq_lengths, descending=True)[0]
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mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
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mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
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mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
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stop_targets = torch.zeros(8, 30, 1).float().to(device)
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speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
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chars_seq = tf.convert_to_tensor(chars_seq.cpu().numpy())
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chars_seq_lengths = tf.convert_to_tensor(chars_seq_lengths.cpu().numpy())
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mel_spec = tf.convert_to_tensor(mel_spec.cpu().numpy())
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return chars_seq, chars_seq_lengths, mel_spec, mel_postnet_spec, mel_lengths,\
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stop_targets, speaker_ids
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def test_train_step(self):
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''' test forward pass '''
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chars_seq, chars_seq_lengths, mel_spec, mel_postnet_spec, mel_lengths,\
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stop_targets, speaker_ids = self.generate_dummy_inputs()
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for idx in mel_lengths:
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stop_targets[:, int(idx.item()):, 0] = 1.0
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stop_targets = stop_targets.view(chars_seq.shape[0],
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stop_targets.size(1) // c.r, -1)
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stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
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model = Tacotron2(num_chars=24, r=c.r, num_speakers=5)
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# training pass
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output = model(chars_seq, chars_seq_lengths, mel_spec, training=True)
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# check model output shapes
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assert np.all(output[0].shape == mel_spec.shape)
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assert np.all(output[1].shape == mel_spec.shape)
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assert output[2].shape[2] == chars_seq.shape[1]
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assert output[2].shape[1] == (mel_spec.shape[1] // model.decoder.r)
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assert output[3].shape[1] == (mel_spec.shape[1] // model.decoder.r)
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# inference pass
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output = model(chars_seq, training=False)
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def test_forward_attention(self,):
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chars_seq, chars_seq_lengths, mel_spec, mel_postnet_spec, mel_lengths,\
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stop_targets, speaker_ids = self.generate_dummy_inputs()
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for idx in mel_lengths:
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stop_targets[:, int(idx.item()):, 0] = 1.0
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stop_targets = stop_targets.view(chars_seq.shape[0],
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stop_targets.size(1) // c.r, -1)
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stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
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model = Tacotron2(num_chars=24, r=c.r, num_speakers=5, forward_attn=True)
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# training pass
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output = model(chars_seq, chars_seq_lengths, mel_spec, training=True)
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# check model output shapes
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assert np.all(output[0].shape == mel_spec.shape)
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assert np.all(output[1].shape == mel_spec.shape)
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assert output[2].shape[2] == chars_seq.shape[1]
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assert output[2].shape[1] == (mel_spec.shape[1] // model.decoder.r)
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assert output[3].shape[1] == (mel_spec.shape[1] // model.decoder.r)
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# inference pass
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output = model(chars_seq, training=False)
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def test_tflite_conversion(self, ): #pylint:disable=no-self-use
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model = Tacotron2(num_chars=24,
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num_speakers=0,
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r=3,
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postnet_output_dim=80,
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decoder_output_dim=80,
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attn_type='original',
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attn_win=False,
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attn_norm='sigmoid',
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prenet_type='original',
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prenet_dropout=True,
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forward_attn=False,
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trans_agent=False,
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forward_attn_mask=False,
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location_attn=True,
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attn_K=0,
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separate_stopnet=True,
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bidirectional_decoder=False,
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enable_tflite=True)
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model.build_inference()
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convert_tacotron2_to_tflite(model, output_path='test_tacotron2.tflite', experimental_converter=True)
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# init tflite model
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tflite_model = load_tflite_model('test_tacotron2.tflite')
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# fake input
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inputs = tf.random.uniform([1, 4], maxval=10, dtype=tf.int32) #pylint:disable=unexpected-keyword-arg
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# run inference
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# get input and output details
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input_details = tflite_model.get_input_details()
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output_details = tflite_model.get_output_details()
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# reshape input tensor for the new input shape
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tflite_model.resize_tensor_input(input_details[0]['index'], inputs.shape) #pylint:disable=unexpected-keyword-arg
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tflite_model.allocate_tensors()
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detail = input_details[0]
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input_shape = detail['shape']
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tflite_model.set_tensor(detail['index'], inputs)
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# run the tflite_model
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tflite_model.invoke()
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# collect outputs
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decoder_output = tflite_model.get_tensor(output_details[0]['index'])
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postnet_output = tflite_model.get_tensor(output_details[1]['index'])
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# remove tflite binary
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os.remove('test_tacotron2.tflite')
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