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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,15 +1,20 @@ | ||
| numpy>=1.16.0 | ||
| torch>=1.5 | ||
| librosa>=0.5.1 | ||
| Unidecode>=0.4.20 | ||
| tensorboard | ||
| tensorflow>=2.2 | ||
| numpy>=1.16.0 | ||
| scipy>=0.19.0 | ||
| numba==0.48 | ||
| librosa==0.7.2 | ||
| phonemizer>=2.2.0 | ||
| unidecode==0.4.20 | ||
| attrdict | ||
| tensorboardX | ||
| matplotlib | ||
| Pillow | ||
| flask | ||
| scipy | ||
| tqdm | ||
| soundfile | ||
| phonemizer | ||
| bokeh==1.4.0 | ||
| inflect | ||
| bokeh==1.4.0 | ||
| soundfile | ||
| nose==1.3.7 | ||
| cardboardlint==1.3.0 | ||
| pylint==2.5.3 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,18 +1,19 @@ | ||
| torch>=1.5 | ||
| tensorflow==2.3rc | ||
| numpy>=1.16.0 | ||
| scipy>=0.19.0 | ||
| numba==0.48 | ||
| torch>=0.4.1 | ||
| tensorflow>=2.2 | ||
| librosa>=0.5.1 | ||
| Unidecode>=0.4.20 | ||
| tensorboard | ||
| librosa==0.7.2 | ||
| phonemizer>=2.2.0 | ||
| unidecode==0.4.20 | ||
| attrdict | ||
| tensorboardX | ||
| matplotlib | ||
| Pillow | ||
| flask | ||
| scipy | ||
| tqdm | ||
| soundfile | ||
| inflect | ||
| phonemizer | ||
| bokeh==1.4.0 | ||
| nose | ||
| soundfile | ||
| nose==1.3.7 | ||
| cardboardlint==1.3.0 |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1 @@ | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,134 @@ | ||
| import os | ||
| import torch | ||
| import unittest | ||
| import numpy as np | ||
| import tensorflow as tf | ||
| tf.get_logger().setLevel('INFO') | ||
|
|
||
| from TTS.utils.io import load_config | ||
| from TTS.tf.models.tacotron2 import Tacotron2 | ||
| from TTS.tf.utils.tflite import convert_tacotron2_to_tflite, load_tflite_model | ||
|
|
||
| #pylint: disable=unused-variable | ||
|
|
||
| torch.manual_seed(1) | ||
| use_cuda = torch.cuda.is_available() | ||
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | ||
|
|
||
| file_path = os.path.dirname(os.path.realpath(__file__)).replace('/tf/', '/') | ||
| c = load_config(os.path.join(file_path, 'test_config.json')) | ||
|
|
||
|
|
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| class TacotronTFTrainTest(unittest.TestCase): | ||
|
|
||
| @staticmethod | ||
| def generate_dummy_inputs(): | ||
| chars_seq = torch.randint(0, 24, (8, 128)).long().to(device) | ||
| chars_seq_lengths = torch.randint(100, 128, (8, )).long().to(device) | ||
| chars_seq_lengths = torch.sort(chars_seq_lengths, descending=True)[0] | ||
| mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device) | ||
| mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device) | ||
| mel_lengths = torch.randint(20, 30, (8, )).long().to(device) | ||
| stop_targets = torch.zeros(8, 30, 1).float().to(device) | ||
| speaker_ids = torch.randint(0, 5, (8, )).long().to(device) | ||
|
|
||
| chars_seq = tf.convert_to_tensor(chars_seq.cpu().numpy()) | ||
| chars_seq_lengths = tf.convert_to_tensor(chars_seq_lengths.cpu().numpy()) | ||
| mel_spec = tf.convert_to_tensor(mel_spec.cpu().numpy()) | ||
| return chars_seq, chars_seq_lengths, mel_spec, mel_postnet_spec, mel_lengths,\ | ||
| stop_targets, speaker_ids | ||
|
|
||
| def test_train_step(self): | ||
| ''' test forward pass ''' | ||
| chars_seq, chars_seq_lengths, mel_spec, mel_postnet_spec, mel_lengths,\ | ||
| stop_targets, speaker_ids = self.generate_dummy_inputs() | ||
|
|
||
| for idx in mel_lengths: | ||
| stop_targets[:, int(idx.item()):, 0] = 1.0 | ||
|
|
||
| stop_targets = stop_targets.view(chars_seq.shape[0], | ||
| stop_targets.size(1) // c.r, -1) | ||
| stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze() | ||
|
|
||
| model = Tacotron2(num_chars=24, r=c.r, num_speakers=5) | ||
| # training pass | ||
| output = model(chars_seq, chars_seq_lengths, mel_spec, training=True) | ||
|
|
||
| # check model output shapes | ||
| assert np.all(output[0].shape == mel_spec.shape) | ||
| assert np.all(output[1].shape == mel_spec.shape) | ||
| assert output[2].shape[2] == chars_seq.shape[1] | ||
| assert output[2].shape[1] == (mel_spec.shape[1] // model.decoder.r) | ||
| assert output[3].shape[1] == (mel_spec.shape[1] // model.decoder.r) | ||
|
|
||
| # inference pass | ||
| output = model(chars_seq, training=False) | ||
|
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||
| def test_forward_attention(self,): | ||
| chars_seq, chars_seq_lengths, mel_spec, mel_postnet_spec, mel_lengths,\ | ||
| stop_targets, speaker_ids = self.generate_dummy_inputs() | ||
|
|
||
| for idx in mel_lengths: | ||
| stop_targets[:, int(idx.item()):, 0] = 1.0 | ||
|
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||
| stop_targets = stop_targets.view(chars_seq.shape[0], | ||
| stop_targets.size(1) // c.r, -1) | ||
| stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze() | ||
|
|
||
| model = Tacotron2(num_chars=24, r=c.r, num_speakers=5, forward_attn=True) | ||
| # training pass | ||
| output = model(chars_seq, chars_seq_lengths, mel_spec, training=True) | ||
|
|
||
| # check model output shapes | ||
| assert np.all(output[0].shape == mel_spec.shape) | ||
| assert np.all(output[1].shape == mel_spec.shape) | ||
| assert output[2].shape[2] == chars_seq.shape[1] | ||
| assert output[2].shape[1] == (mel_spec.shape[1] // model.decoder.r) | ||
| assert output[3].shape[1] == (mel_spec.shape[1] // model.decoder.r) | ||
|
|
||
| # inference pass | ||
| output = model(chars_seq, training=False) | ||
|
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||
| def test_tflite_conversion(self, ): #pylint:disable=no-self-use | ||
| model = Tacotron2(num_chars=24, | ||
| num_speakers=0, | ||
| r=3, | ||
| postnet_output_dim=80, | ||
| decoder_output_dim=80, | ||
| attn_type='original', | ||
| attn_win=False, | ||
| attn_norm='sigmoid', | ||
| prenet_type='original', | ||
| prenet_dropout=True, | ||
| forward_attn=False, | ||
| trans_agent=False, | ||
| forward_attn_mask=False, | ||
| location_attn=True, | ||
| attn_K=0, | ||
| separate_stopnet=True, | ||
| bidirectional_decoder=False, | ||
| enable_tflite=True) | ||
| model.build_inference() | ||
| convert_tacotron2_to_tflite(model, output_path='test_tacotron2.tflite', experimental_converter=True) | ||
| # init tflite model | ||
| tflite_model = load_tflite_model('test_tacotron2.tflite') | ||
| # fake input | ||
| inputs = tf.random.uniform([1, 4], maxval=10, dtype=tf.int32) #pylint:disable=unexpected-keyword-arg | ||
| # run inference | ||
| # get input and output details | ||
| input_details = tflite_model.get_input_details() | ||
| output_details = tflite_model.get_output_details() | ||
| # reshape input tensor for the new input shape | ||
| tflite_model.resize_tensor_input(input_details[0]['index'], inputs.shape) #pylint:disable=unexpected-keyword-arg | ||
| tflite_model.allocate_tensors() | ||
| detail = input_details[0] | ||
| input_shape = detail['shape'] | ||
| tflite_model.set_tensor(detail['index'], inputs) | ||
| # run the tflite_model | ||
| tflite_model.invoke() | ||
| # collect outputs | ||
| decoder_output = tflite_model.get_tensor(output_details[0]['index']) | ||
| postnet_output = tflite_model.get_tensor(output_details[1]['index']) | ||
| # remove tflite binary | ||
| os.remove('test_tacotron2.tflite') | ||
|
|
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