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| import os | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from tests import get_tests_input_path | |
| from TTS.config import load_config | |
| from TTS.encoder.utils.generic_utils import setup_encoder_model | |
| from TTS.encoder.utils.io import save_checkpoint | |
| from TTS.tts.utils.managers import EmbeddingManager | |
| from TTS.utils.audio import AudioProcessor | |
| encoder_config_path = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json") | |
| encoder_model_path = os.path.join(get_tests_input_path(), "checkpoint_0.pth") | |
| sample_wav_path = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0001.wav") | |
| sample_wav_path2 = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0002.wav") | |
| embedding_file_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.json") | |
| embeddings_file_path2 = os.path.join(get_tests_input_path(), "../data/dummy_speakers2.json") | |
| embeddings_file_pth_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.pth") | |
| class EmbeddingManagerTest(unittest.TestCase): | |
| """Test emEeddingManager for loading embedding files and computing embeddings from waveforms""" | |
| def test_speaker_embedding(): | |
| # load config | |
| config = load_config(encoder_config_path) | |
| config.audio.resample = True | |
| # create a dummy speaker encoder | |
| model = setup_encoder_model(config) | |
| save_checkpoint(model, None, None, get_tests_input_path(), 0) | |
| # load audio processor and speaker encoder | |
| manager = EmbeddingManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path) | |
| # load a sample audio and compute embedding | |
| ap = AudioProcessor(**config.audio) | |
| waveform = ap.load_wav(sample_wav_path) | |
| mel = ap.melspectrogram(waveform) | |
| embedding = manager.compute_embeddings(mel) | |
| assert embedding.shape[1] == 256 | |
| # compute embedding directly from an input file | |
| embedding = manager.compute_embedding_from_clip(sample_wav_path) | |
| embedding2 = manager.compute_embedding_from_clip(sample_wav_path) | |
| embedding = torch.FloatTensor(embedding) | |
| embedding2 = torch.FloatTensor(embedding2) | |
| assert embedding.shape[0] == 256 | |
| assert (embedding - embedding2).sum() == 0.0 | |
| # compute embedding from a list of wav files. | |
| embedding3 = manager.compute_embedding_from_clip([sample_wav_path, sample_wav_path2]) | |
| embedding3 = torch.FloatTensor(embedding3) | |
| assert embedding3.shape[0] == 256 | |
| assert (embedding - embedding3).sum() != 0.0 | |
| # remove dummy model | |
| os.remove(encoder_model_path) | |
| def test_embedding_file_processing(self): # pylint: disable=no-self-use | |
| manager = EmbeddingManager(embedding_file_path=embeddings_file_pth_path) | |
| # test embedding querying | |
| embedding = manager.get_embedding_by_clip(manager.clip_ids[0]) | |
| assert len(embedding) == 256 | |
| embeddings = manager.get_embeddings_by_name(manager.embedding_names[0]) | |
| assert len(embeddings[0]) == 256 | |
| embedding1 = manager.get_mean_embedding(manager.embedding_names[0], num_samples=2, randomize=True) | |
| assert len(embedding1) == 256 | |
| embedding2 = manager.get_mean_embedding(manager.embedding_names[0], num_samples=2, randomize=False) | |
| assert len(embedding2) == 256 | |
| assert np.sum(np.array(embedding1) - np.array(embedding2)) != 0 | |
| def test_embedding_file_loading(self): | |
| # test loading a json file | |
| manager = EmbeddingManager(embedding_file_path=embedding_file_path) | |
| self.assertEqual(manager.num_embeddings, 384) | |
| self.assertEqual(manager.embedding_dim, 256) | |
| # test loading a pth file | |
| manager = EmbeddingManager(embedding_file_path=embeddings_file_pth_path) | |
| self.assertEqual(manager.num_embeddings, 384) | |
| self.assertEqual(manager.embedding_dim, 256) | |
| # test loading a pth files with duplicate embedding keys | |
| with self.assertRaises(Exception) as context: | |
| manager = EmbeddingManager(embedding_file_path=[embeddings_file_pth_path, embeddings_file_pth_path]) | |
| self.assertTrue("Duplicate embedding names" in str(context.exception)) | |
| # test loading embedding files with different embedding keys | |
| manager = EmbeddingManager(embedding_file_path=[embeddings_file_pth_path, embeddings_file_path2]) | |
| self.assertEqual(manager.embedding_dim, 256) | |
| self.assertEqual(manager.num_embeddings, 384 * 2) | |