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Running
on
Zero
| 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.speakers import SpeakerManager | |
| 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") | |
| d_vectors_file_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.json") | |
| d_vectors_file_pth_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.pth") | |
| class SpeakerManagerTest(unittest.TestCase): | |
| """Test SpeakerManager for loading embedding files and computing d_vectors 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 | |
| ap = AudioProcessor(**config.audio) | |
| manager = SpeakerManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path) | |
| # load a sample audio and compute embedding | |
| waveform = ap.load_wav(sample_wav_path) | |
| mel = ap.melspectrogram(waveform) | |
| d_vector = manager.compute_embeddings(mel) | |
| assert d_vector.shape[1] == 256 | |
| # compute d_vector directly from an input file | |
| d_vector = manager.compute_embedding_from_clip(sample_wav_path) | |
| d_vector2 = manager.compute_embedding_from_clip(sample_wav_path) | |
| d_vector = torch.FloatTensor(d_vector) | |
| d_vector2 = torch.FloatTensor(d_vector2) | |
| assert d_vector.shape[0] == 256 | |
| assert (d_vector - d_vector2).sum() == 0.0 | |
| # compute d_vector from a list of wav files. | |
| d_vector3 = manager.compute_embedding_from_clip([sample_wav_path, sample_wav_path2]) | |
| d_vector3 = torch.FloatTensor(d_vector3) | |
| assert d_vector3.shape[0] == 256 | |
| assert (d_vector - d_vector3).sum() != 0.0 | |
| # remove dummy model | |
| os.remove(encoder_model_path) | |
| def test_dvector_file_processing(self): | |
| manager = SpeakerManager(d_vectors_file_path=d_vectors_file_path) | |
| self.assertEqual(manager.num_speakers, 1) | |
| self.assertEqual(manager.embedding_dim, 256) | |
| manager = SpeakerManager(d_vectors_file_path=d_vectors_file_pth_path) | |
| self.assertEqual(manager.num_speakers, 1) | |
| self.assertEqual(manager.embedding_dim, 256) | |
| d_vector = manager.get_embedding_by_clip(manager.clip_ids[0]) | |
| assert len(d_vector) == 256 | |
| d_vectors = manager.get_embeddings_by_name(manager.speaker_names[0]) | |
| assert len(d_vectors[0]) == 256 | |
| d_vector1 = manager.get_mean_embedding(manager.speaker_names[0], num_samples=2, randomize=True) | |
| assert len(d_vector1) == 256 | |
| d_vector2 = manager.get_mean_embedding(manager.speaker_names[0], num_samples=2, randomize=False) | |
| assert len(d_vector2) == 256 | |
| assert np.sum(np.array(d_vector1) - np.array(d_vector2)) != 0 | |