mirror of https://github.com/coqui-ai/TTS.git
78 lines
3.4 KiB
Python
78 lines
3.4 KiB
Python
import os
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import unittest
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import numpy as np
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import torch
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from tests import get_tests_input_path
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from TTS.config import load_config
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from TTS.encoder.utils.generic_utils import setup_encoder_model
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from TTS.encoder.utils.io import save_checkpoint
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from TTS.tts.utils.speakers import SpeakerManager
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from TTS.utils.audio import AudioProcessor
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encoder_config_path = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
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encoder_model_path = os.path.join(get_tests_input_path(), "checkpoint_0.pth")
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sample_wav_path = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0001.wav")
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sample_wav_path2 = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0002.wav")
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d_vectors_file_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.json")
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d_vectors_file_pth_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.pth")
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class SpeakerManagerTest(unittest.TestCase):
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"""Test SpeakerManager for loading embedding files and computing d_vectors from waveforms"""
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@staticmethod
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def test_speaker_embedding():
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# load config
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config = load_config(encoder_config_path)
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config.audio.resample = True
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# create a dummy speaker encoder
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model = setup_encoder_model(config)
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save_checkpoint(model, None, None, get_tests_input_path(), 0)
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# load audio processor and speaker encoder
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ap = AudioProcessor(**config.audio)
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manager = SpeakerManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path)
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# load a sample audio and compute embedding
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waveform = ap.load_wav(sample_wav_path)
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mel = ap.melspectrogram(waveform)
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d_vector = manager.compute_embeddings(mel)
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assert d_vector.shape[1] == 256
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# compute d_vector directly from an input file
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d_vector = manager.compute_embedding_from_clip(sample_wav_path)
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d_vector2 = manager.compute_embedding_from_clip(sample_wav_path)
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d_vector = torch.FloatTensor(d_vector)
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d_vector2 = torch.FloatTensor(d_vector2)
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assert d_vector.shape[0] == 256
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assert (d_vector - d_vector2).sum() == 0.0
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# compute d_vector from a list of wav files.
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d_vector3 = manager.compute_embedding_from_clip([sample_wav_path, sample_wav_path2])
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d_vector3 = torch.FloatTensor(d_vector3)
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assert d_vector3.shape[0] == 256
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assert (d_vector - d_vector3).sum() != 0.0
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# remove dummy model
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os.remove(encoder_model_path)
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def test_dvector_file_processing(self):
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manager = SpeakerManager(d_vectors_file_path=d_vectors_file_path)
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self.assertEqual(manager.num_speakers, 1)
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self.assertEqual(manager.embedding_dim, 256)
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manager = SpeakerManager(d_vectors_file_path=d_vectors_file_pth_path)
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self.assertEqual(manager.num_speakers, 1)
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self.assertEqual(manager.embedding_dim, 256)
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d_vector = manager.get_embedding_by_clip(manager.clip_ids[0])
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assert len(d_vector) == 256
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d_vectors = manager.get_embeddings_by_name(manager.speaker_names[0])
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assert len(d_vectors[0]) == 256
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d_vector1 = manager.get_mean_embedding(manager.speaker_names[0], num_samples=2, randomize=True)
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assert len(d_vector1) == 256
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d_vector2 = manager.get_mean_embedding(manager.speaker_names[0], num_samples=2, randomize=False)
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assert len(d_vector2) == 256
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assert np.sum(np.array(d_vector1) - np.array(d_vector2)) != 0
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