mirror of https://github.com/coqui-ai/TTS.git
76 lines
3.2 KiB
Python
76 lines
3.2 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.speaker_encoder.utils.generic_utils import setup_model
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from TTS.speaker_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.tar")
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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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x_vectors_file_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.json")
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class SpeakerManagerTest(unittest.TestCase):
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"""Test SpeakerManager for loading embedding files and computing x_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_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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x_vector = manager.compute_x_vector(mel.T)
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assert x_vector.shape[1] == 256
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# compute x_vector directly from an input file
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x_vector = manager.compute_x_vector_from_clip(sample_wav_path)
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x_vector2 = manager.compute_x_vector_from_clip(sample_wav_path)
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x_vector = torch.FloatTensor(x_vector)
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x_vector2 = torch.FloatTensor(x_vector2)
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assert x_vector.shape[0] == 256
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assert (x_vector - x_vector2).sum() == 0.0
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# compute x_vector from a list of wav files.
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x_vector3 = manager.compute_x_vector_from_clip([sample_wav_path, sample_wav_path2])
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x_vector3 = torch.FloatTensor(x_vector3)
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assert x_vector3.shape[0] == 256
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assert (x_vector - x_vector3).sum() != 0.0
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# remove dummy model
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os.remove(encoder_model_path)
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@staticmethod
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def test_speakers_file_processing():
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manager = SpeakerManager(x_vectors_file_path=x_vectors_file_path)
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print(manager.num_speakers)
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print(manager.x_vector_dim)
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print(manager.clip_ids)
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x_vector = manager.get_x_vector_by_clip(manager.clip_ids[0])
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assert len(x_vector) == 256
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x_vectors = manager.get_x_vectors_by_speaker(manager.speaker_ids[0])
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assert len(x_vectors[0]) == 256
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x_vector1 = manager.get_mean_x_vector(manager.speaker_ids[0], num_samples=2, randomize=True)
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assert len(x_vector1) == 256
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x_vector2 = manager.get_mean_x_vector(manager.speaker_ids[0], num_samples=2, randomize=False)
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assert len(x_vector2) == 256
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assert np.sum(np.array(x_vector1) - np.array(x_vector2)) != 0
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