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Testing Speaker Embedding Management in Coqui-AI TTS

This test suite validates the SpeakerManager component in the Coqui-AI TTS system, focusing on speaker embedding computation and d-vector file processing. It ensures proper functionality of speaker identification and embedding generation for text-to-speech applications.

Test Coverage Overview

The test suite provides comprehensive coverage of the SpeakerManager class functionality, including:

Speaker embedding computation from audio waveforms
D-vector generation from audio clips
Multiple speaker file format handling (JSON and PTH)
Speaker embedding dimension verification
Mean embedding computation with randomization options

Implementation Analysis

The testing approach utilizes unittest framework with two main test cases:

test_speaker_embedding: Validates the speaker encoder model setup and embedding computation
test_dvector_file_processing: Tests d-vector file handling and speaker management functions

Implementation includes PyTorch tensor operations and numpy array comparisons for precise validation.

Technical Details

Key technical components include:

AudioProcessor for waveform processing
PyTorch for tensor operations
Custom speaker encoder model
Configuration management via JSON
Melspectrogram generation
256-dimensional speaker embeddings

Best Practices Demonstrated

The test suite exemplifies several testing best practices:

Isolated test cases with clear objectives
Proper test cleanup (removing dummy models)
Comprehensive assertion checks
Test data management using separate input paths
Effective use of static methods for standalone tests

coqui-ai/tts

tests/aux_tests/test_speaker_manager.py

            
import os
import unittest

import numpy as np
import torch
from trainer.io import save_checkpoint

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.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"""

    @staticmethod
    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(config, model, None, None, 0, 0, get_tests_input_path())

        # 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