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Validating FreeVC Voice Conversion Implementation in Coqui-AI TTS

This test suite validates the FreeVC voice conversion model implementation in the Coqui-AI TTS framework. It covers core functionality including model initialization, audio processing, forward pass operations, and voice conversion capabilities.

Test Coverage Overview

The test suite provides comprehensive coverage of the FreeVC model’s critical components:

Model initialization and configuration validation
Audio processing and feature extraction
Forward pass operations with varying batch sizes
Inference and voice conversion functionality
WavLM feature extraction verification

Implementation Analysis

The testing approach employs unittest framework with PyTorch-specific validations. It uses systematic input generation with controlled random data and precise shape assertions to verify model behavior. The tests incorporate both single and multi-batch scenarios to ensure robust functionality.

Technical Details

Key technical components include:

PyTorch for tensor operations and model implementation
Custom audio configuration parameters
WavLM feature extraction integration
CUDA device handling for GPU support
Automated parameter counting and verification

Best Practices Demonstrated

The test suite exemplifies several testing best practices:

Systematic input generation with controlled parameters
Explicit shape and dimension validation
Separation of training and inference testing
Comprehensive edge case handling
Clear test method organization and naming

coqui-ai/tts

tests/vc_tests/test_freevc.py

            
import os
import unittest

import torch

from tests import get_tests_input_path
from TTS.vc.configs.freevc_config import FreeVCConfig
from TTS.vc.models.freevc import FreeVC

# pylint: disable=unused-variable
# pylint: disable=no-self-use

torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

c = FreeVCConfig()

WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
BATCH_SIZE = 3


def count_parameters(model):
    r"""Count number of trainable parameters in a network"""
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


class TestFreeVC(unittest.TestCase):
    def _create_inputs(self, config, batch_size=2):
        input_dummy = torch.rand(batch_size, 30 * config.audio["hop_length"]).to(device)
        input_lengths = torch.randint(100, 30 * config.audio["hop_length"], (batch_size,)).long().to(device)
        input_lengths[-1] = 30 * config.audio["hop_length"]
        spec = torch.rand(batch_size, 30, config.audio["filter_length"] // 2 + 1).to(device)
        mel = torch.rand(batch_size, 30, config.audio["n_mel_channels"]).to(device)
        spec_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
        spec_lengths[-1] = spec.size(2)
        waveform = torch.rand(batch_size, spec.size(2) * config.audio["hop_length"]).to(device)
        return input_dummy, input_lengths, mel, spec, spec_lengths, waveform

    @staticmethod
    def _create_inputs_inference():
        source_wav = torch.rand(16000)
        target_wav = torch.rand(16000)
        return source_wav, target_wav

    @staticmethod
    def _check_parameter_changes(model, model_ref):
        count = 0
        for param, param_ref in zip(model.parameters(), model_ref.parameters()):
            assert (param != param_ref).any(), "param {} with shape {} not updated!! 
{}
{}".format(
                count, param.shape, param, param_ref
            )
            count += 1

    def test_methods(self):
        config = FreeVCConfig()
        model = FreeVC(config).to(device)
        model.load_pretrained_speaker_encoder()
        model.init_multispeaker(config)
        wavlm_feats = model.extract_wavlm_features(torch.rand(1, 16000))
        assert wavlm_feats.shape == (1, 1024, 49), wavlm_feats.shape

    def test_load_audio(self):
        config = FreeVCConfig()
        model = FreeVC(config).to(device)
        wav = model.load_audio(WAV_FILE)
        wav2 = model.load_audio(wav)
        assert all(torch.isclose(wav, wav2))

    def _test_forward(self, batch_size):
        # create model
        config = FreeVCConfig()
        model = FreeVC(config).to(device)
        model.train()
        print(" > Num parameters for FreeVC model:%s" % (count_parameters(model)))

        _, _, mel, spec, spec_lengths, waveform = self._create_inputs(config, batch_size)

        wavlm_vec = model.extract_wavlm_features(waveform)
        wavlm_vec_lengths = torch.ones(batch_size, dtype=torch.long)

        y = model.forward(wavlm_vec, spec, None, mel, spec_lengths, wavlm_vec_lengths)
        # TODO: assert with training implementation

    def test_forward(self):
        self._test_forward(1)
        self._test_forward(3)

    def _test_inference(self, batch_size):
        config = FreeVCConfig()
        model = FreeVC(config).to(device)
        model.eval()

        _, _, mel, _, _, waveform = self._create_inputs(config, batch_size)

        wavlm_vec = model.extract_wavlm_features(waveform)
        wavlm_vec_lengths = torch.ones(batch_size, dtype=torch.long)

        output_wav = model.inference(wavlm_vec, None, mel, wavlm_vec_lengths)
        assert (
            output_wav.shape[-1] // config.audio.hop_length == wavlm_vec.shape[-1]
        ), f"{output_wav.shape[-1] // config.audio.hop_length} != {wavlm_vec.shape}"

    def test_inference(self):
        self._test_inference(1)
        self._test_inference(3)

    def test_voice_conversion(self):
        config = FreeVCConfig()
        model = FreeVC(config).to(device)
        model.eval()

        source_wav, target_wav = self._create_inputs_inference()
        output_wav = model.voice_conversion(source_wav, target_wav)
        assert (
            output_wav.shape[0] + config.audio.hop_length == source_wav.shape[0]
        ), f"{output_wav.shape} != {source_wav.shape}"

    def test_train_step(self):
        ...

    def test_train_eval_log(self):
        ...

    def test_test_run(self):
        ...

    def test_load_checkpoint(self):
        ...

    def test_get_criterion(self):
        ...

    def test_init_from_config(self):
        ...