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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!! \n{}\n{}".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):
...
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