Medix-AI/coqui-tts
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coqui-tts
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Add custom asserts to tests

This commit is contained in:
Eren Gölge 2021-12-30 12:03:12 +00:00
parent 7129b04d46
commit 497332bd46
2 changed files with 63 additions and 26 deletions
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11
tests/__init__.py
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@ -38,3 +38,14 @@ def run_cli(command):
def get_test_data_config():
return BaseDatasetConfig(name="ljspeech", path="tests/data/ljspeech/", meta_file_train="metadata.csv")
def assertHasAttr(test_obj, obj, intendedAttr):
# from https://stackoverflow.com/questions/48078636/pythons-unittest-lacks-an-asserthasattr-method-what-should-i-use-instead
testBool = hasattr(obj, intendedAttr)
test_obj.assertTrue(testBool, msg=f"obj lacking an attribute. obj: {obj}, intendedAttr: {intendedAttr}")
def assertHasNotAttr(test_obj, obj, intendedAttr):
testBool = hasattr(obj, intendedAttr)
test_obj.assertFalse(testBool, msg=f"obj should not have an attribute. obj: {obj}, intendedAttr: {intendedAttr}")

78
tests/tts_tests/test_vits.py
View File

@ -1,13 +1,14 @@
import os
import torch
import unittest
from TTS.config import load_config
from TTS.tts.models.vits import Vits, VitsArgs
from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.utils.speakers import SpeakerManager
from tests import assertHasAttr, assertHasNotAttr, get_tests_input_path
from TTS.speaker_encoder.utils.generic_utils import setup_speaker_encoder_model
import torch
from tests import assertHasAttr, assertHasNotAttr, get_tests_input_path
from TTS.config import load_config
from TTS.speaker_encoder.utils.generic_utils import setup_speaker_encoder_model
from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.models.vits import Vits, VitsArgs
from TTS.tts.utils.speakers import SpeakerManager
LANG_FILE = os.path.join(get_tests_input_path(), "language_ids.json")
SPEAKER_ENCODER_CONFIG = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
@ -18,21 +19,21 @@ use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#pylint: disable=no-self-use
class TestVits(unittest.TestCase):
def test_init_multispeaker(self):
num_speakers = 10
args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
model = Vits(args)
assertHasAttr(self, model, 'emb_g')
assertHasAttr(self, model, "emb_g")
args = VitsArgs(num_speakers=0, use_speaker_embedding=True)
model = Vits(args)
assertHasNotAttr(self, model, 'emb_g')
assertHasNotAttr(self, model, "emb_g")
args = VitsArgs(num_speakers=10, use_speaker_embedding=False)
model = Vits(args)
assertHasNotAttr(self, model, 'emb_g')
assertHasNotAttr(self, model, "emb_g")
args = VitsArgs(d_vector_dim=101, use_d_vector_file=True)
model = Vits(args)
@ -67,12 +68,12 @@ class TestVits(unittest.TestCase):
aux_input = {"speaker_ids": None, "style_wav": None, "d_vectors": None, "language_ids": None}
args = VitsArgs()
model = Vits(args)
aux_out= model.get_aux_input(aux_input)
aux_out = model.get_aux_input(aux_input)
speaker_id = torch.randint(10, (1,))
language_id = torch.randint(10, (1,))
d_vector = torch.rand(1, 128)
aux_input = {"speaker_ids": speaker_id, "style_wav": None, "d_vectors": d_vector, "language_ids": language_id}
aux_input = {"speaker_ids": speaker_id, "style_wav": None, "d_vectors": d_vector, "language_ids": language_id}
aux_out = model.get_aux_input(aux_input)
self.assertEqual(aux_out["speaker_ids"].shape, speaker_id.shape)
self.assertEqual(aux_out["language_ids"].shape, language_id.shape)
@ -88,8 +89,8 @@ class TestVits(unittest.TestCase):
ref_inp = torch.randn(1, spec_len, 513)
ref_inp_len = torch.randint(1, spec_effective_len, (1,))
ref_spk_id = torch.randint(0, num_speakers, (1,))
tgt_spk_id = torch.randint(0, num_speakers, (1,))
ref_spk_id = torch.randint(1, num_speakers, (1,))
tgt_spk_id = torch.randint(1, num_speakers, (1,))
o_hat, y_mask, (z, z_p, z_hat) = model.voice_conversion(ref_inp, ref_inp_len, ref_spk_id, tgt_spk_id)
self.assertEqual(o_hat.shape, (1, 1, spec_len * 256))
@ -110,7 +111,9 @@ class TestVits(unittest.TestCase):
return input_dummy, input_lengths, spec, spec_lengths, waveform
def _check_forward_outputs(self, config, output_dict, encoder_config=None):
self.assertEqual(output_dict['model_outputs'].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"])
self.assertEqual(
output_dict["model_outputs"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
)
self.assertEqual(output_dict["alignments"].shape, (8, 128, 30))
self.assertEqual(output_dict["alignments"].max(), 1)
self.assertEqual(output_dict["alignments"].min(), 0)
@ -120,13 +123,15 @@ class TestVits(unittest.TestCase):
self.assertEqual(output_dict["logs_p"].shape, (8, config.model_args.hidden_channels, 30))
self.assertEqual(output_dict["m_q"].shape, (8, config.model_args.hidden_channels, 30))
self.assertEqual(output_dict["logs_q"].shape, (8, config.model_args.hidden_channels, 30))
self.assertEqual(output_dict['waveform_seg'].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"])
self.assertEqual(
output_dict["waveform_seg"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
)
if encoder_config:
self.assertEqual(output_dict['gt_spk_emb'].shape, (8, encoder_config.model_params["proj_dim"]))
self.assertEqual(output_dict['syn_spk_emb'].shape, (8, encoder_config.model_params["proj_dim"]))
self.assertEqual(output_dict["gt_spk_emb"].shape, (8, encoder_config.model_params["proj_dim"]))
self.assertEqual(output_dict["syn_spk_emb"].shape, (8, encoder_config.model_params["proj_dim"]))
else:
self.assertEqual(output_dict['gt_spk_emb'], None)
self.assertEqual(output_dict['syn_spk_emb'], None)
self.assertEqual(output_dict["gt_spk_emb"], None)
self.assertEqual(output_dict["syn_spk_emb"], None)
def test_forward(self):
num_speakers = 0
@ -147,7 +152,9 @@ class TestVits(unittest.TestCase):
speaker_ids = torch.randint(0, num_speakers, (8,)).long().to(device)
model = Vits(config).to(device)
output_dict = model.forward(input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids})
output_dict = model.forward(
input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids}
)
self._check_forward_outputs(config, output_dict)
def test_multilingual_forward(self):
@ -162,7 +169,14 @@ class TestVits(unittest.TestCase):
lang_ids = torch.randint(0, num_langs, (8,)).long().to(device)
model = Vits(config).to(device)
output_dict = model.forward(input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids})
output_dict = model.forward(
input_dummy,
input_lengths,
spec,
spec_lengths,
waveform,
aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
)
self._check_forward_outputs(config, output_dict)
def test_secl_forward(self):
@ -175,7 +189,12 @@ class TestVits(unittest.TestCase):
speaker_manager = SpeakerManager()
speaker_manager.speaker_encoder = speaker_encoder
args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, spec_segment_size=10, use_speaker_encoder_as_loss=True)
args = VitsArgs(
language_ids_file=LANG_FILE,
use_language_embedding=True,
spec_segment_size=10,
use_speaker_encoder_as_loss=True,
)
config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
config.audio.sample_rate = 16000
@ -184,7 +203,14 @@ class TestVits(unittest.TestCase):
lang_ids = torch.randint(0, num_langs, (8,)).long().to(device)
model = Vits(config, speaker_manager=speaker_manager).to(device)
output_dict = model.forward(input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids})
output_dict = model.forward(
input_dummy,
input_lengths,
spec,
spec_lengths,
waveform,
aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
)
self._check_forward_outputs(config, output_dict, speaker_encoder_config)
def test_inference(self):
@ -211,4 +237,4 @@ class TestVits(unittest.TestCase):
speaker_ids = torch.randint(0, num_speakers, (1,)).long().to(device)
lang_ids = torch.randint(0, num_langs, (1,)).long().to(device)
model = Vits(config).to(device)
_ = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})
_ = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})