video-dubbing / TTS /tests /tts_tests2 /test_delightful_tts_layers.py
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import torch
from TTS.tts.configs.delightful_tts_config import DelightfulTTSConfig
from TTS.tts.layers.delightful_tts.acoustic_model import AcousticModel
from TTS.tts.models.delightful_tts import DelightfulTtsArgs, VocoderConfig
from TTS.tts.utils.helpers import rand_segments
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.vocoder.models.hifigan_generator import HifiganGenerator
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
args = DelightfulTtsArgs()
v_args = VocoderConfig()
config = DelightfulTTSConfig(
model_args=args,
# compute_f0=True,
# f0_cache_path=os.path.join(output_path, "f0_cache"),
text_cleaner="english_cleaners",
use_phonemes=True,
phoneme_language="en-us",
# phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
)
tokenizer, config = TTSTokenizer.init_from_config(config)
def test_acoustic_model():
dummy_tokens = torch.rand((1, 41)).long().to(device)
dummy_text_lens = torch.tensor([41]).long().to(device)
dummy_spec = torch.rand((1, 100, 207)).to(device)
dummy_spec_lens = torch.tensor([207]).to(device)
dummy_pitch = torch.rand((1, 1, 207)).long().to(device)
dummy_energy = torch.rand((1, 1, 207)).long().to(device)
args.out_channels = 100
args.num_mels = 100
acoustic_model = AcousticModel(args=args, tokenizer=tokenizer, speaker_manager=None).to(device)
acoustic_model = acoustic_model.train()
output = acoustic_model(
tokens=dummy_tokens,
src_lens=dummy_text_lens,
mel_lens=dummy_spec_lens,
mels=dummy_spec,
pitches=dummy_pitch,
energies=dummy_energy,
attn_priors=None,
d_vectors=None,
speaker_idx=None,
)
assert list(output["model_outputs"].shape) == [1, 207, 100]
# output["model_outputs"].sum().backward()
def test_hifi_decoder():
dummy_input = torch.rand((1, 207, 100)).to(device)
dummy_spec_lens = torch.tensor([207]).to(device)
waveform_decoder = HifiganGenerator(
100,
1,
v_args.resblock_type_decoder,
v_args.resblock_dilation_sizes_decoder,
v_args.resblock_kernel_sizes_decoder,
v_args.upsample_kernel_sizes_decoder,
v_args.upsample_initial_channel_decoder,
v_args.upsample_rates_decoder,
inference_padding=0,
cond_channels=0,
conv_pre_weight_norm=False,
conv_post_weight_norm=False,
conv_post_bias=False,
).to(device)
waveform_decoder = waveform_decoder.train()
vocoder_input_slices, slice_ids = rand_segments( # pylint: disable=unused-variable
x=dummy_input.transpose(1, 2),
x_lengths=dummy_spec_lens,
segment_size=32,
let_short_samples=True,
pad_short=True,
)
outputs = waveform_decoder(x=vocoder_input_slices.detach())
assert list(outputs.shape) == [1, 1, 8192]
# outputs.sum().backward()