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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() | |