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| import torch | |
| from torch.utils.data import DataLoader | |
| from synthesizer.hparams import hparams_debug_string | |
| from synthesizer.synthesizer_dataset import SynthesizerDataset, collate_synthesizer | |
| from synthesizer.models.tacotron import Tacotron | |
| from synthesizer.utils.text import text_to_sequence | |
| from synthesizer.utils.symbols import symbols | |
| import numpy as np | |
| from pathlib import Path | |
| from tqdm import tqdm | |
| import platform | |
| def run_synthesis(in_dir, out_dir, model_dir, hparams): | |
| # This generates ground truth-aligned mels for vocoder training | |
| synth_dir = Path(out_dir).joinpath("mels_gta") | |
| synth_dir.mkdir(exist_ok=True) | |
| print(hparams_debug_string()) | |
| # Check for GPU | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| if hparams.synthesis_batch_size % torch.cuda.device_count() != 0: | |
| raise ValueError("`hparams.synthesis_batch_size` must be evenly divisible by n_gpus!") | |
| else: | |
| device = torch.device("cpu") | |
| print("Synthesizer using device:", device) | |
| # Instantiate Tacotron model | |
| model = Tacotron(embed_dims=hparams.tts_embed_dims, | |
| num_chars=len(symbols), | |
| encoder_dims=hparams.tts_encoder_dims, | |
| decoder_dims=hparams.tts_decoder_dims, | |
| n_mels=hparams.num_mels, | |
| fft_bins=hparams.num_mels, | |
| postnet_dims=hparams.tts_postnet_dims, | |
| encoder_K=hparams.tts_encoder_K, | |
| lstm_dims=hparams.tts_lstm_dims, | |
| postnet_K=hparams.tts_postnet_K, | |
| num_highways=hparams.tts_num_highways, | |
| dropout=0., # Use zero dropout for gta mels | |
| stop_threshold=hparams.tts_stop_threshold, | |
| speaker_embedding_size=hparams.speaker_embedding_size).to(device) | |
| # Load the weights | |
| model_dir = Path(model_dir) | |
| model_fpath = model_dir.joinpath(model_dir.stem).with_suffix(".pt") | |
| print("\nLoading weights at %s" % model_fpath) | |
| model.load(model_fpath) | |
| print("Tacotron weights loaded from step %d" % model.step) | |
| # Synthesize using same reduction factor as the model is currently trained | |
| r = np.int32(model.r) | |
| # Set model to eval mode (disable gradient and zoneout) | |
| model.eval() | |
| # Initialize the dataset | |
| in_dir = Path(in_dir) | |
| metadata_fpath = in_dir.joinpath("train.txt") | |
| mel_dir = in_dir.joinpath("mels") | |
| embed_dir = in_dir.joinpath("embeds") | |
| dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams) | |
| data_loader = DataLoader(dataset, | |
| collate_fn=lambda batch: collate_synthesizer(batch, r, hparams), | |
| batch_size=hparams.synthesis_batch_size, | |
| num_workers=2 if platform.system() != "Windows" else 0, | |
| shuffle=False, | |
| pin_memory=True) | |
| # Generate GTA mels | |
| meta_out_fpath = Path(out_dir).joinpath("synthesized.txt") | |
| with open(meta_out_fpath, "w") as file: | |
| for i, (texts, mels, embeds, idx) in tqdm(enumerate(data_loader), total=len(data_loader)): | |
| texts = texts.to(device) | |
| mels = mels.to(device) | |
| embeds = embeds.to(device) | |
| # Parallelize model onto GPUS using workaround due to python bug | |
| if device.type == "cuda" and torch.cuda.device_count() > 1: | |
| _, mels_out, _ = data_parallel_workaround(model, texts, mels, embeds) | |
| else: | |
| _, mels_out, _, _ = model(texts, mels, embeds) | |
| for j, k in enumerate(idx): | |
| # Note: outputs mel-spectrogram files and target ones have same names, just different folders | |
| mel_filename = Path(synth_dir).joinpath(dataset.metadata[k][1]) | |
| mel_out = mels_out[j].detach().cpu().numpy().T | |
| # Use the length of the ground truth mel to remove padding from the generated mels | |
| mel_out = mel_out[:int(dataset.metadata[k][4])] | |
| # Write the spectrogram to disk | |
| np.save(mel_filename, mel_out, allow_pickle=False) | |
| # Write metadata into the synthesized file | |
| file.write("|".join(dataset.metadata[k])) | |