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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]))
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