mirror of
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128 lines
4.6 KiB
Python
128 lines
4.6 KiB
Python
from vocoder.wavernn.models.fatchord_version import WaveRNN
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from vocoder.vocoder_dataset import VocoderDataset, collate_vocoder
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from vocoder.distribution import discretized_mix_logistic_loss
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from vocoder.display import stream, simple_table
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from vocoder.wavernn.gen_wavernn import gen_testset
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from torch.utils.data import DataLoader
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from pathlib import Path
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from torch import optim
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import torch.nn.functional as F
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import vocoder.wavernn.hparams as hp
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import numpy as np
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import time
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import torch
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def train(run_id: str, syn_dir: Path, voc_dir: Path, models_dir: Path, ground_truth: bool,
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save_every: int, backup_every: int, force_restart: bool):
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# Check to make sure the hop length is correctly factorised
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assert np.cumprod(hp.voc_upsample_factors)[-1] == hp.hop_length
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# Instantiate the model
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print("Initializing the model...")
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model = WaveRNN(
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rnn_dims=hp.voc_rnn_dims,
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fc_dims=hp.voc_fc_dims,
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bits=hp.bits,
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pad=hp.voc_pad,
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upsample_factors=hp.voc_upsample_factors,
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feat_dims=hp.num_mels,
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compute_dims=hp.voc_compute_dims,
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res_out_dims=hp.voc_res_out_dims,
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res_blocks=hp.voc_res_blocks,
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hop_length=hp.hop_length,
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sample_rate=hp.sample_rate,
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mode=hp.voc_mode
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)
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if torch.cuda.is_available():
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model = model.cuda()
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device = torch.device('cuda')
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else:
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device = torch.device('cpu')
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# Initialize the optimizer
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optimizer = optim.Adam(model.parameters())
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for p in optimizer.param_groups:
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p["lr"] = hp.voc_lr
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loss_func = F.cross_entropy if model.mode == "RAW" else discretized_mix_logistic_loss
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# Load the weights
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model_dir = models_dir.joinpath(run_id)
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model_dir.mkdir(exist_ok=True)
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weights_fpath = model_dir.joinpath(run_id + ".pt")
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if force_restart or not weights_fpath.exists():
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print("\nStarting the training of WaveRNN from scratch\n")
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model.save(weights_fpath, optimizer)
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else:
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print("\nLoading weights at %s" % weights_fpath)
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model.load(weights_fpath, optimizer)
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print("WaveRNN weights loaded from step %d" % model.step)
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# Initialize the dataset
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metadata_fpath = syn_dir.joinpath("train.txt") if ground_truth else \
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voc_dir.joinpath("synthesized.txt")
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mel_dir = syn_dir.joinpath("mels") if ground_truth else voc_dir.joinpath("mels_gta")
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wav_dir = syn_dir.joinpath("audio")
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dataset = VocoderDataset(metadata_fpath, mel_dir, wav_dir)
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test_loader = DataLoader(dataset,
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batch_size=1,
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shuffle=True,
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pin_memory=True)
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# Begin the training
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simple_table([('Batch size', hp.voc_batch_size),
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('LR', hp.voc_lr),
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('Sequence Len', hp.voc_seq_len)])
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for epoch in range(1, 350):
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data_loader = DataLoader(dataset,
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collate_fn=collate_vocoder,
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batch_size=hp.voc_batch_size,
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num_workers=2,
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shuffle=True,
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pin_memory=True)
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start = time.time()
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running_loss = 0.
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for i, (x, y, m) in enumerate(data_loader, 1):
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if torch.cuda.is_available():
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x, m, y = x.cuda(), m.cuda(), y.cuda()
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# Forward pass
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y_hat = model(x, m)
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if model.mode == 'RAW':
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y_hat = y_hat.transpose(1, 2).unsqueeze(-1)
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elif model.mode == 'MOL':
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y = y.float()
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y = y.unsqueeze(-1)
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# Backward pass
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loss = loss_func(y_hat, y)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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speed = i / (time.time() - start)
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avg_loss = running_loss / i
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step = model.get_step()
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k = step // 1000
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if backup_every != 0 and step % backup_every == 0 :
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model.checkpoint(model_dir, optimizer)
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if save_every != 0 and step % save_every == 0 :
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model.save(weights_fpath, optimizer)
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msg = f"| Epoch: {epoch} ({i}/{len(data_loader)}) | " \
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f"Loss: {avg_loss:.4f} | {speed:.1f} " \
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f"steps/s | Step: {k}k | "
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stream(msg)
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gen_testset(model, test_loader, hp.voc_gen_at_checkpoint, hp.voc_gen_batched,
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hp.voc_target, hp.voc_overlap, model_dir)
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print("")
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