mirror of https://github.com/coqui-ai/TTS.git
69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
import os
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import copy
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import torch
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import unittest
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import numpy as np
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from torch import optim
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from torch import nn
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from utils.generic_utils import load_config
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from layers.losses import L1LossMasked
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from models.tacotron import Tacotron
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torch.manual_seed(1)
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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file_path = os.path.dirname(os.path.realpath(__file__))
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c = load_config(os.path.join(file_path, 'test_config.json'))
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class TacotronTrainTest(unittest.TestCase):
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def test_train_step(self):
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input = torch.randint(0, 24, (8, 128)).long().to(device)
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mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
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linear_spec = torch.rand(8, 30, c.audio['num_freq']).to(device)
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mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
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stop_targets = torch.zeros(8, 30, 1).float().to(device)
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for idx in mel_lengths:
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stop_targets[:, int(idx.item()):, 0] = 1.0
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stop_targets = stop_targets.view(input.shape[0],
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stop_targets.size(1) // c.r, -1)
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stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float()
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criterion = L1LossMasked().to(device)
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criterion_st = nn.BCELoss().to(device)
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model = Tacotron(c.embedding_size, c.audio['num_freq'], c.audio['num_mels'],
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c.r).to(device)
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model.train()
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model_ref = copy.deepcopy(model)
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count = 0
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for param, param_ref in zip(model.parameters(),
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model_ref.parameters()):
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assert (param - param_ref).sum() == 0, param
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count += 1
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optimizer = optim.Adam(model.parameters(), lr=c.lr)
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for i in range(5):
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mel_out, linear_out, align, stop_tokens = model.forward(
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input, mel_spec)
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assert stop_tokens.data.max() <= 1.0
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assert stop_tokens.data.min() >= 0.0
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optimizer.zero_grad()
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loss = criterion(mel_out, mel_spec, mel_lengths)
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stop_loss = criterion_st(stop_tokens, stop_targets)
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loss = loss + criterion(linear_out, linear_spec,
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mel_lengths) + stop_loss
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loss.backward()
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optimizer.step()
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# check parameter changes
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count = 0
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for param, param_ref in zip(model.parameters(),
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model_ref.parameters()):
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# ignore pre-higway layer since it works conditional
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# if count not in [145, 59]:
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assert (param != param_ref).any(
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), "param {} with shape {} not updated!! \n{}\n{}".format(
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count, param.shape, param, param_ref)
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count += 1 |