mirror of https://github.com/coqui-ai/TTS.git
144 lines
6.1 KiB
Python
144 lines
6.1 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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from torch import optim
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from torch import nn
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from TTS.utils.generic_utils import load_config
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from TTS.layers.losses import L1LossMasked
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from TTS.models.tacotron import Tacotron
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from TTS.models.tacotrongst import TacotronGST
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#pylint: disable=unused-variable
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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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def count_parameters(model):
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r"""Count number of trainable parameters in a network"""
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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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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# input_lengths = torch.randint(100, 129, (8, )).long().to(device)
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# input_lengths[-1] = 128
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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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# speaker_ids = torch.randint(0, 5, (8, )).long().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) >
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# 0.0).unsqueeze(2).float().squeeze()
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# criterion = L1LossMasked().to(device)
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# criterion_st = nn.BCEWithLogitsLoss().to(device)
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# model = Tacotron(
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# num_chars=32,
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# num_speakers=5,
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# linear_dim=c.audio['num_freq'],
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# mel_dim=c.audio['num_mels'],
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# r=c.r,
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# memory_size=c.memory_size).to(device) #FIXME: missing num_speakers parameter to Tacotron ctor
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# model.train()
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# print(" > Num parameters for Tacotron model:%s"%(count_parameters(model)))
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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 _ in range(5):
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# mel_out, linear_out, align, stop_tokens = model.forward(
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# input, input_lengths, mel_spec, speaker_ids)
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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
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class TacotronGSTTrainTest(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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input_lengths = torch.randint(100, 129, (8, )).long().to(device)
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input_lengths[-1] = 128
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mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
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linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
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mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
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stop_targets = torch.zeros(8, 120, 1).float().to(device)
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speaker_ids = torch.randint(0, 5, (8, )).long().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) >
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0.0).unsqueeze(2).float().squeeze()
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criterion = L1LossMasked().to(device)
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criterion_st = nn.BCEWithLogitsLoss().to(device)
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model = TacotronGST(
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num_chars=32,
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num_speakers=5,
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linear_dim=c.audio['num_freq'],
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mel_dim=c.audio['num_mels'],
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r=c.r,
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memory_size=c.memory_size).to(device) #FIXME: missing num_speakers parameter to Tacotron ctor
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model.train()
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print(model)
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print(" > Num parameters for Tacotron GST model:%s"%(count_parameters(model)))
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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 _ in range(10):
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mel_out, linear_out, align, stop_tokens = model.forward(
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input, input_lengths, mel_spec, speaker_ids)
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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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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
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