Beispiel #1
0
    def test_train_step():
        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
        input_lengths = torch.randint(100, 129, (8, )).long().to(device)
        input_lengths[-1] = 128
        mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
        linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
        mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
        mel_lengths[-1] = 120
        stop_targets = torch.zeros(8, 120, 1).float().to(device)
        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)

        for idx in mel_lengths:
            stop_targets[:, int(idx.item()):, 0] = 1.0

        stop_targets = stop_targets.view(input_dummy.shape[0],
                                         stop_targets.size(1) // c.r, -1)
        stop_targets = (stop_targets.sum(2) >
                        0.0).unsqueeze(2).float().squeeze()

        criterion = L1LossMasked(seq_len_norm=False).to(device)
        criterion_st = nn.BCEWithLogitsLoss().to(device)
        model = Tacotron(
            num_chars=32,
            num_speakers=5,
            gst=True,
            postnet_output_dim=c.audio['num_freq'],
            decoder_output_dim=c.audio['num_mels'],
            r=c.r,
            memory_size=c.memory_size
        ).to(device)  #FIXME: missing num_speakers parameter to Tacotron ctor
        model.train()
        print(model)
        print(" > Num parameters for Tacotron GST model:%s" %
              (count_parameters(model)))
        model_ref = copy.deepcopy(model)
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            assert (param - param_ref).sum() == 0, param
            count += 1
        optimizer = optim.Adam(model.parameters(), lr=c.lr)
        for _ in range(10):
            mel_out, linear_out, align, stop_tokens = model.forward(
                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
            optimizer.zero_grad()
            loss = criterion(mel_out, mel_spec, mel_lengths)
            stop_loss = criterion_st(stop_tokens, stop_targets)
            loss = loss + criterion(linear_out, linear_spec,
                                    mel_lengths) + stop_loss
            loss.backward()
            optimizer.step()
        # check parameter changes
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            # ignore pre-higway layer since it works conditional
            assert (param != param_ref).any(
            ), "param {} with shape {} not updated!! \n{}\n{}".format(
                count, param.shape, param, param_ref)
            count += 1
Beispiel #2
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    def test_train_step():
        config = config_global.copy()
        config.use_speaker_embedding = False
        config.num_speakers = 1

        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
        input_lengths = torch.randint(100, 129, (8, )).long().to(device)
        input_lengths[-1] = 128
        mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
        linear_spec = torch.rand(8, 30,
                                 config.audio["fft_size"] // 2 + 1).to(device)
        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
        mel_lengths[-1] = mel_spec.size(1)
        stop_targets = torch.zeros(8, 30, 1).float().to(device)

        for idx in mel_lengths:
            stop_targets[:, int(idx.item()):, 0] = 1.0

        stop_targets = stop_targets.view(input_dummy.shape[0],
                                         stop_targets.size(1) // config.r, -1)
        stop_targets = (stop_targets.sum(2) >
                        0.0).unsqueeze(2).float().squeeze()

        criterion = L1LossMasked(seq_len_norm=False).to(device)
        criterion_st = nn.BCEWithLogitsLoss().to(device)
        model = Tacotron(config).to(
            device)  # FIXME: missing num_speakers parameter to Tacotron ctor
        model.train()
        print(" > Num parameters for Tacotron model:%s" %
              (count_parameters(model)))
        model_ref = copy.deepcopy(model)
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            assert (param - param_ref).sum() == 0, param
            count += 1
        optimizer = optim.Adam(model.parameters(), lr=config.lr)
        for _ in range(5):
            outputs = model.forward(input_dummy, input_lengths, mel_spec,
                                    mel_lengths)
            optimizer.zero_grad()
            loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
            stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
            loss = loss + criterion(outputs["model_outputs"], linear_spec,
                                    mel_lengths) + stop_loss
            loss.backward()
            optimizer.step()
        # check parameter changes
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            # ignore pre-higway layer since it works conditional
            # if count not in [145, 59]:
            assert (param != param_ref).any(
            ), "param {} with shape {} not updated!! \n{}\n{}".format(
                count, param.shape, param, param_ref)
            count += 1
Beispiel #3
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    def test_train_step():
        config = config_global.copy()
        config.use_speaker_embedding = True
        config.num_speakers = 10
        config.use_gst = True
        config.gst = GSTConfig()
        # with random gst mel style
        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
        input_lengths = torch.randint(100, 129, (8, )).long().to(device)
        input_lengths[-1] = 128
        mel_spec = torch.rand(8, 120, config.audio["num_mels"]).to(device)
        linear_spec = torch.rand(8, 120,
                                 config.audio["fft_size"] // 2 + 1).to(device)
        mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
        mel_lengths[-1] = 120
        stop_targets = torch.zeros(8, 120, 1).float().to(device)
        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)

        for idx in mel_lengths:
            stop_targets[:, int(idx.item()):, 0] = 1.0

        stop_targets = stop_targets.view(input_dummy.shape[0],
                                         stop_targets.size(1) // config.r, -1)
        stop_targets = (stop_targets.sum(2) >
                        0.0).unsqueeze(2).float().squeeze()

        criterion = L1LossMasked(seq_len_norm=False).to(device)
        criterion_st = nn.BCEWithLogitsLoss().to(device)
        config.use_gst = True
        config.gst = GSTConfig()
        model = Tacotron(config).to(
            device)  # FIXME: missing num_speakers parameter to Tacotron ctor
        model.train()
        # print(model)
        print(" > Num parameters for Tacotron GST model:%s" %
              (count_parameters(model)))
        model_ref = copy.deepcopy(model)
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            assert (param - param_ref).sum() == 0, param
            count += 1
        optimizer = optim.Adam(model.parameters(), lr=config.lr)
        for _ in range(10):
            outputs = model.forward(input_dummy,
                                    input_lengths,
                                    mel_spec,
                                    mel_lengths,
                                    aux_input={"speaker_ids": speaker_ids})
            optimizer.zero_grad()
            loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
            stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
            loss = loss + criterion(outputs["model_outputs"], linear_spec,
                                    mel_lengths) + stop_loss
            loss.backward()
            optimizer.step()
        # check parameter changes
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            # ignore pre-higway layer since it works conditional
            assert (param != param_ref).any(
            ), "param {} with shape {} not updated!! \n{}\n{}".format(
                count, param.shape, param, param_ref)
            count += 1

        # with file gst style
        mel_spec = (torch.FloatTensor(ap.melspectrogram(
            ap.load_wav(WAV_FILE)))[:, :120].unsqueeze(0).transpose(
                1, 2).to(device))
        mel_spec = mel_spec.repeat(8, 1, 1)

        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
        input_lengths = torch.randint(100, 129, (8, )).long().to(device)
        input_lengths[-1] = 128
        linear_spec = torch.rand(8, mel_spec.size(1),
                                 config.audio["fft_size"] // 2 + 1).to(device)
        mel_lengths = torch.randint(20, mel_spec.size(1),
                                    (8, )).long().to(device)
        mel_lengths[-1] = mel_spec.size(1)
        stop_targets = torch.zeros(8, mel_spec.size(1), 1).float().to(device)
        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)

        for idx in mel_lengths:
            stop_targets[:, int(idx.item()):, 0] = 1.0

        stop_targets = stop_targets.view(input_dummy.shape[0],
                                         stop_targets.size(1) // config.r, -1)
        stop_targets = (stop_targets.sum(2) >
                        0.0).unsqueeze(2).float().squeeze()

        criterion = L1LossMasked(seq_len_norm=False).to(device)
        criterion_st = nn.BCEWithLogitsLoss().to(device)
        model = Tacotron(config).to(
            device)  # FIXME: missing num_speakers parameter to Tacotron ctor
        model.train()
        # print(model)
        print(" > Num parameters for Tacotron GST model:%s" %
              (count_parameters(model)))
        model_ref = copy.deepcopy(model)
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            assert (param - param_ref).sum() == 0, param
            count += 1
        optimizer = optim.Adam(model.parameters(), lr=config.lr)
        for _ in range(10):
            outputs = model.forward(input_dummy,
                                    input_lengths,
                                    mel_spec,
                                    mel_lengths,
                                    aux_input={"speaker_ids": speaker_ids})
            optimizer.zero_grad()
            loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
            stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
            loss = loss + criterion(outputs["model_outputs"], linear_spec,
                                    mel_lengths) + stop_loss
            loss.backward()
            optimizer.step()
        # check parameter changes
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            # ignore pre-higway layer since it works conditional
            assert (param != param_ref).any(
            ), "param {} with shape {} not updated!! \n{}\n{}".format(
                count, param.shape, param, param_ref)
            count += 1
Beispiel #4
0
    def test_train_step():
        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
        input_lengths = torch.randint(100, 129, (8, )).long().to(device)
        input_lengths[-1] = 128
        mel_spec = torch.rand(8, 30, c.audio["num_mels"]).to(device)
        linear_spec = torch.rand(8, 30, c.audio["fft_size"]).to(device)
        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
        mel_lengths[-1] = mel_spec.size(1)
        stop_targets = torch.zeros(8, 30, 1).float().to(device)
        speaker_embeddings = torch.rand(8, 55).to(device)

        for idx in mel_lengths:
            stop_targets[:, int(idx.item()):, 0] = 1.0

        stop_targets = stop_targets.view(input_dummy.shape[0],
                                         stop_targets.size(1) // c.r, -1)
        stop_targets = (stop_targets.sum(2) >
                        0.0).unsqueeze(2).float().squeeze()

        criterion = L1LossMasked(seq_len_norm=False).to(device)
        criterion_st = nn.BCEWithLogitsLoss().to(device)
        model = Tacotron(
            num_chars=32,
            num_speakers=5,
            postnet_output_dim=c.audio["fft_size"],
            decoder_output_dim=c.audio["num_mels"],
            gst=True,
            gst_embedding_dim=c.gst["gst_embedding_dim"],
            gst_num_heads=c.gst["gst_num_heads"],
            gst_style_tokens=c.gst["gst_style_tokens"],
            gst_use_speaker_embedding=c.gst["gst_use_speaker_embedding"],
            r=c.r,
            memory_size=c.memory_size,
            speaker_embedding_dim=55,
        ).to(device)  # FIXME: missing num_speakers parameter to Tacotron ctor
        model.train()
        print(" > Num parameters for Tacotron model:%s" %
              (count_parameters(model)))
        model_ref = copy.deepcopy(model)
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            assert (param - param_ref).sum() == 0, param
            count += 1
        optimizer = optim.Adam(model.parameters(), lr=c.lr)
        for _ in range(5):
            mel_out, linear_out, align, stop_tokens = model.forward(
                input_dummy,
                input_lengths,
                mel_spec,
                mel_lengths,
                speaker_embeddings=speaker_embeddings)
            optimizer.zero_grad()
            loss = criterion(mel_out, mel_spec, mel_lengths)
            stop_loss = criterion_st(stop_tokens, stop_targets)
            loss = loss + criterion(linear_out, linear_spec,
                                    mel_lengths) + stop_loss
            loss.backward()
            optimizer.step()
        # check parameter changes
        count = 0
        for name_param, param_ref in zip(model.named_parameters(),
                                         model_ref.parameters()):
            # ignore pre-higway layer since it works conditional
            # if count not in [145, 59]:
            name, param = name_param
            if name == "gst_layer.encoder.recurrence.weight_hh_l0":
                continue
            assert (param != param_ref).any(
            ), "param {} with shape {} not updated!! \n{}\n{}".format(
                count, param.shape, param, param_ref)
            count += 1
Beispiel #5
0
    def test_train_step():
        config = TacotronConfig(
            num_chars=32,
            num_speakers=10,
            use_speaker_embedding=True,
            out_channels=513,
            decoder_output_dim=80,
            use_capacitron_vae=True,
            capacitron_vae=CapacitronVAEConfig(),
            optimizer="CapacitronOptimizer",
            optimizer_params={
                "RAdam": {
                    "betas": [0.9, 0.998],
                    "weight_decay": 1e-6
                },
                "SGD": {
                    "lr": 1e-5,
                    "momentum": 0.9
                },
            },
        )

        batch = dict({})
        batch["text_input"] = torch.randint(0, 24, (8, 128)).long().to(device)
        batch["text_lengths"] = torch.randint(100, 129,
                                              (8, )).long().to(device)
        batch["text_lengths"] = torch.sort(batch["text_lengths"],
                                           descending=True)[0]
        batch["text_lengths"][0] = 128
        batch["linear_input"] = torch.rand(
            8, 120, config.audio["fft_size"] // 2 + 1).to(device)
        batch["mel_input"] = torch.rand(8, 120,
                                        config.audio["num_mels"]).to(device)
        batch["mel_lengths"] = torch.randint(20, 120, (8, )).long().to(device)
        batch["mel_lengths"] = torch.sort(batch["mel_lengths"],
                                          descending=True)[0]
        batch["mel_lengths"][0] = 120
        batch["stop_targets"] = torch.zeros(8, 120, 1).float().to(device)
        batch["stop_target_lengths"] = torch.randint(0, 120, (8, )).to(device)
        batch["speaker_ids"] = torch.randint(0, 5, (8, )).long().to(device)
        batch["d_vectors"] = None

        for idx in batch["mel_lengths"]:
            batch["stop_targets"][:, int(idx.item()):, 0] = 1.0

        batch["stop_targets"] = batch["stop_targets"].view(
            batch["text_input"].shape[0],
            batch["stop_targets"].size(1) // config.r, -1)
        batch["stop_targets"] = (batch["stop_targets"].sum(2) >
                                 0.0).unsqueeze(2).float().squeeze()

        model = Tacotron(config).to(device)
        criterion = model.get_criterion()
        optimizer = model.get_optimizer()
        model.train()
        print(" > Num parameters for Tacotron with Capacitron VAE model:%s" %
              (count_parameters(model)))
        model_ref = copy.deepcopy(model)
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            assert (param - param_ref).sum() == 0, param
            count += 1
        for _ in range(10):
            _, loss_dict = model.train_step(batch, criterion)
            optimizer.zero_grad()
            loss_dict["capacitron_vae_beta_loss"].backward()
            optimizer.first_step()
            loss_dict["loss"].backward()
            optimizer.step()
        # check parameter changes
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            # ignore pre-higway layer since it works conditional
            assert (param != param_ref).any(
            ), "param {} with shape {} not updated!! \n{}\n{}".format(
                count, param.shape, param, param_ref)
            count += 1