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| # -*- coding: utf-8 -*- | |
| # Copyright 2018 Nagoya University (Tomoki Hayashi) | |
| # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) | |
| """TTS Interface realted modules.""" | |
| from espnet.asr.asr_utils import torch_load | |
| try: | |
| import chainer | |
| except ImportError: | |
| Reporter = None | |
| else: | |
| class Reporter(chainer.Chain): | |
| """Reporter module.""" | |
| def report(self, dicts): | |
| """Report values from a given dict.""" | |
| for d in dicts: | |
| chainer.reporter.report(d, self) | |
| class TTSInterface(object): | |
| """TTS Interface for ESPnet model implementation.""" | |
| def add_arguments(parser): | |
| """Add model specific argments to parser.""" | |
| return parser | |
| def __init__(self): | |
| """Initilize TTS module.""" | |
| self.reporter = Reporter() | |
| def forward(self, *args, **kwargs): | |
| """Calculate TTS forward propagation. | |
| Returns: | |
| Tensor: Loss value. | |
| """ | |
| raise NotImplementedError("forward method is not implemented") | |
| def inference(self, *args, **kwargs): | |
| """Generate the sequence of features given the sequences of characters. | |
| Returns: | |
| Tensor: The sequence of generated features (L, odim). | |
| Tensor: The sequence of stop probabilities (L,). | |
| Tensor: The sequence of attention weights (L, T). | |
| """ | |
| raise NotImplementedError("inference method is not implemented") | |
| def calculate_all_attentions(self, *args, **kwargs): | |
| """Calculate TTS attention weights. | |
| Args: | |
| Tensor: Batch of attention weights (B, Lmax, Tmax). | |
| """ | |
| raise NotImplementedError("calculate_all_attentions method is not implemented") | |
| def load_pretrained_model(self, model_path): | |
| """Load pretrained model parameters.""" | |
| torch_load(model_path, self) | |
| def attention_plot_class(self): | |
| """Plot attention weights.""" | |
| from espnet.asr.asr_utils import PlotAttentionReport | |
| return PlotAttentionReport | |
| def base_plot_keys(self): | |
| """Return base key names to plot during training. | |
| The keys should match what `chainer.reporter` reports. | |
| if you add the key `loss`, | |
| the reporter will report `main/loss` and `validation/main/loss` values. | |
| also `loss.png` will be created as a figure visulizing `main/loss` | |
| and `validation/main/loss` values. | |
| Returns: | |
| list[str]: Base keys to plot during training. | |
| """ | |
| return ["loss"] | |