Source code for espnet.nets.tts_interface

# -*- coding: utf-8 -*-

# Copyright 2018 Nagoya University (Tomoki Hayashi)
#  Apache 2.0  (

"""TTS Interface realted modules."""

from espnet.asr.asr_utils import torch_load

    import chainer
except ImportError:
    Reporter = None

[docs] class Reporter(chainer.Chain): """Reporter module."""
[docs] def report(self, dicts): """Report values from a given dict.""" for d in dicts:, self)
[docs]class TTSInterface(object): """TTS Interface for ESPnet model implementation."""
[docs] @staticmethod def add_arguments(parser): """Add model specific argments to parser.""" return parser
def __init__(self): """Initilize TTS module.""" self.reporter = Reporter()
[docs] def forward(self, *args, **kwargs): """Calculate TTS forward propagation. Returns: Tensor: Loss value. """ raise NotImplementedError("forward method is not implemented")
[docs] 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")
[docs] 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")
[docs] def load_pretrained_model(self, model_path): """Load pretrained model parameters.""" torch_load(model_path, self)
@property def attention_plot_class(self): """Plot attention weights.""" from espnet.asr.asr_utils import PlotAttentionReport return PlotAttentionReport @property 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"]