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