256 lines
10 KiB
Python
256 lines
10 KiB
Python
from abc import ABC, abstractmethod
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from typing import List, Optional, Tuple, Union
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from torch import Tensor
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from torchaudio.models import Tacotron2
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class _TextProcessor(ABC):
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@property
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@abstractmethod
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def tokens(self):
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"""The tokens that the each value in the processed tensor represent.
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:type: List[str]
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"""
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@abstractmethod
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def __call__(self, texts: Union[str, List[str]]) -> Tuple[Tensor, Tensor]:
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"""Encode the given (batch of) texts into numerical tensors
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Args:
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text (str or list of str): The input texts.
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Returns:
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(Tensor, Tensor):
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Tensor:
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The encoded texts. Shape: `(batch, max length)`
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Tensor:
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The valid length of each sample in the batch. Shape: `(batch, )`.
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"""
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class _Vocoder(ABC):
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@property
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@abstractmethod
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def sample_rate(self):
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"""The sample rate of the resulting waveform
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:type: float
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"""
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@abstractmethod
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def __call__(self, specgrams: Tensor, lengths: Optional[Tensor] = None) -> Tuple[Tensor, Optional[Tensor]]:
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"""Generate waveform from the given input, such as spectrogram
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Args:
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specgrams (Tensor):
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The input spectrogram. Shape: `(batch, frequency bins, time)`.
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The expected shape depends on the implementation.
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lengths (Tensor, or None, optional):
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The valid length of each sample in the batch. Shape: `(batch, )`.
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(Default: `None`)
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Returns:
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(Tensor, Optional[Tensor]):
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Tensor:
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The generated waveform. Shape: `(batch, max length)`
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Tensor or None:
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The valid length of each sample in the batch. Shape: `(batch, )`.
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"""
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class Tacotron2TTSBundle(ABC):
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"""Data class that bundles associated information to use pretrained Tacotron2 and vocoder.
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This class provides interfaces for instantiating the pretrained model along with
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the information necessary to retrieve pretrained weights and additional data
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to be used with the model.
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Torchaudio library instantiates objects of this class, each of which represents
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a different pretrained model. Client code should access pretrained models via these
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instances.
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Please see below for the usage and the available values.
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Example - Character-based TTS pipeline with Tacotron2 and WaveRNN
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>>> import torchaudio
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>>>
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>>> text = "Hello, T T S !"
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>>> bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_CHAR_LJSPEECH
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>>>
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>>> # Build processor, Tacotron2 and WaveRNN model
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>>> processor = bundle.get_text_processor()
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>>> tacotron2 = bundle.get_tacotron2()
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Downloading:
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100%|███████████████████████████████| 107M/107M [00:01<00:00, 87.9MB/s]
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>>> vocoder = bundle.get_vocoder()
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Downloading:
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100%|███████████████████████████████| 16.7M/16.7M [00:00<00:00, 78.1MB/s]
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>>>
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>>> # Encode text
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>>> input, lengths = processor(text)
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>>>
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>>> # Generate (mel-scale) spectrogram
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>>> specgram, lengths, _ = tacotron2.infer(input, lengths)
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>>>
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>>> # Convert spectrogram to waveform
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>>> waveforms, lengths = vocoder(specgram, lengths)
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>>>
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>>> torchaudio.save('hello-tts.wav', waveforms, vocoder.sample_rate)
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Example - Phoneme-based TTS pipeline with Tacotron2 and WaveRNN
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>>>
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>>> # Note:
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>>> # This bundle uses pre-trained DeepPhonemizer as
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>>> # the text pre-processor.
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>>> # Please install deep-phonemizer.
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>>> # See https://github.com/as-ideas/DeepPhonemizer
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>>> # The pretrained weight is automatically downloaded.
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>>>
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>>> import torchaudio
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>>>
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>>> text = "Hello, TTS!"
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>>> bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH
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>>>
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>>> # Build processor, Tacotron2 and WaveRNN model
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>>> processor = bundle.get_text_processor()
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Downloading:
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100%|███████████████████████████████| 63.6M/63.6M [00:04<00:00, 15.3MB/s]
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>>> tacotron2 = bundle.get_tacotron2()
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Downloading:
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100%|███████████████████████████████| 107M/107M [00:01<00:00, 87.9MB/s]
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>>> vocoder = bundle.get_vocoder()
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Downloading:
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100%|███████████████████████████████| 16.7M/16.7M [00:00<00:00, 78.1MB/s]
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>>>
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>>> # Encode text
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>>> input, lengths = processor(text)
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>>>
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>>> # Generate (mel-scale) spectrogram
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>>> specgram, lengths, _ = tacotron2.infer(input, lengths)
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>>>
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>>> # Convert spectrogram to waveform
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>>> waveforms, lengths = vocoder(specgram, lengths)
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>>>
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>>> torchaudio.save('hello-tts.wav', waveforms, vocoder.sample_rate)
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"""
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# Using the inner class so that these interfaces are not directly exposed on
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# `torchaudio.pipelines`, but still listed in documentation.
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# The thing is, text processing and vocoder are generic and we do not know what kind of
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# new text processing and vocoder will be added in the future, so we want to make these
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# interfaces specific to this Tacotron2TTS pipeline.
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class TextProcessor(_TextProcessor):
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"""Interface of the text processing part of Tacotron2TTS pipeline
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See :func:`torchaudio.pipelines.Tacotron2TTSBundle.get_text_processor` for the usage.
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"""
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class Vocoder(_Vocoder):
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"""Interface of the vocoder part of Tacotron2TTS pipeline
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See :func:`torchaudio.pipelines.Tacotron2TTSBundle.get_vocoder` for the usage.
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"""
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@abstractmethod
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def get_text_processor(self, *, dl_kwargs=None) -> TextProcessor:
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"""Create a text processor
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For character-based pipeline, this processor splits the input text by character.
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For phoneme-based pipeline, this processor converts the input text (grapheme) to
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phonemes.
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If a pre-trained weight file is necessary,
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:func:`torch.hub.download_url_to_file` is used to downloaded it.
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Args:
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dl_kwargs (dictionary of keyword arguments,):
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Passed to :func:`torch.hub.download_url_to_file`.
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Returns:
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TextProcessor:
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A callable which takes a string or a list of strings as input and
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returns Tensor of encoded texts and Tensor of valid lengths.
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The object also has ``tokens`` property, which allows to recover the
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tokenized form.
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Example - Character-based
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>>> text = [
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>>> "Hello World!",
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>>> "Text-to-speech!",
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>>> ]
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>>> bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_CHAR_LJSPEECH
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>>> processor = bundle.get_text_processor()
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>>> input, lengths = processor(text)
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>>>
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>>> print(input)
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tensor([[19, 16, 23, 23, 26, 11, 34, 26, 29, 23, 15, 2, 0, 0, 0],
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[31, 16, 35, 31, 1, 31, 26, 1, 30, 27, 16, 16, 14, 19, 2]],
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dtype=torch.int32)
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>>>
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>>> print(lengths)
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tensor([12, 15], dtype=torch.int32)
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>>>
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>>> print([processor.tokens[i] for i in input[0, :lengths[0]]])
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['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd', '!']
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>>> print([processor.tokens[i] for i in input[1, :lengths[1]]])
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['t', 'e', 'x', 't', '-', 't', 'o', '-', 's', 'p', 'e', 'e', 'c', 'h', '!']
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Example - Phoneme-based
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>>> text = [
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>>> "Hello, T T S !",
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>>> "Text-to-speech!",
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>>> ]
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>>> bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH
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>>> processor = bundle.get_text_processor()
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Downloading:
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100%|███████████████████████████████| 63.6M/63.6M [00:04<00:00, 15.3MB/s]
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>>> input, lengths = processor(text)
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>>>
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>>> print(input)
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tensor([[54, 20, 65, 69, 11, 92, 44, 65, 38, 2, 0, 0, 0, 0],
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[81, 40, 64, 79, 81, 1, 81, 20, 1, 79, 77, 59, 37, 2]],
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dtype=torch.int32)
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>>>
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>>> print(lengths)
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tensor([10, 14], dtype=torch.int32)
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>>>
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>>> print([processor.tokens[i] for i in input[0]])
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['HH', 'AH', 'L', 'OW', ' ', 'W', 'ER', 'L', 'D', '!', '_', '_', '_', '_']
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>>> print([processor.tokens[i] for i in input[1]])
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['T', 'EH', 'K', 'S', 'T', '-', 'T', 'AH', '-', 'S', 'P', 'IY', 'CH', '!']
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"""
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@abstractmethod
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def get_vocoder(self, *, dl_kwargs=None) -> Vocoder:
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"""Create a vocoder module, based off of either WaveRNN or GriffinLim.
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If a pre-trained weight file is necessary,
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:func:`torch.hub.load_state_dict_from_url` is used to downloaded it.
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Args:
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dl_kwargs (dictionary of keyword arguments):
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Passed to :func:`torch.hub.load_state_dict_from_url`.
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Returns:
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Vocoder:
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A vocoder module, which takes spectrogram Tensor and an optional
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length Tensor, then returns resulting waveform Tensor and an optional
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length Tensor.
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"""
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@abstractmethod
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def get_tacotron2(self, *, dl_kwargs=None) -> Tacotron2:
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"""Create a Tacotron2 model with pre-trained weight.
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Args:
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dl_kwargs (dictionary of keyword arguments):
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Passed to :func:`torch.hub.load_state_dict_from_url`.
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Returns:
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Tacotron2:
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The resulting model.
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"""
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