Python symbols.symbols方法代码示例

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def __init__(self,
                 n_src_vocab=len(symbols)+1,
                 len_max_seq=hp.max_sep_len,
                 d_word_vec=hp.word_vec_dim,
                 n_layers=hp.encoder_n_layer,
                 n_head=hp.encoder_head,
                 d_k=64,
                 d_v=64,
                 d_model=hp.word_vec_dim,
                 d_inner=hp.encoder_conv1d_filter_size,
                 dropout=hp.dropout):

        super(Encoder, self).__init__()

        n_position = len_max_seq + 1

        self.src_word_emb = nn.Embedding(
            n_src_vocab, d_word_vec, padding_idx=Constants.PAD)

        self.position_enc = nn.Embedding.from_pretrained(
            get_sinusoid_encoding_table(n_position, d_word_vec, padding_idx=0),
            freeze=True)

        self.layer_stack = nn.ModuleList([FFTBlock(
            d_model, d_inner, n_head, d_k, d_v, dropout=dropout) for _ in range(n_layers)]) 

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def __init__(self, embedding_size):
        """

        :param embedding_size: dimension of embedding
        """
        super(Encoder, self).__init__()
        self.embedding_size = embedding_size
        self.embed = nn.Embedding(len(symbols), embedding_size)
        self.prenet = Prenet(embedding_size, hp.hidden_size * 2, hp.hidden_size)
        self.cbhg = CBHG(hp.hidden_size) 

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def text_to_sequence(text, cleaner_names):
  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.

    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."

    Args:
      text: string to convert to a sequence
      cleaner_names: names of the cleaner functions to run the text through

    Returns:
      List of integers corresponding to the symbols in the text
  '''
  sequence = []

  # Check for curly braces and treat their contents as ARPAbet:
  while len(text):
    m = _curly_re.match(text)
    if not m:
      sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
      break
    sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
    sequence += _arpabet_to_sequence(m.group(2))
    text = m.group(3)

  # Append EOS token
  sequence.append(_symbol_to_id['~'])
  return sequence 

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def _symbols_to_sequence(symbols):
  return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)] 

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def __init__(self, embedding_size, num_hidden):
        super(EncoderPrenet, self).__init__()
        self.embedding_size = embedding_size
        self.embed = nn.Embedding(len(symbols), embedding_size, padding_idx=0)

        self.conv1 = Conv(in_channels=embedding_size,
                          out_channels=num_hidden,
                          kernel_size=5,
                          padding=int(np.floor(5 / 2)),
                          w_init='relu')
        self.conv2 = Conv(in_channels=num_hidden,
                          out_channels=num_hidden,
                          kernel_size=5,
                          padding=int(np.floor(5 / 2)),
                          w_init='relu')

        self.conv3 = Conv(in_channels=num_hidden,
                          out_channels=num_hidden,
                          kernel_size=5,
                          padding=int(np.floor(5 / 2)),
                          w_init='relu')

        self.batch_norm1 = nn.BatchNorm1d(num_hidden)
        self.batch_norm2 = nn.BatchNorm1d(num_hidden)
        self.batch_norm3 = nn.BatchNorm1d(num_hidden)

        self.dropout1 = nn.Dropout(p=0.2)
        self.dropout2 = nn.Dropout(p=0.2)
        self.dropout3 = nn.Dropout(p=0.2)
        self.projection = Linear(num_hidden, num_hidden) 

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def convert_to_en_symbols():
    '''Converts built-in korean symbols to english, to be used for english training
    
'''
    global _symbol_to_id, _id_to_symbol, isEn
    if not isEn:
        print(" [!] Converting to english mode")
    _symbol_to_id = {s: i for i, s in enumerate(en_symbols)}
    _id_to_symbol = {i: s for i, s in enumerate(en_symbols)}
    isEn=True 

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def _text_to_sequence(text, cleaner_names, as_token):
    '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.

        The text can optionally have ARPAbet sequences enclosed in curly braces embedded
        in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."

        Args:
            text: string to convert to a sequence
            cleaner_names: names of the cleaner functions to run the text through

        Returns:
            List of integers corresponding to the symbols in the text
    '''
    sequence = []

    # Check for curly braces and treat their contents as ARPAbet:
    while len(text):
        m = _curly_re.match(text)
        if not m:
            sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
            break
        sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
        sequence += _arpabet_to_sequence(m.group(2))
        text = m.group(3)

    # Append EOS token
    sequence.append(_symbol_to_id[EOS])  # [14, 29, 45, 2, 27, 62, 20, 21, 4, 39, 45, 1]

    if as_token:
        return sequence_to_text(sequence, combine_jamo=True)
    else:
        return np.array(sequence, dtype=np.int32) 

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def _symbols_to_sequence(symbols):
    return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)] 

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def text_to_sequence(text, cleaner_names):
    '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.

      The text can optionally have ARPAbet sequences enclosed in curly braces embedded
      in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."

      Args:
        text: string to convert to a sequence
        cleaner_names: names of the cleaner functions to run the text through

      Returns:
        List of integers corresponding to the symbols in the text
    '''
    sequence = []

    # Check for curly braces and treat their contents as ARPAbet:
    while len(text):
        m = _curly_re.match(text)
        if not m:
            sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
            break
        sequence += _symbols_to_sequence(
            _clean_text(m.group(1), cleaner_names))
        sequence += _arpabet_to_sequence(m.group(2))
        text = m.group(3)

    return sequence 

# 需要导入模块: from text import symbols [as 别名]
# 或者: from text.symbols import symbols [as 别名]
def text_to_sequence(text, cleaner_names):
  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.

    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."

    Args:
      text: string to convert to a sequence
      cleaner_names: names of the cleaner functions to run the text through

    Returns:
      List of integers corresponding to the symbols in the text
  '''
  sequence = []

  # Check for curly braces and treat their contents as ARPAbet:
  while len(text):
    m = _curly_re.match(text)
    if not m:
      sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
      break
    sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
    sequence += _arpabet_to_sequence(m.group(2))
    text = m.group(3)

  return sequence