fix ci for waveflow, test=tts

pull/1451/head
TianYuan 3 years ago
parent f51097618b
commit 67ec6242c3

@ -208,7 +208,7 @@ def verbalize_digit(value_string: str, alt_one=False) -> str:
result_symbols = [DIGITS[digit] for digit in value_string] result_symbols = [DIGITS[digit] for digit in value_string]
result = ''.join(result_symbols) result = ''.join(result_symbols)
if alt_one: if alt_one:
result.replace("", "") result = result.replace("", "")
return result return result

@ -33,11 +33,11 @@ def fold(x, n_group):
"""Fold audio or spectrogram's temporal dimension in to groups. """Fold audio or spectrogram's temporal dimension in to groups.
Args: Args:
x(Tensor): The input tensor. shape=(\*, time_steps) x(Tensor): The input tensor. shape=(*, time_steps)
n_group(int): The size of a group. n_group(int): The size of a group.
Returns: Returns:
Tensor: Folded tensor. shape=(\*, time_steps // n_group, group) Tensor: Folded tensor. shape=(*, time_steps // n_group, group)
""" """
spatial_shape = list(x.shape[:-1]) spatial_shape = list(x.shape[:-1])
time_steps = paddle.shape(x)[-1] time_steps = paddle.shape(x)[-1]
@ -98,11 +98,11 @@ class UpsampleNet(nn.LayerList):
trim_conv_artifact(bool, optional, optional): Trim deconvolution artifact at each layer. Defaults to False. trim_conv_artifact(bool, optional, optional): Trim deconvolution artifact at each layer. Defaults to False.
Returns: Returns:
Tensor: The upsampled spectrogram. shape=(batch_size, input_channels, time_steps \* upsample_factor) Tensor: The upsampled spectrogram. shape=(batch_size, input_channels, time_steps * upsample_factor)
Notes: Notes:
If trim_conv_artifact is ``True``, the output time steps is less If trim_conv_artifact is ``True``, the output time steps is less
than ``time_steps \* upsample_factors``. than ``time_steps * upsample_factors``.
""" """
x = paddle.unsqueeze(x, 1) # (B, C, T) -> (B, 1, C, T) x = paddle.unsqueeze(x, 1) # (B, C, T) -> (B, 1, C, T)
for layer in self: for layer in self:
@ -641,7 +641,7 @@ class ConditionalWaveFlow(nn.LayerList):
mel(np.ndarray): Mel spectrogram of an utterance(in log-magnitude). shape=(C_mel, T_mel) mel(np.ndarray): Mel spectrogram of an utterance(in log-magnitude). shape=(C_mel, T_mel)
Returns: Returns:
Tensor: The synthesized audio, where``T <= T_mel \* upsample_factors``. shape=(B, T) Tensor: The synthesized audio, where``T <= T_mel * upsample_factors``. shape=(B, T)
""" """
start = time.time() start = time.time()
condition = self.encoder(mel, trim_conv_artifact=True) # (B, C, T) condition = self.encoder(mel, trim_conv_artifact=True) # (B, C, T)

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