def forward(self, spectrogram: torch.FloatTensor) -> torch.FloatTensor:
        r"""
        Converts a log-mel spectrogram into a speech waveform. Passing a batch of log-mel spectrograms returns a batch
        of speech waveforms. Passing a single, un-batched log-mel spectrogram returns a single, un-batched speech
        waveform.

        Args:
            spectrogram (`torch.FloatTensor`):
                Tensor containing the log-mel spectrograms. Can be batched and of shape `(batch_size, sequence_length,
                config.model_in_dim)`, or un-batched and of shape `(sequence_length, config.model_in_dim)`.

        Returns:
            `torch.FloatTensor`: Tensor containing the speech waveform. If the input spectrogram is batched, will be of
            shape `(batch_size, num_frames,)`. If un-batched, will be of shape `(num_frames,)`.
        """
        if self.config.normalize_before:
            spectrogram = (spectrogram - self.mean) / self.scale

        is_batched = spectrogram.dim() == 3
        if not is_batched:
            spectrogram = spectrogram.unsqueeze(0)

        hidden_states = spectrogram.transpose(2, 1)

        hidden_states = self.conv_pre(hidden_states)
        for i in range(self.num_upsamples):
            hidden_states = nn.functional.leaky_relu(hidden_states, self.config.leaky_relu_slope)
            hidden_states = self.upsampler[i](hidden_states)

            res_state = self.resblocks[i * self.num_kernels](hidden_states)
            for j in range(1, self.num_kernels):
                res_state += self.resblocks[i * self.num_kernels + j](hidden_states)
            hidden_states = res_state / self.num_kernels

        hidden_states = nn.functional.leaky_relu(hidden_states)
        hidden_states = self.conv_post(hidden_states)
        hidden_states = torch.tanh(hidden_states)

        if not is_batched:
            # remove batch dim and collapse tensor to 1-d audio waveform
            waveform = hidden_states.squeeze(0).transpose(1, 0).view(-1)
        else:
            # remove seq-len dim since this collapses to 1
            waveform = hidden_states.squeeze(1)

        return waveform

        Converts a log-mel spectrogram into a speech waveform. Passing a batch of log-mel spectrograms returns a batch
        of speech waveforms. Passing a single, un-batched log-mel spectrogram returns a single, un-batched speech
        waveform.

        Args:
            spectrogram (`torch.FloatTensor`):
                Tensor containing the log-mel spectrograms. Can be batched and of shape `(batch_size, sequence_length,
                config.model_in_dim)`, or un-batched and of shape `(sequence_length, config.model_in_dim)`.

        Returns:
            `torch.FloatTensor`: Tensor containing the speech waveform. If the input spectrogram is batched, will be of
            shape `(batch_size, num_frames,)`. If un-batched, will be of shape `(num_frames,)`.
        
dpkd/transformers/src/transformers/models/speecht5/modeling_speecht5.py