diff --git a/hfgl/cli.py b/hfgl/cli.py
index 9b2ca86..e83da6e 100644
--- a/hfgl/cli.py
+++ b/hfgl/cli.py
@@ -119,7 +119,7 @@ def synthesize(
         exists=True,
         dir_okay=False,
         file_okay=True,
-        help="The path to a torch file containing time-oriented spectral features [T (frames), K (Mel bands)]",
+        help="The path to a torch file containing Mel band-oriented spectral features [K (Mel bands), T (frames)]",
         autocompletion=complete_path,
     ),
     generator_path: Path = typer.Option(
@@ -132,20 +132,41 @@ def synthesize(
         help="The path to a trained EveryVoice spec-to-wav model",
         autocompletion=complete_path,
     ),
+    time_oriented: bool = typer.Option(
+        False,
+        help="By default, EveryVoice assumes your spectrograms are of the shape [K (Mel bands), T (frames)]. If instead your spectrograms are of shape [T (frames), K (Mel bands)] then please add this flag to transpose the dimensions.",
+    ),
 ):
     """Given some Mel spectrograms and a trained model, generate some audio. i.e. perform *copy synthesis*"""
     import sys
 
     with spinner():
         import torch
+        import torchaudio
         from pydantic import ValidationError
-        from scipy.io.wavfile import write
 
         from .utils import load_hifigan_from_checkpoint, synthesize_data
 
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     checkpoint = torch.load(generator_path, map_location=device)
     data = torch.load(data_path, map_location=device)
+    if time_oriented:
+        data = data.transpose(0, 1)
+    data_size = data.size()
+    if (
+        checkpoint["hyper_parameters"]["config"]["preprocessing"]["audio"]["n_mels"]
+        not in data_size
+    ):
+        raise ValueError(
+            f"Your model expects a spectrogram of dimensions [K (Mel bands), T (frames)] where K == {checkpoint['hyper_parameters']['config']['preprocessing']['audio']['n_mels']} but you provided a tensor of size {data_size}"
+        )
+    if (
+        data_size[0]
+        != checkpoint["hyper_parameters"]["config"]["preprocessing"]["audio"]["n_mels"]
+    ):
+        raise ValueError(
+            f"We expected the first dimension of your Mel spectrogram to correspond with the number of Mel bands declared by your model ({checkpoint['hyper_parameters']['config']['preprocessing']['audio']['n_mels']}). Instead, we found you model has the dimensions {data_size}. If your spectrogram is time-oriented, please re-run this command with the '--time-oriented' flag."
+        )
     try:
         vocoder_model, vocoder_config = load_hifigan_from_checkpoint(checkpoint, device)
     except (TypeError, ValidationError) as e:
@@ -153,7 +174,9 @@ def synthesize(
         sys.exit(1)
     wav, sr = synthesize_data(data, vocoder_model, vocoder_config)
     logger.info(f"Writing file {data_path}.wav")
-    write(f"{data_path}.wav", sr, wav)
+    torchaudio.save(
+        f"{data_path}.wav", wav, sr, format="wav", encoding="PCM_S", bits_per_sample=16
+    )
 
 
 if __name__ == "__main__":
diff --git a/hfgl/utils.py b/hfgl/utils.py
index 2e88274..d955c20 100644
--- a/hfgl/utils.py
+++ b/hfgl/utils.py
@@ -54,7 +54,7 @@ def synthesize_data(
     """Synthesize a batch of waveforms from spectral features
 
     Args:
-        data (Tensor): data tensor, expects output from feature prediction network to be size (b=batch_size, t=number_of_frames, k=n_mels)
+        data (Tensor): data tensor, expects output from feature prediction network to be size (b=batch_size, k=n_mels, t=number_of_frames,)
         ckpt (dict): HiFiGANLightning checkpoint, expects checkpoint to have a 'hyper_parameters.config' key and HiFiGANConfig object value as well as a 'state_dict' key with model weight as the value
     Returns:
         Tuple[np.ndarray, int]: a B, T array of the synthesized audio and the sampling rate
@@ -67,7 +67,7 @@ def synthesize_data(
             model.generator.post_n_fft // 4,
         ).to(data.device)
         with torch.no_grad():
-            mag, phase = model.generator(data.transpose(1, 2))
+            mag, phase = model.generator(data)
         # We can remove this once the fix for https://github.com/pytorch/pytorch/issues/119088 is merged
         if mag.device.type == "mps" or phase.device.type == "mps":
             logger.warning(
@@ -79,10 +79,9 @@ def synthesize_data(
         wavs = inverse_spectral_transform(mag * torch.exp(phase * 1j)).unsqueeze(-2)
     else:
         with torch.no_grad():
-            wavs = model.generator(data.transpose(1, 2))
-    # squeeze to remove the channel dimension
+            wavs = model.generator(data)
     return (
-        wavs.squeeze(1).cpu().numpy(),
+        wavs.cpu(),
         config.preprocessing.audio.output_sampling_rate,
     )