@@ -30,9 +30,9 @@ class DanceDiffusionPipeline(DiffusionPipeline):
     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
 
     Parameters:
-        unet ([`UNet1DModel`]): U-Net architecture to denoise the encoded image.
+        unet ([`UNet1DModel`]): U-Net architecture to denoise the encoded audio.
         scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
+            A scheduler to be used in combination with `unet` to denoise the encoded audio. Can be one of
             [`IPNDMScheduler`].
     """
 
@@ -54,7 +54,7 @@ def __call__(
             batch_size (`int`, *optional*, defaults to 1):
                 The number of audio samples to generate.
             num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality audio sample at
+                The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at
                 the expense of slower inference.
             generator (`torch.Generator`, *optional*):
                 One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
@@ -67,7 +67,7 @@ def __call__(
 
         Returns:
             [`~pipelines.AudioPipelineOutput`] or `tuple`: [`~pipelines.utils.AudioPipelineOutput`] if `return_dict` is
-            True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
+            True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated audio.
         """
 
         if audio_length_in_s is None:
@@ -94,7 +94,7 @@ def __call__(
             )
         sample_size = int(sample_size)
 
-        dtype = next(iter(self.unet.parameters())).dtype
+        dtype = next(self.unet.parameters()).dtype
         shape = (batch_size, self.unet.config.in_channels, sample_size)
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
@@ -112,7 +112,7 @@ def __call__(
             # 1. predict noise model_output
             model_output = self.unet(audio, t).sample
 
-            # 2. compute previous image: x_t -> t_t-1
+            # 2. compute previous audio sample: x_t -> t_t-1
             audio = self.scheduler.step(model_output, t, audio).prev_sample
 
         audio = audio.clamp(-1, 1).float().cpu().numpy()