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< |
source |
> |
( |
batch_size: int = 1 |
audio_file: str = None |
raw_audio: ndarray = None |
slice: int = 0 |
start_step: int = 0 |
steps: int = None |
generator: Generator = None |
mask_start_secs: float = 0 |
mask_end_secs: float = 0 |
step_generator: Generator = None |
eta: float = 0 |
noise: Tensor = None |
encoding: Tensor = None |
return_dict = True |
) |
→ |
List[PIL Image] |
Parameters |
batch_size (int) — number of samples to generate |
audio_file (str) — must be a file on disk due to Librosa limitation or |
raw_audio (np.ndarray) — audio as numpy array |
slice (int) — slice number of audio to convert |
start_step (int) — step to start from |
steps (int) — number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM) |
generator (torch.Generator) — random number generator or None |
mask_start_secs (float) — number of seconds of audio to mask (not generate) at start |
mask_end_secs (float) — number of seconds of audio to mask (not generate) at end |
step_generator (torch.Generator) — random number generator used to de-noise or None |
eta (float) — parameter between 0 and 1 used with DDIM scheduler |
noise (torch.Tensor) — noise tensor of shape (batch_size, 1, height, width) or None |
encoding (torch.Tensor) — for UNet2DConditionModel shape (batch_size, seq_length, cross_attention_dim) |
return_dict (bool) — if True return AudioPipelineOutput, ImagePipelineOutput else Tuple |
Returns |
List[PIL Image] |
mel spectrograms (float, List[np.ndarray]): sample rate and raw audios |
Generate random mel spectrogram from audio input and convert to audio. |
encode |
< |
source |
> |
( |
images: typing.List[PIL.Image.Image] |
steps: int = 50 |
) |
→ |
np.ndarray |
Parameters |
images (List[PIL Image]) — list of images to encode |
steps (int) — number of encoding steps to perform (defaults to 50) |
Returns |
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