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Running
on
Zero
| import numpy as np | |
| import torch | |
| from typing import Optional, List, Tuple, NamedTuple, Union | |
| from models import PipelineWrapper | |
| class PromptEmbeddings(NamedTuple): | |
| embedding_hidden_states: torch.Tensor | |
| embedding_class_lables: torch.Tensor | |
| boolean_prompt_mask: torch.Tensor | |
| def load_audio(audio_path: Union[str, np.array], fn_STFT, left: int = 0, right: int = 0, device: Optional[torch.device] = None | |
| ) -> torch.tensor: | |
| if type(audio_path) is str: | |
| import audioldm | |
| import audioldm.audio | |
| duration = min(audioldm.utils.get_duration(audio_path), 30) | |
| mel, _, _ = audioldm.audio.wav_to_fbank(audio_path, target_length=int(duration * 102.4), fn_STFT=fn_STFT) | |
| mel = mel.unsqueeze(0) | |
| else: | |
| mel = audio_path | |
| c, h, w = mel.shape | |
| left = min(left, w-1) | |
| right = min(right, w - left - 1) | |
| mel = mel[:, :, left:w-right] | |
| mel = mel.unsqueeze(0).to(device) | |
| return mel | |
| def get_height_of_spectrogram(length: int, ldm_stable: PipelineWrapper) -> int: | |
| vocoder_upsample_factor = np.prod(ldm_stable.model.vocoder.config.upsample_rates) / \ | |
| ldm_stable.model.vocoder.config.sampling_rate | |
| if length is None: | |
| length = ldm_stable.model.unet.config.sample_size * ldm_stable.model.vae_scale_factor * \ | |
| vocoder_upsample_factor | |
| height = int(length / vocoder_upsample_factor) | |
| # original_waveform_length = int(length * ldm_stable.model.vocoder.config.sampling_rate) | |
| if height % ldm_stable.model.vae_scale_factor != 0: | |
| height = int(np.ceil(height / ldm_stable.model.vae_scale_factor)) * ldm_stable.model.vae_scale_factor | |
| print( | |
| f"Audio length in seconds {length} is increased to {height * vocoder_upsample_factor} " | |
| f"so that it can be handled by the model. It will be cut to {length} after the " | |
| f"denoising process." | |
| ) | |
| return height | |
| def get_text_embeddings(target_prompt: List[str], target_neg_prompt: List[str], ldm_stable: PipelineWrapper | |
| ) -> Tuple[torch.Tensor, PromptEmbeddings, PromptEmbeddings]: | |
| text_embeddings_hidden_states, text_embeddings_class_labels, text_embeddings_boolean_prompt_mask = \ | |
| ldm_stable.encode_text(target_prompt) | |
| uncond_embedding_hidden_states, uncond_embedding_class_lables, uncond_boolean_prompt_mask = \ | |
| ldm_stable.encode_text(target_neg_prompt) | |
| text_emb = PromptEmbeddings(embedding_hidden_states=text_embeddings_hidden_states, | |
| boolean_prompt_mask=text_embeddings_boolean_prompt_mask, | |
| embedding_class_lables=text_embeddings_class_labels) | |
| uncond_emb = PromptEmbeddings(embedding_hidden_states=uncond_embedding_hidden_states, | |
| boolean_prompt_mask=uncond_boolean_prompt_mask, | |
| embedding_class_lables=uncond_embedding_class_lables) | |
| return text_embeddings_class_labels, text_emb, uncond_emb | |