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), 45) 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