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import numpy as np
import torch
from typing import Optional, List, Tuple, NamedTuple, Union
from models import PipelineWrapper
import torchaudio
from audioldm.utils import get_duration
MAX_DURATION = None
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,
return_wav: bool = False, stft: bool = False, model_sr: Optional[int] = None) -> torch.Tensor:
if stft: # AudioLDM/tango loading to spectrogram
if type(audio_path) is str:
import audioldm
import audioldm.audio
duration = get_duration(audio_path)
if MAX_DURATION is not None:
duration = min(duration, MAX_DURATION)
mel, _, wav = 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)
if return_wav:
return mel, 16000, duration, wav
return mel, model_sr, duration
else:
waveform, sr = torchaudio.load(audio_path)
if sr != model_sr:
waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=model_sr)
# waveform = waveform.numpy()[0, ...]
def normalize_wav(waveform):
waveform = waveform - torch.mean(waveform)
waveform = waveform / (torch.max(torch.abs(waveform)) + 1e-8)
return waveform * 0.5
waveform = normalize_wav(waveform)
# waveform = waveform[None, ...]
# waveform = pad_wav(waveform, segment_length)
# waveform = waveform[0, ...]
waveform = torch.FloatTensor(waveform)
if MAX_DURATION is not None:
duration = min(waveform.shape[-1] / model_sr, MAX_DURATION)
waveform = waveform[:, :int(duration * model_sr)]
# cut waveform
duration = waveform.shape[-1] / model_sr
return waveform, model_sr, duration
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
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