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from typing import Iterable, Tuple
import torch
import numpy as np
from PIL import Image
from tqdm.auto import tqdm
from librosa.beat import beat_track
from diffusers import DDPMPipeline, DDPMScheduler
from .mel import Mel
VERSION = "1.1.3"
class AudioDiffusion:
def __init__(self,
model_id: str = "teticio/audio-diffusion-256",
resolution: int = 256,
cuda: bool = torch.cuda.is_available(),
progress_bar: Iterable = tqdm):
"""Class for generating audio using Denoising Diffusion Probabilistic Models.
Args:
model_id (String): name of model (local directory or Hugging Face Hub)
resolution (int): size of square mel spectrogram in pixels
cuda (bool): use CUDA?
progress_bar (iterable): iterable callback for progress updates or None
"""
self.mel = Mel(x_res=resolution, y_res=resolution)
self.model_id = model_id
self.ddpm = DDPMPipeline.from_pretrained(self.model_id)
if cuda:
self.ddpm.to("cuda")
self.progress_bar = progress_bar or (lambda _: _)
def generate_spectrogram_and_audio(
self,
generator: torch.Generator = None
) -> Tuple[Image.Image, Tuple[int, np.ndarray]]:
"""Generate random mel spectrogram and convert to audio.
Args:
generator (torch.Generator): random number generator or None
Returns:
PIL Image: mel spectrogram
(float, np.ndarray): sample rate and raw audio
"""
images = self.ddpm(output_type="numpy", generator=generator)["sample"]
images = (images * 255).round().astype("uint8").transpose(0, 3, 1, 2)
image = Image.fromarray(images[0][0])
audio = self.mel.image_to_audio(image)
return image, (self.mel.get_sample_rate(), audio)
@torch.no_grad()
def generate_spectrogram_and_audio_from_audio(
self,
audio_file: str = None,
raw_audio: np.ndarray = None,
slice: int = 0,
start_step: int = 0,
steps: int = None,
generator: torch.Generator = None
) -> Tuple[Image.Image, Tuple[int, np.ndarray]]:
"""Generate random mel spectrogram from audio input and convert to audio.
Args:
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 to perform (defaults to num_train_timesteps)
generator (torch.Generator): random number generator or None
Returns:
PIL Image: mel spectrogram
(float, np.ndarray): sample rate and raw audio
"""
# It would be better to derive a class from DDPMDiffusionPipeline
# but currently the return type ImagePipelineOutput cannot be imported.
if steps is None:
steps = self.ddpm.scheduler.num_train_timesteps
scheduler = DDPMScheduler(num_train_timesteps=steps)
scheduler.set_timesteps(steps)
images = torch.randn(
(1, self.ddpm.unet.in_channels, self.ddpm.unet.sample_size,
self.ddpm.unet.sample_size),
generator=generator,
)
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(audio_file, raw_audio)
input_image = self.mel.audio_slice_to_image(slice)
input_image = np.frombuffer(input_image.tobytes(),
dtype="uint8").reshape(
(input_image.width,
input_image.height))
input_image = ((input_image / 255) * 2 - 1)
if start_step > 0:
images[0][0] = scheduler.add_noise(
torch.tensor(input_image[np.newaxis, np.newaxis, :]),
images, steps - start_step)
images = images.to(self.ddpm.device)
for t in self.progress_bar(scheduler.timesteps[start_step:]):
model_output = self.ddpm.unet(images, t)['sample']
images = scheduler.step(model_output,
t,
images,
generator=generator)['prev_sample']
images = (images / 2 + 0.5).clamp(0, 1)
images = images.cpu().permute(0, 2, 3, 1).numpy()
images = (images * 255).round().astype("uint8").transpose(0, 3, 1, 2)
image = Image.fromarray(images[0][0])
audio = self.mel.image_to_audio(image)
return image, (self.mel.get_sample_rate(), audio)
@staticmethod
def loop_it(audio: np.ndarray,
sample_rate: int,
loops: int = 12) -> np.ndarray:
"""Loop audio
Args:
audio (np.ndarray): audio as numpy array
sample_rate (int): sample rate of audio
loops (int): number of times to loop
Returns:
(float, np.ndarray): sample rate and raw audio or None
"""
_, beats = beat_track(y=audio, sr=sample_rate, units='samples')
for beats_in_bar in [16, 12, 8, 4]:
if len(beats) > beats_in_bar:
return np.tile(audio[beats[0]:beats[beats_in_bar]], loops)
return None
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