teticio's picture
add abilithy to generate audio from another audio
e66133f
raw
history blame
5.23 kB
from typing import Iterable, Tuple
import torch
import numpy as np
from PIL import Image
from tqdm.auto import tqdm
from diffusers import DDPMPipeline
from librosa.beat import beat_track
from .mel import Mel
VERSION = "1.1.1"
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 = 1000,
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
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.
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] = self.ddpm.scheduler.add_noise(
torch.tensor(input_image[np.newaxis, np.newaxis, :]), images,
steps - start_step)
images = images.to(self.ddpm.device)
self.ddpm.scheduler.set_timesteps(steps)
for t in self.progress_bar(self.ddpm.scheduler.timesteps[start_step:]):
model_output = self.ddpm.unet(images, t)['sample']
images = self.ddpm.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