Spaces:
Runtime error
Runtime error
File size: 5,485 Bytes
e66133f d533c9c c17b696 e66133f d533c9c 072978d c17b696 072978d c17b696 e66133f c17b696 e66133f c17b696 e66133f c17b696 e66133f c17b696 e66133f c17b696 e66133f c17b696 e66133f 072978d e66133f 072978d e66133f 072978d e66133f 072978d e66133f 072978d e66133f 072978d e66133f c17b696 d533c9c e66133f b7f49a5 e66133f b7f49a5 e66133f b7f49a5 e66133f d533c9c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
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
|