File size: 5,230 Bytes
e66133f
 
 
d533c9c
c17b696
e66133f
c17b696
d533c9c
c17b696
 
 
e66133f
c17b696
 
 
 
 
e66133f
 
 
 
c17b696
 
 
 
 
 
e66133f
c17b696
 
 
 
 
 
e66133f
c17b696
e66133f
 
 
 
c17b696
 
e66133f
 
 
c17b696
 
e66133f
c17b696
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
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