File size: 2,544 Bytes
438ebe2 | 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 | import warnings
from typing import Callable, Union
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
warnings.filterwarnings("ignore")
import numpy as np
import librosa
from PIL import Image
class Mel(ConfigMixin, SchedulerMixin):
config_name = "mel_config.json"
@register_to_config
def __init__(
self,
x_res: int = 256,
y_res: int = 256,
sample_rate: int = 22050,
n_fft: int = 2048,
hop_length: int = 512,
top_db: float = 80.0,
n_iter: int = 32,
):
self.hop_length = hop_length
self.sr = sample_rate
self.n_fft = n_fft
self.top_db = top_db
self.audio = None
self.n_iter = n_iter
self.set_resolution(x_res, y_res)
def set_resolution(self, x_res: int, y_res: int):
self.x_res = x_res
self.y_res = y_res
self.n_mels = self.y_res
self.slice_size = self.x_res * self.hop_length - 1
def load_audio(self, audio_file: str = None, raw_audio: np.ndarray = None):
if audio_file is not None:
self.audio, _ = librosa.load(audio_file, mono=True, sr=self.sr)
else:
self.audio = raw_audio
if len(self.audio) < self.x_res * self.hop_length:
self.audio = np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio),))])
def get_number_of_slices(self) -> int:
return len(self.audio) // self.slice_size
def get_audio_slice(self, slice: int = 0) -> int:
return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
def get_sample_rate(self) -> int:
return self.sr
def audio_slice_to_image(self, slice: int, ref: Union[float, Callable] = np.max) -> Image.Image:
S = librosa.feature.melspectrogram(
y=self.get_audio_slice(slice),
sr=self.sr,
n_fft=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
)
log_S = librosa.power_to_db(S, ref=ref, top_db=self.top_db)
spec_data = (((log_S + self.top_db) * 255 / self.top_db).clip(0, 255) + 0.5).astype(np.uint8)
return Image.fromarray(spec_data)
def image_to_audio(self, image: Image.Image) -> np.ndarray:
spec_data = np.frombuffer(image.tobytes(), dtype=np.uint8).reshape((image.height, image.width))
log_S = spec_data.astype("float") * self.top_db / 255 - self.top_db
S = librosa.db_to_power(log_S)
return librosa.feature.inverse.mel_to_audio(
S, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_iter=self.n_iter
) |