Upload audio_diffusion.py

audio_diffusion.py

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+import tensorflow as tf
+import tensorflow_io as tfio
+import IPython.display as ipd
+import matplotlib.pyplot as plt
+import scipy as sp
+import PIL.Image
+import numpy as np
+
+def wav_to_tf(filename):
+    bits = tf.io.read_file(filename)
+    x = tfio.audio.decode_wav(bits,dtype=tf.int16)[:,0]
+    x = tf.cast(x,tf.float32)
+    x = x - tf.math.reduce_mean(x);
+    x = x / tf.math.reduce_std(x)
+    return tf.Variable(x)
+
+def play(x,rate=24000):
+    ipd.display(ipd.Audio(x,rate=rate,autoplay=False))
+
+def slog(x):
+    return tf.sign(x) * tf.math.log(1+ tf.math.abs(x) )
+    
+def show(X,clim=(-3,3), xlim=(0,300), ylim=(0,100)):
+    plt.figure(figsize=(15,6),dpi=200)
+    plt.imshow(tf.transpose(X),origin='lower',cmap='RdBu')
+    plt.colorbar()
+    plt.clim(clim)
+    plt.xlim(xlim)
+    plt.ylim(ylim)
+    
+def mdct(x,L=624):
+    X = tf.signal.mdct(x,L);
+    return tf.Variable(X)
+
+def imdct(X):
+    y = tf.signal.inverse_mdct(X)
+    return y
+
+Γ = sp.special.gamma
+
+def F(x,μ,σ,γ):
+    return sp.stats.gennorm.cdf(x, beta=γ, loc=μ, scale=σ)
+
+def Finv(x,μ,σ,γ):
+    return sp.stats.gennorm.ppf(x, beta=γ, loc=μ, scale=σ)
+
+def r(γ):
+    return Γ(1/γ)*Γ(3/γ)/Γ(2/γ)
+
+def estimate_GGD(X):
+    μ = tf.math.reduce_mean(X)
+    σ = tf.math.reduce_std(X)
+    E = tf.math.reduce_mean(tf.abs(X - μ))
+    ρ = tf.square(σ/E)
+    
+    γ = sp.optimize.bisect(lambda γ:r(γ)-ρ, 0.3, 1.5,maxiter=50)
+    return μ,σ,γ
+
+def tf_to_pil(x):
+    x = np.array(x)
+    return PIL.Image.fromarray(x,mode="L")
+def pil_to_tf(x):
+    x = np.array(x)
+    return tf.convert_to_tensor(x)
+
+def σ_prior(band):
+    def sc(z,μ,σ,γ):
+        return sp.stats.skewcauchy.pdf(z, γ, loc=μ, scale=σ)
+    return 10000*(2*sc(band,20,100,0.9)+sc(band,22,12,0.5))
+
+def img_to_mdct(img):
+    X = []
+    q = 256;
+    Y = pil_to_tf(img)
+    Y = tf.cast(Y,tf.float32)/q
+    for i_band in range(512):
+        band = Y[:,i_band]
+        σ = σ_prior(i_band)
+        X.append(Finv(band,0,σ,0.85))
+    X = tf.stack(X)
+    X = tf.transpose(X)
+    X = tf.where(tf.math.is_inf(X), tf.ones_like(X), X)
+    return tf.cast(X,tf.float32)
+
+def mdct_to_img(X):
+    Y = []
+    q = 256;
+    for i_band in range(512):
+        band = X[:,i_band]
+        σ = σ_prior(i_band)
+        Y.append(F(band,0,σ,0.85))
+    Y = tf.stack(Y)
+    Y = tf.transpose(Y)
+    Y = tf.round(q*Y)
+    Y = tf.cast(Y,tf.uint8)
+    return tf_to_pil(Y)