playing with denoiseing

data.py

@@ -22,6 +22,9 @@ def process_file(file_path):
         end = start_pos + size
         if end <= sample_len:
             chunk = y[start_pos:end]
+            chunk = librosa.feature.melspectrogram(y=chunk, sr=SAMPLE_RATE)
+            chunk = ((librosa.amplitude_to_db(chunk,ref=np.max)+80)/80)
+
             #chunk = librosa.feature.melspectrogram(y=chunk,sr=SAMPLE_RATE)
             #chunk = ((librosa.amplitude_to_db(chunk,ref=np.max)+40)/40)
             file_chunks.append(chunk)

dataInit.py

@@ -1,13 +1,14 @@
 import data
 import numpy as np
 
-x,y = data.dataset(data.load())
+x = data.load()
 size=len(x)
+print(size)
 
 x_np = np.stack(x)
 x_np = np.expand_dims(x_np, axis=1)
 
-y_np = np.stack(y)
-y_np = np.expand_dims(y_np, axis=1)
+#y_np = np.stack(y)
+#y_np = np.expand_dims(y_np, axis=1)
 
-np.savez_compressed("data",x_np,y_np)
+np.savez_compressed("data",x_np)

model.py

@@ -1,34 +1,73 @@
 from tinygrad import Tensor, nn
 
-class Gen:
-    def __init__(self, height=128, width=216, latent_dim=128):
+class gen:
+    def __init__(self, input_channels=1, height=128, width=431, latent_dim=64):
+        self.height = height
+        self.width = width
+        self.latent_dim = latent_dim
+
         self.w = width // 4
         self.h = height // 4
-        self.flat = 128 * self.h * self.w
-        self.ld = latent_dim
-        self.d1 = nn.Linear(latent_dim, self.flat)
-        self.d2 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1)
-        self.d3 = nn.ConvTranspose2d(64, 1, kernel_size=3, stride=2, padding=1, output_padding=1)
-
-    def __call__(self, noise: Tensor) -> Tensor:
-        x = self.d1(noise).relu()
-        x = x.reshape(noise.shape[0], 128, self.h, self.w)
-        x = self.d2(x).relu()
-        x = self.d3(x)
-        return x.tanh()
+        self.h = 32  # Output height after 2 strides
+        self.w = 108 # Output width after 2 strides
+        self.flattened_size = 128 * self.h * self.w
 
 
-class Check:
-    def __init__(self, height=128, width=216):
-        self.w = width // 4
-        self.h = height // 4
-        self.flat = 128 * self.h * self.w
-        self.e1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=1)
+        self.e1 = nn.Conv2d(input_channels, 64, kernel_size=3, stride=2, padding=1)
         self.e2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
-        self.out = nn.Linear(self.flat, 1)
+
+        self.el = nn.Linear(self.flattened_size, self.latent_dim)
+
+        self.q = nn.Linear(self.latent_dim,self.latent_dim)
+        self.k = nn.Linear(self.latent_dim,self.latent_dim)
+        self.v = nn.Linear(self.latent_dim,self.latent_dim)
+
+        self.dl = nn.Linear(self.latent_dim, self.flattened_size)
+
+        self.d1 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1)
+        self.d2 = nn.ConvTranspose2d(64, input_channels, kernel_size=3, stride=2, padding=1, output_padding=1)
 
     def __call__(self, x: Tensor) -> Tensor:
-        x = self.e1(x).relu()
-        x = self.e2(x).relu()
-        x = x.reshape(x.shape[0], -1)
-        return self.out(x)#.sigmoid()
+        y, shape = self.encode(x)
+        z = self.atten(y)
+        return self.decode(z, shape)
+
+    def encode(self, x: Tensor):
+        x = self.e1(x).leakyrelu()
+        x = self.e2(x).leakyrelu()
+        b, c, h, w = x.shape
+
+        flattened_size = c * h * w
+
+
+        x = x.reshape(shape=(b, flattened_size))
+        z = self.el(x)
+        return z, (c, h, w)
+
+    def atten(self, x: Tensor):
+        q = self.q(x).relu()
+        k = self.k(x).relu()
+        v = self.v(x).relu()
+        return q.scaled_dot_product_attention(k,v)
+
+    def decode(self, z: Tensor, shape):
+        x = self.dl(z).leakyrelu()
+        x = x.reshape(shape=(-1, 128, self.h, self.w))
+        x = self.d1(x).leakyrelu()
+        x = self.d2(x).sigmoid()
+
+        # Crop or pad to match input size
+        out_h, out_w = x.shape[2], x.shape[3]
+        if out_h > self.height:
+            x = x[:, :, :self.height, :]
+        elif out_h < self.height:
+            pad_h = self.height - out_h
+            x = x.pad2d((0, 0, 0, pad_h))
+
+        if out_w > self.width:
+            x = x[:, :, :, :self.width]
+        elif out_w < self.width:
+            pad_w = self.width - out_w
+            x = x.pad2d((0, pad_w, 0, 0))
+
+        return x

train.py

@@ -1,106 +1,71 @@
-#!/usr/bin/env python
-# coding: utf-8
-import data
-import model as model
-import show
 import mlflow
 import numpy as np
-from tinygrad import nn,TinyJit,Tensor
+from tinygrad import Device,Tensor,nn,TinyJit
+import matplotlib.pyplot as plt
+import time
+import show
+from model import gen
 
+BATCH_SIZE = 16
+EPOCHS = 100
+LEARNING_RATE = 1e-5
+print(Device.DEFAULT)
+mdl = gen()
+opt = nn.optim.AdamW(nn.state.get_parameters(mdl), lr=LEARNING_RATE)
+volume = 0.1
+
+def spec_loss(pred, target, eps=1e-6):
+    # spectral convergence
+    sc = ((target - pred).square().sum()) ** 0.5 / ((target.square().sum()) ** 0.5 + eps)
+    # log magnitude difference
+    log_mag = ((target.abs() + eps).log() - (pred.abs() + eps).log()).abs().mean()
+    return sc + log_mag
+
+
+@TinyJit
+def step_gen(x):
+    Tensor.training = True
+    noise = Tensor.rand_like(x).tanh()
+    y = x+(noise*volume)
+    y = y.clamp(0,1)
+    loss = spec_loss(mdl(y),x)
+    opt.zero_grad()
+    loss.backward()
+    opt.step()
+    return loss.numpy()
+
+print("loading")
+x = np.load("data.npz")["arr_0"]
+#x= x[0:64]
+run_name = f"tinygrad_autoencoder_{int(time.time())}"
 mlflow.set_tracking_uri("http://127.0.0.1:5000")
-mlflow.start_run(experiment_id=804883409598823668)
-#hyper
-BACH_SIZE=32
-BATCH_SIZE=BACH_SIZE
-glr=2e-4
-dlr=1e-5
-epochs=100
+mlflow.start_run()
+mlflow.log_params({"batch_size": BATCH_SIZE, "epochs": EPOCHS, "lr": LEARNING_RATE, "data size":len(x)})
 
+show.logSpec(Tensor(x[0:1]).numpy()[0][0],"default")
 
-#dataset
-x = data.load()
-size=len(x)
-x_np = np.stack(x)
-x_np = np.expand_dims(x_np, axis=1)
-permutation = np.random.permutation(size)
-x_np = x_np[permutation]
-
-train = x_np[30:]
-test = x_np[0:30]
-
-print("Train:"+str(len(train)))
-print("Test:"+str(len(test)))
-
-
-#model
-gen = model.Gen()
-dif = model.Check()
-genOpt = nn.optim.AdamW(nn.state.get_parameters(gen), lr=glr)
-difOpt = nn.optim.AdamW(nn.state.get_parameters(dif), lr=dlr)
-
-
-#train
-
-@TinyJit
-def step_dis(x:Tensor):
-    Tensor.training = True
-    real = Tensor.ones((BATCH_SIZE,1))
-    fake = Tensor.zeros((BACH_SIZE,1))
-    noise = Tensor.randn(BACH_SIZE, gen.ld)
-    fake_data = gen(noise).detach()
-    fake_loss = dif(fake_data).binary_crossentropy_logits(fake)
-    real_loss = dif(x).binary_crossentropy_logits(real)
-    loss = (fake_loss + real_loss)/2
-    loss.backward()
-    difOpt.step()
-    return loss.numpy()
-
-@TinyJit
-def step_gen():
-    Tensor.training = True
-    real = Tensor.ones((BATCH_SIZE,1))
-    noise = Tensor.randn(BACH_SIZE, gen.ld)
-    fake_data = gen(noise).detach()
-    loss = dif(fake_data).binary_crossentropy_logits(real)
-    loss.backward()
-    genOpt.step()
-    return loss.numpy()
-
-
-eshape = (BACH_SIZE, 1, 128, 216)
-
-mlflow.log_param("generator_learning_rate", glr)
-mlflow.log_param("discim_learning_rate", dlr)
-mlflow.log_param("epochs", epochs)
-mlflow.log_param("train size", len(train))
-mlflow.log_param("test size", len(test))
-for e in range(0,epochs):
-    print(f"\n--- Starting Epoch {e} ---\n")
-    dl=0
-    gl=0
-
-    for i in range(0,size,BACH_SIZE):
-        tx=Tensor(train[i:i+BACH_SIZE])
+print("training")
+pl = 0
+eshape = (BATCH_SIZE, 1, 128, 431)
+for epoch in range(0,EPOCHS):
+    print(f"\n--- Starting Epoch {epoch} ---\n")
+    loss=0
+    for i in range(0,len(x),BATCH_SIZE):
+        tx=Tensor(x[i:i+BATCH_SIZE])
         if(tx.shape != eshape):
             continue
-        #steps
-        dl+=step_dis(tx)
-        gl+=step_gen()
+        loss += step_gen(tx)
 
-    dl /= (size/BACH_SIZE)
-    gl /= (size/BACH_SIZE)
-    if e%5==0:
-        noise = Tensor.randn(BACH_SIZE, gen.ld)
-        show.logSpec(gen(noise).numpy()[0][0],e)
-    #todo test on test data
-    mlflow.log_metric("gen_loss", gl, step=e)
-    mlflow.log_metric("dis_loss", dl, step=e)
-    print(f"loss of gen:{gl} dis:{dl}")
+    loss /= (len(x)/BATCH_SIZE)
+    if epoch%5==0:
+        noise = Tensor.rand_like(Tensor(x[0:1])).tanh()
+        y = Tensor(x[0:1]) + (noise*volume)
+        show.logSpec(mdl(y).numpy()[0][0],epoch)
+        if(pl - loss < 0.03 and epoch > 25):
+            show.logSpec(y.numpy()[0][0],f"volume_{volume}")
+            volume *= 2
+    pl = loss
 
-
-#save
-noise = Tensor.randn(BACH_SIZE, gen.ld)
-show.logSpec(gen(noise).numpy()[0][0],epochs)
-from tinygrad.nn.state import safe_save, get_state_dict
-safe_save(get_state_dict(gen),"music.safetensors")
-mlflow.log_artifact("music.safetensors")
+    mlflow.log_metric("volume", volume, step=epoch)
+    mlflow.log_metric("loss", loss, step=epoch)
+    print(f"loss of {loss}")