make usageparams a class

.gitignore

@@ -165,7 +165,7 @@ cython_debug/
 data/
 .vscode/
 .DS_Store
-outputs/training_progress
+outputs/spectrogram_images
 outputs/generated_audio.wav
 outputs/generated_audio_[0-9]*
 test.wav

paper/paper.md

@@ -6,6 +6,8 @@ Continuation of UCLA COSMOS 2024 Research
 
 ## 1. Abstract
 
+Existing convolutional aproaches to audio generation often are limited to producing low-fidelity, single-channel, monophonic audio, while demanding significant computational resources for both training and inference. To address these challenges, this work introduces StereoSampleGAN, a novel audio generation architecture that combines a Deep Convolutional Wasserstein GAN (WGAN), attention mechanisms, and loss optimization techniques. StereoSampleGAN allows high-fidelity, stereo audio generation for audio samples while being remaining computationally efficient. Training on three distinct sample datasets with varying spectral overlap–two of kick drums and one of tonal one shots–StereoSampleGAN demonstrates promising results in generating high quality simple stereo sounds. However, the model displays notable limitations when generating audio structures with greater amounts of spectral variation, indicating areas for further improvement.
+
 ## 2. Introduction
 
 ## 3. Data Manipulation

src/data_processing/audio_processing_validation.py

@@ -4,21 +4,24 @@
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 import random
-from usage_params import compiled_data_path, training_sample_length
+from usage_params import UsageParams
 from utils.signal_helpers import (
     stft_and_istft,
 )
 
+# Initialize sample selection
+params = UsageParams()
+
 
 def choose_random_sample():
     audio_files = [
         f
-        for f in os.listdir(compiled_data_path)
-        if os.path.isfile(os.path.join(compiled_data_path, f))
+        for f in os.listdir(params.compiled_data_path)
+        if os.path.isfile(os.path.join(params.compiled_data_path, f))
     ]
     if audio_files:
         sample_name = random.choice(audio_files)
-        sample_path = os.path.join(compiled_data_path, sample_name)
+        sample_path = os.path.join(params.compiled_data_path, sample_name)
         return sample_path, sample_name
     else:
         return None, None
@@ -27,4 +30,4 @@ def choose_random_sample():
 # Analyze fourier transform audio degradation
 sample_path, sample_name = choose_random_sample()
 
-stft_and_istft(sample_path, "test", training_sample_length)
+stft_and_istft(sample_path, "test", params.training_sample_length)

src/data_processing/encode_audio_data.py

@@ -8,18 +8,19 @@
     load_loudness_data,
 )
 from utils.signal_helpers import encode_sample_directory
-from usage_params import training_audio_dir, compiled_data_path
+from usage_params import UsageParams
 
 # Encode audio samples
+params = UsageParams()
 if len(sys.argv) > 1:
     visualize = sys.argv[1].lower() == "visualize"
 else:
     visualize = False
 
 
-encode_sample_directory(training_audio_dir, compiled_data_path, visualize)
+encode_sample_directory(params.training_audio_dir, params.compiled_data_path, visualize)
 
 real_data = load_loudness_data(
-    compiled_data_path
+    params.compiled_data_path
 )  # datapts, channels, frames, freq bins
-print(f"{training_audio_dir} data shape: {str(real_data.shape)}")
+print(f"{params.training_audio_dir} data shape: {str(real_data.shape)}")

src/generate.py

@@ -6,4 +6,4 @@
 
 
 # Generate based on usage_params
-generate_audio(model_to_generate_with, training_sample_length)
+generate_audio(model_to_generate_with, training_sample_length, True)

src/stereo_sample_gan.py

@@ -10,20 +10,23 @@
     load_loudness_data,
 )
 
-from usage_params import compiled_data_path
+from usage_params import UsageParams
 
 # Constants
 LR_G = 0.003
 LR_C = 0.004
 
 # Load data
-all_spectrograms = load_loudness_data(compiled_data_path)
+params = UsageParams()
+all_spectrograms = load_loudness_data(params.compiled_data_path)
 all_spectrograms = torch.FloatTensor(all_spectrograms)
+
 train_size = int(0.8 * len(all_spectrograms))
 val_size = len(all_spectrograms) - train_size
 train_dataset, val_dataset = random_split(
     TensorDataset(all_spectrograms), [train_size, val_size]
 )
+
 train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
 val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)
 

src/usage_params.py

@@ -1,16 +1,22 @@
 # Main params
-audio_generation_count = 2  # Audio examples to generate
+class UsageParams:
+    def __init__(self):
+        self.audio_generation_count = 2  # Audio examples to generate
 
-# Training params
-training_sample_length = 1.5  # seconds
-outputs_dir = "outputs"  # Where to save your generated audio & model
+        # Training params
+        self.training_sample_length = 1.5  # seconds
+        self.outputs_dir = "outputs"  # Where to save your generated audio & model
 
-model_save_name = "StereoSampleGAN-InstrumentOneShot"  # What to name your model save
-training_audio_dir = "data/one_shots"  # Your training data path
-compiled_data_path = "data/compiled_data.npy"  # Your compiled data/output path
-model_save_path = f"{outputs_dir}/{model_save_name}.pth"
+        self.model_save_name = (
+            "StereoSampleGAN-InstrumentOneShot"  # What to name your model save
+        )
+        self.training_audio_dir = "data/one_shots"  # Your training data path
+        self.compiled_data_path = (
+            "data/compiled_data.npy"  # Your compiled data/output path
+        )
+        self.model_save_path = f"{self.outputs_dir}/{self.model_save_name}.pth"
 
-# Generating audio
-model_to_generate_with = model_save_path  # Generation model path
-generated_audio_name = "generated_audio"  # Output file name
-visualize_generated = True  # Show generated audio spectrograms
+        # Generating audio
+        self.model_to_generate_with = self.model_save_path  # Generation model path
+        self.generated_audio_name = "generated_audio"  # Output file name
+        self.visualize_generated = True  # Show generated audio spectrograms

src/utils/file_helpers.py

@@ -3,9 +3,10 @@
 import torch
 import soundfile as sf
 
-from usage_params import model_save_path
+from usage_params import UsageParams
 
 # Constants
+params = UsageParams()
 GLOBAL_SR = 44100
 
 
@@ -26,9 +27,9 @@ def save_model(model):
     # Save model
     torch.save(
         model.state_dict(),
-        model_save_path,
+        params.model_save_path,
     )
-    print(f"Model saved at {model_save_path}")
+    print(f"Model saved at {params.model_save_path}")
 
 
 def get_device():

src/utils/generation_helpers.py

@@ -1,18 +1,16 @@
 import os
 import torch
 from architecture import Generator, LATENT_DIM
-from usage_params import (
-    outputs_dir,
-    generated_audio_name,
-    audio_generation_count,
-    visualize_generated,
-)
+from usage_params import UsageParams
 from utils.file_helpers import get_device, save_audio
 from utils.signal_helpers import audio_to_norm_db, graph_spectrogram, norm_db_to_audio
 
 
 # Generation function
-def generate_audio(generation_model_save, len_audio_in):
+params = UsageParams()
+
+
+def generate_audio(generation_model_save, len_audio_in, save_images=False):
     device = get_device()
 
     generator = Generator()
@@ -26,24 +24,28 @@ def generate_audio(generation_model_save, len_audio_in):
     generator.eval()
 
     # Generate audio
-    z = torch.randn(audio_generation_count, LATENT_DIM, 1, 1)
+    z = torch.randn(params.audio_generation_count, LATENT_DIM, 1, 1)
     with torch.no_grad():
         generated_output = generator(z)
 
     generated_output = generated_output.squeeze().numpy()
     print("Generated output shape:", generated_output.shape)
 
     # Visualize and save audio
-    for i in range(audio_generation_count):
+    for i in range(params.audio_generation_count):
         current_sample = generated_output[i]
 
         audio_info = norm_db_to_audio(current_sample, len_audio_in)
         audio_save_path = os.path.join(
-            outputs_dir, f"{generated_audio_name}_{i + 1}.wav"
+            params.outputs_dir, f"{params.generated_audio_name}_{i + 1}.wav"
         )
 
         save_audio(audio_save_path, audio_info)
 
-        if visualize_generated is True:
+        if params.visualize_generated is True:
             vis_signal_after_istft = audio_to_norm_db(audio_info)
-            graph_spectrogram(vis_signal_after_istft, "generated audio (after istft)")
+            graph_spectrogram(
+                vis_signal_after_istft,
+                f"{params.generated_audio_name}_{i + 1}",
+                save_images,
+            )

src/utils/signal_helpers.py

@@ -10,10 +10,7 @@
 import plotly.subplots as sp
 import scipy
 
-from usage_params import (
-    training_sample_length,
-    outputs_dir,
-)
+from usage_params import UsageParams
 from utils.file_helpers import (
     GLOBAL_SR,
     delete_DSStore,
@@ -26,8 +23,9 @@
 DATA_SHAPE = 256
 
 # STFT Helpers
+params = UsageParams()
 GLOBAL_WIN = 510
-GLOBAL_HOP = int(training_sample_length * GLOBAL_SR) // (DATA_SHAPE - 1)
+GLOBAL_HOP = int(params.training_sample_length * GLOBAL_SR) // (DATA_SHAPE - 1)
 window = scipy.signal.windows.kaiser(GLOBAL_WIN, beta=12)
 
 
@@ -126,7 +124,9 @@ def load_audio(path):
     y, sr = librosa.load(path, sr=GLOBAL_SR, mono=False)
     if y.ndim == 1:
         y = np.stack((y, y), axis=0)
-    y = librosa.util.fix_length(y, size=int(training_sample_length * GLOBAL_SR), axis=1)
+    y = librosa.util.fix_length(
+        y, size=int(params.training_sample_length * GLOBAL_SR), axis=1
+    )
     return y
 
 
@@ -165,7 +165,7 @@ def scale_data_to_range(data, new_min, new_max):
 
 
 # Validation helpers
-def graph_spectrogram(audio_data, sample_name, save=False):
+def graph_spectrogram(audio_data, sample_name, save_images=False):
     fig = sp.make_subplots(rows=2, cols=1)
 
     for i in range(2):
@@ -197,19 +197,19 @@ def graph_spectrogram(audio_data, sample_name, save=False):
     )
     fig.update_layout(title_text=f"{sample_name}")
 
-    if save is False:
+    if save_images is False:
         fig.show()
     else:
-        fig.write_image(f"outputs/training_progress/{sample_name}")
+        fig.write_image(f"outputs/spectrogram_images/{sample_name}.png")
 
 
 def generate_sine_impulses(num_impulses=1, outPath="model"):
     amplitude = 1
     for i in range(num_impulses):
-        t = np.arange(0, training_sample_length, 1 / GLOBAL_SR)
+        t = np.arange(0, params.training_sample_length, 1 / GLOBAL_SR)
         freq = np.random.uniform(0, 20000)
         audio_wave = amplitude * np.sin(2 * np.pi * freq * t)
-        num_samples = int(training_sample_length * GLOBAL_SR)
+        num_samples = int(params.training_sample_length * GLOBAL_SR)
         audio_signal = np.zeros(num_samples)
 
         audio_wave = audio_wave[:num_samples]
@@ -240,7 +240,7 @@ def stft_and_istft(path, file_name, len_audio_in):
         vis_istft.shape,
     )
 
-    save_path = os.path.join(outputs_dir, f"{file_name}.wav")
+    save_path = os.path.join(params.outputs_dir, f"{file_name}.wav")
     save_audio(save_path, istft)
 
     graph_spectrogram(stft, "stft")