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import joblib |
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import numpy as np |
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from generate_synthetic_data_online import generate_synth_dataset_log_512, generate_synth_dataset_log_muted_512 |
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from tools import show_spc, spc_to_VAE_input, VAE_out_put_to_spc, np_log10 |
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import torch.utils.data as data |
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class Data_cache(): |
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"""This is a class that stores synthetic data.""" |
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def __init__(self, synthetic_data, external_sources): |
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self.n_synthetic = np.shape(synthetic_data)[0] |
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self.synthetic_data = synthetic_data.astype(np.float32) |
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self.external_sources = external_sources.astype(np.float32) |
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self.epsilon = 1e-20 |
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def get_all_data(self): |
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return np.vstack([self.synthetic_data, self.external_sources]) |
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def refresh(self): |
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self.synthetic_data = generate_synth_dataset(self.n_synthetic, mute=True) |
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def get_data_loader(self, shuffle=True, BATCH_SIZE=8, new_way=False): |
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all_data = self.get_all_data() |
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our_data = [] |
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for i in range(len(all_data)): |
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if new_way: |
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spectrogram = VAE_out_put_to_spc(np.reshape(all_data[i], (1, 512, 256))) |
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log_spectrogram = np.log10(spectrogram + self.epsilon) |
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our_data.append(log_spectrogram) |
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else: |
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our_data.append(np.reshape(all_data[i], (1, 512, 256))) |
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iterator = data.DataLoader(our_data, shuffle=shuffle, batch_size=BATCH_SIZE) |
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return iterator |
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def generate_synth_dataset(n_synthetic, mute=False): |
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"""Preprocessing for synthetic data""" |
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n_synthetic_sample = n_synthetic |
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if mute: |
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Input0 = generate_synth_dataset_log_muted_512(n_synthetic_sample) |
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else: |
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Input0 = generate_synth_dataset_log_512(n_synthetic_sample) |
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Input0 = spc_to_VAE_input(Input0) |
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Input0 = Input0.reshape(Input0.shape[0], Input0.shape[1], Input0.shape[2], 1) |
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return Input0 |
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def read_data(data_path): |
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"""Read external sources""" |
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data = np.array(joblib.load(data_path)) |
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data = spc_to_VAE_input(data) |
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data = data.reshape(data.shape[0], data.shape[1], data.shape[2], 1) |
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return data |
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def load_data(n_synthetic): |
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"""Generate the hybrid dataset.""" |
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Input_synthetic = generate_synth_dataset(n_synthetic) |
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Input_AU = read_data("./data/external_data/ARTURIA_data") |
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print("ARTURIA dataset loaded.") |
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Input_NSynth = read_data("./data/external_data/NSynth_data") |
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print("NSynth dataset loaded.") |
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Input_SF = read_data("./data/external_data/soundfonts_data") |
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Input_SF_256 = np.zeros((337, 512, 256, 1)) |
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Input_SF_256[:,:,:251,:] += Input_SF |
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Input_SF =Input_SF_256 |
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print("SoundFonts dataset loaded.") |
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Input_google = read_data("./data/external_data/WaveNet_samples") |
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Input_external = np.vstack([Input_AU, Input_NSynth, Input_SF, Input_google]) |
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data_cache = Data_cache(Input_synthetic, Input_external) |
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print(f"Data loaded, data shape: {np.shape(data_cache.get_all_data())}") |
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return data_cache |
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def show_data(dataset_name, n_sample=3, index=-1, new_way=False): |
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"""Show and return a certain dataset. |
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Parameters |
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---------- |
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dataset_name: String |
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Name of the dataset to show. |
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n_samples: int |
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Number of samples to show. |
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index: int |
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Setting 'index' larger equal 0 shows the 'index'-th sample in the desired dataset. |
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Returns |
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------- |
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np.ndarray: |
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The showed dataset. |
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""" |
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if dataset_name == "ARTURIA": |
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data = read_data("./data/external_data/ARTURIA_data") |
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elif dataset_name == "NSynth": |
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data = read_data("./data/external_data/NSynth_data") |
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elif dataset_name == "SoundFonts": |
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data = read_data("./data/external_data/soundfonts_data") |
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elif dataset_name == "Synthetic": |
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data = generate_synth_dataset(int(n_sample * 3)) |
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else: |
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print("Example command: \"!python thesis_main.py show_data -s [ARTURIA, NSynth, SoundFonts, Synthetic] -n 5\"") |
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return |
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if index >= 0: |
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show_spc(VAE_out_put_to_spc(data[index])) |
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else: |
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for i in range(n_sample): |
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index = np.random.randint(0,len(data)) |
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print(index) |
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show_spc(VAE_out_put_to_spc(data[index])) |
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return data |
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def show_data(tensor_batch, index=-1, new_way=False): |
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if index < 0: |
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index = np.random.randint(0, tensor_batch.shape[0]) |
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if new_way: |
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sample = tensor_batch[index].detach().numpy() |
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spectrogram = 10.0 ** sample |
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print(f"The {index}-th sample:") |
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show_spc(spectrogram) |
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else: |
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sample = tensor_batch[index].detach().numpy() |
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show_spc(VAE_out_put_to_spc(sample)) |
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