ghostofdivinity commited on
Commit
910b02d
·
1 Parent(s): bbd0842

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +8 -23
app.py CHANGED
@@ -1,27 +1,9 @@
1
  import gradio as gr
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- import os
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  import torch
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  import torchaudio
 
 
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  from torch import nn
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- from torch.utils.data import DataLoader, Dataset
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- from torchvision import transforms
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- import numpy as np
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- import IPython.display as ipd
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-
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- # Generate new kick drum samples
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- generator.eval()
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- with torch.no_grad():
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- for i in range(num_generated_samples):
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- noise = torch.randn(1, latent_dim).to(device)
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- generated_sample = generator(noise).squeeze().cpu()
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-
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- # Save the generated sample
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- output_filename = f"generated_kick_{i+1}.wav"
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- torchaudio.save(output_filename, generated_sample.unsqueeze(0), 16000)
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-
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- # Play the generated sample
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- print(f"Generated Sample {i+1}:")
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- display(ipd.Audio(output_filename))
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  # Load the saved generator model
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  class Generator(nn.Module):
@@ -39,11 +21,13 @@ class Generator(nn.Module):
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  def forward(self, x):
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  return self.generator(x)
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  latent_dim = 100
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  generator = Generator(latent_dim).to(device)
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- generator_model_path = "generator_model.pkl"
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- generator.load_state_dict(torch.load(generator_model_path))
 
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  def generate_kick_drums():
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  # Define the number of samples you want to generate
@@ -65,10 +49,11 @@ def generate_kick_drums():
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  return tuple(output_files)
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  def gradio_interface():
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  generate_button = gr.Interface(fn=generate_kick_drums,
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  inputs=None,
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- outputs=[gr.Audio(type='filepath', label=f"generated_kick_{i+1}") for i in range(3)],
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  live=True)
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  generate_button.launch(debug=True)
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  import gradio as gr
 
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  import torch
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  import torchaudio
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+ from google.colab import drive
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+ drive.mount('/content/drive')
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  from torch import nn
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Load the saved generator model
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  class Generator(nn.Module):
 
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  def forward(self, x):
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  return self.generator(x)
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+
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  latent_dim = 100
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  generator = Generator(latent_dim).to(device)
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+ generator_model_path = '/content/drive/MyDrive/generator_model.pkl'
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+ generator.load_state_dict(torch.load(generator_model_path, map_location=device))
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+
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  def generate_kick_drums():
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  # Define the number of samples you want to generate
 
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  return tuple(output_files)
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+ # Define Gradio interface
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  def gradio_interface():
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  generate_button = gr.Interface(fn=generate_kick_drums,
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  inputs=None,
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+ outputs=[gr.Audio(type='filepath', label=f"generated_kick_{i}") for i in range(3)],
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  live=True)
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  generate_button.launch(debug=True)
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