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import gradio as gr
import torch
import soundfile as sf
import numpy as np
import yaml
from inference import MasteringStyleTransfer
from utils import download_youtube_audio
from config import args
import pyloudnorm as pyln
import tempfile
import os
import pandas as pd
mastering_transfer = MasteringStyleTransfer(args)
def denormalize_audio(audio, dtype=np.int16):
"""
Denormalize the audio from the range [-1, 1] to the full range of the specified dtype.
"""
if dtype == np.int16:
audio = np.clip(audio, -1, 1) # Ensure the input is in the range [-1, 1]
return (audio * 32767).astype(np.int16)
elif dtype == np.float32:
return audio.astype(np.float32)
else:
raise ValueError("Unsupported dtype. Use np.int16 or np.float32.")
def loudness_normalize(audio, sample_rate, target_loudness=-12.0):
# Ensure audio is float32
if audio.dtype != np.float32:
audio = audio.astype(np.float32)
# If audio is mono, reshape to (samples, 1)
if audio.ndim == 1:
audio = audio.reshape(-1, 1)
meter = pyln.Meter(sample_rate) # create BS.1770 meter
loudness = meter.integrated_loudness(audio)
loudness_normalized_audio = pyln.normalize.loudness(audio, loudness, target_loudness)
return loudness_normalized_audio
def process_youtube_url(url):
try:
audio, sr = download_youtube_audio(url)
return (sr, audio)
except Exception as e:
return None, f"Error processing YouTube URL: {str(e)}"
def process_audio_with_youtube(input_audio, input_youtube_url, reference_audio, reference_youtube_url):
if input_youtube_url:
input_audio, error = process_youtube_url(input_youtube_url)
if error:
return None, None, error
if reference_youtube_url:
reference_audio, error = process_youtube_url(reference_youtube_url)
if error:
return None, None, error
if input_audio is None or reference_audio is None:
return None, None, "Both input and reference audio are required."
return process_audio(input_audio, reference_audio)
def process_audio(input_audio, reference_audio):
output_audio, predicted_params, sr = mastering_transfer.process_audio(
input_audio, reference_audio, reference_audio
)
param_output = mastering_transfer.get_param_output_string(predicted_params)
# Convert output_audio to numpy array if it's a tensor
if isinstance(output_audio, torch.Tensor):
output_audio = output_audio.cpu().numpy()
if output_audio.ndim == 1:
output_audio = output_audio.reshape(-1, 1)
elif output_audio.ndim > 2:
output_audio = output_audio.squeeze()
# Ensure the audio is in the correct shape (samples, channels)
if output_audio.shape[1] > output_audio.shape[0]:
output_audio = output_audio.transpose(1,0)
print(output_audio.shape)
print(f"sr: {sr}")
# Normalize output audio
output_audio = loudness_normalize(output_audio, sr)
# Denormalize the audio to int16
output_audio = denormalize_audio(output_audio, dtype=np.int16)
return (sr, output_audio), param_output
def perform_ito(input_audio, reference_audio, ito_reference_audio, num_steps, optimizer, learning_rate, af_weights):
if ito_reference_audio is None:
ito_reference_audio = reference_audio
af_weights = [float(w.strip()) for w in af_weights.split(',')]
ito_config = {
'optimizer': optimizer,
'learning_rate': learning_rate,
'num_steps': num_steps,
'af_weights': af_weights,
'sample_rate': args.sample_rate
}
input_tensor = mastering_transfer.preprocess_audio(input_audio, args.sample_rate)
reference_tensor = mastering_transfer.preprocess_audio(reference_audio, args.sample_rate)
ito_reference_tensor = mastering_transfer.preprocess_audio(ito_reference_audio, args.sample_rate)
initial_reference_feature = mastering_transfer.get_reference_embedding(reference_tensor)
ito_log = ""
loss_values = []
all_results = []
for log_entry, current_output, current_params, step, loss in mastering_transfer.inference_time_optimization(
input_tensor, ito_reference_tensor, ito_config, initial_reference_feature
):
ito_log += log_entry
ito_param_output = mastering_transfer.get_param_output_string(current_params)
loss_values.append({"step": int(step), "loss": loss})
# Convert current_output to numpy array if it's a tensor
if isinstance(current_output, torch.Tensor):
current_output = current_output.cpu().numpy()
if current_output.ndim == 1:
current_output = current_output.reshape(-1, 1)
elif current_output.ndim > 2:
current_output = current_output.squeeze()
# Ensure the audio is in the correct shape (samples, channels)
if current_output.shape[1] > current_output.shape[0]:
current_output = current_output.transpose(1,0)
# Loudness normalize output audio
current_output = loudness_normalize(current_output, args.sample_rate)
# Denormalize the audio to int16
current_output = denormalize_audio(current_output, dtype=np.int16)
all_results.append({
'step': step,
'audio': current_output,
'params': ito_param_output,
'log': log_entry,
'loss': loss
})
yield (args.sample_rate, current_output), ito_param_output, step, ito_log, pd.DataFrame(loss_values), all_results
def update_ito_output(all_results, selected_step):
print(all_results)
print(selected_step)
selected_result = all_results[selected_step - 1]
return (args.sample_rate, selected_result['audio']), selected_result['params'], selected_result['log']
""" APP display """
with gr.Blocks() as demo:
gr.Markdown("# ITO-Master: Inference Time Optimization for Mastering Style Transfer")
gr.Markdown("# Step 1: Mastering Style Transfer")
with gr.Tab("Upload Audio"):
with gr.Row():
input_audio = gr.Audio(label="Input Audio")
reference_audio = gr.Audio(label="Reference Audio")
process_button = gr.Button("Process Mastering Style Transfer")
with gr.Row():
output_audio = gr.Audio(label="Output Audio", type='numpy')
param_output = gr.Textbox(label="Predicted Parameters", lines=5)
process_button.click(
process_audio,
inputs=[input_audio, reference_audio],
outputs=[output_audio, param_output]
)
with gr.Tab("YouTube Audio"):
with gr.Row():
input_youtube_url = gr.Textbox(label="Input YouTube URL")
reference_youtube_url = gr.Textbox(label="Reference YouTube URL")
with gr.Row():
input_audio_yt = gr.Audio(label="Input Audio (Do not put when using YouTube URL)")
reference_audio_yt = gr.Audio(label="Reference Audio (Do not put when using YouTube URL)")
process_button_yt = gr.Button("Process Mastering Style Transfer")
with gr.Row():
output_audio_yt = gr.Audio(label="Output Audio", type='numpy')
param_output_yt = gr.Textbox(label="Predicted Parameters", lines=5)
error_message_yt = gr.Textbox(label="Error Message", visible=False)
def process_and_handle_errors(input_audio, input_youtube_url, reference_audio, reference_youtube_url):
result = process_audio_with_youtube(input_audio, input_youtube_url, reference_audio, reference_youtube_url)
if len(result) == 3 and isinstance(result[2], str): # Error occurred
return None, None, gr.update(visible=True, value=result[2])
return result[0], result[1], gr.update(visible=False, value="")
process_button_yt.click(
process_and_handle_errors,
inputs=[input_audio_yt, input_youtube_url, reference_audio_yt, reference_youtube_url],
outputs=[output_audio_yt, param_output_yt, error_message_yt]
)
gr.Markdown("## Step 2: Inference Time Optimization (ITO)")
with gr.Row():
ito_reference_audio = gr.Audio(label="ITO Reference Audio (optional)")
with gr.Column():
num_steps = gr.Slider(minimum=1, maximum=100, value=10, step=1, label="Number of Steps")
optimizer = gr.Dropdown(["Adam", "RAdam", "SGD"], value="RAdam", label="Optimizer")
learning_rate = gr.Slider(minimum=0.0001, maximum=0.1, value=0.001, step=0.0001, label="Learning Rate")
af_weights = gr.Textbox(label="AudioFeatureLoss Weights (comma-separated)", value="0.1,0.001,1.0,1.0,0.1")
ito_button = gr.Button("Perform ITO")
with gr.Row():
with gr.Column():
ito_output_audio = gr.Audio(label="ITO Output Audio")
ito_param_output = gr.Textbox(label="ITO Predicted Parameters", lines=15)
ito_step_slider = gr.Slider(minimum=1, maximum=100, step=1, label="ITO Step", interactive=True)
with gr.Column():
ito_loss_plot = gr.LinePlot(
x="step",
y="loss",
title="ITO Loss Curve",
x_title="Step",
y_title="Loss",
height=300,
width=600,
)
ito_log = gr.Textbox(label="ITO Log", lines=10)
all_results = gr.State([])
min_loss_step = gr.State(0)
def on_ito_complete(results, min_step, loss_df):
all_results.value = results
min_loss_step.value = min_step
return loss_df, gr.update(maximum=len(results), value=min_step+1)
ito_button.click(
perform_ito,
inputs=[input_audio, reference_audio, ito_reference_audio, num_steps, optimizer, learning_rate, af_weights],
outputs=[ito_output_audio, ito_param_output, ito_step_slider, ito_log, ito_loss_plot, all_results]
).then(
update_ito_output,
inputs=[all_results, ito_step_slider],
outputs=[ito_output_audio, ito_param_output, ito_log]
)
ito_step_slider.change(
update_ito_output,
inputs=[all_results, ito_step_slider],
outputs=[ito_output_audio, ito_param_output, ito_log]
)
demo.launch()
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