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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
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_audio(input_audio, reference_audio):
output_audio, predicted_params, _, _, _, sr = mastering_transfer.process_audio(
input_audio, reference_audio, reference_audio, {}, False
)
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()
# Normalize output audio
output_audio = loudness_normalize(output_audio, sr)
# Denormalize the audio to int16
output_audio = denormalize_audio(output_audio, dtype=np.int16)
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(param_output)
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
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 = ""
for log_entry, current_output, current_params, step 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)
# Convert current_output to numpy array if it's a tensor
if isinstance(current_output, torch.Tensor):
current_output = current_output.detach().cpu().numpy()
# 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)
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)
yield (args.sample_rate, current_output), ito_param_output, step, ito_log
with gr.Blocks() as demo:
gr.Markdown("# Mastering Style Transfer Demo")
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=10)
process_button.click(
process_audio,
inputs=[input_audio, reference_audio],
outputs=[output_audio, param_output]
)
gr.Markdown("## Inference Time Optimization (ITO)")
with gr.Row():
with gr.Column(scale=2):
ito_reference_audio = gr.Audio(label="ITO Reference Audio (optional)")
num_steps = gr.Slider(minimum=1, maximum=1000, value=100, 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")
ito_output_audio = gr.Audio(label="ITO Output Audio")
ito_param_output = gr.Textbox(label="ITO Predicted Parameters", lines=10)
ito_steps_taken = gr.Number(label="ITO Steps Taken")
with gr.Column(scale=1):
ito_log = gr.Textbox(label="ITO Log", lines=30)
def run_ito(input_audio, reference_audio, ito_reference_audio, num_steps, optimizer, learning_rate, af_weights):
af_weights = [float(w.strip()) for w in af_weights.split(',')]
ito_generator = perform_ito(
input_audio, reference_audio, ito_reference_audio, num_steps, optimizer, learning_rate, af_weights
)
# Initialize variables to store the final results
final_audio = None
final_params = None
final_steps = 0
final_log = ""
# Iterate through the generator to get the final results
for audio, params, steps, log in ito_generator:
final_audio = audio
final_params = params
final_steps = steps
final_log = log
return final_audio, final_params, final_steps, final_log
ito_button.click(
run_ito,
inputs=[input_audio, reference_audio, ito_reference_audio, num_steps, optimizer, learning_rate, af_weights],
outputs=[ito_output_audio, ito_param_output, ito_steps_taken, ito_log]
)
demo.launch()
# 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
# mastering_transfer = MasteringStyleTransfer(args)
# def process_audio(input_audio, reference_audio, perform_ito, ito_reference_audio=None):
# # Process the audio files
# output_audio, predicted_params, ito_output_audio, ito_predicted_params, ito_log, sr = mastering_transfer.process_audio(
# input_audio, reference_audio, ito_reference_audio if ito_reference_audio else reference_audio, {}, perform_ito
# )
# # Generate parameter output strings
# param_output = mastering_transfer.get_param_output_string(predicted_params)
# ito_param_output = mastering_transfer.get_param_output_string(ito_predicted_params) if ito_predicted_params is not None else "ITO not performed"
# # Generate top 10 differences if ITO was performed
# top_10_diff = mastering_transfer.get_top_10_diff_string(predicted_params, ito_predicted_params) if ito_predicted_params is not None else "ITO not performed"
# return "output_mastered.wav", "ito_output_mastered.wav" if ito_output_audio is not None else None, param_output, ito_param_output, top_10_diff, ito_log
# def process_with_ito(input_audio, reference_audio, perform_ito, use_same_reference, ito_reference_audio):
# ito_ref = reference_audio if use_same_reference else ito_reference_audio
# return process_audio(input_audio, reference_audio, perform_ito, ito_ref)
# def process_youtube_with_ito(input_url, reference_url, perform_ito, use_same_reference, ito_reference_url):
# input_audio = download_youtube_audio(input_url)
# reference_audio = download_youtube_audio(reference_url)
# ito_ref = reference_audio if use_same_reference else download_youtube_audio(ito_reference_url)
# output_audio, predicted_params, ito_output_audio, ito_predicted_params, ito_log, sr = mastering_transfer.process_audio(
# input_audio, reference_audio, ito_ref, {}, perform_ito, log_ito=True
# )
# param_output = mastering_transfer.get_param_output_string(predicted_params)
# ito_param_output = mastering_transfer.get_param_output_string(ito_predicted_params) if ito_predicted_params is not None else "ITO not performed"
# top_10_diff = mastering_transfer.get_top_10_diff_string(predicted_params, ito_predicted_params) if ito_predicted_params is not None else "ITO not performed"
# return "output_mastered_yt.wav", "ito_output_mastered_yt.wav" if ito_output_audio is not None else None, param_output, ito_param_output, top_10_diff, ito_log
# with gr.Blocks() as demo:
# gr.Markdown("# Mastering Style Transfer Demo")
# with gr.Tab("Upload Audio"):
# input_audio = gr.Audio(label="Input Audio")
# reference_audio = gr.Audio(label="Reference Audio")
# perform_ito = gr.Checkbox(label="Perform ITO")
# with gr.Column(visible=False) as ito_options:
# use_same_reference = gr.Checkbox(label="Use same reference audio for ITO", value=True)
# ito_reference_audio = gr.Audio(label="ITO Reference Audio", visible=False)
# def update_ito_options(perform_ito):
# return gr.Column.update(visible=perform_ito)
# def update_ito_reference(use_same):
# return gr.Audio.update(visible=not use_same)
# perform_ito.change(fn=update_ito_options, inputs=perform_ito, outputs=ito_options)
# use_same_reference.change(fn=update_ito_reference, inputs=use_same_reference, outputs=ito_reference_audio)
# submit_button = gr.Button("Process")
# output_audio = gr.Audio(label="Output Audio")
# ito_output_audio = gr.Audio(label="ITO Output Audio")
# param_output = gr.Textbox(label="Predicted Parameters", lines=10)
# ito_param_output = gr.Textbox(label="ITO Predicted Parameters", lines=10)
# top_10_diff = gr.Textbox(label="Top 10 Parameter Differences", lines=10)
# ito_log = gr.Textbox(label="ITO Log", lines=20)
# submit_button.click(
# process_with_ito,
# inputs=[input_audio, reference_audio, perform_ito, use_same_reference, ito_reference_audio],
# outputs=[output_audio, ito_output_audio, param_output, ito_param_output, top_10_diff, ito_log]
# )
# with gr.Tab("YouTube URLs"):
# input_url = gr.Textbox(label="Input YouTube URL")
# reference_url = gr.Textbox(label="Reference YouTube URL")
# perform_ito_yt = gr.Checkbox(label="Perform ITO")
# with gr.Column(visible=False) as ito_options_yt:
# use_same_reference_yt = gr.Checkbox(label="Use same reference audio for ITO", value=True)
# ito_reference_url = gr.Textbox(label="ITO Reference YouTube URL", visible=False)
# def update_ito_options_yt(perform_ito):
# return gr.Column.update(visible=perform_ito)
# def update_ito_reference_yt(use_same):
# return gr.Textbox.update(visible=not use_same)
# perform_ito_yt.change(fn=update_ito_options_yt, inputs=perform_ito_yt, outputs=ito_options_yt)
# use_same_reference_yt.change(fn=update_ito_reference_yt, inputs=use_same_reference_yt, outputs=ito_reference_url)
# submit_button_yt = gr.Button("Process")
# output_audio_yt = gr.Audio(label="Output Audio")
# ito_output_audio_yt = gr.Audio(label="ITO Output Audio")
# param_output_yt = gr.Textbox(label="Predicted Parameters", lines=10)
# ito_param_output_yt = gr.Textbox(label="ITO Predicted Parameters", lines=10)
# top_10_diff_yt = gr.Textbox(label="Top 10 Parameter Differences", lines=10)
# ito_log_yt = gr.Textbox(label="ITO Log", lines=20)
# submit_button_yt.click(
# process_youtube_with_ito,
# inputs=[input_url, reference_url, perform_ito_yt, use_same_reference_yt, ito_reference_url],
# outputs=[output_audio_yt, ito_output_audio_yt, param_output_yt, ito_param_output_yt, top_10_diff_yt, ito_log_yt]
# )
# demo.launch() |