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import gradio as gr
import numpy as np
from spleeter.separator import Separator
import soundfile as sf
import base64
# Spleeter separation
def spleeter_separate(audio):
separator = Separator('spleeter:5stems')
prediction = separator.separate(audio)
return prediction['vocals'], prediction['accompaniment'], prediction['bass'], prediction['drums'], prediction['other']
# Function to control the volume of each stem
def adjust_volume(stems, volumes):
adjusted_stems = []
for stem, volume in zip(stems, volumes):
adjusted_stem = stem * volume
adjusted_stems.append(adjusted_stem)
return adjusted_stems
# Function to handle the separation and volume adjustment
def process_audio(audio, volumes):
stems = spleeter_separate(audio)
adjusted_stems = adjust_volume(stems, volumes)
reconstructed_audio = sum(adjusted_stems)
return reconstructed_audio.astype(np.float32)
# Gradio interface
def separate_audio(audio, vocals, accompaniment, bass, drums, other):
audio = np.frombuffer(base64.b64decode(audio), dtype=np.float32)
volumes = [vocals, accompaniment, bass, drums, other]
reconstructed_audio = process_audio(audio, volumes)
return base64.b64encode(reconstructed_audio.tobytes()).decode()
iface = gr.Interface(
fn=separate_audio,
inputs=[
gr.inputs.Audio(label="Audio file"),
gr.inputs.Slider(0.0, 1.0, step=0.1, label="Vocals"),
gr.inputs.Slider(0.0, 1.0, step=0.1, label="Accompaniment"),
gr.inputs.Slider(0.0, 1.0, step=0.1, label="Bass"),
gr.inputs.Slider(0.0, 1.0, step=0.1, label="Drums"),
gr.inputs.Slider(0.0, 1.0, step=0.1, label="Other")
],
outputs=gr.outputs.Audio(label="Separated Audio", type="numpy"),
title="Song Stem Separation",
description="Isolate vocals, accompaniment, bass, and drums of any song using the Spleeter model."
)
iface.launch()
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