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d1a97be a0afde1 d1a97be a0afde1 d1a97be a0afde1 d1a97be | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import os
import shutil
import subprocess
import tempfile
from pathlib import Path
import gradio as gr
import torchaudio
import torch
import soundfile as sf
# ================================
# CONFIG
# ================================
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MODEL = "htdemucs" # best quality vocal separation
def resave(src, dst):
wav, sr = torchaudio.load(str(src))
data = wav.numpy().T
sf.write(dst, data, sr)
# ================================
# SPLIT FUNCTION
# ================================
def split_vocals(audio_file):
if audio_file is None:
return None, None, "❌ Please upload an audio file."
work_dir = tempfile.mkdtemp(prefix="demucs_")
try:
print(f"[Demucs] Input: {audio_file}")
print(f"[Demucs] Device: {DEVICE}")
# Run Demucs
result = subprocess.run(
[
"python", "-m", "demucs",
"--two-stems", "vocals", # only split vocals vs everything else
"-n", MODEL,
"-o", work_dir,
audio_file,
],
capture_output=True,
text=True,
)
if result.returncode != 0:
error = result.stderr[-1000:] # last 1000 chars to avoid huge dumps
print(f"[Demucs] Error:\n{error}")
return None, None, f"❌ Demucs failed:\n{error}"
# Find output files
track_name = Path(audio_file).stem
demucs_out = Path(work_dir) / MODEL / track_name
vocals_src = demucs_out / "vocals.wav"
background_src = demucs_out / "no_vocals.wav"
if not vocals_src.exists() or not background_src.exists():
return None, None, "❌ Demucs ran but output files not found."
# Copy to stable paths
vocals_dst = str(Path(work_dir) / "vocals.wav")
background_dst = str(Path(work_dir) / "background.wav")
resave(vocals_src, vocals_dst)
resave(background_src, background_dst)
print(f"[Demucs] ✅ Done. Vocals: {vocals_dst}")
return vocals_dst, background_dst, "✅ Split complete!"
except Exception as e:
return None, None, f"❌ Exception: {str(e)}"
# ================================
# GRADIO UI
# ================================
with gr.Blocks(title="CleanSong AI — Demucs Splitter") as demo:
gr.Markdown("# 🎸 CleanSong AI — Vocal Splitter")
gr.Markdown(
"Upload a song → get the isolated **vocals** and **background** tracks back separately. "
"Powered by Demucs `htdemucs` model."
)
with gr.Row():
with gr.Column():
audio_input = gr.Audio(
label="Upload Song",
type="filepath",
sources=["upload"],
)
split_btn = gr.Button("🎸 Split Vocals", variant="primary", size="lg")
with gr.Column():
status = gr.Textbox(label="Status", interactive=False)
vocals_out = gr.Audio(label="Vocals Only", type="filepath")
background_out = gr.Audio(label="Background / Instrumental", type="filepath")
split_btn.click(
fn=split_vocals,
inputs=[audio_input],
outputs=[vocals_out, background_out, status],
)
gr.Markdown(
"""
---
**Part of the CleanSong AI pipeline.**
- Upload your song here first to get isolated vocals
- Feed the vocals into the Whisper Transcriber Space
- The background track is kept intact for the final remix
"""
)
if __name__ == "__main__":
demo.launch()
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