Update install_spleeter.py

install_spleeter.py

@@ -1,159 +1,9 @@
-import gradio as gr
-import numpy as np
-import soundfile as sf
-import onnxruntime as ort
-from scipy.signal import stft, istft
-import os
-import uuid
+import subprocess
 
-# ====== Load Model ======
-MODEL_PATH = "models/mdxnet.onnx"
+# Install spleeter without dependencies
+subprocess.run(
+    ["pip", "install", "--no-deps", "spleeter==2.3.2"],
+    check=True
+)
 
-print("Loading MDX-Net model...")
-sess = ort.InferenceSession(MODEL_PATH, providers=["CPUExecutionProvider"])
-input_name = sess.get_inputs()[0].name
-input_shape = sess.get_inputs()[0].shape  # ['batch_size', 4, 3072, 256]
-output_name = sess.get_outputs()[0].name
-print("Loaded model with input shape:", input_shape)
-
-
-# ====== PARAMETERS (from UVR MDX pipeline) ======
-N_FFT = 6144
-HOP = 1536
-
-# For reconstruction (ISTFT) — correct value
-F_BIN_FULL = N_FFT // 2 + 1  # 3073
-
-# For MDX model input (fixed)
-F_BIN_MODEL = 3072
-
-CHUNK_T = 256
-N_CHANNELS = 4
-
-
-def prepare_spectrogram(audio, sr):
-    """Return stacked model input (4×3072×T) and full 3073-bin complex STFT."""
-
-    # Convert stereo → mono
-    if audio.ndim == 2:
-        audio = np.mean(audio, axis=1)
-
-    audio = audio.astype(np.float32)
-
-    # STFT
-    f, t, Zxx = stft(
-        audio,
-        fs=sr,
-        nperseg=N_FFT,
-        noverlap=N_FFT - HOP
-    )
-    Zxx = Zxx.astype(np.complex64)
-
-    # Full resolution for ISTFT later (3073)
-    original_spec = Zxx[:F_BIN_FULL, :]
-
-    # Model input uses ONLY 3072 bins
-    spec = Zxx[:F_BIN_MODEL, :]
-
-    mag = np.abs(spec).astype(np.float32)
-    phase = np.angle(spec).astype(np.float32)
-
-    ch1 = mag
-    ch2 = np.sin(phase).astype(np.float32)
-    ch3 = np.cos(phase).astype(np.float32)
-    ch4 = (mag ** 2).astype(np.float32)
-
-    stacked = np.stack([ch1, ch2, ch3, ch4], axis=0).astype(np.float32)
-    return stacked, original_spec
-
-
-def run_mdx(stacked):
-    _, _, T = stacked.shape
-    out = np.zeros((F_BIN_MODEL, T), dtype=np.float32)
-
-    for i in range(0, T, CHUNK_T):
-        chunk = stacked[:, :, i:i+CHUNK_T]
-
-        if chunk.shape[2] < CHUNK_T:
-            pad = CHUNK_T - chunk.shape[2]
-            chunk = np.pad(chunk, ((0,0),(0,0),(0,pad)), mode="constant")
-
-        inp = chunk[np.newaxis, :, :, :].astype(np.float32)
-
-        pred = sess.run([output_name], {input_name: inp})[0].astype(np.float32)
-        pred = pred[0, 0]  # mask channel
-
-        out[:, i:i+CHUNK_T] = pred[:, :out[:, i:i+CHUNK_T].shape[1]]
-
-    return out
-
-
-def reconstruct_audio(mask, original_spec, sr):
-    """Pad mask to 3073 bins, apply to full STFT, then ISTFT."""
-
-    # Pad 1 missing bin (3072 → 3073)
-    if mask.shape[0] == F_BIN_MODEL:
-        mask = np.pad(mask, ((0, 1), (0, 0)), mode="constant")
-
-    masked = mask * original_spec  # complex spectrogram
-
-    # ISTFT that requires N_FFT = 6144 → 3073 bins OK
-    _, audio = istft(
-        masked,
-        fs=sr,
-        nperseg=N_FFT,
-        noverlap=N_FFT - HOP
-    )
-    return audio
-
-
-def separate_audio(input_path):
-    if input_path is None:
-        return None, None
-
-    job = str(uuid.uuid4())
-    out_dir = f"output/{job}"
-    os.makedirs(out_dir, exist_ok=True)
-
-    audio, sr = sf.read(input_path)
-
-    # ---- Prepare spectrogram ----
-    stacked, original_spec = prepare_spectrogram(audio, sr)
-
-    # ---- Run MDX-Net ----
-    print("Running MDX-Net...")
-    mask = run_mdx(stacked)
-
-    mask = mask.astype(np.float32)
-    original_spec = original_spec.astype(np.complex64)
-
-    # ---- Reconstruct audio ----
-    vocals = reconstruct_audio(mask, original_spec, sr)
-
-    # Length-safe subtraction
-    instrumental = audio[:len(vocals)] - vocals[:len(audio)]
-
-    # ---- Save ----
-    v_path = os.path.join(out_dir, "vocals.wav")
-    i_path = os.path.join(out_dir, "instrumental.wav")
-
-    sf.write(v_path, vocals, sr)
-    sf.write(i_path, instrumental, sr)
-
-    return v_path, i_path
-
-
-# ===== Gradio UI =====
-with gr.Blocks(title="MDX-Net Vocal Remover (UVR)") as app:
-    gr.Markdown("# 🎵 MDX-Net Vocal Remover (UVR)")
-    gr.Markdown("Extract **Vocals** and **Instrumental** using the real UVR MDX-Net pipeline.")
-
-    inp = gr.Audio(type="filepath", label="Upload Audio")
-    btn = gr.Button("Separate", variant="primary")
-
-    out_v = gr.Audio(type="filepath", label="Vocals")
-    out_i = gr.Audio(type="filepath", label="Instrumental")
-
-    btn.click(separate_audio, inp, [out_v, out_i])
-
-app.launch()
+print("Spleeter installed without dependencies.")