Create app.py

app.py

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+import gradio as gr
+import librosa
+import soundfile as sf
+import os
+import tempfile
+import shutil
+import torch
+
+from demucs.pretrained import get_model as get_demucs_model
+from demucs.apply import apply_model
+from spleeter.separator import Separator
+from matchering import match
+from so_vits_svc_fork.inference.core import Svc
+import whisper
+import madmom
+
+# --- 1. Audio Separation (Demucs/Spleeter) ---
+def separate_audio(audio):
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        tmp.write(audio.read())
+        tmp_path = tmp.name
+    # Demucs
+    model = get_demucs_model('htdemucs')
+    wav, sr = librosa.load(tmp_path, sr=44100, mono=False)
+    sources = apply_model(model, torch.tensor(wav).unsqueeze(0), device='cpu', split=True)
+    out_dir = tempfile.mkdtemp()
+    stems = {}
+    for i, name in enumerate(model.sources):
+        out_path = os.path.join(out_dir, f"{name}.wav")
+        sf.write(out_path, sources[0, i].cpu().numpy().T, sr)
+        stems[name] = out_path
+    return stems
+
+# --- 2. Pattern Extraction & Genre Detection ---
+def extract_pattern(audio):
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        tmp.write(audio.read())
+        tmp_path = tmp.name
+    y, sr = librosa.load(tmp_path, sr=None)
+    tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
+    onset_env = librosa.onset.onset_strength(y=y, sr=sr)
+    onsets = librosa.onset.onset_detect(onset_envelope=onset_env, sr=sr)
+    # Genre detection (replace with ML model if needed)
+    genre = "dj bantengan" if tempo > 120 else "pop"
+    return {
+        "tempo": float(tempo),
+        "beats": beats.tolist(),
+        "onsets": onsets.tolist(),
+        "genre": genre
+    }
+
+# --- 3. Genre-Aware Pattern Generator (Magenta/MusicGen style transfer) ---
+def generate_pattern(reference_audio, creativity=0.2):
+    # TODO: Integrate with MusicGen/Magenta for real pattern generation
+    # For now, return extracted pattern as placeholder
+    return extract_pattern(reference_audio)
+
+# --- 4. Mixing/Mastering (Matchering) ---
+def mix_and_master(input_audio, reference_audio):
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_in, \
+         tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_ref:
+        tmp_in.write(input_audio.read())
+        tmp_ref.write(reference_audio.read())
+        in_path = tmp_in.name
+        ref_path = tmp_ref.name
+    out_path = in_path.replace(".wav", "_mastered.wav")
+    match(in_path, ref_path, out_path)
+    return out_path
+
+# --- 5. Vocal Processing (so-vits-svc, Spleeter) ---
+def change_vocal(audio, model_path):
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        tmp.write(audio.read())
+        tmp_path = tmp.name
+    svc = Svc(model_path)
+    out_wav_path = svc.infer(tmp_path)
+    return out_wav_path
+
+# --- 6. Denoising (RNNoise, Demucs) ---
+def denoise_audio(audio):
+    # TODO: Integrate with RNNoise or Demucs for real denoising
+    # For now, just return input
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        tmp.write(audio.read())
+        tmp_path = tmp.name
+    return tmp_path
+
+# --- 7. Multi-vocal Lyric Detection (Whisper) ---
+def detect_lyrics(audio):
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        tmp.write(audio.read())
+        tmp_path = tmp.name
+    model = whisper.load_model("base")
+    result = model.transcribe(tmp_path)
+    # For multi-vocal, you can use Spleeter/Demucs to split vocals, then transcribe each
+    return {"lyrics": result["text"]}
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("# DAW AI Ultra-Premium Pipeline (All-in-One, Real Pipeline)")
+
+    with gr.Tab("Separate Audio"):
+        audio_in = gr.Audio(type="file", label="Input Audio")
+        out = gr.JSON(label="Separated Stems (vocals, drums, bass, other)")
+        btn = gr.Button("Separate")
+        btn.click(separate_audio, inputs=audio_in, outputs=out)
+
+    with gr.Tab("Extract Pattern"):
+        audio_in2 = gr.Audio(type="file", label="Input Audio")
+        out2 = gr.JSON(label="Pattern Info")
+        btn2 = gr.Button("Extract")
+        btn2.click(extract_pattern, inputs=audio_in2, outputs=out2)
+
+    with gr.Tab("Generate Pattern"):
+        ref_audio = gr.Audio(type="file", label="Reference Audio")
+        creativity = gr.Slider(0, 1, value=0.2, label="Creativity")
+        out3 = gr.JSON(label="Generated Pattern")
+        btn3 = gr.Button("Generate")
+        btn3.click(generate_pattern, inputs=[ref_audio, creativity], outputs=out3)
+
+    with gr.Tab("Mix/Master"):
+        audio_in3 = gr.Audio(type="file", label="Input Audio")
+        ref_audio2 = gr.Audio(type="file", label="Reference Audio")
+        out4 = gr.Audio(label="Mastered Output")
+        btn4 = gr.Button("Master")
+        btn4.click(mix_and_master, inputs=[audio_in3, ref_audio2], outputs=out4)
+
+    with gr.Tab("Vocal Change"):
+        audio_in4 = gr.Audio(type="file", label="Input Vocal Audio")
+        model_path = gr.Textbox(label="Voice Model Path")
+        out5 = gr.Audio(label="Changed Vocal Output")
+        btn5 = gr.Button("Change Vocal")
+        btn5.click(change_vocal, inputs=[audio_in4, model_path], outputs=out5)
+
+    with gr.Tab("Denoise"):
+        audio_in5 = gr.Audio(type="file", label="Input Audio")
+        out6 = gr.Audio(label="Denoised Output")
+        btn6 = gr.Button("Denoise")
+        btn6.click(denoise_audio, inputs=audio_in5, outputs=out6)
+
+    with gr.Tab("Detect Lyrics (Multi-Vocal)"):
+        audio_in6 = gr.Audio(type="file", label="Input Audio")
+        out7 = gr.JSON(label="Detected Lyrics per Vocal")
+        btn7 = gr.Button("Detect Lyrics")
+        btn7.click(detect_lyrics, inputs=audio_in6, outputs=out7)
+
+demo.launch()