initial commit

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README.md

@@ -4,7 +4,7 @@ emoji: 💻
 colorFrom: blue
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.42.0
+sdk_version: 5.28.0
 app_file: app.py
 pinned: false
 short_description: ' Anticipatory Music Transformer wrapped in HARP'

app.py

@@ -0,0 +1,166 @@
+import gradio as gr
+import torch
+from dataclasses import asdict
+
+from pyharp.core import ModelCard, build_endpoint
+from pyharp.labels import LabelList
+
+from transformers import AutoModelForCausalLM
+from anticipation.sample import generate
+from anticipation.convert import events_to_midi, midi_to_events
+from anticipation import ops
+from anticipation.tokenize import extract_instruments
+
+# === Model Choices ===
+SMALL_MODEL = "stanford-crfm/music-small-800k"
+MEDIUM_MODEL = "stanford-crfm/music-medium-800k"
+LARGE_MODEL = "stanford-crfm/music-large-800k"
+
+# === HARP Model Card ===
+model_card = ModelCard(
+    name="Anticipatory Music Transformer",
+    description="Generate accompaniment for a monophonic melody using AMT. ZeroGPU + HARP-compatible.",
+    author="John Thickstun, David Hall, Chris Donahue, Percy Liang",
+    tags=["midi", "generation", "accompaniment", "transformer"]
+)
+
+# === Core AMT pipeline (ZeroGPU-friendly: loads model each request) ===
+def generate_accompaniment(
+    input_midi_path: str,
+    model_choice: str,
+    selected_midi_program: int,
+    history_length: float
+):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    # Load model per request (ZeroGPU ephemeral runtime)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_choice,
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+        low_cpu_mem_usage=True
+    ).to(device)
+    model.eval()
+
+    # Parse input MIDI → events
+    events = midi_to_events(input_midi_path)
+    total_time = round(ops.max_time(events, seconds=True))
+
+    # Extract melody by MIDI program
+    events, melody = extract_instruments(events, [selected_midi_program])
+    if not melody:
+        return None, "No melody events found for the chosen MIDI program. Try another program number."
+
+    # Use initial history
+    history = ops.clip(events, 0, history_length, clip_duration=False)
+
+    # Generate accompaniment from history_length → total_time
+    accompaniment = generate(
+        model,
+        start_time=history_length,
+        end_time=total_time,
+        inputs=history,
+        controls=melody,
+        top_p=0.95,
+        debug=False
+    )
+
+    # Merge melody + accompaniment, clip to duration
+    output_events = ops.clip(
+        ops.combine(accompaniment, melody),
+        0,
+        total_time,
+        clip_duration=True
+    )
+
+    # Save to MIDI file
+    output_midi_path = "amt_output.mid"
+    mid = events_to_midi(output_events)
+    mid.save(output_midi_path)
+
+    return output_midi_path, None
+
+
+# === HARP process function ===
+def process_fn(input_midi_path, model_choice, selected_midi_program, history_length):
+    """
+    Inputs order must match the input_components order below.
+    - input_midi_path: filepath string (gr.File with type='filepath')
+    - model_choice: str
+    - selected_midi_program: int
+    - history_length: float
+    Returns:
+    - output_midi_path: str or None (gr.File expects a filepath)
+    - labels_json: dict-like (for gr.JSON)
+    """
+    output_midi_path, error_message = generate_accompaniment(
+        input_midi_path,
+        model_choice,
+        int(selected_midi_program),
+        float(history_length),
+    )
+
+    if error_message:
+        return None, {"message": error_message}
+
+    labels = LabelList()  # optional: fill with MidiLabel entries if you have them
+    return output_midi_path, asdict(labels)
+
+
+# === Gradio + HARP UI ===
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎼 Anticipatory Music Transformer (ZeroGPU + HARP)")
+
+    # Inputs
+    input_midi = gr.File(
+        file_types=[".mid", ".midi"],
+        label="Input MIDI",
+        type="filepath",
+    ).harp_required(True)
+
+    model_dropdown = gr.Dropdown(
+        choices=[SMALL_MODEL, MEDIUM_MODEL, LARGE_MODEL],
+        value=MEDIUM_MODEL,
+        label="AMT Model",
+    )
+
+    midi_program_slider = gr.Slider(
+        minimum=0,
+        maximum=127,
+        step=1,
+        value=1,
+        label="Melody Instrument (MIDI Program #)"
+    )
+
+    history_slider = gr.Slider(
+        minimum=1,
+        maximum=30,
+        step=1,
+        value=5,
+        label="History Length (seconds)"
+    )
+
+    # Outputs
+    output_midi = gr.File(
+        file_types=[".mid", ".midi"],
+        label="Generated MIDI",
+        type="filepath",
+    )
+    output_labels = gr.JSON(label="Labels / Metadata")
+
+    # Build HARP endpoint
+    _ = build_endpoint(
+        model_card=model_card,
+        input_components=[
+            input_midi,
+            model_dropdown,
+            midi_program_slider,
+            history_slider,
+        ],
+        output_components=[
+            output_midi,
+            output_labels,
+        ],
+        process_fn=process_fn
+    )
+
+demo.queue().launch(show_error=True, debug=True)

requirements.txt

@@ -0,0 +1,14 @@
+# PyHARP from correct branch
+git+https://github.com/TEAMuP-dev/pyharp.git@cb/gen-inputs
+midi2audio == 0.1.1
+mido == 1.2.10
+numpy >= 1.22.4
+torch >= 2.0.1
+transformers == 4.29.2
+safetensors
+accelerate
+tqdm == 4.65.0
+
+# Additional dependencies for AMT
+git+https://github.com/jthickstun/anticipation.git
+