Add DAM voice biomarker inference app

README.md

@@ -1,13 +1,14 @@
 ---
-title: Dam Inference
-emoji: 🏢
-colorFrom: pink
-colorTo: pink
+title: Loop Mind DAM Inference
+emoji: 🧬
+colorFrom: indigo
+colorTo: purple
 sdk: gradio
-sdk_version: 6.12.0
+sdk_version: 5.23.0
 app_file: app.py
 pinned: false
-short_description: Voice biomarker inference using KintsugiHealth/DAM
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Voice biomarker inference for Loop Mind using KintsugiHealth/DAM.
+Returns depression/anxiety severity scores from acoustic voice features.

app.py

@@ -0,0 +1,110 @@
+"""
+Loop Mind — DAM Voice Biomarker Inference Space
+================================================
+Wraps KintsugiHealth/DAM for voice biomarker analysis.
+Accepts audio, returns depression/anxiety severity scores.
+
+Deployed as a Hugging Face Space (Gradio).
+Called by the Loop Mind /voice-biomarker Supabase Edge Function.
+"""
+
+import sys
+import os
+import subprocess
+import warnings
+import json
+
+import gradio as gr
+import torch
+import torchaudio
+
+warnings.filterwarnings("ignore")
+
+# Download the DAM model on first run
+if not os.path.exists("dam"):
+    print("Downloading KintsugiHealth/DAM model (~1GB)...")
+    subprocess.run(["git", "clone", "https://huggingface.co/KintsugiHealth/dam"])
+
+sys.path.append(os.path.abspath("dam"))
+
+print("Loading DAM pipeline...")
+try:
+    from pipeline import Pipeline
+    dam_pipeline = Pipeline()
+    print("DAM model loaded successfully.")
+except Exception as e:
+    print(f"Failed to load DAM model: {e}")
+    dam_pipeline = None
+
+
+DEP_LABELS = {0: "none", 1: "mild-moderate", 2: "severe"}
+ANX_LABELS = {0: "none", 1: "mild", 2: "moderate", 3: "severe"}
+
+
+def analyze_audio(audio_filepath: str) -> str:
+    """
+    Accepts an audio file path, runs DAM inference, returns JSON string
+    with depression/anxiety scores (both quantized and raw).
+    """
+    if audio_filepath is None:
+        return json.dumps({"error": "No audio provided"})
+
+    if dam_pipeline is None:
+        return json.dumps({"error": "Model not loaded"})
+
+    try:
+        # Pre-process: convert to mono if needed
+        waveform, sample_rate = torchaudio.load(audio_filepath)
+        if waveform.shape[0] > 1:
+            waveform = torch.mean(waveform, dim=0, keepdim=True)
+            audio_filepath = "temp_mono.wav"
+            torchaudio.save(audio_filepath, waveform, sample_rate)
+
+        # Run inference (both quantized and raw)
+        res_q = dam_pipeline.run_on_file(audio_filepath, quantize=True)
+        res_r = dam_pipeline.run_on_file(audio_filepath, quantize=False)
+
+        # Extract and normalize scores
+        dep_q = int(res_q.get("depression", 0).item() if hasattr(res_q.get("depression", 0), "item") else res_q.get("depression", 0))
+        anx_q = int(res_q.get("anxiety", 0).item() if hasattr(res_q.get("anxiety", 0), "item") else res_q.get("anxiety", 0))
+        dep_r = float(res_r.get("depression", 0.0).item() if hasattr(res_r.get("depression", 0.0), "item") else res_r.get("depression", 0.0))
+        anx_r = float(res_r.get("anxiety", 0.0).item() if hasattr(res_r.get("anxiety", 0.0), "item") else res_r.get("anxiety", 0.0))
+
+        result = {
+            "depression": dep_q,
+            "depression_label": DEP_LABELS.get(dep_q, "unknown"),
+            "anxiety": anx_q,
+            "anxiety_label": ANX_LABELS.get(anx_q, "unknown"),
+            "raw_depression": round(dep_r, 4),
+            "raw_anxiety": round(anx_r, 4),
+            "model": "KintsugiHealth/dam",
+        }
+
+        return json.dumps(result)
+
+    except Exception as e:
+        return json.dumps({"error": str(e)})
+
+
+# Clean up temp file after processing
+def analyze_and_cleanup(audio_filepath: str) -> str:
+    result = analyze_audio(audio_filepath)
+    # Delete temp mono file if created
+    if os.path.exists("temp_mono.wav"):
+        try:
+            os.unlink("temp_mono.wav")
+        except OSError:
+            pass
+    return result
+
+
+demo = gr.Interface(
+    fn=analyze_and_cleanup,
+    inputs=gr.Audio(type="filepath", label="Upload audio or record (30+ seconds recommended)"),
+    outputs=gr.Textbox(label="Analysis Result (JSON)", lines=10),
+    title="Loop Mind — Voice Biomarker Analysis",
+    description="Powered by KintsugiHealth/DAM. Returns depression and anxiety severity scores from voice acoustic features. For research and wellness tracking only — not a clinical diagnosis.",
+    theme="soft",
+)
+
+demo.launch()

packages.txt

@@ -0,0 +1 @@
+ffmpeg

requirements.txt

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+torch
+torchaudio
+transformers
+soundfile
+peft
+torchcodec