Initial commit: Sound Generation Survey

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README.md

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+---
+title: Subjective Noise Reduction Survey
+emoji: 🎧
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 5.47.1
+app_file: app.py
+pinned: false
+---
+
+This Space serves a single-page scrollable survey for subjective evaluation of noise reduction quality.
+
+Instructions:
+- Each question has a Noise Reference (if available), plus two anonymized audios: Audio A (1‑prefixed) and Audio B (2‑prefixed).
+- Task: select which audio contains less of the original noise.
+- Click "Submit All" to export a CSV with your results.
+
+Scoring note: any audio filename containing `t0.99` is treated as the wrong option; the other one is counted as correct.
+
+Build/runtime:
+- `requirements.txt` pins `gradio>=4.44.1` to avoid schema issues.

app.py

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+import gradio as gr
+import os
+import json
+from datetime import datetime
+import uuid
+import re
+from typing import List, Dict, Tuple, Optional
+
+# =========================
+# Configuration
+# =========================
+
+# Root directory containing question subfolders: question1, question2, ...
+QUESTIONS_ROOT = os.path.join(os.path.dirname(__file__), "questions")
+# How many question folders to scan (question1 ... questionN)
+NUM_QUESTIONS = 16
+
+# Regex to detect g values in filenames, e.g., "_g0_", "-g0.3-", "g1.0"
+G_VALUE_PATTERN = re.compile(r"(?:^|[_-])g([0-9]+(?:\.[0-9]+)?)", re.IGNORECASE)
+# Exact g==0 detection (g0, g0.0, g0.00, ...)
+G0_PATTERN = re.compile(r"(?:^|[_-])g0(?:\.0+)?(?:[_-]|$)", re.IGNORECASE)
+# Regex to detect mix value, e.g., "_mix1.00_", "-mix0.60-"
+MIX_VALUE_PATTERN = re.compile(r"(?:^|[_-])mix([0-9]+(?:\.[0-9]+)?)", re.IGNORECASE)
+
+
+# =========================
+# Data discovery
+# =========================
+
+def discover_questions() -> List[Dict]:
+    """
+    Scan QUESTIONS_ROOT/question1..questionN and build a question list.
+
+    Rules:
+    - Noise reference: any .wav not starting with '1' or '2' (optional).
+    - Audio A: .wav starting with '1'
+    - Audio B: .wav starting with '2'
+    - Image (optional): first *.jpg/*.jpeg/*.png/*.gif in the folder
+    - Correctness heuristic:
+        * Prefer 'g' rule: if exactly one side has g==0, that side is WRONG.
+        * Fallback 'mix' rule when no 'g' param on either: if exactly one side has mix==1.0, that side is WRONG.
+      We store which side is WRONG in field 't099_is' for backward compatibility.
+    """
+    questions = []
+    print(f"[disc] Scanning: {QUESTIONS_ROOT}")
+
+    for i in range(1, NUM_QUESTIONS + 1):
+        qdir = os.path.join(QUESTIONS_ROOT, f"question{i}")
+        if not os.path.isdir(qdir):
+            print(f"[disc] Skip missing dir: {qdir}")
+            continue
+
+        # Collect files
+        all_files = [f for f in os.listdir(qdir) if f.lower().endswith(".wav")]
+        noise_candidates = [f for f in all_files if not (f.startswith("1") or f.startswith("2"))]
+        one_candidates = sorted([f for f in all_files if f.startswith("1")])
+        two_candidates = sorted([f for f in all_files if f.startswith("2")])
+
+        image_candidates = [f for f in os.listdir(qdir)
+                            if f.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))]
+
+        # Resolve absolute paths
+        noise_path = os.path.join(qdir, noise_candidates[0]) if noise_candidates else None
+        a_path = os.path.join(qdir, one_candidates[0]) if one_candidates else None
+        b_path = os.path.join(qdir, two_candidates[0]) if two_candidates else None
+        image_path = os.path.join(qdir, image_candidates[0]) if image_candidates else None
+
+        if not (a_path and b_path):
+            print(f"[disc] Missing A/B in {qdir}: A={a_path}, B={b_path}")
+            continue
+
+        # Sanity checks (non-fatal)
+        for p in [a_path, b_path, noise_path, image_path]:
+            if p and not os.path.exists(p):
+                print(f"[disc] File not found (non-fatal): {p}")
+
+        # Correctness heuristic
+        fname_a = os.path.basename(a_path)
+        fname_b = os.path.basename(b_path)
+
+        a_has_g = bool(G_VALUE_PATTERN.search(fname_a))
+        b_has_g = bool(G_VALUE_PATTERN.search(fname_b))
+        a_is_g0 = bool(G0_PATTERN.search(fname_a))
+        b_is_g0 = bool(G0_PATTERN.search(fname_b))
+
+        a_is_mix1 = False
+        b_is_mix1 = False
+        if not (a_has_g or b_has_g):
+            ma = MIX_VALUE_PATTERN.search(fname_a)
+            mb = MIX_VALUE_PATTERN.search(fname_b)
+            try:
+                a_is_mix1 = (abs(float(ma.group(1)) - 1.0) < 1e-9) if ma else False
+            except Exception:
+                a_is_mix1 = False
+            try:
+                b_is_mix1 = (abs(float(mb.group(1)) - 1.0) < 1e-9) if mb else False
+            except Exception:
+                b_is_mix1 = False
+
+        wrong_label = None
+        if a_has_g or b_has_g:
+            if a_is_g0 and not b_is_g0:
+                wrong_label = "A"
+            elif b_is_g0 and not a_is_g0:
+                wrong_label = "B"
+        else:
+            if a_is_mix1 and not b_is_mix1:
+                wrong_label = "A"
+            elif b_is_mix1 and not a_is_mix1:
+                wrong_label = "B"
+
+        if wrong_label == "A":
+            correct_label = "B"
+        elif wrong_label == "B":
+            correct_label = "A"
+        else:
+            correct_label = None
+
+        questions.append({
+            "id": f"question{i}",
+            "index": i,
+            "noise": noise_path,
+            "A": a_path,
+            "B": b_path,
+            "image": image_path,
+            "correct": correct_label,
+            # For compatibility: which option is considered "wrong" by heuristic
+            "t099_is": wrong_label,
+        })
+
+    print(f"[disc] Found {len(questions)} valid questions.")
+    return questions
+
+
+# =========================
+# Upload to RESULTS dataset (no Space restart)
+# =========================
+def upload_to_results_dataset(local_path: str, dest_dir: str = "submissions") -> str:
+    """
+    Upload a local file into a dedicated dataset repo.
+    Unlike committing to the Space repo, this does NOT trigger rebuilds/restarts.
+
+    Requires Space secrets:
+      - HF_TOKEN: with write permissions
+      - RESULTS_REPO: dataset repo id (e.g., 'qiuyiding/sound-survey-results')
+    """
+    from huggingface_hub import upload_file, create_repo
+
+    repo_id = os.environ.get("RESULTS_REPO", "qiuyiding/sound-survey-results")
+    hf_token = os.environ.get("HF_TOKEN")
+    if not hf_token:
+        raise RuntimeError("Missing HF_TOKEN. Set it in Settings → Repository secrets.")
+
+    if "/" not in repo_id:
+        raise ValueError(f"RESULTS_REPO looks invalid: {repo_id!r}. Expected 'owner/dataset-name'.")
+
+    create_repo(repo_id, repo_type="dataset", exist_ok=True, token=hf_token)
+
+    remote_path = f"{dest_dir}/{os.path.basename(local_path)}"
+    upload_file(
+        path_or_fileobj=local_path,
+        path_in_repo=remote_path,
+        repo_id=repo_id,
+        repo_type="dataset",   
+        token=hf_token,
+        commit_message=f"Add survey result {os.path.basename(local_path)}",
+    )
+    return f"{repo_id}:{remote_path}"
+
+# =========================
+# Export + Summary
+# =========================
+
+def finish_and_export_json(questions: List[Dict], responses: List[Dict]) -> Tuple[str, Optional[str]]:
+    """
+    Build a JSON payload, save to a local path (for front-end download),
+    then also upload it to the RESULTS dataset repo (so you can view results on the Hub).
+    Returns (summary_text, local_file_path_for_download).
+    """
+    total = len(questions)
+    answered = len(responses)
+    num_correct = sum(1 for r in responses if r.get("is_correct") is True)
+    num_incorrect = sum(1 for r in responses if r.get("is_correct") is False)
+    num_undetermined = answered - num_correct - num_incorrect
+
+    payload = {
+        "meta": {
+            "timestamp": datetime.now().isoformat(timespec="seconds"),
+            "total_questions": total,
+            "answered": answered,
+            "correct": num_correct,
+            "wrong": num_incorrect,
+            "undetermined": num_undetermined,
+        },
+        "results": sorted(responses, key=lambda x: x["index"]),
+    }
+
+    # Prefer /tmp as it is always writable in Spaces runtime
+    out_name = f"survey_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}.json"
+    save_attempts = [
+        os.path.join("/tmp", out_name),
+        os.path.join(os.path.dirname(__file__), out_name),
+        os.path.join(os.getcwd(), out_name),
+    ]
+    local_path = None
+    for save_path in save_attempts:
+        try:
+            with open(save_path, "w", encoding="utf-8") as f:
+                json.dump(payload, f, ensure_ascii=False, indent=2)
+            local_path = save_path
+            print(f"[export] Saved JSON: {save_path}")
+            break
+        except Exception as e:
+            print(f"[export] Save failed at {save_path}: {e}")
+
+    if local_path is None:
+        # Last resort: create a temp file
+        import tempfile
+        tf = tempfile.NamedTemporaryFile(delete=False, suffix=".json", mode="w", encoding="utf-8")
+        json.dump(payload, tf, ensure_ascii=False, indent=2)
+        tf.close()
+        local_path = tf.name
+        print(f"[export] Saved JSON to temp: {local_path}")
+
+    # Upload to results dataset (does NOT restart Space)
+    hub_loc = None
+    hub_err = None
+    try:
+        hub_loc = upload_to_results_dataset(local_path, dest_dir="submissions")
+        print(f"[export] Uploaded to dataset: {hub_loc}")
+    except Exception as e:
+        hub_err = str(e)
+        print(f"[export] Upload to dataset failed: {hub_err}")
+
+    # Human-readable summary shown in the textbox
+    lines = [
+        f"Total: {total} questions, Answered: {answered}",
+        f"Correct: {num_correct}, Wrong: {num_incorrect}, Undetermined: {num_undetermined}",
+        f"Saved locally (for download): {local_path}",
+        (f"Uploaded to results dataset as: {hub_loc}" if hub_loc else f"Upload to results dataset failed: {hub_err or 'see Logs'}"),
+        "\nPer-question results:",
+    ]
+    for r in payload["results"]:
+        correctness = (
+            "Correct" if r.get("is_correct") is True else
+            ("Wrong" if r.get("is_correct") is False else "Undetermined")
+        )
+        lines.append(
+            f"- {r['question_id']}: Selected {r['choice']}, Result: {correctness} (wrong-side heuristic: {r.get('t099_is')})"
+        )
+    return "\n".join(lines), local_path
+
+
+# =========================
+# UI App
+# =========================
+
+def create_survey_interface():
+    questions = discover_questions()
+    n = len(questions)
+
+    with gr.Blocks(title="Sound Generation Survey") as demo:
+        # Top instructions (combined and dynamic count)
+        gr.Markdown(
+            f"""
+            # Sound Generation Survey
+
+            Below are {n} pairs of audios processed with different noise reduction methods.  
+            Please listen carefully and select **which audio sounds cleaner and contains less of the original noise**.  
+
+            It may take some time to load all the audios.  
+            If any loading error occurs, please refresh the webpage and try again.  
+            We truly appreciate your time and patience in participating in this study!  
+
+            ---
+
+            ## Instructions
+            - Each question shows a **Noise Reference** (if available) and two anonymized audios: **Audio A** and **Audio B**.  
+            - **Task:** Select which audio has **less** of the original noise.  
+            - **Tip:** First play the Noise Reference to memorize noise characteristics, then compare A and B.  
+            - If the two audios sound the same, please choose the one that sounds more pleasant and has less noise.
+            """
+        )
+
+        radios = []
+
+        # Render questions
+        for idx, q in enumerate(questions):
+            with gr.Accordion(label=f"Question {idx+1}: {q['id']}", open=True):
+                # Optional noise reference + optional image
+                with gr.Row():
+                    if q["noise"] and q["image"]:
+                        with gr.Column(scale=1):
+                            gr.Image(
+                                value=q["image"], label="",
+                                height=200, width=200, show_download_button=False
+                            )
+                        with gr.Column(scale=2):
+                            gr.Audio(value=q["noise"], label="Noise Reference", interactive=False)
+                    elif q["noise"]:
+                        gr.Audio(value=q["noise"], label="Noise Reference", interactive=False)
+                    elif q["image"]:
+                        gr.Image(
+                            value=q["image"], label="",
+                            height=200, width=200, show_download_button=False
+                        )
+                    else:
+                        gr.Markdown("*No noise reference or image available*")
+
+                # Audio A and B
+                with gr.Row():
+                    with gr.Column():
+                        gr.Audio(value=q["A"], label="Audio A", interactive=False)
+                    with gr.Column():
+                        gr.Audio(value=q["B"], label="Audio B", interactive=False)
+
+                # Single radio selection for A/B
+                r = gr.Radio(["A", "B"], label="Select which audio has LESS noise", value=None)
+                radios.append(r)
+
+        # Submit / Reset
+        with gr.Row():
+            submit_btn = gr.Button("Submit All", variant="primary")
+            reset_btn = gr.Button("Reset All")
+
+        summary = gr.Textbox(label="Results (summary)", interactive=False, lines=12)
+        download = gr.File(label="Download JSON", interactive=False)
+
+        # Submit callback
+        def submit_all(*choices):
+            try:
+                responses = []
+                for i, q in enumerate(questions):
+                    choice_label = choices[i] if i < len(choices) else None
+                    if choice_label not in ("A", "B"):
+                        continue
+
+                    timestamp = datetime.now().isoformat(timespec="seconds")
+                    is_wrong = q.get("t099_is") == choice_label if q.get("t099_is") else None
+
+                    entry = {
+                        "timestamp": timestamp,
+                        "question_id": q["id"],
+                        "index": q["index"],
+                        "choice": choice_label,
+                        "is_correct": None if is_wrong is None else (not is_wrong),
+                        "correct_label": q.get("correct"),
+                        "t099_is": q.get("t099_is"),
+                        "noise": q["noise"],
+                        "A": q["A"],
+                        "B": q["B"],
+                        "chosen_path": q.get(choice_label),
+                        "chosen_has_g0_or_mix1": bool(q.get("t099_is") == choice_label),
+                    }
+                    responses.append(entry)
+
+                if not responses:
+                    return "Please make at least one selection before submitting.", None
+
+                summary_text, path = finish_and_export_json(questions, responses)
+                return summary_text, path
+            except Exception as e:
+                import traceback
+                traceback.print_exc()
+                return f"Error occurred: {str(e)}", None
+
+        # Reset callback: set all radios back to None
+        def reset_all():
+            return [None] * len(radios)
+
+        submit_btn.click(fn=submit_all, inputs=radios, outputs=[summary, download])
+        reset_btn.click(fn=reset_all, inputs=None, outputs=radios)
+
+    # Use queue for multi-user safety; avoid unsupported args (no concurrency_count here)
+    demo.queue()
+    return demo
+
+
+# Expose a module-level `demo` so Spaces can find and launch it
+demo = create_survey_interface()
+
+# Local dev entry
+if __name__ == "__main__":
+    demo.launch()

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