Add files using upload-large-folder tool

.gitattributes

@@ -33,3 +33,50 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_NHU05058025_clip_000001_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_NHU05007095_clip_000010_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_07000017_clip_000001_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/FreeSound_392499_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_07041147_clip_000038_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/tau_urban_bus-paris-31-980-a_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YN02I1dSTiSc_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/tau_urban_public_square-vienna-124-3695-a_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/FreeSound_117507_clip_000025_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YG-cQP0Naz0I_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_Yddm8gvmItHU_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_Y6PSsSboko5E_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/FreeSound_434092_clip_000025_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_07049072_clip_000004_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/manifest.json filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YSfSrm6NEJkA_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/FreeSound_246405_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_07032152_clip_000002_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/FreeSound_434092_clip_000017_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_Yy85HEwP81uk_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_Y7gn2ijaGups_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YZoLBbbv2ez4_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_07007014_clip_000015_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YOTun6NSJ6ac_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/sonyc_17_009533_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/FreeSound_164798_clip_000004_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_Ym-Yw009COyI_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YyoVl4OSIhgA_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_NHU05030114_clip_000001_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/FreeSound_477970_clip_000002_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_07002280_clip_000003_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YHiWLW88p7MU_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_07013034_clip_000008_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YKqtFSJBt2Bk_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/tau_urban_public_square-london-113-3280-a_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YggWUrF1gI-g_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YOk3byOaHGlc_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/FreeSound_362811_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_NHU05094125_clip_000011_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YV5EO_u92VJI_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_Y8vasA2D2_Kc_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_Ye4tMBJqRbv8_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/BBC_Sound_Effects_NHU05011105_clip_000004_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YSQLWYmqRAuE_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_YgDBJlsRdSfQ_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/AudioSet_SL_Y_rytZgjgjMk_clip_000000_rank_01.wav filter=lfs diff=lfs merge=lfs -text
+pool/audio/white_noise/FreeSound_547056_clip_000003_rank_01.wav filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,95 @@
 ---
-title: Hiding Sound
-emoji: 🐠
-colorFrom: indigo
+title: Hiding Sound Listening Study
+emoji: 🎧
+colorFrom: green
 colorTo: blue
 sdk: gradio
-sdk_version: 6.14.0
-python_version: '3.13'
+sdk_version: 4.40.0
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Hiding Sound — Tier 4 Human Study
+
+Participant-facing form for the Hiding Sound paper's human listening
+evaluation. Each session draws a fresh random subset (default 20 per
+condition × 5 conditions = 100 trials) from `pool/manifest.json`. The
+participant listens to each clip and picks an option (A/B/C/D). On
+submit, the responses are saved as a JSON file the participant can
+download.
+
+## Files
+
+```
+app.py             ← Gradio app
+requirements.txt
+pool/
+  manifest.json    ← all available trials (generated by 01_build_pool.py)
+  audio/
+    <condition>/<stem>.wav
+```
+
+## Deploying as a Hugging Face Space
+
+1. Build the pool (default = include every test stem per condition,
+   so each session can sample from the full set):
+   ```bash
+   python ../scripts/tier4/01_build_pool.py            # ~15 k wavs, ~4.7 GB
+   # or cap pool size for a lighter Space:
+   python ../scripts/tier4/01_build_pool.py --pool-size 200
+   ```
+   (Output goes here, into `tier4_app/pool/`.)
+
+2. Push to a Space. **Recommended path** (no `git lfs` needed — uses the
+   `huggingface_hub` Python API):
+   ```bash
+   pip install -U huggingface_hub
+   huggingface-cli login        # paste a write token, one-time
+
+   cd src/evaluation/eval/tier4_app
+   python deploy_space.py --repo-id <user>/<space-name>
+   ```
+   This idempotently creates the Space if it doesn't exist and uploads
+   everything in this folder (audio LFS is handled automatically). Re-run
+   to push updates.
+
+   Alternative (if you have `git-lfs` installed and prefer a git workflow):
+   ```bash
+   # install git-lfs once
+   conda install -c conda-forge git-lfs   # or: apt install git-lfs
+   git lfs install
+
+   cd src/evaluation/eval/tier4_app
+   git init
+   git lfs track "pool/audio/**/*.wav"
+   git add .gitattributes
+   git add .
+   git commit -m "initial deploy"
+   git remote add origin https://huggingface.co/spaces/<user>/<space>
+   git push -u origin main
+   ```
+
+3. (Optional) Auto-upload submissions to a private HF Dataset. Set these
+   secrets on the Space settings page:
+   - `HF_TOKEN` — a write-access token
+   - `HF_RESULTS_REPO` — e.g. `<user>/hidingsound-tier4-results` (must
+     already exist as a HF Dataset; the app will push JSONs into a
+     `submissions/` subfolder).
+
+   With both set, every submission is pushed to the dataset alongside the
+   download link the participant gets.
+
+## Local testing
+
+```bash
+pip install -r requirements.txt
+python app.py
+# → opens http://127.0.0.1:7860
+```
+
+## Re-sampling per visit
+
+The manifest contains the full pool. Per-session trial selection happens
+inside the Gradio handler (`sample_session_trials(...)`) which fires on
+every `start_btn` click — so refreshing or re-opening the link gives a
+fresh draw.

app.py

@@ -0,0 +1,323 @@
+"""Hiding Sound — Tier 4 human-study Gradio app.
+
+Deploy as a Hugging Face Space. On every fresh page-load the app draws a
+new random subset of trials from `pool/manifest.json` (default 20 per
+condition × 5 conditions = 100 trials). Trials are presented one at a time
+with audio playback and 4 multiple-choice options. On submit, the app
+generates a JSON file that the participant can download AND, if the Space
+has the `HF_TOKEN` + `HF_RESULTS_REPO` secrets set, pushes the JSON to a
+private HF dataset for automated collection.
+
+Knobs (env vars):
+  HF_TOKEN          HF write-access token (set as a Space secret)
+  HF_RESULTS_REPO   target dataset, e.g. "ymdou/hidingsound-tier4-results"
+  N_PER_CONDITION   override per-condition sample size (default 20)
+
+Run locally:
+  python app.py
+"""
+from __future__ import annotations
+
+import json
+import os
+import random
+import time
+import uuid
+from datetime import datetime
+from pathlib import Path
+
+import gradio as gr
+
+POOL_DIR = Path(__file__).parent / "pool"
+MANIFEST_PATH = POOL_DIR / "manifest.json"
+N_PER_CONDITION = int(os.environ.get("N_PER_CONDITION", "1"))   # smoke default
+HF_TOKEN = os.environ.get("HF_TOKEN")
+HF_RESULTS_REPO = os.environ.get("HF_RESULTS_REPO")  # e.g. "user/dataset"
+
+
+# ──────────────────────────── pool loading ───────────────────────────────
+
+with open(MANIFEST_PATH) as _f:
+    _MANIFEST = json.load(_f)
+ALL_TRIALS = _MANIFEST["trials"]
+META = _MANIFEST.get("_meta", {})
+CONDITIONS = META.get("conditions", sorted({t["condition"] for t in ALL_TRIALS}))
+N_OPTIONS = META.get("n_options", 4)
+TOTAL_PER_SESSION = N_PER_CONDITION * len(CONDITIONS)
+
+
+def sample_session_trials(seed: int | None = None) -> list[dict]:
+    """Pick N_PER_CONDITION trials from each condition. Order shuffled."""
+    rng = random.Random(seed)
+    by_cond: dict[str, list[dict]] = {c: [] for c in CONDITIONS}
+    for t in ALL_TRIALS:
+        if t["condition"] in by_cond:
+            by_cond[t["condition"]].append(t)
+    chosen: list[dict] = []
+    for c in CONDITIONS:
+        pool = by_cond[c]
+        if not pool:
+            continue
+        n = min(N_PER_CONDITION, len(pool))
+        chosen.extend(rng.sample(pool, n))
+    rng.shuffle(chosen)
+    return chosen
+
+
+# ──────────────────────────── submission storage ─────────────────────────
+
+def upload_to_hf(submission: dict) -> str | None:
+    if not (HF_TOKEN and HF_RESULTS_REPO):
+        return None
+    try:
+        from huggingface_hub import HfApi
+        api = HfApi(token=HF_TOKEN)
+        ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+        pid = submission.get("participant_id", uuid.uuid4().hex[:8])
+        fname = f"submissions/{ts}_{pid}.json"
+        local = Path("/tmp") / f"sub_{ts}_{pid}.json"
+        local.write_text(json.dumps(submission, ensure_ascii=False, indent=2))
+        api.upload_file(
+            path_or_fileobj=str(local),
+            path_in_repo=fname,
+            repo_id=HF_RESULTS_REPO,
+            repo_type="dataset",
+        )
+        return f"{HF_RESULTS_REPO}:{fname}"
+    except Exception as e:
+        print(f"[upload_to_hf] failed: {e!r}", flush=True)
+        return None
+
+
+# ──────────────────────────── UI building blocks ─────────────────────────
+
+INTRO_MD = f"""
+# Hiding Sound — Listening Study
+
+Thanks for participating! You will hear **{TOTAL_PER_SESSION} short audio
+clip(s)** (currently configured as {N_PER_CONDITION} per condition ×
+{len(CONDITIONS)} conditions). For each clip, listen carefully and click
+the option button whose description best matches the dominant sound you
+hear. Clicking an option records your answer and moves on automatically.
+
+When you finish, your responses will be saved as a JSON file you can
+download (and auto-uploaded if the host has configured a results dataset).
+
+Click **Start** below when you're ready.
+"""
+
+
+def render_trial(idx: int, trial: dict) -> str:
+    head = (f"### Trial {idx+1} / {TOTAL_PER_SESSION}\n\n"
+            f"Listen to the audio, then click the option that best "
+            f"describes the dominant sound.")
+    return head
+
+
+# ──────────────────────────── main app ───────────────────────────────────
+
+def make_app() -> gr.Blocks:
+    with gr.Blocks(title="Hiding Sound Listening Study") as demo:
+        gr.Markdown(INTRO_MD)
+
+        # session state
+        trials_state = gr.State(value=[])
+        idx_state    = gr.State(value=0)
+        answers_state = gr.State(value=[])  # list of {"trial_id":int, "letter":str|None}
+        pid_state    = gr.State(value="")
+
+        # intro page (no participant-id box; we auto-generate)
+        with gr.Group(visible=True) as intro_group:
+            start_btn = gr.Button("Start", variant="primary")
+
+        # trial page — 4 option buttons (label updated per-trial); clicking
+        # an option records the answer and auto-advances.
+        with gr.Group(visible=False) as trial_group:
+            progress_md = gr.Markdown("")
+            audio_player = gr.Audio(label="Audio", autoplay=False, type="filepath")
+            opt_btn_a = gr.Button("", variant="secondary")
+            opt_btn_b = gr.Button("", variant="secondary")
+            opt_btn_c = gr.Button("", variant="secondary")
+            opt_btn_d = gr.Button("", variant="secondary")
+            prev_btn  = gr.Button("◀ Previous")
+
+        # submit page
+        with gr.Group(visible=False) as submit_group:
+            submit_btn = gr.Button("Submit my responses", variant="primary")
+            submit_status = gr.Markdown("")
+            download_file = gr.File(label="Download your responses (JSON)",
+                                     visible=False)
+
+        # ─── helpers ──────────────────────────────────────────────────
+
+        def _trial_ui_updates(i: int, trials: list[dict], answers: list[dict]):
+            """Returns [progress_md, audio_path, btn_A_label, btn_B_label,
+            btn_C_label, btn_D_label] for the trial at index i."""
+            t = trials[i]
+            audio_path = str(POOL_DIR / t["audio"])
+            head = render_trial(i, t)
+            chosen = answers[i].get("letter")
+            btn_labels: list[str] = []
+            for opt in t["options"]:
+                tag = "✓ " if chosen == opt["letter"] else ""
+                btn_labels.append(f"{tag}({opt['letter']}) {opt['description']}")
+            # Pad to 4 in case n_options < 4 for some reason.
+            while len(btn_labels) < 4:
+                btn_labels.append("")
+            return [head, audio_path, *btn_labels[:4]]
+
+        # ─── handlers ─────────────────────────────────────────────────
+
+        def on_start():
+            pid = uuid.uuid4().hex[:8]
+            trials = sample_session_trials(seed=int(time.time() * 1000) & 0xffff_ffff)
+            answers = [{"trial_id": i, "letter": None}
+                       for i in range(len(trials))]
+            ui = _trial_ui_updates(0, trials, answers)
+            return (
+                gr.update(visible=False),            # intro
+                gr.update(visible=True),             # trial
+                gr.update(visible=False),            # submit
+                trials, 0, answers, pid,
+                *ui,                                 # progress, audio, btn A/B/C/D
+            )
+
+        def _advance(trials, i, answers, letter):
+            """Record answer and advance; auto-show submit page on last."""
+            if 0 <= i < len(trials):
+                answers[i]["letter"] = letter
+            if i + 1 < len(trials):
+                i += 1
+                ui = _trial_ui_updates(i, trials, answers)
+                return (
+                    gr.update(visible=True),     # trial
+                    gr.update(visible=False),    # submit
+                    trials, i, answers, *ui,
+                )
+            # Last trial answered → flip to submit.
+            ui = _trial_ui_updates(i, trials, answers)  # filler outputs
+            return (
+                gr.update(visible=False),        # trial
+                gr.update(visible=True),         # submit
+                trials, i, answers, *ui,
+            )
+
+        def make_pick(letter):
+            def _pick(trials, i, answers):
+                return _advance(trials, i, answers, letter)
+            return _pick
+
+        def on_prev(trials, i, answers):
+            if i > 0:
+                i -= 1
+            ui = _trial_ui_updates(i, trials, answers)
+            return (
+                gr.update(visible=True),     # trial visible
+                gr.update(visible=False),    # submit hidden
+                trials, i, answers, *ui,
+            )
+
+        def on_submit(trials, answers, pid):
+            now = datetime.now().isoformat()
+            sub = {
+                "participant_id": pid,
+                "submitted_at": now,
+                "n_options": N_OPTIONS,
+                "n_per_condition": N_PER_CONDITION,
+                "conditions": CONDITIONS,
+                "responses": [
+                    {
+                        "trial_id": i,
+                        "stem": t["stem"],
+                        "condition": t["condition"],
+                        "gt_letter": t["gt_letter"],
+                        "user_letter": a["letter"],
+                        "correct": (a["letter"] is not None
+                                    and a["letter"] == t["gt_letter"]),
+                        "options": t["options"],
+                    }
+                    for i, (t, a) in enumerate(zip(trials, answers))
+                ],
+            }
+            # Compute per-condition accuracy on the fly.
+            from collections import defaultdict
+            per_cond = defaultdict(lambda: [0, 0])  # [n_correct, n_total]
+            for r in sub["responses"]:
+                if r["user_letter"] is None:
+                    continue
+                per_cond[r["condition"]][1] += 1
+                per_cond[r["condition"]][0] += int(r["correct"])
+            sub["per_condition_accuracy"] = {
+                c: {"correct": v[0], "n": v[1],
+                    "accuracy": (v[0] / v[1] if v[1] else 0.0)}
+                for c, v in per_cond.items()
+            }
+
+            # Local download path.
+            ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+            local = Path("/tmp") / f"hidingsound_tier4_{pid}_{ts}.json"
+            local.write_text(json.dumps(sub, ensure_ascii=False, indent=2))
+
+            uploaded = upload_to_hf(sub)
+            status_lines = ["**Thank you!** Your responses are recorded."]
+            if uploaded:
+                status_lines.append(f"Uploaded to: `{uploaded}`")
+            else:
+                status_lines.append(
+                    "Auto-upload is **disabled** for this Space. "
+                    "Please download the JSON and email it back to the study "
+                    "organizers."
+                )
+            status_lines.append("")
+            status_lines.append("Per-condition accuracy on this submission:")
+            for c, v in sub["per_condition_accuracy"].items():
+                status_lines.append(f"- **{c}**: {v['correct']}/{v['n']} = "
+                                    f"{100*v['accuracy']:.1f}%")
+            return ("\n".join(status_lines), str(local),
+                    gr.update(visible=True))
+
+        # ─── wiring ───────────────────────────────────────────────────
+
+        trial_outputs = [
+            trial_group, submit_group,
+            trials_state, idx_state, answers_state,
+            progress_md, audio_player,
+            opt_btn_a, opt_btn_b, opt_btn_c, opt_btn_d,
+        ]
+
+        start_btn.click(
+            on_start,
+            inputs=[],
+            outputs=[
+                intro_group, trial_group, submit_group,
+                trials_state, idx_state, answers_state, pid_state,
+                progress_md, audio_player,
+                opt_btn_a, opt_btn_b, opt_btn_c, opt_btn_d,
+            ],
+        )
+
+        for btn, letter in [(opt_btn_a, "A"), (opt_btn_b, "B"),
+                             (opt_btn_c, "C"), (opt_btn_d, "D")]:
+            btn.click(
+                make_pick(letter),
+                inputs=[trials_state, idx_state, answers_state],
+                outputs=trial_outputs,
+            )
+
+        prev_btn.click(
+            on_prev,
+            inputs=[trials_state, idx_state, answers_state],
+            outputs=trial_outputs,
+        )
+
+        submit_btn.click(
+            on_submit,
+            inputs=[trials_state, answers_state, pid_state],
+            outputs=[submit_status, download_file, download_file],
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    make_app().launch()

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