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225e725 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 | """Subtext Arena environment.
Episode flow:
1. reset() picks a random MUStARD clip (Pivot Set oversampled 3x).
Returns: clip_id + speaker + duration, no transcript yet — the agent
must call get_transcript and/or audio tools to investigate.
2. step(SubtextArenaAction) executes one tool call:
- get_transcript -> literal text + conversational context
- get_prosody_features -> pitch, energy, pause text summary
- get_pitch_contour -> ASCII contour
- submit_belief -> terminates episode with label + confidence
Reward = per-step delta (small + for tool use, penalties for malformed
actions) + final composite reward when submit_belief fires.
3. After max_steps (default 6) without a submission, the episode is force-
terminated with the no_submission penalty.
The trained policy is a TEXT LLM (Path A). Audio is processed by the env's
frozen prosody-feature pipeline; the agent only ever sees text. Audio is
load-bearing because the Pivot Set explicitly contains clips where the literal
transcript alone leads to the wrong answer — the agent must consult prosody
to score on those.
"""
from __future__ import annotations
import os
import random
from typing import Optional
from uuid import uuid4
from openenv.core.env_server.interfaces import Environment
from openenv.core.env_server.types import State
try:
from ..models import SubtextArenaAction, SubtextArenaObservation
except ImportError:
from models import SubtextArenaAction, SubtextArenaObservation # type: ignore[no-redef]
try:
from .scenarios import load_scenarios, sample_clip
from .audio_tools import (
render_transcript,
render_prosody_features,
render_pitch_contour,
)
from .grader import step_reward, final_reward
except ImportError:
from server.scenarios import load_scenarios, sample_clip # type: ignore[no-redef]
from server.audio_tools import ( # type: ignore[no-redef]
render_transcript,
render_prosody_features,
render_pitch_contour,
)
from server.grader import step_reward, final_reward # type: ignore[no-redef]
VALID_TOOLS = {
"get_transcript",
"get_prosody_features",
"get_pitch_contour",
"submit_belief",
}
AUDIO_TOOLS = {"get_prosody_features", "get_pitch_contour"}
class SubtextArenaEnvironment(Environment):
"""OpenEnv environment for sarcasm-vs-sincere classification on MUStARD."""
SUPPORTS_CONCURRENT_SESSIONS: bool = True
def __init__(self, max_steps: int = 6, seed: Optional[int] = None):
self._scenarios = load_scenarios()
self._max_steps = max_steps
self._rng = random.Random(seed if seed is not None else os.urandom(4))
self._state = State(episode_id=str(uuid4()), step_count=0)
self._current_clip_id: Optional[str] = None
self._n_audio_calls = 0
self._n_total_calls = 0
self._terminated = False
# When set (via FORCE_CLIP_ID env var), reset() picks this clip instead
# of sampling. Used by eval_pivot_set.py to walk specific clips.
self._force_next_clip_id: Optional[str] = None
def force_next_reset(self, clip_id: str) -> None:
"""Force the next reset() to pick the given clip ID.
Called by eval scripts that need to evaluate on specific clips
(e.g. all 50 Prosody-Pivot clips) rather than random sampling.
Auto-clears after one reset.
"""
if clip_id not in self._scenarios:
raise ValueError(
f"Unknown clip_id {clip_id!r}; not in MUStARD scenarios."
)
self._force_next_clip_id = clip_id
# ------------------------------------------------------------------
# Reset
# ------------------------------------------------------------------
def reset(self) -> SubtextArenaObservation:
if self._force_next_clip_id is not None:
clip_id = self._force_next_clip_id
self._force_next_clip_id = None
else:
# Honor FORCE_CLIP_ID env var as a fallback (works through HTTP too)
forced = os.environ.get("FORCE_CLIP_ID", "").strip()
if forced and forced in self._scenarios:
clip_id = forced
else:
clip_id = sample_clip(self._scenarios, self._rng)
clip = self._scenarios[clip_id]
prosody = clip.get("prosody") or {}
self._state = State(episode_id=str(uuid4()), step_count=0)
self._current_clip_id = clip_id
self._n_audio_calls = 0
self._n_total_calls = 0
self._terminated = False
return SubtextArenaObservation(
clip_id=clip_id,
speaker=clip.get("speaker", ""),
duration_s=float(prosody.get("duration_s", 0.0)),
is_pivot=bool(clip.get("is_pivot", False)),
tool_used="reset",
tool_output=(
f"Episode started. Clip {clip_id}, speaker {clip.get('speaker', '?')}, "
f"duration {prosody.get('duration_s', 0.0):.2f}s. "
f"You have {self._max_steps} tool calls before forced submission. "
f"Available tools: get_transcript, get_prosody_features, get_pitch_contour, submit_belief."
),
step=0,
max_steps=self._max_steps,
audio_calls_so_far=0,
done=False,
reward=0.0,
)
# ------------------------------------------------------------------
# Step
# ------------------------------------------------------------------
def step(self, action: SubtextArenaAction) -> SubtextArenaObservation: # type: ignore[override]
if self._current_clip_id is None or self._terminated:
# Episode ended — return done=True with no reward
return SubtextArenaObservation(
clip_id=self._current_clip_id or "",
tool_used="",
tool_output="Episode terminated. Call reset() to start a new episode.",
step=self._state.step_count,
max_steps=self._max_steps,
audio_calls_so_far=self._n_audio_calls,
done=True,
reward=0.0,
error="episode_terminated",
)
clip = self._scenarios[self._current_clip_id]
prosody = clip.get("prosody") or {}
self._state.step_count += 1
self._n_total_calls += 1
tool = (action.tool or "").strip()
args = action.tool_args or {}
error: Optional[str] = None
tool_output: str = ""
if tool not in VALID_TOOLS:
error = f"unknown tool '{tool}'. Valid: {sorted(VALID_TOOLS)}"
tool_output = f"[error] {error}"
reward = step_reward(tool, error)
return SubtextArenaObservation(
clip_id=self._current_clip_id,
speaker=clip.get("speaker", ""),
duration_s=float(prosody.get("duration_s", 0.0)),
is_pivot=bool(clip.get("is_pivot", False)),
tool_used=tool,
tool_output=tool_output,
step=self._state.step_count,
max_steps=self._max_steps,
audio_calls_so_far=self._n_audio_calls,
done=False,
reward=reward,
error=error,
)
if tool == "get_transcript":
tool_output = render_transcript(self._current_clip_id, self._scenarios)
elif tool == "get_prosody_features":
tool_output = render_prosody_features(self._current_clip_id, prosody, args)
self._n_audio_calls += 1
elif tool == "get_pitch_contour":
tool_output = render_pitch_contour(self._current_clip_id, prosody, args)
self._n_audio_calls += 1
elif tool == "submit_belief":
return self._submit_and_terminate(args, clip, prosody)
# Per-step delta for non-terminal actions
per_step = step_reward(tool, error)
forced_terminate = self._state.step_count >= self._max_steps
if forced_terminate:
# Force a submission with no label -> apply no_submission penalty
return self._submit_and_terminate(
{"label": None, "confidence": 0.0},
clip,
prosody,
forced=True,
preceding_reward=per_step,
)
return SubtextArenaObservation(
clip_id=self._current_clip_id,
speaker=clip.get("speaker", ""),
duration_s=float(prosody.get("duration_s", 0.0)),
is_pivot=bool(clip.get("is_pivot", False)),
tool_used=tool,
tool_output=tool_output,
step=self._state.step_count,
max_steps=self._max_steps,
audio_calls_so_far=self._n_audio_calls,
done=False,
reward=per_step,
error=error,
)
def _submit_and_terminate(
self,
args: dict,
clip: dict,
prosody: dict,
forced: bool = False,
preceding_reward: float = 0.0,
) -> SubtextArenaObservation:
label = args.get("label")
if isinstance(label, str):
label = label.strip().lower()
if label not in {"sarcastic", "sincere"}:
label = None
else:
label = None
confidence = float(args.get("confidence", 0.5) or 0.5)
gold = "sarcastic" if clip.get("sarcasm") else "sincere"
components = final_reward(
submitted_label=label,
submitted_confidence=confidence,
gold_label=gold,
is_pivot=bool(clip.get("is_pivot", False)),
n_audio_calls=self._n_audio_calls,
n_total_calls=self._n_total_calls,
)
total_reward = components["_total"] + preceding_reward
if forced:
tool_output = (
f"[forced termination after {self._max_steps} steps without submit_belief]\n"
f"Gold label: {gold}. Reward components: {components}"
)
else:
verdict = "CORRECT" if (label == gold) else "WRONG"
tool_output = (
f"Submitted: label={label}, confidence={confidence:.2f}. "
f"Gold: {gold}. {verdict}. Reward components: {components}"
)
self._terminated = True
return SubtextArenaObservation(
clip_id=self._current_clip_id or "",
speaker=clip.get("speaker", ""),
duration_s=float(prosody.get("duration_s", 0.0)),
is_pivot=bool(clip.get("is_pivot", False)),
tool_used="submit_belief",
tool_output=tool_output,
step=self._state.step_count,
max_steps=self._max_steps,
audio_calls_so_far=self._n_audio_calls,
done=True,
reward=round(total_reward, 4),
metadata={
"gold": gold,
"submitted_label": label,
"submitted_confidence": confidence,
"n_audio_calls": self._n_audio_calls,
"n_total_calls": self._n_total_calls,
"is_pivot": bool(clip.get("is_pivot", False)),
"reward_components": components,
},
)
# ------------------------------------------------------------------
# State
# ------------------------------------------------------------------
@property
def state(self) -> State:
return self._state
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