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d9af7ee | 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 | """Service layer wiring UI callbacks to domain logic."""
from __future__ import annotations
from contextlib import contextmanager
from dataclasses import dataclass
import time
from typing import Dict, Iterable, Iterator, List, Sequence, Tuple
import numpy as np
from .audio import load_audio_file, play_until_boundary, get_patch_info
from .constraints import (
ConstraintDefinition,
default_thresholds_for_metric,
get_constraint_definition,
guard_range_for_metric,
iter_constraint_definitions,
)
from .data_loader import PrecomputedRepository
from .patching import apply_patching, compute_patch_statistics
from .visualization import create_visualization
MetricName = str # "Entropy" or "Perplexity"
def normalize_metric_name(metric: MetricName) -> str:
key = metric.lower()
if key not in {"entropy", "perplexity"}:
raise ValueError(f"Unsupported metric '{metric}'")
return key
@dataclass
class PatchService:
"""Stateful helper for the Gradio UI callbacks."""
repository: PrecomputedRepository
@classmethod
def from_data_root(cls) -> "PatchService":
repo = PrecomputedRepository()
repo.load()
return cls(repository=repo)
# ------------------------------------------------------------------ #
# Data lookups
# ------------------------------------------------------------------ #
def audio_ids(self) -> Sequence[str]:
return list(self.repository.ids())
def get_data(self, file_id: str) -> Dict[str, object]:
return self.repository.get(file_id)
def file_info_markdown(self, file_id: str) -> str:
if not file_id or file_id not in self.repository.records:
return "No file selected"
data = self.repository.get(file_id)
return (
"### Audio Information\n\n"
f"**File ID:** `{data['file_id']}`\n\n"
f"**Duration:** {data['audio_duration_sec']:.2f}s\n\n"
f"**Tokens:** {data['num_tokens']}\n\n"
"**Entropy Statistics:**\n"
f"- Average: {data['avg_entropy']:.2f} bits\n"
f"- Min: {data['min_entropy']:.2f} bits\n"
f"- Max: {data['max_entropy']:.2f} bits\n\n"
"**Perplexity Statistics:**\n"
f"- Average: {data['avg_perplexity']:.2f}\n\n"
"**Transcript:**\n"
f"> {data['transcript']}\n"
)
# ------------------------------------------------------------------ #
# Constraint helpers
# ------------------------------------------------------------------ #
def constraint_definitions(self) -> Iterable[ConstraintDefinition]:
return iter_constraint_definitions()
def guard_slider_config(self, metric: MetricName):
return guard_range_for_metric(metric)
def threshold_defaults(self, metric: MetricName) -> Dict[str, float]:
return dict(default_thresholds_for_metric(metric))
# ------------------------------------------------------------------ #
# Patching pipeline
# ------------------------------------------------------------------ #
def run_patching(
self,
file_id: str,
metric_name: MetricName,
selected_constraints: Sequence[str],
guard_threshold: float,
thresholds: Dict[str, float],
) -> Tuple[object, str, Tuple[int, np.ndarray], Tuple[int, np.ndarray], List[List[int]]]:
"""Apply patching constraints and return visualization artefacts."""
metric_key = normalize_metric_name(metric_name)
data = self.repository.get(file_id)
if not selected_constraints:
raise ValueError("At least one constraint must be selected")
metric = np.array(
data["entropies"] if metric_key == "entropy" else data["perplexities"]
)
constraints = {name: thresholds[name] for name in selected_constraints}
constraints["min_metric"] = float(guard_threshold)
with log_phase("apply_patching()"):
patches = apply_patching(metric, constraints=constraints)
with log_phase("compute_patch_statistics()"):
stats = compute_patch_statistics(patches)
display_threshold = thresholds[selected_constraints[0]]
with log_phase("create_visualization()"):
figure = create_visualization(
data=data,
patches=patches,
metric_name=metric_key,
threshold=display_threshold,
)
stats_text = self._format_stats(
metric_key=metric_key,
guard_threshold=guard_threshold,
selected_constraints=selected_constraints,
thresholds=thresholds,
stats=stats,
data=data,
)
with log_phase("load_audio_file(original)"):
original_audio = load_audio_file(data["audio_path"])
with log_phase("load_audio_file(reconstructed)"):
reconstructed_audio = load_audio_file(data["reconstructed_audio_path"])
return figure, stats_text, original_audio, reconstructed_audio, patches
def _format_stats(
self,
metric_key: str,
guard_threshold: float,
selected_constraints: Sequence[str],
thresholds: Dict[str, float],
stats: Dict[str, float],
data: Dict[str, object],
) -> str:
guard = guard_range_for_metric(metric_key)
lines = []
for name in selected_constraints:
definition = get_constraint_definition(name)
lines.append(
f" - **{definition.label}**: {thresholds[name]:.2f} ({definition.description})"
)
guard_label = guard.label or "Minimum Metric for Boundary"
guard_value = f"{guard_threshold:.2f}" if metric_key == "entropy" else f"{guard_threshold:.1f}"
lines.append(f" - **{guard_label}**: {guard_value} ({guard.description})")
constraints_display = "\n".join(lines)
return f"""
### Patch Statistics
**Active Constraints** (OR logic):
{constraints_display}
- **Total Patches:** {stats['num_patches']}
- **Average Size:** {stats['avg_size']:.2f} tokens ({stats['avg_size']/40:.3f}s)
- **Min Size:** {stats['min_size']} tokens
- **Max Size:** {stats['max_size']} tokens
- **Median Size:** {stats['median_size']:.1f} tokens
- **Std Dev:** {stats['std_size']:.2f} tokens
**Inference Efficiency:**
- Entropy model steps: {data['num_tokens']}
- BLT global transformer steps: {stats['num_patches']}
- **Speedup:** {data['num_tokens'] / stats['num_patches']:.2f}x
"""
# ------------------------------------------------------------------ #
# Audio utilities
# ------------------------------------------------------------------ #
def play_until_boundary(
self, file_id: str, current_token: int, patches: Sequence[Sequence[int]]
) -> Tuple[Tuple[int, np.ndarray], str, int]:
if not patches:
raise ValueError("Patches must be computed before playback.")
data = self.repository.get(file_id)
with log_phase("play_until_boundary()"):
sr_audio = play_until_boundary(data["audio_path"], patches, current_token)
patch_info = None
next_token = 0
for idx, patch in enumerate(patches):
if current_token in patch:
patch_info = get_patch_info(patches, idx)
if idx + 1 < len(patches):
next_token = patches[idx + 1][0]
else:
next_token = patches[0][0]
break
if not patch_info:
return sr_audio, f"Token {current_token} not found in any patch", next_token
info_text = (
f"**Playing Patch #{patch_info['patch_index']} / {len(patches)}**\n\n"
f"- Tokens: {patch_info['start_token']} → {patch_info['end_token']} "
f"({patch_info['num_tokens']} tokens)\n"
f"- Time: {patch_info['start_time_sec']:.2f}s → {patch_info['end_time_sec']:.2f}s\n"
f"- Duration: {patch_info['duration_sec']:.2f}s\n"
)
return sr_audio, info_text, next_token
@contextmanager
def log_phase(label: str) -> Iterator[None]:
"""Profile a phase and emit duration in seconds."""
start = time.perf_counter()
yield
elapsed = time.perf_counter() - start
print(f" {label}: {elapsed:.3f}s")
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