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35d483e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 | """Canonical record schema and validation for turn-detection datasets.
The upstream smart-turn v3.2 schema uses ``endpoint_bool``, ``midfiller`` and
``endfiller``. In particular, filler values are nullable: ``None`` means that
the example was not annotated for that auxiliary task and must not silently be
converted to ``False``.
"""
from __future__ import annotations
from dataclasses import dataclass, field
import math
from typing import Any, Mapping
_ENDPOINT_FIELDS = ("endpoint_bool", "endpoint", "is_endpoint", "label", "target")
_MID_FILLER_FIELDS = ("midfiller", "mid_filler", "is_midfiller")
_END_FILLER_FIELDS = ("endfiller", "end_filler", "is_endfiller")
_SYNTHETIC_FIELDS = ("synthetic", "is_synthetic")
_ID_FIELDS = ("id", "record_id", "example_id", "utterance_id")
_DATASET_FIELDS = ("dataset", "source_dataset", "data_source")
_TRUE_VALUES = {
"1",
"true",
"t",
"yes",
"y",
"end",
"ended",
"endpoint",
"complete",
"completed",
"finished",
"final",
}
_FALSE_VALUES = {
"0",
"false",
"f",
"no",
"n",
"hold",
"continue",
"continuation",
"incomplete",
"not_endpoint",
"non_endpoint",
"nonendpoint",
}
_NULL_VALUES = {"", "na", "n/a", "nan", "none", "null", "unknown", "unlabeled"}
@dataclass(frozen=True)
class ValidationIssue:
"""A machine-readable validation finding."""
severity: str
code: str
message: str
field: str | None = None
def to_dict(self) -> dict[str, str]:
result = {"severity": self.severity, "code": self.code, "message": self.message}
if self.field is not None:
result["field"] = self.field
return result
class RecordValidationError(ValueError):
"""Raised when strict normalization encounters one or more invalid fields."""
def __init__(self, issues: list[ValidationIssue]) -> None:
self.issues = tuple(issues)
summary = "; ".join(f"{item.field or 'record'}: {item.message}" for item in issues)
super().__init__(summary)
@dataclass(frozen=True)
class TurnRecord:
"""Normalized representation of one labeled audio example."""
record_id: str
audio: Any
language: str | None
endpoint: bool | None
midfiller: bool | None
endfiller: bool | None
synthetic: bool | None
dataset: str | None
spoken_text: str | None
source_file: str | None = None
source_row: int | None = None
metadata: Mapping[str, Any] = field(default_factory=dict)
def to_dict(self, *, include_audio: bool = True) -> dict[str, Any]:
"""Convert to a plain mapping, optionally excluding the heavy audio value."""
result: dict[str, Any] = {
"record_id": self.record_id,
"language": self.language,
"endpoint": self.endpoint,
"midfiller": self.midfiller,
"endfiller": self.endfiller,
"synthetic": self.synthetic,
"dataset": self.dataset,
"spoken_text": self.spoken_text,
"source_file": self.source_file,
"source_row": self.source_row,
"metadata": dict(self.metadata),
}
if include_audio:
result["audio"] = self.audio
return result
@dataclass(frozen=True)
class NormalizationResult:
"""A permissively normalized record together with validation findings."""
record: TurnRecord
issues: tuple[ValidationIssue, ...]
@property
def errors(self) -> tuple[ValidationIssue, ...]:
return tuple(item for item in self.issues if item.severity == "error")
@property
def warnings(self) -> tuple[ValidationIssue, ...]:
return tuple(item for item in self.issues if item.severity == "warning")
def normalize_nullable_bool(value: Any, *, field_name: str = "value") -> bool | None:
"""Normalize a nullable boolean without conflating missing and false labels.
Accepted values include booleans, numeric 0/1 and common textual forms. A
null-like value returns ``None``. Other values raise ``ValueError``.
"""
if value is None:
return None
if isinstance(value, bool):
return value
if isinstance(value, float) and math.isnan(value):
return None
if isinstance(value, (int, float)) and value in (0, 1):
return bool(value)
if isinstance(value, str):
normalized = value.strip().casefold().replace("-", "_").replace(" ", "_")
if normalized in _NULL_VALUES:
return None
if normalized in _TRUE_VALUES:
return True
if normalized in _FALSE_VALUES:
return False
raise ValueError(f"{field_name} must be boolean, 0/1, or null; got {value!r}")
def _first_present(record: Mapping[str, Any], names: tuple[str, ...]) -> tuple[str | None, Any]:
for name in names:
if name in record:
return name, record[name]
return None, None
def _optional_text(value: Any) -> str | None:
if value is None:
return None
text = str(value).strip()
return text or None
def _optional_int(value: Any) -> int | None:
if value is None:
return None
try:
return int(value)
except (TypeError, ValueError):
return None
def normalize_record_with_issues(record: Mapping[str, Any]) -> NormalizationResult:
"""Normalize a raw row and retain all validation errors and warnings."""
issues: list[ValidationIssue] = []
endpoint_key, endpoint_raw = _first_present(record, _ENDPOINT_FIELDS)
endpoint: bool | None
if endpoint_key is None:
endpoint = None
issues.append(
ValidationIssue("error", "missing_endpoint", "required endpoint label is missing", "endpoint")
)
else:
try:
endpoint = normalize_nullable_bool(endpoint_raw, field_name=endpoint_key)
except ValueError as exc:
endpoint = None
issues.append(ValidationIssue("error", "invalid_endpoint", str(exc), endpoint_key))
if endpoint is None:
issues.append(
ValidationIssue("error", "null_endpoint", "endpoint label cannot be null", endpoint_key)
)
normalized_booleans: dict[str, bool | None] = {}
for canonical, aliases in (
("midfiller", _MID_FILLER_FIELDS),
("endfiller", _END_FILLER_FIELDS),
("synthetic", _SYNTHETIC_FIELDS),
):
key, raw = _first_present(record, aliases)
if key is None:
normalized_booleans[canonical] = None
continue
try:
normalized_booleans[canonical] = normalize_nullable_bool(raw, field_name=key)
except ValueError as exc:
normalized_booleans[canonical] = None
issues.append(ValidationIssue("error", f"invalid_{canonical}", str(exc), key))
id_key, raw_id = _first_present(record, _ID_FIELDS)
source_file = _optional_text(record.get("__source_file", record.get("source_file")))
source_row = _optional_int(record.get("__source_row", record.get("source_row")))
record_id = _optional_text(raw_id)
if record_id is None:
if source_file is not None and source_row is not None:
record_id = f"{source_file}#{source_row}"
else:
record_id = ""
issues.append(ValidationIssue("warning", "missing_id", "record has no explicit id", id_key or "id"))
audio = record.get("audio")
if audio is None:
issues.append(ValidationIssue("error", "missing_audio", "audio value is missing", "audio"))
language = _optional_text(record.get("language", record.get("lang")))
if language is None:
issues.append(ValidationIssue("warning", "missing_language", "language is missing", "language"))
dataset_key, raw_dataset = _first_present(record, _DATASET_FIELDS)
dataset = _optional_text(raw_dataset)
if dataset is None:
issues.append(
ValidationIssue("warning", "missing_dataset", "source dataset is missing", dataset_key or "dataset")
)
spoken_text = _optional_text(record.get("spoken_text", record.get("transcript", record.get("text"))))
consumed = {
"audio",
"language",
"lang",
"spoken_text",
"transcript",
"text",
"__source_file",
"source_file",
"__source_row",
"source_row",
*_ENDPOINT_FIELDS,
*_MID_FILLER_FIELDS,
*_END_FILLER_FIELDS,
*_SYNTHETIC_FIELDS,
*_ID_FIELDS,
*_DATASET_FIELDS,
}
metadata = {key: value for key, value in record.items() if key not in consumed}
normalized = TurnRecord(
record_id=record_id,
audio=audio,
language=language,
endpoint=endpoint,
midfiller=normalized_booleans["midfiller"],
endfiller=normalized_booleans["endfiller"],
synthetic=normalized_booleans["synthetic"],
dataset=dataset,
spoken_text=spoken_text,
source_file=source_file,
source_row=source_row,
metadata=metadata,
)
return NormalizationResult(normalized, tuple(issues))
def normalize_record(record: Mapping[str, Any], *, strict: bool = True) -> TurnRecord:
"""Return a canonical record, raising on invalid fields in strict mode."""
result = normalize_record_with_issues(record)
if strict and result.errors:
raise RecordValidationError(list(result.errors))
return result.record
def validate_record(record: Mapping[str, Any] | TurnRecord) -> tuple[ValidationIssue, ...]:
"""Validate a raw mapping or an already-normalized :class:`TurnRecord`."""
if isinstance(record, Mapping):
return normalize_record_with_issues(record).issues
issues: list[ValidationIssue] = []
if not record.record_id:
issues.append(ValidationIssue("warning", "missing_id", "record has no explicit id", "record_id"))
if record.audio is None:
issues.append(ValidationIssue("error", "missing_audio", "audio value is missing", "audio"))
if not isinstance(record.endpoint, bool):
issues.append(ValidationIssue("error", "invalid_endpoint", "endpoint must be boolean", "endpoint"))
for field_name in ("midfiller", "endfiller", "synthetic"):
value = getattr(record, field_name)
if value is not None and not isinstance(value, bool):
issues.append(
ValidationIssue("error", f"invalid_{field_name}", f"{field_name} must be boolean or null", field_name)
)
return tuple(issues)
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