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# Created by: Patrick Lumbantobing, VertoX-AI
# Copyright (c) 2026 VertoX-AI. All rights reserved.
#
# This work is licensed under the Apache-2.0 License.
# To view a copy of this license, visit
# https://www.apache.org/licenses/LICENSE-2.0
"""Benchmark identical ordered metadata requests through injected neutral backends."""
from __future__ import annotations
import itertools
import math
from collections.abc import Mapping
from dataclasses import dataclass
from pathlib import Path
from speech_text_data_aligners.application.annotate import (
_execute_backend,
_prepare_rows,
)
from speech_text_data_aligners.benchmarking.metrics import (
BenchmarkObservation,
GoldReference,
RowKey,
calculate_gold_metrics,
calculate_pairwise_metrics,
)
from speech_text_data_aligners.benchmarking.reports import (
BackendReport,
BenchmarkReport,
)
from speech_text_data_aligners.core.errors import BenchmarkError
from speech_text_data_aligners.core.extraction import extract_boundary_fields
from speech_text_data_aligners.core.protocols import AlignmentBackend
from speech_text_data_aligners.data.metadata import MetadataTable, read_metadata
@dataclass(frozen=True, slots=True)
class BenchmarkOptions:
"""Control benchmark row-failure behavior.
Parameters
----------
strict: Abort on the first preflight, backend, mapping, or validation failure.
"""
strict: bool = False
_DEFAULT_BENCHMARK_OPTIONS = BenchmarkOptions()
def _parse_optional_seconds(value: str, *, row_ordinal: int) -> float | None:
"""Parse one optional finite, non-negative gold timestamp.
Parameters
----------
value: Exact CSV cell; empty means missing reference.
row_ordinal: Zero-based reference row ordinal used in diagnostics.
Returns
-------
Parsed seconds or ``None`` for an empty cell.
Raises
------
BenchmarkError: If a non-empty value is invalid, negative, NaN, or infinite.
"""
if value == "":
return None
try:
seconds = float(value)
except ValueError as error:
raise BenchmarkError(
f"reference row {row_ordinal} has an invalid first_word_end_s"
) from error
if not math.isfinite(seconds) or seconds < 0:
raise BenchmarkError(
f"reference row {row_ordinal} has an invalid first_word_end_s"
)
return seconds
def _read_references(table: MetadataTable) -> dict[RowKey, GoldReference]:
"""Parse the supported sparse reference fields with unambiguous joins.
Parameters
----------
table: Reference table preserving exact CSV cells.
Returns
-------
References keyed by ``(line_id, audio_id)``.
Raises
------
BenchmarkError: If fields are missing, keys repeat, or timestamps are invalid.
"""
required = ("first_word_text", "first_word_end_s")
if any(table.fieldnames.count(name) != 1 for name in required):
raise BenchmarkError("reference CSV lacks the supported first-word fields")
references: dict[RowKey, GoldReference] = {}
for ordinal in range(len(table.rows)):
row = table.row_dict(ordinal)
reference = GoldReference(
line_id=row["line_id"],
audio_id=row["audio_id"],
first_word_text=row["first_word_text"] or None,
first_word_end_s=_parse_optional_seconds(
row["first_word_end_s"], row_ordinal=ordinal
),
)
if reference.key in references:
raise BenchmarkError("reference CSV contains a duplicate join key")
references[reference.key] = reference
return references
def benchmark_backends(
*,
backends: Mapping[str, AlignmentBackend],
metadata_csv: Path,
reference_csv: Path,
audio_dir: Path,
options: BenchmarkOptions = _DEFAULT_BENCHMARK_OPTIONS,
) -> BenchmarkReport:
"""Run identical ordered requests through each injected backend and score results.
Parameters
----------
backends: Insertion-ordered report name to protocol instance mapping.
metadata_csv: Metadata rows to align.
reference_csv: Sparse first-word gold/reference CSV.
audio_dir: Directory containing WAVs resolved from ``audio_id``.
options: Strict or continue-on-error policy.
Returns
-------
Typed per-backend gold/runtime and pairwise-disagreement report.
Raises
------
BenchmarkError: If backend names/reference joins are invalid.
SpeechTextAlignerError: If strict preparation/alignment fails.
Side Effects:
Probes source WAVs and calls each backend exactly once with the same request
tuple; it does not write reports or mutate metadata.
"""
if not backends:
raise BenchmarkError("at least one backend is required")
if any(not name for name in backends):
raise BenchmarkError("benchmark backend report names cannot be empty")
prepared = _prepare_rows(metadata_csv, audio_dir, strict=options.strict)
references = _read_references(read_metadata(reference_csv))
metadata_keys = {
(
prepared.table.row_dict(index)["line_id"],
prepared.table.row_dict(index)["audio_id"],
)
for index in range(len(prepared.table.rows))
}
if set(references).difference(metadata_keys):
raise BenchmarkError("reference CSV contains rows absent from metadata")
request_by_id = {request.request_id: request for request in prepared.requests}
backend_reports: list[BackendReport] = []
for report_name, backend in backends.items():
outcomes = _execute_backend(prepared, backend, strict=options.strict)
observations: list[BenchmarkObservation] = []
for ordinal, outcome in enumerate(outcomes):
row = prepared.table.row_dict(ordinal)
boundaries = None
runtime = None
failure_code = None
if outcome.result is not None:
request = request_by_id[outcome.request_id]
boundaries = extract_boundary_fields(request, outcome.result)
runtime = outcome.result.runtime_s
native_artifacts = outcome.result.provenance.artifacts
else:
assert outcome.failure is not None
failure_code = outcome.failure.code
native_artifacts = (
outcome.failure.provenance.artifacts
if outcome.failure.provenance is not None
else ()
)
observations.append(
BenchmarkObservation(
backend_name=report_name,
line_id=row["line_id"],
audio_id=row["audio_id"],
boundaries=boundaries,
runtime_s=runtime,
failure_code=failure_code,
native_artifacts=native_artifacts,
)
)
observations_tuple = tuple(observations)
backend_reports.append(
BackendReport(
backend_name=report_name,
observations=observations_tuple,
gold_metrics=calculate_gold_metrics(observations_tuple, references),
)
)
pairwise = tuple(
calculate_pairwise_metrics(left.observations, right.observations)
for left, right in itertools.combinations(backend_reports, 2)
)
return BenchmarkReport(tuple(backend_reports), pairwise)
def benchmark_backend(
*,
backend: AlignmentBackend,
metadata_csv: Path,
reference_csv: Path,
audio_dir: Path,
options: BenchmarkOptions = _DEFAULT_BENCHMARK_OPTIONS,
) -> BenchmarkReport:
"""Benchmark one injected backend as a convenience wrapper.
Parameters
----------
backend: Backend-neutral protocol instance.
metadata_csv: Metadata rows to align.
reference_csv: Sparse first-word gold/reference CSV.
audio_dir: Directory containing WAVs resolved from ``audio_id``.
options: Strict or continue-on-error policy.
Returns
-------
A typed report containing one backend and no pairwise comparisons.
Raises
------
SpeechTextAlignerError: If validation/preparation fails as documented by
:func:`benchmark_backends`.
"""
return benchmark_backends(
backends={backend.name: backend},
metadata_csv=metadata_csv,
reference_csv=reference_csv,
audio_dir=audio_dir,
options=options,
)
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