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AI Slop Code RIsk Monitor

m-bain/whisperX

WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)

11.5 Moderate pattern signal View on GitHub

Maintenance and Governance Review

This report summarizes scanner evidence for m-bain/whisperX, a Python project with 24,457 GitHub stars. SynthScan examined 4,169 lines of code across 26 source files, recording 41 pattern matches distributed across 8 syntactic categories. The legacy adjusted pattern signal is 11.5 (Moderate pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

11.5
Adjusted Signal
11.5
Raw Pattern Score
100%
Time Factor
2026-09-26
Last Push
24.5K
Stars
Python
Language
4.2K
Lines of Code
26
Files
41
Pattern Hits
2026-10-12
Scan Date
0.00
HC Hit Rate

Risk Review · v3

Use each vector to guide manual verification of the underlying findings.

Cognitive Ownership Risk 37.4/100 · MEDIUM

Can maintainers explain intent, assumptions, and trade-offs? 33 finding(s).

Hallucinated Dependency & Supply Chain Risk 6.9/100 · LOW

Are imports declared and package/API names verified? 6 finding(s).

Agent Over-Engineering Ratio 24.1/100 · LOW

Are wrappers, guards, and repeated structures necessary? 19 finding(s).

PR Review Velocity Impact 41.7/100 · MEDIUM

Which changes need focused human review? 41 finding(s).

What These Metrics Mean

Adjusted Signal
Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score
Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor
A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits
Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate
High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files
Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 0MEDIUM 2LOW 39

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 41 distinct pattern matches across 8 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Hyper-Verbose Identifiers14 hits · 17 pts
SeverityFileLineSnippetContext
LOWwhisperx/asr.py23def find_numeral_symbol_tokens(tokenizer):CODE
LOWwhisperx/SubtitlesProcessor.py47 def estimate_timestamp_for_word(self, words, i, next_segment_start_time=None):CODE
LOWwhisperx/SubtitlesProcessor.py99 def determine_advanced_split_points(self, segment, next_segment_start_time=None):CODE
LOWwhisperx/SubtitlesProcessor.py141 def generate_subtitles_from_split_points(self, segment, split_points, next_start_time=None):CODE
LOW⚡tests/test_word_timestamp_interpolation.py88 def test_known_chars_get_timestamps(self):CODE
LOW⚡tests/test_word_timestamp_interpolation.py96 def test_unknown_word_gets_timestamps(self):CODE
LOW⚡tests/test_word_timestamp_interpolation.py105 def test_mixed_word_gets_timestamps(self):CODE
LOW⚡tests/test_word_timestamp_interpolation.py114 def test_unknown_word_does_not_corrupt_neighbors(self):CODE
LOW⚡tests/test_word_timestamp_interpolation.py124 def test_all_unknown_segment_gets_timestamps(self):CODE
LOW⚡tests/test_word_timestamp_interpolation.py132 def test_timestamps_are_ordered(self):CODE
LOWtests/test_word_timestamp_interpolation.py155 def test_ignore_does_not_crash(self):CODE
LOWtests/test_word_timestamp_interpolation.py164 def test_ignore_segments_have_valid_timestamps(self):CODE
LOWtests/test_word_timestamp_interpolation.py177 def test_ignore_preserves_nans(self):CODE
LOWtests/test_word_timestamp_interpolation.py188 def test_ignore_word_assignment_integration(self):CODE
Deep Nesting12 hits · 12 pts
SeverityFileLineSnippetContext
LOWwhisperx/alignment.py117CODE
LOWwhisperx/asr.py462CODE
LOWwhisperx/asr.py38CODE
LOWwhisperx/asr.py140CODE
LOWwhisperx/asr.py257CODE
LOWwhisperx/diarize.py185CODE
LOWwhisperx/utils.py252CODE
LOWwhisperx/utils.py262CODE
LOWwhisperx/transcribe.py20CODE
LOWwhisperx/SubtitlesProcessor.py76CODE
LOWwhisperx/SubtitlesProcessor.py99CODE
LOWwhisperx/vads/pyannote.py108CODE
Unused Imports6 hits · 6 pts
SeverityFileLineSnippetContext
LOWwhisperx/vads/__init__.py1CODE
LOWwhisperx/vads/__init__.py2CODE
LOWwhisperx/vads/__init__.py3CODE
LOWwhisperx/vads/vad.py3CODE
LOWwhisperx/vads/vad.py4CODE
LOWwhisperx/vads/vad.py4CODE
Excessive Try-Catch Wrapping2 hits · 3 pts
SeverityFileLineSnippetContext
LOWwhisperx/alignment.py103 except Exception as e:CODE
MEDIUMwhisperx/alignment.py105 print(f"Error loading model from huggingface, check https://huggingface.co/models for finetuned wav2vec2.0 mCODE
AI Slop Vocabulary1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUMwhisperx/alignment.py325 # increment word_idx, nltk word tokenization would probably be more robust here, but us space for now...COMMENT
AI Structural Patterns3 hits · 3 pts
SeverityFileLineSnippetContext
LOWwhisperx/asr.py462CODE
LOWwhisperx/asr.py257CODE
LOWtests/test_word_timestamp_interpolation.py86CODE
Redundant / Tautological Comments2 hits · 3 pts
SeverityFileLineSnippetContext
LOWwhisperx/diarize.py237 # Assign speaker to wordsCOMMENT
LOWwhisperx/vads/pyannote.py34 # Check if the resolved model file existsCOMMENT
Over-Commented Block1 hit · 1 pts
SeverityFileLineSnippetContext
LOWwhisperx/alignment.py461def backtrack(trellis, emission, tokens, blank_id=0):COMMENT