Repository Analysis

m-bain/whisperX

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

11.5 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of m-bain/whisperX, a Python project with 23,062 GitHub stars. SynthScan v2.0 examined 4,000 lines of code across 26 source files, recording 39 pattern matches distributed across 8 syntactic categories. The overall adjusted score of 11.5 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

11.5
Adjusted Score
11.5
Raw Score
100%
Time Factor
2026-07-13
Last Push
23.1K
Stars
Python
Language
4.0K
Lines of Code
26
Files
39
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 2LOW 37

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 39 distinct pattern matches across 8 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. 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 diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Hyper-Verbose Identifiers14 hits · 17 pts
SeverityFileLineSnippetContext
LOWwhisperx/asr.py22def 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
LOWtests/test_word_timestamp_interpolation.py88 def test_known_chars_get_timestamps(self):CODE
LOWtests/test_word_timestamp_interpolation.py96 def test_unknown_word_gets_timestamps(self):CODE
LOWtests/test_word_timestamp_interpolation.py105 def test_mixed_word_gets_timestamps(self):CODE
LOWtests/test_word_timestamp_interpolation.py114 def test_unknown_word_does_not_corrupt_neighbors(self):CODE
LOWtests/test_word_timestamp_interpolation.py124 def test_all_unknown_segment_gets_timestamps(self):CODE
LOWtests/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 Nesting10 hits · 10 pts
SeverityFileLineSnippetContext
LOWwhisperx/alignment.py117CODE
LOWwhisperx/asr.py315CODE
LOWwhisperx/asr.py114CODE
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.py315CODE
LOWwhisperx/asr.py197CODE
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