Faster Whisper transcription with CTranslate2
This report summarizes scanner evidence for SYSTRAN/faster-whisper, a Python project with 25,810 GitHub stars. SynthScan examined 6,253 lines of code across 28 source files, recording 35 pattern matches distributed across 8 syntactic categories. The legacy adjusted pattern signal is 8.5 (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.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 27 finding(s).
Are imports declared and package/API names verified? 7 finding(s).
Are wrappers, guards, and repeated structures necessary? 17 finding(s).
Which changes need focused human review? 35 finding(s).
Run additional scans to build a history of the legacy pattern signal. Score changes are not a measure of AI adoption or code quality.
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.
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.
The scanner identified 35 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.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM⚡ | faster_whisper/transcribe.py | 1298 | # skip silence before possible hallucinations | COMMENT |
| MEDIUM⚡ | faster_whisper/transcribe.py | 1302 | # if first segment might be a hallucination, skip leading silence | COMMENT |
| MEDIUM⚡ | faster_whisper/transcribe.py | 1310 | # skip silence before any possible hallucination that is surrounded | COMMENT |
| MEDIUM⚡ | faster_whisper/transcribe.py | 1311 | # by silence or more hallucinations | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_utils.py | 26 | def test_download_model_in_cache(tmpdir): | CODE |
| LOW | tests/test_tokenizer.py | 105 | def test_suppressed_tokens_minus_value(): | CODE |
| LOW | tests/test_transcribe.py | 100 | def test_prefix_with_timestamps(jfk_path): | CODE |
| LOW | tests/test_transcribe.py | 160 | def test_multilingual_transcription(data_dir): | CODE |
| LOW | tests/test_transcribe.py | 237 | def test_transcribe_signature(): | CODE |
| LOW | tests/test_transcribe.py | 247 | def test_monotonic_timestamps(physcisworks_path): | CODE |
| LOW | tests/test_transcribe.py | 274 | def test_cliptimestamps_segments(jfk_path): | CODE |
| LOW | tests/test_transcribe.py | 295 | def test_cliptimestamps_timings(physcisworks_path): | CODE |
| LOW | faster_whisper/transcribe.py | 582 | def _batched_segments_generator( | CODE |
| LOW | faster_whisper/transcribe.py | 1029 | def _split_segments_by_timestamps( | CODE |
| LOW | faster_whisper/transcribe.py | 1855 | def restore_speech_timestamps( | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | faster_whisper/__init__.py | 1 | CODE | |
| LOW | faster_whisper/__init__.py | 2 | CODE | |
| LOW | faster_whisper/__init__.py | 2 | CODE | |
| LOW | faster_whisper/__init__.py | 3 | CODE | |
| LOW | faster_whisper/__init__.py | 3 | CODE | |
| LOW | faster_whisper/__init__.py | 3 | CODE | |
| LOW | faster_whisper/__init__.py | 4 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | faster_whisper/utils.py | 57 | Downloads a CTranslate2 Whisper model from the Hugging Face Hub. Args: size_or_id: Size of the model to downl | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | faster_whisper/feature_extractor.py | 68 | CODE | |
| LOW | faster_whisper/transcribe.py | 254 | CODE | |
| LOW | faster_whisper/transcribe.py | 623 | CODE | |
| LOW | faster_whisper/transcribe.py | 749 | CODE | |
| LOW | faster_whisper/vad.py | 384 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | faster_whisper/transcribe.py | 254 | CODE | |
| LOW | faster_whisper/transcribe.py | 731 | CODE | |
| LOW | faster_whisper/transcribe.py | 1108 | CODE | |
| LOW | faster_whisper/transcribe.py | 1572 | CODE | |
| LOW | faster_whisper/vad.py | 51 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | faster_whisper/__init__.py | 6 | __all__ = [ | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | faster_whisper/vad.py | 57 | """This method is used for splitting long audios into speech chunks using silero VAD. | STRING |