Deezer source separation library including pretrained models.
This report summarizes scanner evidence for deezer/spleeter, a Python project with 28,480 GitHub stars. SynthScan examined 5,374 lines of code across 56 source files, recording 24 pattern matches distributed across 6 syntactic categories. The legacy adjusted pattern signal is 9.7 (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? 23 finding(s).
Are imports declared and package/API names verified? 0 finding(s).
Are wrappers, guards, and repeated structures necessary? 14 finding(s).
Which changes need focused human review? 24 finding(s).
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.
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 24 distinct pattern matches across 6 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 |
|---|---|---|---|---|
| HIGH | spleeter/utils/configuration.py | 21 | Load configuration from the given descriptor. Could be either a `spleeter:` prefixed embedded configuration nam | STRING |
| HIGH | spleeter/utils/tensor.py | 97 | Pad and partition a tensor into segment of len `segment_len` along the first dimension. The tensor is padded wi | STRING |
| HIGH | spleeter/audio/ffmpeg.py | 72 | Loads the audio file denoted by the given path and returns it data as a waveform. Parameters: | STRING |
| HIGH | spleeter/audio/spectrogram.py | 96 | Time stretch a spectrogram preserving shape with random ratio in tensorflow. Applies time_stretch to spectrogra | STRING |
| HIGH | spleeter/model/__init__.py | 393 | Extend mask, from reduced number of frequency bin to the number of frequency bin in the STFT. | STRING |
| HIGH | spleeter/model/provider/__init__.py | 60 | Ensures required model is available at given location. Parameters: model_directory (str): | STRING |
| HIGH | spleeter/model/provider/github.py | 100 | Downloads and returns reference checksum for the given model name. Parameters: name (str): | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | spleeter/separator.py | 110 | def _get_prediction_generator(self, data: dict) -> Generator: | CODE |
| LOW | spleeter/dataset.py | 297 | def check_parameters_compatibility(self): | CODE |
| LOW | spleeter/audio/convertor.py | 118 | def db_uint_spectrogram_to_gain( | CODE |
| LOW | spleeter/model/__init__.py | 55 | def get_input_dict_placeholders(self): | CODE |
| LOW | spleeter/model/__init__.py | 71 | def get_input_dict_placeholders(self): | CODE |
| LOW | spleeter/model/__init__.py | 352 | def _build_mwf_output_waveform(self) -> Dict: | CODE |
| LOW | spleeter/model/__init__.py | 466 | def _build_manual_output_waveform(self, masked_stft: Dict) -> Dict: | CODE |
| LOW | spleeter/model/functions/unet.py | 46 | def _get_conv_activation_layer(params: Dict) -> Any: | CODE |
| LOW | spleeter/model/functions/unet.py | 64 | def _get_deconv_activation_layer(params: Dict) -> Any: | CODE |
| LOW | tests/test_train.py | 50 | def generate_fake_training_dataset( | CODE |
| LOW | tests/test_eval.py | 27 | def generate_fake_eval_dataset(path): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | spleeter/dataset.py | 517 | CODE | |
| LOW | spleeter/__main__.py | 110 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | spleeter/model/provider/github.py | 131 | CODE | |
| LOW | tests/test_eval.py | 46 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | docker/conda-entrypoint.sh | 1 | #!/bin/bash | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | spleeter/audio/adapter.py | 119 | except Exception as e: | CODE |