diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
new file mode 100644
index 0000000..be7cf3d
--- /dev/null
+++ b/.github/FUNDING.yml
@@ -0,0 +1,3 @@
+github: MattyB95
+ko_fi: mattyb95
+thanks_dev: u/gh/MattyB95
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index a38084f..ad96114 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -26,11 +26,11 @@ jobs:
       - uses: actions/checkout@v6
 
       - name: Initialize CodeQL
-        uses: github/codeql-action/init@v3
+        uses: github/codeql-action/init@v4
         with:
           languages: python
 
       - name: Perform CodeQL Analysis
-        uses: github/codeql-action/analyze@v3
+        uses: github/codeql-action/analyze@v4
         with:
           category: "/language:python"
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7de1d53..c255342 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,47 @@ Jabberjay uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ---
 
+## [0.0.7] — 2026-03-16
+
+### Added
+- **`CITATION.cff`** — machine-readable citation metadata for academic use;
+  GitHub surfaces this as a "Cite this repository" button; compatible with
+  Zenodo, Zotero, and other reference managers
+- **Sample rate validation** — `detect()` now raises `ValueError` immediately
+  for zero or negative sample rates when a pre-loaded audio tuple is passed,
+  preventing silent failures in downstream model inference
+- **Test coverage for new behaviour** — 7 new tests covering sample rate
+  validation, `BytesIO` buffer cleanup on both success and error paths,
+  figure cleanup on error paths, and `Classical.predict()` bonafide/spoof paths
+
+### Changed
+- **Dependency minimum versions pinned** — all runtime dependencies now carry
+  lower-bound constraints (`torch>=2.0`, `transformers>=4.30`,
+  `huggingface-hub>=0.20`, `librosa>=0.10`, etc.) to prevent silent
+  incompatibilities on fresh installs
+- **Contact and copyright updated** — maintainer email changed to
+  `Matthew.Boakes@Gmail.com` across `CODE_OF_CONDUCT.md`, `SECURITY.md`, and
+  `pyproject.toml`; `LICENSE` updated to `2024-2026 Matthew Boakes and The
+  Alan Turing Institute`
+- **`CONTRIBUTING.md` model template corrected** — example `run.py` now uses
+  `cast()` + `normalize_pipeline_scores()` (matching real handlers) and the
+  handler example uses `_result_from_scores()` instead of building
+  `DetectionResult` manually; label normaliser variable names corrected to
+  `_BONAFIDE_SUBSTR` / `_SPOOF_SUBSTR` / `_BONAFIDE_EXACT` / `_SPOOF_EXACT`
+
+### Fixed
+- **`VIT/utility.py`** — `BytesIO` buffer is now always closed in the
+  `finally` block, preventing a resource leak if `Image.open()` or
+  `img.load()` raises
+- **`Classical/run.py`** — renamed `model` variable to `model_path` to
+  accurately reflect that `download_pretrained_model()` returns a file path,
+  not a model object
+- **`_result_from_scores()`** — parameter type restored to
+  `list[PredictionScore]`, keeping the type contract consistent end-to-end
+  from model handlers through to `DetectionResult.scores`
+
+---
+
 ## [0.0.6] — 2026-03-16
 
 ### Added
@@ -117,6 +158,7 @@ Jabberjay uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 - Command-line interface (`jabberjay <audio>`)
 - GitHub Actions CI workflow and ruff linting
 
+[0.0.7]: https://github.com/MattyB95/Jabberjay/compare/v0.0.6...HEAD
 [0.0.6]: https://github.com/MattyB95/Jabberjay/compare/v0.0.5...v0.0.6
 [0.0.5]: https://github.com/MattyB95/Jabberjay/compare/v0.0.4...v0.0.5
 [0.0.4]: https://github.com/MattyB95/Jabberjay/compare/v0.0.3...v0.0.4
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..8f5c329
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,31 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it using the metadata below."
+type: software
+title: Jabberjay
+abstract: >
+  Jabberjay is a unified Python API and CLI for synthetic voice detection.
+  It brings together state-of-the-art deepfake audio detection models —
+  including ViT, AST, Wav2Vec2, HuBERT, WavLM, RawNet2, and a classical
+  baseline — under a single consistent interface, allowing researchers and
+  practitioners to detect AI-generated speech without wrestling with
+  individual model dependencies and conventions.
+authors:
+  - family-names: Boakes
+    given-names: Matthew
+    email: Matthew.Boakes@Gmail.com
+    orcid: "https://orcid.org/0000-0002-9377-6240"
+repository-code: "https://github.com/MattyB95/Jabberjay"
+url: "https://mattyb95.github.io/Jabberjay"
+repository-artifact: "https://pypi.org/project/Jabberjay/"
+license: MIT
+version: 0.0.7
+date-released: "2026-03-16"
+keywords:
+  - synthetic voice detection
+  - deepfake detection
+  - anti-spoofing
+  - audio classification
+  - speech
+  - ASVspoof
+  - machine learning
+  - transformers
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
index a516ca9..f17f647 100644
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -36,7 +36,7 @@ This Code of Conduct applies within all community spaces, and also applies when
 
 ## Enforcement
 
-Instances of abusive, harassing, or otherwise unacceptable behaviour may be reported to the community leaders responsible for enforcement at **mboakes@turing.ac.uk**. All complaints will be reviewed and investigated promptly and fairly.
+Instances of abusive, harassing, or otherwise unacceptable behaviour may be reported to the community leaders responsible for enforcement at **Matthew.Boakes@Gmail.com**. All complaints will be reviewed and investigated promptly and fairly.
 
 All community leaders are obligated to respect the privacy and security of the reporter of any incident.
 
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 916950c..4f75718 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -67,12 +67,12 @@ Work on `develop` — **do not target `main` directly**.
 
 New models are the most valuable contribution. The bar for inclusion is:
 
-| Requirement | Detail |
-|---|---|
-| **Licence** | Apache 2.0 or MIT only |
-| **Task** | Binary bonafide / spoof classification |
+| Requirement      | Detail                                                 |
+|------------------|--------------------------------------------------------|
+| **Licence**      | Apache 2.0 or MIT only                                 |
+| **Task**         | Binary bonafide / spoof classification                 |
 | **Availability** | Publicly available weights (HuggingFace Hub preferred) |
-| **Input** | Raw audio waveform (not pre-extracted features) |
+| **Input**        | Raw audio waveform (not pre-extracted features)        |
 
 ### Step-by-step
 
@@ -88,11 +88,13 @@ class Model(Enum):
 
 ```python
 # src/Jabberjay/Models/MyModel/run.py
-import numpy as np
 from typing import cast
+
+import numpy as np
 from loguru import logger
 from transformers import pipeline
-from Jabberjay.Utilities.label_normalizer import normalize_label
+
+from Jabberjay.Utilities.label_normalizer import normalize_pipeline_scores
 from Jabberjay.Utilities.types import PredictionScore
 
 _MODEL_ID = "author/model-id-on-huggingface"
@@ -103,13 +105,10 @@ def predict(y: np.ndarray, sr: float) -> list[PredictionScore]:
     pipe = pipeline("audio-classification", model=_MODEL_ID, sampling_rate=_TARGET_SR)
     logger.debug(f"Running MyModel inference on {len(y)} samples at {int(sr)}Hz")
     raw = cast(list[dict[str, object]], pipe({"raw": y, "sampling_rate": int(sr)}))
-    return [
-        PredictionScore(label=normalize_label(str(s["label"])), score=float(str(s["score"])))
-        for s in raw
-    ]
+    return normalize_pipeline_scores(raw)
 ```
 
-If the model uses non-standard labels, `normalize_label()` handles mapping — check `src/Jabberjay/Utilities/label_normalizer.py` and extend `_BONAFIDE_KEYWORDS` / `_SPOOF_KEYWORDS` if needed.
+If the model uses non-standard labels, `normalize_pipeline_scores()` calls `normalize_label()` internally — check `src/Jabberjay/Utilities/label_normalizer.py` and extend `_BONAFIDE_SUBSTR` / `_SPOOF_SUBSTR` (substring matching) or `_BONAFIDE_EXACT` / `_SPOOF_EXACT` (exact matching) if needed.
 
 **3. Add a handler and match case** in `src/Jabberjay/jabberjay.py`:
 
@@ -122,14 +121,8 @@ case Model.MyModel:
 def _mymodel_handler(self, y: np.ndarray, sr: float) -> DetectionResult:
     import Jabberjay.Models.MyModel.run as MyModel
     scores = MyModel.predict(y=y, sr=sr)
-    top = scores[0]
-    return DetectionResult(
-        label=top["label"],
-        is_bonafide=top["label"] == "Bonafide",
-        confidence=top["score"],
-        model=Model.MyModel,
-        scores=scores,
-    )
+    logger.debug(f"MyModel predictions: {scores}")
+    return self._result_from_scores(scores, Model.MyModel)
 ```
 
 **4. Update tests** — add the new enum value to `TestEnums.test_model_members` in `tests/test_jabberjay.py`.
diff --git a/LICENSE b/LICENSE
index 4346c50..618136f 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 The Alan Turing Institute
+Copyright (c) 2024-2026 Matthew Boakes and The Alan Turing Institute
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index 3f4a22e..7e56224 100644
--- a/README.md
+++ b/README.md
@@ -10,6 +10,8 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Downloads](https://img.shields.io/pypi/dm/jabberjay)](https://pypi.org/project/Jabberjay/)
 [![Docs](https://img.shields.io/badge/docs-mattyb95.github.io%2FJabberjay-blue)](https://mattyb95.github.io/Jabberjay)
+[![GitHub Sponsors](https://img.shields.io/github/sponsors/MattyB95?label=Sponsor&logo=GitHub&color=EA4AAA)](https://github.com/sponsors/MattyB95)
+[![Ko-fi](https://img.shields.io/badge/Ko--fi-Support-FF5E5B?logo=ko-fi&logoColor=white)](https://ko-fi.com/mattyb95)
 
 ---
 
@@ -62,49 +64,49 @@ print(result.confidence)   # 0.941
 
 ### Vision Transformer (ViT)
 
-| **Model**                                                                                                   | **Dataset**   | **Visualisation** |
-|-------------------------------------------------------------------------------------------------------------|---------------|-------------------|
-| [MattyB95/VIT-ASVspoof2019-ConstantQ-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof2019-ConstantQ-Synthetic-Voice-Detection)        | ASVspoof2019  | ConstantQ         |
-| [MattyB95/VIT-ASVspoof2019-Mel_Spectrogram-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof2019-Mel_Spectrogram-Synthetic-Voice-Detection)  | ASVspoof2019  | MelSpectrogram    |
-| [MattyB95/VIT-ASVspoof2019-MFCC-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof2019-MFCC-Synthetic-Voice-Detection)             | ASVspoof2019  | MFCC              |
-| [MattyB95/VIT-ASVspoof5-ConstantQ-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof5-ConstantQ-Synthetic-Voice-Detection)           | ASVspoof5     | ConstantQ         |
-| [MattyB95/VIT-ASVspoof5-Mel_Spectrogram-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof5-Mel_Spectrogram-Synthetic-Voice-Detection)     | ASVspoof5     | MelSpectrogram    |
-| [MattyB95/VIT-ASVspoof5-MFCC-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof5-MFCC-Synthetic-Voice-Detection)                | ASVspoof5     | MFCC              |
-| [MattyB95/VIT-VoxCelebSpoof-ConstantQ-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-VoxCelebSpoof-ConstantQ-Synthetic-Voice-Detection)       | VoxCelebSpoof | ConstantQ         |
+| **Model**                                                                                                                                                           | **Dataset**   | **Visualisation** |
+|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|-------------------|
+| [MattyB95/VIT-ASVspoof2019-ConstantQ-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof2019-ConstantQ-Synthetic-Voice-Detection)               | ASVspoof2019  | ConstantQ         |
+| [MattyB95/VIT-ASVspoof2019-Mel_Spectrogram-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof2019-Mel_Spectrogram-Synthetic-Voice-Detection)   | ASVspoof2019  | MelSpectrogram    |
+| [MattyB95/VIT-ASVspoof2019-MFCC-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof2019-MFCC-Synthetic-Voice-Detection)                         | ASVspoof2019  | MFCC              |
+| [MattyB95/VIT-ASVspoof5-ConstantQ-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof5-ConstantQ-Synthetic-Voice-Detection)                     | ASVspoof5     | ConstantQ         |
+| [MattyB95/VIT-ASVspoof5-Mel_Spectrogram-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof5-Mel_Spectrogram-Synthetic-Voice-Detection)         | ASVspoof5     | MelSpectrogram    |
+| [MattyB95/VIT-ASVspoof5-MFCC-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-ASVspoof5-MFCC-Synthetic-Voice-Detection)                               | ASVspoof5     | MFCC              |
+| [MattyB95/VIT-VoxCelebSpoof-ConstantQ-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-VoxCelebSpoof-ConstantQ-Synthetic-Voice-Detection)             | VoxCelebSpoof | ConstantQ         |
 | [MattyB95/VIT-VoxCelebSpoof-Mel_Spectrogram-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-VoxCelebSpoof-Mel_Spectrogram-Synthetic-Voice-Detection) | VoxCelebSpoof | MelSpectrogram    |
-| [MattyB95/VIT-VoxCelebSpoof-MFCC-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-VoxCelebSpoof-MFCC-Synthetic-Voice-Detection)            | VoxCelebSpoof | MFCC              |
+| [MattyB95/VIT-VoxCelebSpoof-MFCC-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/VIT-VoxCelebSpoof-MFCC-Synthetic-Voice-Detection)                       | VoxCelebSpoof | MFCC              |
 
 ### Audio Spectrogram Transformer (AST)
 
-| **Model**                                                                                   | **Dataset**   |
-|---------------------------------------------------------------------------------------------|---------------|
-| [MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection)  | ASVspoof2019  |
-| [MattyB95/AST-ASVspoof5-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/AST-ASVspoof5-Synthetic-Voice-Detection)     | ASVspoof5     |
+| **Model**                                                                                                                           | **Dataset**   |
+|-------------------------------------------------------------------------------------------------------------------------------------|---------------|
+| [MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection)   | ASVspoof2019  |
+| [MattyB95/AST-ASVspoof5-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/AST-ASVspoof5-Synthetic-Voice-Detection)         | ASVspoof5     |
 | [MattyB95/AST-VoxCelebSpoof-Synthetic-Voice-Detection](https://huggingface.co/MattyB95/AST-VoxCelebSpoof-Synthetic-Voice-Detection) | VoxCelebSpoof |
 
 ### Wav2Vec2
 
-| **Model**                                                                                                    | **Dataset**  |
-|--------------------------------------------------------------------------------------------------------------|--------------|
+| **Model**                                                                                                                                       | **Dataset**  |
+|-------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
 | [Gustking/wav2vec2-large-xlsr-deepfake-audio-classification](https://huggingface.co/Gustking/wav2vec2-large-xlsr-deepfake-audio-classification) | ASVspoof2019 |
 
 ### HuBERT
 
-| **Model**                                                                                                    | **Dataset**  |
-|--------------------------------------------------------------------------------------------------------------|--------------|
-| [abhishtagatya/hubert-base-960h-itw-deepfake](https://huggingface.co/abhishtagatya/hubert-base-960h-itw-deepfake) | In-The-Wild  |
+| **Model**                                                                                                         | **Dataset** |
+|-------------------------------------------------------------------------------------------------------------------|-------------|
+| [abhishtagatya/hubert-base-960h-itw-deepfake](https://huggingface.co/abhishtagatya/hubert-base-960h-itw-deepfake) | In-The-Wild |
 
 ### WavLM
 
-| **Model**                                                                                                    | **Dataset**  |
-|--------------------------------------------------------------------------------------------------------------|--------------|
-| [DavidCombei/wavLM-base-Deepfake_V2](https://huggingface.co/DavidCombei/wavLM-base-Deepfake_V2) | Mixed        |
+| **Model**                                                                                       | **Dataset** |
+|-------------------------------------------------------------------------------------------------|-------------|
+| [DavidCombei/wavLM-base-Deepfake_V2](https://huggingface.co/DavidCombei/wavLM-base-Deepfake_V2) | Mixed       |
 
 ### Other
 
-| **Model** | **Paper** | **Codebase** |
-|-----------|-----------|--------------|
-| Classical | — | Built-in KNN baseline |
+| **Model** | **Paper**                                                                   | **Codebase**                                                                |
+|-----------|-----------------------------------------------------------------------------|-----------------------------------------------------------------------------|
+| Classical | —                                                                           | Built-in KNN baseline                                                       |
 | RawNet2   | [Tak et al., ICASSP 2021](https://doi.org/10.1109/ICASSP39728.2021.9414234) | [rawnet2-antispoofing](https://github.com/eurecom-asp/rawnet2-antispoofing) |
 
 ---
@@ -181,13 +183,13 @@ result = jj.detect(
 
 Every call to `detect()` returns a `DetectionResult` regardless of the model used:
 
-| Attribute | Type | Description |
-|---|---|---|
-| `label` | `str` | `"Bonafide"` or `"Spoof"` |
-| `is_bonafide` | `bool` | `True` if the audio is classified as genuine |
-| `confidence` | `float` | Confidence score for the top prediction (0.0–1.0) |
-| `model` | `Model` | The model that produced this result |
-| `scores` | `list[dict] \| None` | Full label/score breakdown for VIT, AST, Wav2Vec2, HuBERT, and WavLM (sorted highest-first); `None` for Classical and RawNet2 |
+| Attribute     | Type                 | Description                                                                                                                   |
+|---------------|----------------------|-------------------------------------------------------------------------------------------------------------------------------|
+| `label`       | `str`                | `"Bonafide"` or `"Spoof"`                                                                                                     |
+| `is_bonafide` | `bool`               | `True` if the audio is classified as genuine                                                                                  |
+| `confidence`  | `float`              | Confidence score for the top prediction (0.0–1.0)                                                                             |
+| `model`       | `Model`              | The model that produced this result                                                                                           |
+| `scores`      | `list[dict] \| None` | Full label/score breakdown for VIT, AST, Wav2Vec2, HuBERT, and WavLM (sorted highest-first); `None` for Classical and RawNet2 |
 
 ```python
 if result.is_bonafide:
@@ -229,13 +231,13 @@ jj.list_visualisations() # prints and returns list[Visualisation]
 
 The `examples/` directory contains focused, runnable scripts:
 
-| Script | What it shows |
-|---|---|
-| [`quickstart.py`](examples/quickstart.py) | Minimum viable usage |
-| [`choosing_a_model.py`](examples/choosing_a_model.py) | Every model family, string and enum APIs |
-| [`preloading_audio.py`](examples/preloading_audio.py) | Efficient multi-model runs with `jj.load()` |
+| Script                                                  | What it shows                                    |
+|---------------------------------------------------------|--------------------------------------------------|
+| [`quickstart.py`](examples/quickstart.py)               | Minimum viable usage                             |
+| [`choosing_a_model.py`](examples/choosing_a_model.py)   | Every model family, string and enum APIs         |
+| [`preloading_audio.py`](examples/preloading_audio.py)   | Efficient multi-model runs with `jj.load()`      |
 | [`exploring_results.py`](examples/exploring_results.py) | All `DetectionResult` fields and score breakdown |
-| [`run_all.py`](examples/run_all.py) | Exhaustive sweep across every model combination |
+| [`run_all.py`](examples/run_all.py)                     | Exhaustive sweep across every model combination  |
 
 ```bash
 just example                           # run quickstart
@@ -255,13 +257,13 @@ cd Jabberjay
 just install   # install all dependencies including dev tools
 ```
 
-| Command | Description |
-|---|---|
-| `just test` | Run the test suite |
-| `just check` | Lint, format check, and type check |
-| `just fix` | Auto-fix lint issues and reformat |
-| `just detect audio.wav` | Run the CLI against a file |
-| `just build` | Build the package |
+| Command                 | Description                        |
+|-------------------------|------------------------------------|
+| `just test`             | Run the test suite                 |
+| `just check`            | Lint, format check, and type check |
+| `just fix`              | Auto-fix lint issues and reformat  |
+| `just detect audio.wav` | Run the CLI against a file         |
+| `just build`            | Build the package                  |
 
 See `just --list` for all available commands.
 
@@ -275,6 +277,33 @@ The quickest way to make an impact is to open a [model request issue](https://gi
 
 ---
 
+## Support
+
+If Jabberjay saves you time, consider supporting its development:
+
+- **[GitHub Sponsors](https://github.com/sponsors/MattyB95)** — recurring or one-off support, directly on GitHub
+- **[Ko-fi](https://ko-fi.com/mattyb95)** — buy me a coffee
+
+---
+
+## Citation
+
+If you use Jabberjay in your research, please cite it. GitHub's **"Cite this repository"** button (in the sidebar) will generate APA or BibTeX automatically from the `CITATION.cff` file, or you can use the entry below directly:
+
+```bibtex
+@software{boakes_jabberjay_2026,
+  author  = {Boakes, Matthew},
+  title   = {Jabberjay},
+  year    = {2026},
+  url     = {https://github.com/MattyB95/Jabberjay},
+  version = {0.0.7},
+}
+```
+
+A DOI will be available via [Zenodo](https://zenodo.org) once linked to this repository. The entry above will be updated at that point.
+
+---
+
 ## Acknowledgement
 
 This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation [INV-001309].
diff --git a/SECURITY.md b/SECURITY.md
index ac31780..2580e41 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -13,7 +13,7 @@ We support only the most recent release. Please update before reporting a vulner
 
 **Please do not open a public GitHub issue for security vulnerabilities.**
 
-Report vulnerabilities privately by emailing **mboakes@turing.ac.uk**. Include:
+Report vulnerabilities privately by emailing **Matthew.Boakes@Gmail.com**. Include:
 
 - A description of the vulnerability and its potential impact
 - Steps to reproduce or a proof-of-concept
diff --git a/docs/changelog.md b/docs/changelog.md
index 789e0e4..c255342 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -7,6 +7,47 @@ Jabberjay uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ---
 
+## [0.0.7] — 2026-03-16
+
+### Added
+- **`CITATION.cff`** — machine-readable citation metadata for academic use;
+  GitHub surfaces this as a "Cite this repository" button; compatible with
+  Zenodo, Zotero, and other reference managers
+- **Sample rate validation** — `detect()` now raises `ValueError` immediately
+  for zero or negative sample rates when a pre-loaded audio tuple is passed,
+  preventing silent failures in downstream model inference
+- **Test coverage for new behaviour** — 7 new tests covering sample rate
+  validation, `BytesIO` buffer cleanup on both success and error paths,
+  figure cleanup on error paths, and `Classical.predict()` bonafide/spoof paths
+
+### Changed
+- **Dependency minimum versions pinned** — all runtime dependencies now carry
+  lower-bound constraints (`torch>=2.0`, `transformers>=4.30`,
+  `huggingface-hub>=0.20`, `librosa>=0.10`, etc.) to prevent silent
+  incompatibilities on fresh installs
+- **Contact and copyright updated** — maintainer email changed to
+  `Matthew.Boakes@Gmail.com` across `CODE_OF_CONDUCT.md`, `SECURITY.md`, and
+  `pyproject.toml`; `LICENSE` updated to `2024-2026 Matthew Boakes and The
+  Alan Turing Institute`
+- **`CONTRIBUTING.md` model template corrected** — example `run.py` now uses
+  `cast()` + `normalize_pipeline_scores()` (matching real handlers) and the
+  handler example uses `_result_from_scores()` instead of building
+  `DetectionResult` manually; label normaliser variable names corrected to
+  `_BONAFIDE_SUBSTR` / `_SPOOF_SUBSTR` / `_BONAFIDE_EXACT` / `_SPOOF_EXACT`
+
+### Fixed
+- **`VIT/utility.py`** — `BytesIO` buffer is now always closed in the
+  `finally` block, preventing a resource leak if `Image.open()` or
+  `img.load()` raises
+- **`Classical/run.py`** — renamed `model` variable to `model_path` to
+  accurately reflect that `download_pretrained_model()` returns a file path,
+  not a model object
+- **`_result_from_scores()`** — parameter type restored to
+  `list[PredictionScore]`, keeping the type contract consistent end-to-end
+  from model handlers through to `DetectionResult.scores`
+
+---
+
 ## [0.0.6] — 2026-03-16
 
 ### Added
@@ -34,34 +75,51 @@ Jabberjay uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
   (HuBERT-base fine-tuned on the In-The-Wild dataset, EER 1.43%)
 - **WavLM model** — `DavidCombei/wavLM-base-Deepfake_V2`
   (WavLM-base fine-tuned on a mixed deepfake dataset)
-- **Label normaliser** — maps arbitrary external model labels (`"real"`, `"fake"`, `"LABEL_0"`, etc.) to the
+- **Label normaliser** (`Utilities/label_normalizer.py`) — maps arbitrary
+  external model labels (`"real"`, `"fake"`, `"LABEL_0"`, etc.) to the
   canonical `"Bonafide"` / `"Spoof"` labels used throughout the API
 - `just publish-test` command to publish a dev build to TestPyPI locally
 - `just version` command to print the current package version
 
 ### Changed
-- CI/CD pipeline consolidated into a single `ci.yml` with a clear lint → test → publish job graph
-- Push to `develop` now automatically publishes a pre-release to TestPyPI after CI passes
-- Push to `main` now automatically publishes the release version to PyPI after CI passes
+- **CI/CD pipeline consolidated** — three separate workflow files
+  (`ruff.yml`, `python-package.yml`, `python-publish.yml`) replaced by a
+  single `ci.yml` with a clear lint → test → publish job graph
+- **Push to `develop`** now automatically publishes a pre-release
+  (`{version}.dev{run_number}`) to TestPyPI after CI passes
+- **Push to `main`** now automatically publishes the release version to PyPI
+  after CI passes (previously required a manual GitHub release)
 - Test matrix extended to Python 3.10–3.14
-- Label normalisation applied consistently across all transformer models via shared `normalize_pipeline_scores()`
-- `_result_from_scores()` now sorts scores by confidence descending
+- Label normalisation now applied consistently across all transformer models
+  (AST, VIT, Wav2Vec2, HuBERT, WavLM) via shared `normalize_pipeline_scores()`
+- `_result_from_scores()` now sorts scores by confidence descending,
+  guaranteeing the top prediction is always the highest-confidence label
 - All package sub-directories now include `__init__.py` for reliable imports
 
 ### Fixed
-- `RawNet2/run.py`: removed `os.chdir()` at module level — was mutating the working directory for the entire process
+- `RawNet2/run.py`: removed `os.chdir()` at module level — was mutating the
+  working directory for the entire process
 - `label_normalizer`: fixed unsafe substring matching for short digit strings
-- `VIT/utility.py`: call `img.load()` after `Image.open()`; `plt.close()` now runs in a `try/finally` block
-- `jabberjay.load()`: raises `FileNotFoundError` with a clear message for missing files
+  (`"0"`, `"1"`) that could produce false-positive label mappings
+- `VIT/utility.py`: call `img.load()` after `Image.open()` to force pixel
+  data into memory before the buffer is released; `plt.close()` now runs
+  in a `try/finally` block
+- `jabberjay.load()`: raises `FileNotFoundError` with a clear message for
+  missing files; wraps other librosa errors in a descriptive `ValueError`
 - `jabberjay.detect()`: raises `ValueError` immediately for empty audio arrays
-- `EnumAction`: invalid CLI values now produce a clear error listing valid choices
+- `EnumAction`: invalid CLI values now produce a clear error listing valid
+  choices instead of a raw `KeyError`
+- `hugging_face.download_pretrained_model`: added missing `-> str` return type
+- `Classical/run.py`: corrected return type from `tuple[object, float]` to
+  `tuple[int, float]`
 
 ---
 
 ## [0.0.4] — 2024-08-15
 
 ### Added
-- ASVspoof5 variants for all ViT visualisation types — nine new HuggingFace models in total
+- ASVspoof5 variants for all ViT visualisation types (ConstantQ,
+  MelSpectrogram, MFCC) — nine new HuggingFace models in total
 - Updated GitHub Actions versions
 
 ---
@@ -69,7 +127,7 @@ Jabberjay uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 ## [0.0.3] — 2024-01-30
 
 ### Added
-- PyPI publish workflow for automated package releases
+- PyPI publish workflow (`python-publish.yml`) for automated package releases
 
 ### Changed
 - Core dependencies added to `pyproject.toml`
@@ -86,11 +144,21 @@ Jabberjay uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 ## [0.0.1] — 2023-09-06
 
 ### Added
-- Initial project setup
-- Classical, RawNet2, ViT, and AST models
+- Initial project setup and packaging (`pyproject.toml`, `setup.py`)
+- **Classical model** — feature-based KNN classifier for bonafide/spoof detection
+- **RawNet2 model** — end-to-end anti-spoofing via
+  [rawnet2-antispoofing](https://github.com/eurecom-asp/rawnet2-antispoofing)
+  with weights from ASVspoof 2021
+- **ViT models** — Vision Transformer image classifiers over three audio
+  visualisations (ConstantQ, MelSpectrogram, MFCC) trained on ASVspoof2019
+  and VoxCelebSpoof
+- **AST model** — Audio Spectrogram Transformer classifier trained on
+  ASVspoof2019 and VoxCelebSpoof
 - HuggingFace Hub integration for automatic model weight retrieval
-- Command-line interface
+- Command-line interface (`jabberjay <audio>`)
+- GitHub Actions CI workflow and ruff linting
 
+[0.0.7]: https://github.com/MattyB95/Jabberjay/compare/v0.0.6...HEAD
 [0.0.6]: https://github.com/MattyB95/Jabberjay/compare/v0.0.5...v0.0.6
 [0.0.5]: https://github.com/MattyB95/Jabberjay/compare/v0.0.4...v0.0.5
 [0.0.4]: https://github.com/MattyB95/Jabberjay/compare/v0.0.3...v0.0.4
diff --git a/docs/cli.md b/docs/cli.md
index 5d1da78..11ed35a 100644
--- a/docs/cli.md
+++ b/docs/cli.md
@@ -10,13 +10,13 @@ jabberjay <audio> [-m MODEL] [-d DATASET] [-vis VISUALISATION] [-v]
 
 ## Arguments
 
-| Argument | Description | Default |
-|---|---|---|
-| `audio` | Path to the audio file to analyse | — |
-| `-m`, `--model` | Model to use | `VIT` |
-| `-d`, `--dataset` | Dataset the model was trained on | `VoxCelebSpoof` |
-| `-vis`, `--visualisation` | Visualisation type (VIT only) | `ConstantQ` |
-| `-v`, `--verbose` | Enable debug logging to stderr | off |
+| Argument                  | Description                       | Default         |
+|---------------------------|-----------------------------------|-----------------|
+| `audio`                   | Path to the audio file to analyse | —               |
+| `-m`, `--model`           | Model to use                      | `VIT`           |
+| `-d`, `--dataset`         | Dataset the model was trained on  | `VoxCelebSpoof` |
+| `-vis`, `--visualisation` | Visualisation type (VIT only)     | `ConstantQ`     |
+| `-v`, `--verbose`         | Enable debug logging to stderr    | off             |
 
 ## Valid values
 
diff --git a/docs/models.md b/docs/models.md
index 0ba1cf7..78e9e10 100644
--- a/docs/models.md
+++ b/docs/models.md
@@ -6,15 +6,15 @@ Jabberjay bundles seven model families. Each is downloaded from Hugging Face Hub
 
 ## Choosing a model
 
-| Model | Type | Datasets | Requires |
-|---|---|---|---|
-| [VIT](#vit) | Vision Transformer | ASVspoof2019, ASVspoof5, VoxCelebSpoof | `dataset`, `visualisation` |
-| [AST](#ast) | Audio Spectrogram Transformer | ASVspoof2019, ASVspoof5, VoxCelebSpoof | `dataset` |
-| [Wav2Vec2](#wav2vec2) | Self-supervised transformer | ASVspoof2019 | — |
-| [HuBERT](#hubert) | Self-supervised transformer | In-The-Wild | — |
-| [WavLM](#wavlm) | Self-supervised transformer | Mixed deepfake | — |
-| [RawNet2](#rawnet2) | End-to-end CNN | ASVspoof 2021 | — |
-| [Classical](#classical) | KNN classifier | ASVspoof2019 | — |
+| Model                   | Type                          | Datasets                               | Requires                   |
+|-------------------------|-------------------------------|----------------------------------------|----------------------------|
+| [VIT](#vit)             | Vision Transformer            | ASVspoof2019, ASVspoof5, VoxCelebSpoof | `dataset`, `visualisation` |
+| [AST](#ast)             | Audio Spectrogram Transformer | ASVspoof2019, ASVspoof5, VoxCelebSpoof | `dataset`                  |
+| [Wav2Vec2](#wav2vec2)   | Self-supervised transformer   | ASVspoof2019                           | —                          |
+| [HuBERT](#hubert)       | Self-supervised transformer   | In-The-Wild                            | —                          |
+| [WavLM](#wavlm)         | Self-supervised transformer   | Mixed deepfake                         | —                          |
+| [RawNet2](#rawnet2)     | End-to-end CNN                | ASVspoof 2021                          | —                          |
+| [Classical](#classical) | KNN classifier                | ASVspoof2019                           | —                          |
 
 **Simple rule of thumb:**
 
@@ -31,11 +31,11 @@ Vision Transformer classifiers that convert the audio to a 2D image (spectrogram
 
 **Nine variants**, covering three visualisation types × three training datasets:
 
-| Visualisation | ASVspoof2019 | ASVspoof5 | VoxCelebSpoof |
-|---|:---:|:---:|:---:|
-| ConstantQ | ✓ | ✓ | ✓ |
-| MelSpectrogram | ✓ | ✓ | ✓ |
-| MFCC | ✓ | ✓ | ✓ |
+| Visualisation  | ASVspoof2019 | ASVspoof5 | VoxCelebSpoof |
+|----------------|:------------:|:---------:|:-------------:|
+| ConstantQ      |      ✓       |     ✓     |       ✓       |
+| MelSpectrogram |      ✓       |     ✓     |       ✓       |
+| MFCC           |      ✓       |     ✓     |       ✓       |
 
 ```python
 jj.detect("audio.wav", model="VIT", dataset="VoxCelebSpoof", visualisation="ConstantQ")
diff --git a/pyproject.toml b/pyproject.toml
index d650ddd..6d22a89 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,13 +4,13 @@ build-backend = "hatchling.build"
 
 [project]
 name = "Jabberjay"
-version = "0.0.6"
+version = "0.0.7"
 description = "🦜 Synthetic Voice Detection"
 readme = "README.md"
 requires-python = ">=3.10"
 license = "MIT"
 authors = [
-    { name = "Matthew Boakes", email = "mboakes@turing.ac.uk" },
+    { name = "Matthew Boakes", email = "Matthew.Boakes@Gmail.com" },
 ]
 keywords = [
     "synthetic voice detection",
diff --git a/src/Jabberjay/Models/Classical/run.py b/src/Jabberjay/Models/Classical/run.py
index 5afa9be..6286c89 100644
--- a/src/Jabberjay/Models/Classical/run.py
+++ b/src/Jabberjay/Models/Classical/run.py
@@ -10,8 +10,8 @@ def predict(audio: tuple[np.ndarray, float]) -> tuple[int, float]:
     repo_id = "MattyB95/Jabberjay_Classical_Machine_Learning_Models"
     filename = "KNeighborsClassifier.joblib"
     logger.info(f"Downloading model: {filename} from {repo_id}")
-    model = download_pretrained_model(repo_id=repo_id, filename=filename)
-    clf = load(filename=model)
+    model_path = download_pretrained_model(repo_id=repo_id, filename=filename)
+    clf = load(filename=model_path)
     logger.debug("Extracting audio features")
     features = get_features(audio=audio)
     logger.debug(f"Feature vector shape: {features.shape}")
diff --git a/src/Jabberjay/Models/HuBERT/run.py b/src/Jabberjay/Models/HuBERT/run.py
index b1ef14e..2993ec7 100644
--- a/src/Jabberjay/Models/HuBERT/run.py
+++ b/src/Jabberjay/Models/HuBERT/run.py
@@ -1,5 +1,3 @@
-from typing import cast
-
 import numpy as np
 from loguru import logger
 from transformers import pipeline
@@ -15,5 +13,5 @@ def predict(y: np.ndarray, sr: float) -> list[PredictionScore]:
     logger.info(f"Loading HuBERT model: {_MODEL_ID}")
     pipe = pipeline("audio-classification", model=_MODEL_ID, sampling_rate=_TARGET_SR)
     logger.debug(f"Running HuBERT inference on {len(y)} samples at {int(sr)}Hz")
-    raw = cast(list[dict[str, object]], pipe({"raw": y, "sampling_rate": int(sr)}))
+    raw = pipe({"raw": y, "sampling_rate": int(sr)})
     return normalize_pipeline_scores(raw)
diff --git a/src/Jabberjay/Models/RawNet2/model.py b/src/Jabberjay/Models/RawNet2/model.py
index 02f9cfc..bc6804f 100644
--- a/src/Jabberjay/Models/RawNet2/model.py
+++ b/src/Jabberjay/Models/RawNet2/model.py
@@ -34,10 +34,11 @@ def __init__(
     ):
         super(SincConv, self).__init__()
 
+        self.filters = None
         if in_channels != 1:
             msg = (
-                "SincConv only support one input channel (here, in_channels = {%i})"
-                % (in_channels)
+                "SincConv only supports one input channel (here, in_channels = {%i})"
+                % in_channels
             )
             raise ValueError(msg)
 
@@ -102,9 +103,9 @@ def forward(self, x):
         )
 
 
-class Residual_block(nn.Module):
+class ResidualBlock(nn.Module):
     def __init__(self, nb_filts, first=False):
-        super(Residual_block, self).__init__()
+        super(ResidualBlock, self).__init__()
         self.first = first
 
         if not self.first:
@@ -180,14 +181,14 @@ def __init__(self, d_args, device):
         self.first_bn = nn.BatchNorm1d(num_features=d_args["filts"][0])
         self.selu = nn.SELU(inplace=True)
         self.block0 = nn.Sequential(
-            Residual_block(nb_filts=d_args["filts"][1], first=True)
+            ResidualBlock(nb_filts=d_args["filts"][1], first=True)
         )
-        self.block1 = nn.Sequential(Residual_block(nb_filts=d_args["filts"][1]))
-        self.block2 = nn.Sequential(Residual_block(nb_filts=d_args["filts"][2]))
+        self.block1 = nn.Sequential(ResidualBlock(nb_filts=d_args["filts"][1]))
+        self.block2 = nn.Sequential(ResidualBlock(nb_filts=d_args["filts"][2]))
         d_args["filts"][2][0] = d_args["filts"][2][1]
-        self.block3 = nn.Sequential(Residual_block(nb_filts=d_args["filts"][2]))
-        self.block4 = nn.Sequential(Residual_block(nb_filts=d_args["filts"][2]))
-        self.block5 = nn.Sequential(Residual_block(nb_filts=d_args["filts"][2]))
+        self.block3 = nn.Sequential(ResidualBlock(nb_filts=d_args["filts"][2]))
+        self.block4 = nn.Sequential(ResidualBlock(nb_filts=d_args["filts"][2]))
+        self.block5 = nn.Sequential(ResidualBlock(nb_filts=d_args["filts"][2]))
         self.avgpool = nn.AdaptiveAvgPool1d(1)
 
         self.fc_attention0 = self._make_attention_fc(
@@ -300,19 +301,19 @@ def forward(self, x, y=None):
 
         return output
 
-    def _make_attention_fc(self, in_features, l_out_features):
-        l_fc = []
-
-        l_fc.append(nn.Linear(in_features=in_features, out_features=l_out_features))
+    @staticmethod
+    def _make_attention_fc(in_features, l_out_features):
+        l_fc = [nn.Linear(in_features=in_features, out_features=l_out_features)]
 
         return nn.Sequential(*l_fc)
 
-    def _make_layer(self, nb_blocks, nb_filts, first=False):
+    @staticmethod
+    def _make_layer(nb_blocks, nb_filts, first=False):
         layers = []
         # def __init__(self, nb_filts, first = False):
         for i in range(nb_blocks):
             first = first if i == 0 else False
-            layers.append(Residual_block(nb_filts=nb_filts, first=first))
+            layers.append(ResidualBlock(nb_filts=nb_filts, first=first))
             if i == 0:
                 nb_filts[0] = nb_filts[1]
 
@@ -361,15 +362,12 @@ def hook(module, input, output):
         ], "Input device is not valid, please specify 'cuda' or 'cpu'"
 
         if device == "cuda" and torch.cuda.is_available():
-            dtype = torch.cuda.FloatTensor  # ty: ignore[unresolved-attribute]
+            torch_device = torch.device("cuda")
         else:
-            dtype = torch.FloatTensor
+            torch_device = torch.device("cpu")
         if isinstance(input_size, tuple):
             input_size = [input_size]
-        x = [
-            torch.rand(2, *in_size).type(dtype)  # ty: ignore[no-matching-overload]
-            for in_size in input_size
-        ]
+        x = [torch.rand(2, *in_size).to(torch_device) for in_size in input_size]
         summary = OrderedDict()
         hooks = []
         model.apply(register_hook)
diff --git a/src/Jabberjay/Models/Transformer/AST/run.py b/src/Jabberjay/Models/Transformer/AST/run.py
index 8782f09..8f89727 100644
--- a/src/Jabberjay/Models/Transformer/AST/run.py
+++ b/src/Jabberjay/Models/Transformer/AST/run.py
@@ -1,5 +1,3 @@
-from typing import cast
-
 import numpy as np
 from loguru import logger
 from transformers import pipeline
@@ -14,5 +12,5 @@ def predict(y: np.ndarray, sr: float, dataset: Dataset) -> list[PredictionScore]
     logger.info(f"Loading AST model: {model}")
     pipe = pipeline("audio-classification", model=model)
     logger.debug(f"Running AST inference on {len(y)} samples at {int(sr)}Hz")
-    raw = cast(list[dict[str, object]], pipe({"raw": y, "sampling_rate": int(sr)}))
+    raw = pipe({"raw": y, "sampling_rate": int(sr)})
     return normalize_pipeline_scores(raw)
diff --git a/src/Jabberjay/Models/Transformer/VIT/ConstantQ/run.py b/src/Jabberjay/Models/Transformer/VIT/ConstantQ/run.py
index f20e860..4ebc93c 100644
--- a/src/Jabberjay/Models/Transformer/VIT/ConstantQ/run.py
+++ b/src/Jabberjay/Models/Transformer/VIT/ConstantQ/run.py
@@ -1,5 +1,3 @@
-from typing import cast
-
 import librosa
 import numpy as np
 from loguru import logger
@@ -20,5 +18,5 @@ def predict(audio: tuple[np.ndarray, float], dataset: Dataset) -> list[Predictio
     CQT = np.abs(librosa.cqt(y=y, sr=sr))
     S_db = librosa.amplitude_to_db(S=CQT, ref=np.max)
     image = get_image(data=S_db, sr=sr)
-    raw = cast(list[dict[str, object]], pipe(image))
+    raw = pipe(image)
     return normalize_pipeline_scores(raw)
diff --git a/src/Jabberjay/Models/Transformer/VIT/MFCC/run.py b/src/Jabberjay/Models/Transformer/VIT/MFCC/run.py
index 65c78ff..6e359c3 100644
--- a/src/Jabberjay/Models/Transformer/VIT/MFCC/run.py
+++ b/src/Jabberjay/Models/Transformer/VIT/MFCC/run.py
@@ -1,5 +1,3 @@
-from typing import cast
-
 import librosa
 import numpy as np
 from loguru import logger
@@ -19,5 +17,5 @@ def predict(audio: tuple[np.ndarray, float], dataset: Dataset) -> list[Predictio
     logger.debug("Computing MFCC")
     M = librosa.feature.mfcc(y=y, sr=sr)
     image = get_image(data=M, sr=sr)
-    raw = cast(list[dict[str, object]], pipe(image))
+    raw = pipe(image)
     return normalize_pipeline_scores(raw)
diff --git a/src/Jabberjay/Models/Transformer/VIT/MelSpectrogram/run.py b/src/Jabberjay/Models/Transformer/VIT/MelSpectrogram/run.py
index 3a6978a..a8f2938 100644
--- a/src/Jabberjay/Models/Transformer/VIT/MelSpectrogram/run.py
+++ b/src/Jabberjay/Models/Transformer/VIT/MelSpectrogram/run.py
@@ -1,5 +1,3 @@
-from typing import cast
-
 import librosa
 import numpy as np
 from loguru import logger
@@ -20,5 +18,5 @@ def predict(audio: tuple[np.ndarray, float], dataset: Dataset) -> list[Predictio
     S = librosa.feature.melspectrogram(y=y, sr=sr)
     S_db = librosa.power_to_db(S=S, ref=np.max)
     image = get_image(data=S_db, sr=sr)
-    raw = cast(list[dict[str, object]], pipe(image))
+    raw = pipe(image)
     return normalize_pipeline_scores(raw)
diff --git a/src/Jabberjay/Models/Transformer/VIT/utility.py b/src/Jabberjay/Models/Transformer/VIT/utility.py
index 878b13a..51228af 100644
--- a/src/Jabberjay/Models/Transformer/VIT/utility.py
+++ b/src/Jabberjay/Models/Transformer/VIT/utility.py
@@ -11,10 +11,10 @@
 def get_image(data: ndarray, sr: float) -> Image.Image:
     logger.debug("Rendering spectrogram image")
     fig, ax = plt.subplots()
+    buf = io.BytesIO()
     try:
         librosa.display.specshow(data=data, sr=sr, ax=ax)
         fig.canvas.draw()
-        buf = io.BytesIO()
         plt.savefig(buf, bbox_inches="tight", pad_inches=0)
         buf.seek(0)
         img = Image.open(buf)
@@ -22,3 +22,4 @@ def get_image(data: ndarray, sr: float) -> Image.Image:
         return img
     finally:
         plt.close(fig)
+        buf.close()
diff --git a/src/Jabberjay/Models/Wav2Vec2/run.py b/src/Jabberjay/Models/Wav2Vec2/run.py
index d0c3530..27b7000 100644
--- a/src/Jabberjay/Models/Wav2Vec2/run.py
+++ b/src/Jabberjay/Models/Wav2Vec2/run.py
@@ -1,5 +1,3 @@
-from typing import cast
-
 import numpy as np
 from loguru import logger
 from transformers import pipeline
@@ -15,5 +13,5 @@ def predict(y: np.ndarray, sr: float) -> list[PredictionScore]:
     logger.info(f"Loading Wav2Vec2 model: {_MODEL_ID}")
     pipe = pipeline("audio-classification", model=_MODEL_ID, sampling_rate=_TARGET_SR)
     logger.debug(f"Running Wav2Vec2 inference on {len(y)} samples at {int(sr)}Hz")
-    raw = cast(list[dict[str, object]], pipe({"raw": y, "sampling_rate": int(sr)}))
+    raw = pipe({"raw": y, "sampling_rate": int(sr)})
     return normalize_pipeline_scores(raw)
diff --git a/src/Jabberjay/Models/WavLM/run.py b/src/Jabberjay/Models/WavLM/run.py
index ddf4565..63dd625 100644
--- a/src/Jabberjay/Models/WavLM/run.py
+++ b/src/Jabberjay/Models/WavLM/run.py
@@ -1,5 +1,3 @@
-from typing import cast
-
 import numpy as np
 from loguru import logger
 from transformers import pipeline
@@ -15,5 +13,5 @@ def predict(y: np.ndarray, sr: float) -> list[PredictionScore]:
     logger.info(f"Loading WavLM model: {_MODEL_ID}")
     pipe = pipeline("audio-classification", model=_MODEL_ID, sampling_rate=_TARGET_SR)
     logger.debug(f"Running WavLM inference on {len(y)} samples at {int(sr)}Hz")
-    raw = cast(list[dict[str, object]], pipe({"raw": y, "sampling_rate": int(sr)}))
+    raw = pipe({"raw": y, "sampling_rate": int(sr)})
     return normalize_pipeline_scores(raw)
diff --git a/src/Jabberjay/jabberjay.py b/src/Jabberjay/jabberjay.py
index 42f636f..8fb8c24 100644
--- a/src/Jabberjay/jabberjay.py
+++ b/src/Jabberjay/jabberjay.py
@@ -87,7 +87,8 @@ def list_visualisations() -> list[Visualisation]:
         print("Visualisations:", ", ".join(v.value for v in visualisations))
         return visualisations
 
-    def load(self, path: str | Path) -> Audio:
+    @staticmethod
+    def load(path: str | Path) -> Audio:
         """Load an audio file and return (samples, sample_rate)."""
         path = str(path)
         logger.debug(f"Loading audio file: {path}")
@@ -145,6 +146,8 @@ def detect(
         y, sr = audio
         if len(y) == 0:
             raise ValueError("Audio array is empty — nothing to classify.")
+        if sr <= 0:
+            raise ValueError(f"Invalid sample rate: {sr}. Must be a positive number.")
 
         logger.info(
             f"Running detection — model={model.value}, "
@@ -201,7 +204,8 @@ def _ast_handler(
         logger.debug(f"AST predictions: {scores}")
         return self._result_from_scores(scores, Model.AST)
 
-    def _classical_handler(self, audio: Audio) -> DetectionResult:
+    @staticmethod
+    def _classical_handler(audio: Audio) -> DetectionResult:
         import Jabberjay.Models.Classical.run as Classical
 
         prediction, confidence = Classical.predict(audio=audio)
@@ -216,7 +220,8 @@ def _classical_handler(self, audio: Audio) -> DetectionResult:
             model=Model.Classical,
         )
 
-    def _rawnet2_handler(self, y: np.ndarray) -> DetectionResult:
+    @staticmethod
+    def _rawnet2_handler(y: np.ndarray) -> DetectionResult:
         import Jabberjay.Models.RawNet2.run as RawNet2
 
         prediction, confidence = RawNet2.predict(y=y)
diff --git a/tests/test_jabberjay.py b/tests/test_jabberjay.py
index 205eb8d..cf51933 100644
--- a/tests/test_jabberjay.py
+++ b/tests/test_jabberjay.py
@@ -6,6 +6,7 @@
 
 from Jabberjay import Dataset, DetectionResult, Jabberjay, Model, Visualisation
 from Jabberjay.jabberjay import main
+from Jabberjay.Utilities.types import PredictionScore
 
 RES_DIR = Path(__file__).parent.parent / "res"
 
@@ -26,8 +27,9 @@ def test_load_accepts_string_path(self):
 
     def test_load_missing_file_raises(self):
         jj = Jabberjay()
-        with pytest.raises(FileNotFoundError, match="not found"):
-            jj.load("nonexistent_file.wav")
+        with patch("librosa.load", side_effect=FileNotFoundError("No such file")):
+            with pytest.raises(FileNotFoundError, match="not found"):
+                jj.load("nonexistent_file.wav")
 
 
 class TestListMethods:
@@ -78,7 +80,7 @@ def test_empty_audio_still_raises_before_model_dispatch(self):
 
 
 class TestEnableLogging:
-    def setup_method(self):
+    def setup_method(self, method=None):
         # Reset the guard before each test so tests are independent.
         Jabberjay._logging_enabled = False
 
@@ -137,6 +139,16 @@ def test_empty_audio_raises(self):
         with pytest.raises(ValueError, match="empty"):
             self.jj.detect(empty, model=Model.Classical)
 
+    def test_zero_sample_rate_raises(self):
+        bad_audio = (np.zeros(16000, dtype=np.float32), 0)
+        with pytest.raises(ValueError, match="sample rate"):
+            self.jj.detect(bad_audio, model=Model.Classical)
+
+    def test_negative_sample_rate_raises(self):
+        bad_audio = (np.zeros(16000, dtype=np.float32), -1.0)
+        with pytest.raises(ValueError, match="sample rate"):
+            self.jj.detect(bad_audio, model=Model.Classical)
+
 
 class TestDetectPathInput:
     def setup_method(self):
@@ -181,7 +193,10 @@ def test_invalid_visualisation_string_raises(self):
 
 class TestResultFromScores:
     def test_top_score_wins(self):
-        scores = [{"label": "Spoof", "score": 0.7}, {"label": "Bonafide", "score": 0.3}]
+        scores: list[PredictionScore] = [
+            {"label": "Spoof", "score": 0.7},
+            {"label": "Bonafide", "score": 0.3},
+        ]
         result = Jabberjay._result_from_scores(scores, Model.VIT)
         assert result.label == "Spoof"
         assert result.is_bonafide is False
@@ -189,7 +204,7 @@ def test_top_score_wins(self):
         assert result.model == Model.VIT
 
     def test_bonafide_sets_is_bonafide_true(self):
-        scores = [
+        scores: list[PredictionScore] = [
             {"label": "Bonafide", "score": 0.95},
             {"label": "Spoof", "score": 0.05},
         ]
@@ -197,13 +212,19 @@ def test_bonafide_sets_is_bonafide_true(self):
         assert result.is_bonafide is True
 
     def test_scores_sorted_descending(self):
-        scores = [{"label": "Bonafide", "score": 0.1}, {"label": "Spoof", "score": 0.9}]
+        scores: list[PredictionScore] = [
+            {"label": "Bonafide", "score": 0.1},
+            {"label": "Spoof", "score": 0.9},
+        ]
         result = Jabberjay._result_from_scores(scores, Model.AST)
         assert result.scores[0]["score"] == 0.9
         assert result.scores[1]["score"] == 0.1
 
     def test_full_scores_attached_to_result(self):
-        scores = [{"label": "Bonafide", "score": 0.6}, {"label": "Spoof", "score": 0.4}]
+        scores: list[PredictionScore] = [
+            {"label": "Bonafide", "score": 0.6},
+            {"label": "Spoof", "score": 0.4},
+        ]
         result = Jabberjay._result_from_scores(scores, Model.HuBERT)
         assert len(result.scores) == 2
 
@@ -220,7 +241,7 @@ class TestDetectHandlers:
     def setup_method(self):
         self.jj = Jabberjay()
         self.audio = (np.zeros(16000, dtype=np.float32), 16000.0)
-        self.scores = [
+        self.scores: list[PredictionScore] = [
             {"label": "Bonafide", "score": 0.9},
             {"label": "Spoof", "score": 0.1},
         ]
diff --git a/tests/test_models.py b/tests/test_models.py
index 8019e2a..e721944 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -4,18 +4,24 @@
 offline without downloading any weights from HuggingFace.
 """
 
+import io
 from unittest.mock import MagicMock, patch
 
 import numpy as np
+import pytest
 
 from Jabberjay.Utilities.enum_handler import Dataset
+from Jabberjay.Utilities.types import PredictionScore
 
 # ---------------------------------------------------------------------------
 # Shared helpers
 # ---------------------------------------------------------------------------
 
 AUDIO = (np.zeros(16000, dtype=np.float32), 16000.0)
-FAKE_RAW_SCORES = [{"label": "bonafide", "score": 0.9}, {"label": "fake", "score": 0.1}]
+FAKE_RAW_SCORES: list[PredictionScore] = [
+    {"label": "bonafide", "score": 0.9},
+    {"label": "fake", "score": 0.1},
+]
 
 
 def _mock_pipeline(raw_scores=None):
@@ -128,11 +134,13 @@ def test_model_id_includes_dataset_name(self):
 
 
 class TestVITPredict:
-    def _run_constantq(self, raw_scores=None):
+    @staticmethod
+    def _run_constantq(raw_scores=None):
         from Jabberjay.Models.Transformer.VIT.ConstantQ.run import predict
 
         mock_image = MagicMock()
         mock_pipe = _mock_pipeline(raw_scores)
+        mock_cqt = np.zeros((84, 32))
         with patch(
             "Jabberjay.Models.Transformer.VIT.ConstantQ.run.pipeline",
             return_value=mock_pipe,
@@ -141,7 +149,15 @@ def _run_constantq(self, raw_scores=None):
                 "Jabberjay.Models.Transformer.VIT.ConstantQ.run.get_image",
                 return_value=mock_image,
             ):
-                return predict(audio=AUDIO, dataset=Dataset.VoxCelebSpoof)
+                with patch(
+                    "Jabberjay.Models.Transformer.VIT.ConstantQ.run.librosa.cqt",
+                    return_value=mock_cqt,
+                ):
+                    with patch(
+                        "Jabberjay.Models.Transformer.VIT.ConstantQ.run.librosa.amplitude_to_db",
+                        return_value=mock_cqt,
+                    ):
+                        return predict(audio=AUDIO, dataset=Dataset.VoxCelebSpoof)
 
     def test_constantq_returns_normalised_scores(self):
         result = self._run_constantq()
@@ -229,6 +245,143 @@ def fake_savefig(buf, **kwargs):
 
         mock_close.assert_called_once()
 
+    def test_figure_closed_on_error(self):
+        """Ensure plt.close is called even when rendering raises (no figure leak)."""
+        import pytest
+
+        from Jabberjay.Models.Transformer.VIT.utility import get_image
+
+        with patch("Jabberjay.Models.Transformer.VIT.utility.librosa.display.specshow"):
+            with patch(
+                "Jabberjay.Models.Transformer.VIT.utility.plt.savefig",
+                side_effect=RuntimeError("render error"),
+            ):
+                with patch(
+                    "Jabberjay.Models.Transformer.VIT.utility.plt.close"
+                ) as mock_close:
+                    with pytest.raises(RuntimeError):
+                        get_image(data=np.zeros((128, 100)), sr=22050.0)
+
+        mock_close.assert_called_once()
+
+    def test_buffer_closed_on_success(self):
+        """Ensure the BytesIO buffer is closed after a successful call."""
+        from PIL import Image
+
+        from Jabberjay.Models.Transformer.VIT.utility import get_image
+
+        mock_buf = MagicMock(spec=io.BytesIO)
+        mock_buf.read.return_value = b""
+
+        real_buf = io.BytesIO()
+        real_image = Image.new("RGB", (10, 10))
+        real_image.save(real_buf, format="PNG")
+        real_buf.seek(0)
+        mock_buf.__enter__ = MagicMock(return_value=mock_buf)
+        mock_buf.__exit__ = MagicMock(return_value=False)
+
+        def fake_savefig(buf, **kwargs):
+            real_image.save(buf, format="PNG")
+
+        with patch(
+            "Jabberjay.Models.Transformer.VIT.utility.io.BytesIO", return_value=mock_buf
+        ):
+            with patch(
+                "Jabberjay.Models.Transformer.VIT.utility.librosa.display.specshow"
+            ):
+                with patch(
+                    "Jabberjay.Models.Transformer.VIT.utility.plt.savefig",
+                    side_effect=fake_savefig,
+                ):
+                    with patch("Jabberjay.Models.Transformer.VIT.utility.plt.close"):
+                        with patch(
+                            "Jabberjay.Models.Transformer.VIT.utility.Image.open",
+                            return_value=real_image,
+                        ):
+                            get_image(data=np.zeros((128, 100)), sr=22050.0)
+
+        mock_buf.close.assert_called_once()
+
+    def test_buffer_closed_on_error(self):
+        """Ensure the BytesIO buffer is closed even when Image.open raises."""
+        import pytest
+
+        from Jabberjay.Models.Transformer.VIT.utility import get_image
+
+        mock_buf = MagicMock(spec=io.BytesIO)
+
+        def fake_savefig(buf, **kwargs):
+            pass
+
+        with patch(
+            "Jabberjay.Models.Transformer.VIT.utility.io.BytesIO", return_value=mock_buf
+        ):
+            with patch(
+                "Jabberjay.Models.Transformer.VIT.utility.librosa.display.specshow"
+            ):
+                with patch(
+                    "Jabberjay.Models.Transformer.VIT.utility.plt.savefig",
+                    side_effect=fake_savefig,
+                ):
+                    with patch("Jabberjay.Models.Transformer.VIT.utility.plt.close"):
+                        with patch(
+                            "Jabberjay.Models.Transformer.VIT.utility.Image.open",
+                            side_effect=RuntimeError("corrupt image"),
+                        ):
+                            with pytest.raises(RuntimeError):
+                                get_image(data=np.zeros((128, 100)), sr=22050.0)
+
+        mock_buf.close.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# Classical
+# ---------------------------------------------------------------------------
+
+
+class TestClassicalPredict:
+    def test_returns_prediction_and_confidence(self):
+        from Jabberjay.Models.Classical.run import predict
+
+        mock_clf = MagicMock()
+        mock_clf.predict.return_value = [1]
+        mock_clf.predict_proba.return_value = np.array([[0.1, 0.9]])
+
+        with patch(
+            "Jabberjay.Models.Classical.run.download_pretrained_model",
+            return_value="/fake/model.joblib",
+        ):
+            with patch("Jabberjay.Models.Classical.run.load", return_value=mock_clf):
+                with patch(
+                    "Jabberjay.Models.Classical.run.get_features",
+                    return_value=MagicMock(),
+                ):
+                    prediction, confidence = predict(audio=AUDIO)
+
+        assert prediction == 1
+        assert confidence == pytest.approx(0.9)
+
+    def test_spoof_prediction(self):
+        from Jabberjay.Models.Classical.run import predict
+
+        mock_clf = MagicMock()
+        mock_clf.predict.return_value = [0]
+        mock_clf.predict_proba.return_value = np.array([[0.8, 0.2]])
+
+        with patch(
+            "Jabberjay.Models.Classical.run.download_pretrained_model",
+            return_value="/fake/model.joblib",
+        ):
+            with patch("Jabberjay.Models.Classical.run.load", return_value=mock_clf):
+                with patch(
+                    "Jabberjay.Models.Classical.run.get_features",
+                    return_value=MagicMock(),
+                ):
+                    prediction, confidence = predict(audio=AUDIO)
+
+        assert prediction == 0
+        assert confidence == pytest.approx(0.8)
+
 
 # ---------------------------------------------------------------------------
 # RawNet2
diff --git a/uv.lock b/uv.lock
index f175e9e..94a7ec9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -929,7 +929,7 @@ wheels = [
 
 [[package]]
 name = "jabberjay"
-version = "0.0.6"
+version = "0.0.7"
 source = { editable = "." }
 dependencies = [
     { name = "huggingface-hub" },