| --- |
| tags: |
| - audiotranscription-segmentation |
| - radio |
| - radio-live-a-la-carte |
| - rlac |
| - random-forest |
| - transcription |
| model_index: |
| - name: RLAC Audio-transcription Segmenter - Chroniques (Random Forest) |
| --- |
| |
| # RLAC Audio-transcription Segmenter - Chroniques (Random Forest) |
|
|
| ## Description |
| This model uses a **Random Forest** machine learning approach to detect radio chronicle segments from textual transcriptions (SRT files). It is designed to be a lightweight and efficient alternative for text-based segmentation. |
|
|
| Hugging Face link: [eglantinefonrose/rlac-audiotranscript-segmenter-chroniques-randomforest](https://huggingface.co/eglantinefonrose/rlac-audiotranscript-segmenter-chroniques-randomforest) |
|
|
| ## Model Details |
|
|
| The model is a single-file classifier (`.pkl`) serialized with `joblib`. It operates by analyzing individual transcription segments and their immediate context. |
|
|
| ### 1. Feature Engineering |
| The model extracts several key features from the SRT segments: |
| * **Temporal Data**: Precise air time and segment duration. |
| * **Linguistic Statistics**: Word count, character count, and average word length. |
| * **Punctuation Analysis**: Detection of question marks and exclamation points to identify rhetorical styles. |
| * **TF-IDF Vectorization**: A statistical measure used to evaluate the importance of words in the transcript relative to radio chronicle vocabulary. |
| * **Jingle Detection**: Specific binary markers for identified radio jingles. |
|
|
| ### 2. Contextual Awareness (Sliding Window) |
| To improve accuracy, the model employs a **sliding window** technique. It doesn't just look at a segment in isolation; it incorporates features from the surrounding segments (e.g., the 2 segments before and 2 segments after) to capture the flow and continuity of the radio broadcast. |
|
|
| ### 3. Core Classifier |
| The underlying algorithm is a **RandomForestClassifier** from the Scikit-Learn library, optimized with 200 estimators to handle the balance between precision and recall for chronicle detection. |
|
|
| ## Advantages |
| * **CPU-Only**: Does not require any GPU or heavy deep learning frameworks. |
| * **High Speed**: Processing an hour-long show takes less than a second. |
| * **Efficiency**: Extremely low memory footprint. |
|
|
| ## Usage |
| The model is integrated into the local `predict.py` workflow: |
| ```python |
| from train import RadioChroniqueClassifier |
| classifier = RadioChroniqueClassifier.load_model("models/radio_chronique_rf.pkl") |
| ``` |
|
|
| ## Author |
| Maintained by eglantinefonrose. |
|
|
