| --- |
| title: Language Attrition - derive & model |
| emoji: 📊 |
| colorFrom: indigo |
| colorTo: pink |
| sdk: gradio |
| sdk_version: 4.44.1 |
| python_version: "3.10" |
| app_file: app.py |
| pinned: false |
| license: mit |
| --- |
| |
| # Language Attrition: derive the numbers, then model them |
|
|
| A point-and-click tool for the *Bye-lingual* seminar. No transcribing, no |
| Spanish, no Praat, no code. Runs on CPU (it does **not** transcribe audio). |
|
|
| **Tab 1 - Derive.** Upload a recording **and its transcript** (the existing |
| word-timestamped `.json`). The app derives ~25 linguistic factors: disfluency |
| (filled / empty pauses, repetitions, retractions), fluency (speech / |
| articulation rate, phonation %), complexity (MLU, TTR, MTLD), and acoustics (F0, |
| jitter, shimmer, HNR, spectral tilt, MFCCs). Stack several speakers and download |
| the table. |
|
|
| **Tab 2 - Model.** Use the table you built (or upload a CSV joined with your |
| questionnaire predictors), pick two variables, and get the scatter, the |
| correlation (Pearson or Spearman), and the regression line. A correlation |
| heatmap shows all variables at once. |
|
|
| The numbers are identical to the course pipeline (same `markers.py` / |
| `acoustic_markers.py`). Transcription is **not** done here: this Space consumes an |
| existing transcript. Phase-2 markers (VOT, vowel space, rhythm) need forced |
| alignment (MFA) and are not produced. |
|
|
| ## Transcript format |
| A JSON file, either a bare list or `{"chunks": [...]}`, where each chunk is: |
| ```json |
| {"text": "Pues", "timestamp": {"start": 2.94, "end": 3.08}} |
| ``` |
| The `[*]` disfluency markers must be kept. Per-speaker transcripts can be |
| exported from the pipeline with `pipeline/export_transcripts_json.py`. |
|
|
| ## Files |
| - `app.py` - Gradio UI (Derive + Model tabs) |
| - `features.py` - parse transcript → extract_markers → flat feature row |
| - `markers.py`, `acoustic_markers.py`, `alignment_markers.py` - vendored from `pipeline/` |
| |
