README.md

metadata

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:

{"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/