README.md

metadata

title: GLAM Web App
emoji: 📈
colorFrom: green
colorTo: purple
sdk: gradio
sdk_version: 6.18.0
python_version: '3.13'
app_file: app.py
pinned: false
license: mit
short_description: Respiratory monitoring model

GLAM Pipeline

This repository includes a respiratory audio separation pipeline built around a local SepFormer model and a clinical reasoning/GNN inference path.

Key Files

model.py

Loads the SepFormer separation model using local pretrained files in pretrained_sepformer.

Wraps encoder/masknet/decoder into UnifiedSepFormer.

interface.py

Provides shared audio loading, separation, and saving helpers.

Includes:

• separate_audio_file(...)
• predict_sources(...)
• save_separated_sources(...)

for pipeline reuse.

Still supports the existing Gradio UI workflow via:

• separate_audio(...)

pipeline.py

Runs separation over a directory of mixture audio files.

Writes separated WAVs and waveform PNGs to an output folder.

Produces:

• pipeline_summary.json
• pipeline_summary.csv

gnn.py

Defines the GNN model architecture used for respiratory audio reasoning.

Provides utilities for:

• loading a GNN checkpoint
• estimating breathing rate
• patient state tracking
• clinical alert generation

reasoning_pipeline.py

Processes GNN run outputs:

• window_report.json
• window_report.csv

Generates reasoning summary CSV/JSON files.

Includes visualization helpers for per-patient summary plots.

full_pipeline.py

Runs the complete flow:

1. Separate mixture audio files
2. Run Wav2Vec2 + GNN inference
3. Save per-run artifacts
4. Aggregate reasoning summaries

Running a Simulation

To test the system using the provided sample data:

1. Open the Patient Registration page.
2. Navigate to the Sample_Audio folder.
3. Upload S1, S2, and S3 as individual patient recordings.
4. Click Register to register each patient.

Next:

1. Open the Separation page.
2. Upload Sample_0_mix.wav from the Sample_Audio folder as the mixed audio recording.
3. Select the registered patients (S1, S2, and S3).
4. Click Run Separation.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference