intron-innovation/AfriSpeech-Dialog

A dataset of long-form African accented English conversation for evaluation diarization, ASR, and summarization # AfriSpeech-Dialog ## Project Overview **AfriSpeech-Dialog** is a project to evaluate Automatic Speech Recognition (ASR), speaker diarization, and multi-agent summarization on medical and non-medical African-accented conversations (long-form), which include code-switching. ### Main Contributions: The project and associated paper make the following contributions: - Introduces a dataset of ~50 simulated medical/non-medical conversations with African accents. - Evaluates state-of-the-art (SOTA) speaker diarization models on accented speech. - Compares the performance of open multilingual ASR models (e.g., Whisper, Conformer, MMS, XLS-R) on long-form accented speech, benchmarked against datasets from other continents. - Evaluates multi-agent summarization of medical/non-medical conversation transcripts. --- ## Installation To set up the environment, follow these steps: 1. Create a conda environment with Python 3.10: ```bash conda create -n afrispeech_dialog python=3.10 ``` 2. Activate the environment: ```bash conda activate afrispeech_dialog ``` 3. Install the required dependencies: ```bash pip install -r requirements.txt ``` --- ## Data Setup The `data/` directory contains audio samples referenced in `afrispeech_dialog_v1_47.csv`. This CSV file includes the following columns: - `path`: The relative path to the audio files. - `transcript`: The text transcript of the conversations. - We have provided additional columns for demographic details about the speakers. ## Running Experiments ### ASR Experiments 1. **Model Subclassing**: - To benchmark a specific model, subclass the `Model` class to create a custom class for that model. You can use `src/models/whisper.py` as a guide. - Once you've created the class, modify `bin/main_predictions.py` to include a condition for running your custom class (around line 28). 2. **Main Script**: - The main entry point for running experiments is `bin/main_predictions.py`. This script handles data preprocessing (for ASR, …