README.md

---
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: audio_duration
    dtype: float64
  - name: number_of_speakers
    dtype: int64
  - name: text
    dtype: string
  - name: gender
    dtype: string
  - name: age
    dtype: string
  - name: accents
    dtype: string
  - name: formality
    dtype: string
  - name: semantic_content
    dtype: string
  - name: data_source
    dtype: string
  - name: acoustic_environment
    dtype: string
  - name: spontaneous
    dtype: int64
  splits:
  - name: test
    num_bytes: 402008962.0
    num_examples: 344
  download_size: 387412980
  dataset_size: 402008962.0
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# PSRB - Hugging Face Format

**All credits for the original dataset go to [PartAI/PSRB](https://huggingface.co/datasets/PartAI/PSRB).**

## Dataset Overview
This repository contains a formatted version of the PSRB dataset designed to work out-of-the-box with the Hugging Face `datasets` and `transformers` ecosystem for Automatic Speech Recognition (ASR) tasks. 

## What Was Done
The original data, which was formatted as a CSV with local audio paths, was processed into a Hugging Face DatasetDict. Specifically:
* The raw Pandas DataFrame was converted to a Hugging Face Dataset.
* The name of columns has been fixed. For example the text was saved in audio_duration column, the audio_duration was saved in number_of_speakers column, etc. Now each column contains correct related data.
* **The audio is in the `audio` column and the text in the `text` column.**
* The audio paths were cast to the `Audio(sampling_rate=16000)` feature, meaning the dataset will automatically read and decode the raw waveforms into 16kHz PyTorch/NumPy arrays when queried.
* All original metadata columns (`audio_duration`, `number_of_speakers`, `gender`, `age`, `accents`, `formality`, `semantic_content`, `data_source`, `acoustic_environment`, `spontaneous`) were strictly preserved to allow for detailed WER analysis across different demographics and acoustic environments.

## Quick Start

You can load and use the dataset directly without worrying about local file paths:

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your-username/your-dataset-name")

# Access the first sample's audio array and transcription
sample = dataset["test"][0]

audio_array = sample["audio"]["array"]
transcription = sample["text"]

print(f"Transcription: {transcription}")
print(f"Speaker Gender: {sample['gender']}")
```