README.md

---
dataset_info:
  features:
  - name: file
    dtype: string
  - name: audio
    struct:
    - name: array
      sequence: float64
    - name: path
      dtype: string
    - name: sampling_rate
      dtype: int64
  - name: text
    dtype: string
  - name: speaker_id
    dtype: int64
  - name: chapter_id
    dtype: int64
  - name: id
    dtype: string
  splits:
  - name: train.clean.100
    num_bytes: 1623641436
    num_examples: 1000
  - name: train.clean.360
    num_bytes: 1572285643
    num_examples: 1000
  - name: train.other.500
    num_bytes: 1502809029
    num_examples: 1000
  - name: validation.clean
    num_bytes: 65591952
    num_examples: 100
  - name: validation.other
    num_bytes: 76760504
    num_examples: 100
  - name: test.clean
    num_bytes: 85852252
    num_examples: 100
  - name: test.other
    num_bytes: 58550856
    num_examples: 100
  download_size: 1181170369
  dataset_size: 4985491672
configs:
- config_name: default
  data_files:
  - split: train.clean.100
    path: data/train.clean.100-*
  - split: train.clean.360
    path: data/train.clean.360-*
  - split: train.other.500
    path: data/train.other.500-*
  - split: validation.clean
    path: data/validation.clean-*
  - split: validation.other
    path: data/validation.other-*
  - split: test.clean
    path: data/test.clean-*
  - split: test.other
    path: data/test.other-*
---

# Condensed LibriSpeech ASR

This dataset is a condensed version of the [LibriSpeech ASR dataset](https://huggingface.co/datasets/openslr/librispeech_asr), created by subsampling approximately 10% of the original data from each split. It is intended for quick experimentation, prototyping, and debugging when working with Automatic Speech Recognition (ASR) tasks.

## Dataset Details

- **Original Dataset:** [LibriSpeech ASR](https://huggingface.co/datasets/openslr/librispeech_asr)
- **Condensation Ratio:** Approximately 10% of the full dataset
- **Splits Included:**
  - `train.clean.100`
  - `train.clean.360`
  - `train.other.500`
  - `validation.clean`
  - `validation.other`
  - `test.clean`
  - `test.other`

For each split, a default number of examples was extracted:
- **Training Splits:** 1,000 examples each
- **Validation/Test Splits:** 100 examples each

## Data Format

Each sample in the dataset contains the following fields:

- **file:**  
  A path to the original audio file (FLAC format).

- **audio:**  
  A dictionary containing:
  - `path`: Path to the audio file.
  - `array`: The decoded audio waveform as a NumPy array.
  - `sampling_rate`: The sampling rate (typically 16 kHz).

- **text:**  
  The transcription corresponding to the audio.

- **id:**  
  A unique identifier for the sample.

- **speaker_id:**  
  A unique identifier for the speaker.

- **chapter_id:**  
  An identifier corresponding to the audiobook chapter.

## How Was This Dataset Created?

The condensed dataset was generated by streaming the full [LibriSpeech ASR dataset](https://huggingface.co/datasets/openslr/librispeech_asr) using the Hugging Face Datasets library and selecting approximately 10% of each split. This process preserves the original structure and fields, enabling seamless use with models and workflows designed for LibriSpeech.

## Usage Example

Below is a Python snippet to load and inspect the dataset:

```python
from datasets import load_dataset

# Load the condensed dataset from the Hugging Face Hub
dataset = load_dataset("nyalpatel/condensed_librispeech_asr")

# Access a specific split (e.g., test.clean)
test_dataset = dataset["test.clean"]

# Display the first example in the test set
print(test_dataset[0])