---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: speaker_embeddings
    sequence: float32
  splits:
  - name: train
    num_bytes: 1726884990.125
    num_examples: 11247
  download_size: 1723089571
  dataset_size: 1726884990.125
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# English Technical Speech Dataset

## Overview

The **English Technical Speech Dataset** is a curated collection of English technical vocabulary recordings, designed for applications like Text-to-Speech (TTS), Automatic Speech Recognition (ASR), and Audio Classification. The dataset includes **11,247 entries** and provides audio files, transcriptions, and speaker embeddings to support the development of robust technical language models.

- **Language**: English (technical focus)
- **Total Entries**: 11,247
- **File Format**: Parquet
- **Sampling Rate**: 16 kHz

## Domain and Use Cases

### Primary Domain: Technical Speech Processing

This dataset is ideal for use in:
- **Text-to-Speech (TTS) Systems**: Facilitating the generation of technical language audio.
- **Automatic Speech Recognition (ASR)**: Improving transcription accuracy on technical vocabulary.
- **Customer Support AI**: Enhancing systems that recognize and respond to complex terminology.

### Use Cases
- **ASR for Technical Support**: Optimized for recognizing industry-specific vocabulary in customer service.
- **Educational Transcriptions**: Useful for e-learning platforms focusing on technical material.
- **Technical Support Tools**: Enhances AI tools in areas such as IT help desks.

## Data Structure

The dataset is stored in a Parquet file and has three main columns:

1. **audio**: Contains the audio data recorded at a 16 kHz sampling rate.
2. **text**: Transcriptions of the audio content.
3. **speaker_embeddings**: Speaker embeddings generated with **SpeechBrain's x-vector model** for each audio file, providing vector representations of speaker characteristics.

#### Sample Data Structure
| Column              | Description                                        |
|---------------------|----------------------------------------------------|
| `audio`             | Audio file in 16 kHz WAV format                    |
| `text`              | Text transcription of the corresponding audio      |
| `speaker_embeddings`| Vectorized embeddings representing speaker identity|

### Speaker Embeddings

Speaker embeddings were generated using the SpeechBrain x-vector model to capture speaker characteristics. This vector data is provided in the `speaker_embeddings` column and can be used for speaker identification or verification.

## Getting Started

To load and work with this dataset, you can use the `datasets` library from Hugging Face:

```python
from datasets import load_dataset

ds = load_dataset("Tejasva-Maurya/English-Technical-Speech-Dataset", split = "train")
```

### Example Data

Each row in the dataset includes:
- **Audio**: WAV audio data with a 16 kHz sampling rate
- **Text**: Corresponding transcription for each audio sample
- **Speaker Embedding**: Vectorized representation of speaker identity

## Dataset Composition and Sources

This dataset combines:
- **Custom Audio Recordings**: **Self-recorded** technical vocabulary, with the assistance of **[Saurabh Kumar](https://huggingface.co/Sana1207)** for additional recordings.
- **Open-Source Data from GitHub**:Sample set (Pure-set) from
  - [NPTEL2020-Indian-English-Speech-Dataset](https://github.com/AI4Bharat/NPTEL2020-Indian-English-Speech-Dataset?tab=readme-ov-file)
- **Technical Speech Data from Hugging Face**:
  - [TTS_English_Technical_data](https://huggingface.co/datasets/Yassmen/TTS_English_Technical_data)

These sources contribute to the dataset’s focus on high-quality technical language audio and transcription accuracy.

## License and Citation

This dataset is licensed under **Creative Commons Attribution 4.0 International (CC BY 4.0)**. If you use this dataset, please cite it.

## Acknowledgments

Special thanks to Saurabh Kumar for assisting with custom audio recordings, and to AI4Bharat, Yassmen, and other contributors for their open-source datasets. This dataset is part of a larger effort to improve technical language understanding and processing in AI.

---