---
license: mit
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- audio
- speech
- transcription
- asr
- voice-recording
size_categories:
- n<1K
dataset_info:
  features:
  - name: audio
    dtype: audio
    sample_rate: 16000
  - name: transcript
    dtype: string
  splits:
  - name: train
    num_examples: 99
---

# Audio Transcription Dataset

This dataset contains 99 audio recordings with their corresponding transcriptions for automatic speech recognition (ASR) tasks.

## Dataset Description

This dataset includes:
- **Audio files**: High-quality voice recordings (.wav format)
- **Transcriptions**: Accurate text transcriptions of the spoken content
- **Proper Audio feature type**: Ready for model training (not just file paths!)

## Dataset Statistics

- **Total samples**: 99
- **Audio format**: WAV files at 16kHz sampling rate
- **Average transcript length**: 40.8 characters
- **Language**: English

## Sample Data

| Audio File | Transcript |
|------------|------------|
| R1.wav | my name is Rocky. |
| R10.wav | I am speaking English for a voice recording. |
| R11.wav | This is a test sentence for training the model. |

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Aashish17405/audio-dataset")

# Access audio data (proper Audio type, not string!)
audio_sample = dataset['train'][0]['audio']
print(f"Sampling rate: {audio_sample['sampling_rate']}")
print(f"Audio array shape: {audio_sample['array'].shape}")
print(f"Transcript: {dataset['train'][0]['transcript']}")

# Ready for model training with transformers
from transformers import WhisperProcessor, WhisperForConditionalGeneration

processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")

# Process audio
inputs = processor(audio_sample["array"], sampling_rate=audio_sample["sampling_rate"], return_tensors="pt")
```

## Features

✅ **Proper Audio Type**: Audio column shows as "Audio" feature, not "string"  
✅ **High Quality**: Clear voice recordings  
✅ **Diverse Content**: Various sentences and topics  
✅ **Training Ready**: Formatted for immediate use with speech models  

## Use Cases

- Fine-tuning speech recognition models (Whisper, Wav2Vec2, etc.)
- Voice training and accent recognition
- Speech-to-text model development
- Audio processing research

## License

MIT License - Free to use for research and commercial purposes.