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---
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license: mit
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task_categories:
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- automatic-speech-recognition
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language:
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- en
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tags:
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- audio
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- speech
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- transcription
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- asr
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- voice-recording
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size_categories:
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- n<1K
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dataset_info:
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features:
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- name: audio
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dtype: audio
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sample_rate: 16000
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- name: transcript
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dtype: string
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splits:
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- name: train
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num_examples: 297
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---
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# Audio Transcription Dataset
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This dataset contains 297 audio recordings with their corresponding transcriptions for automatic speech recognition (ASR) tasks.
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## Dataset Description
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This dataset includes:
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- **Audio files**: High-quality voice recordings (.wav format)
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- **Transcriptions**: Accurate text transcriptions of the spoken content
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- **Proper Audio feature type**: Ready for model training (not just file paths!)
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## Dataset Statistics
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- **Total samples**: 297
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- **Audio format**: WAV files at 16kHz sampling rate
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- **Average transcript length**: 50.1 characters
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- **Language**: English
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## Sample Data
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| Audio File | Transcript |
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|------------|------------|
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| R1.wav | Hello! My name is Rocky. |
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| R10.wav | I am speaking English for a voice recording. |
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| R11.wav | This is a test sentence for training the model. |
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## Usage
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```python
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from datasets import load_dataset
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# Load the dataset
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dataset = load_dataset("Aashish17405/audio-dataset")
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# Access audio data (proper Audio type, not string!)
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audio_sample = dataset['train'][0]['audio']
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print(f"Sampling rate: {audio_sample['sampling_rate']}")
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print(f"Audio array shape: {audio_sample['array'].shape}")
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print(f"Transcript: {dataset['train'][0]['transcript']}")
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# Ready for model training with transformers
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
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model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
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# Process audio
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inputs = processor(audio_sample["array"], sampling_rate=audio_sample["sampling_rate"], return_tensors="pt")
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```
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## Features
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✅ **Proper Audio Type**: Audio column shows as "Audio" feature, not "string"
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✅ **High Quality**: Clear voice recordings
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✅ **Diverse Content**: Various sentences and topics
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✅ **Training Ready**: Formatted for immediate use with speech models
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## Use Cases
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- Fine-tuning speech recognition models (Whisper, Wav2Vec2, etc.)
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- Voice training and accent recognition
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- Speech-to-text model development
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- Audio processing research
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## License
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MIT License - Free to use for research and commercial purposes.
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