---
license: mit
language: en
tags:
- pathological-speech
- speech-synthesis
- tts
- voice-conversion
- healthy
- librispeech
---

# Librispeech Female Dataset

## Overview

This dataset contains healthy speech samples from a female speaker (211) in the LibriSpeech corpus, prepared for pathological speech synthesis research.

**Speaker Information:**
- **Speaker ID:** 211
- **Corpus:** LibriSpeech
- **Gender:** Female
- **Speech Status:** Healthy
- **Disorder Type:** None
- **Severity:** None

## Dataset Statistics

- **Total Samples:** 160
- **Total Duration:** 0.41 hours
- **Sampling Rate:** 24,000 Hz
- **Format:** Audio arrays with transcriptions

### Training Split
- **Samples:** 130
- **Duration:** 0.33 hours
- **Avg Duration:** 9.1s
- **Duration Range:** 2.0s - 17.1s
- **Avg Text Length:** 141 characters

### Test Split  
- **Samples:** 30
- **Duration:** 0.08 hours
- **Avg Duration:** 9.3s
- **Duration Range:** 2.4s - 16.3s
- **Avg Text Length:** 142 characters


### Loading the Dataset

```python
from datasets import load_dataset

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

# Access train and test splits
train_data = dataset['train']
test_data = dataset['test']

# Each sample contains:
# - 'audio': {'array': numpy_array, 'sampling_rate': 24000}
# - 'text': str (normalized transcription)

# Example usage
sample = train_data[0]
audio_array = sample['audio']['array']
transcription = sample['text']
sampling_rate = sample['audio']['sampling_rate']
```

### Direct Training with Transformers

```python
from transformers import Trainer
from datasets import load_dataset

# Load and use directly with Trainer (no preprocessing needed)
dataset = load_dataset("your-username/librispeech_female")
trainer = Trainer(
    train_dataset=dataset['train'],
    eval_dataset=dataset['test'],
    # ... other trainer arguments
)
```