---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- en
tags:
- speech
- audio
- dataset
- tts
- asr
- merged-dataset
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: "data.csv"
  default: true
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: text
    dtype: string
  - name: speaker_id
    dtype: string
  - name: emotion
    dtype: string
  - name: language
    dtype: string
  splits:
  - name: train
    num_examples: 1994
  config_name: default
---

# test6

This is a merged speech dataset containing 1994 audio segments from 2 source datasets.

## Dataset Information

- **Total Segments**: 1994
- **Speakers**: 3
- **Languages**: en
- **Emotions**: neutral, negative_surprise, positive_surprise, distress, relief, contentment, adoration, interest, confusion, happy, sadness, triumph, fear, disappointment, awe, realization, angry
- **Original Datasets**: 2

## Dataset Structure

Each example contains:
- `audio`: Audio file (WAV format, 16kHz sampling rate)
- `text`: Transcription of the audio
- `speaker_id`: Unique speaker identifier (made unique across all merged datasets)
- `emotion`: Detected emotion (neutral, happy, sad, etc.)
- `language`: Language code (en, es, fr, etc.)

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Codyfederer/test6")

# Access the training split
train_data = dataset["train"]

# Example: Get first sample
sample = train_data[0]
print(f"Text: {sample['text']}")
print(f"Speaker: {sample['speaker_id']}")
print(f"Language: {sample['language']}")
print(f"Emotion: {sample['emotion']}")

# Play audio (requires audio libraries)
# sample['audio']['array'] contains the audio data
# sample['audio']['sampling_rate'] contains the sampling rate
```

### Alternative: Load from CSV

```python
import pandas as pd
from datasets import Dataset, Audio, Features, Value

# Load the CSV file
df = pd.read_csv("data.csv")

# Define features
features = Features({
    "audio": Audio(sampling_rate=16000),
    "text": Value("string"),
    "speaker_id": Value("string"),
    "emotion": Value("string"),
    "language": Value("string")
})

# Create dataset
dataset = Dataset.from_pandas(df, features=features)
```

### Dataset Structure

The dataset includes:
- `data.csv` - Main dataset file with all columns
- `*.wav` - Audio files in the root directory  
- `load_dataset.txt` - Python script for loading the dataset (rename to .py to use)

CSV columns:
- `audio`: Audio filename (in root directory)
- `text`: Transcription of the audio
- `speaker_id`: Unique speaker identifier
- `emotion`: Detected emotion
- `language`: Language code

## Speaker ID Mapping

Speaker IDs have been made unique across all merged datasets to avoid conflicts.
For example:
- Original Dataset A: `speaker_0`, `speaker_1`
- Original Dataset B: `speaker_0`, `speaker_1`
- Merged Dataset: `speaker_0`, `speaker_1`, `speaker_2`, `speaker_3`

Original dataset information is preserved in the metadata for reference.

## Data Quality

This dataset was created using the Vyvo Dataset Builder with:
- Automatic transcription and diarization
- Quality filtering for audio segments
- Music and noise filtering
- Emotion detection
- Language identification

## License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

## Citation

```bibtex
@dataset{vyvo_merged_dataset,
  title={test6},
  author={Vyvo Dataset Builder},
  year={2025},
  url={https://huggingface.co/datasets/Codyfederer/test6}
}
```

This dataset was created using the Vyvo Dataset Builder tool.