metadata

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

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

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

@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.