README.md

---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- tr
tags:
- speech
- audio
- dataset
- tts
- asr
- merged-dataset
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: "train-*.parquet"
  default: true
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: null
  - name: text
    dtype: string
  - name: speaker_id
    dtype: string
  - name: language
    dtype: string
  - name: emotion
    dtype: string
  - name: original_dataset
    dtype: string
  - name: original_filename
    dtype: string
  - name: start_time
    dtype: float32
  - name: end_time
    dtype: float32
  - name: duration
    dtype: float32
  splits:
  - name: train
    num_examples: 1293
  config_name: default
---

# FTTRTEST

This is a merged speech dataset containing 1293 audio segments from 5 source datasets.

## Dataset Information

- **Total Segments**: 1293
- **Speakers**: 7
- **Languages**: tr
- **Emotions**: happy, angry, neutral
- **Original Datasets**: 5

## Dataset Structure

Each example contains:
- `audio`: Audio file (WAV format, original sampling rate preserved)
- `text`: Transcription of the audio
- `speaker_id`: Unique speaker identifier (made unique across all merged datasets)
- `language`: Language code (en, es, fr, etc.)
- `emotion`: Detected emotion (neutral, happy, sad, etc.)
- `original_dataset`: Name of the source dataset this segment came from
- `original_filename`: Original filename in the source dataset
- `start_time`: Start time of the segment in seconds
- `end_time`: End time of the segment in seconds
- `duration`: Duration of the segment in seconds

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

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

# 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']}")
print(f"Original Dataset: {sample['original_dataset']}")
print(f"Duration: {sample['duration']}s")

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

### Alternative: Load from JSONL

```python
from datasets import Dataset, Audio, Features, Value
import json

# Load the JSONL file
rows = []
with open("data.jsonl", "r", encoding="utf-8") as f:
    for line in f:
        rows.append(json.loads(line))

features = Features({
    "audio": Audio(sampling_rate=None),
    "text": Value("string"),
    "speaker_id": Value("string"),
    "language": Value("string"),
    "emotion": Value("string"),
    "original_dataset": Value("string"),
    "original_filename": Value("string"),
    "start_time": Value("float32"),
    "end_time": Value("float32"),
    "duration": Value("float32")
})

dataset = Dataset.from_list(rows, features=features)
```

### Dataset Structure

The dataset includes:
- `data.jsonl` - Main dataset file with all columns (JSON Lines)
- `*.wav` - Audio files under `audio_XXX/` subdirectories  
- `load_dataset.txt` - Python script for loading the dataset (rename to .py to use)

JSONL keys:
- `audio`: Relative audio path (e.g., `audio_000/segment_000000_speaker_0.wav`)
- `text`: Transcription of the audio
- `speaker_id`: Unique speaker identifier
- `language`: Language code
- `emotion`: Detected emotion
- `original_dataset`: Name of the source dataset
- `original_filename`: Original filename in the source dataset
- `start_time`: Start time of the segment in seconds
- `end_time`: End time of the segment in seconds
- `duration`: Duration of the segment in seconds

## 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={FTTRTEST},
  author={Vyvo Dataset Builder},
  year={2025},
  url={https://huggingface.co/datasets/Codyfederer/fttrtest}
}
```

This dataset was created using the Vyvo Dataset Builder tool.