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Datasets:
Codyfederer
/
tr123

Tasks:
Automatic Speech Recognition
Text-to-Speech
Modalities:
Audio
Languages:
Turkish
Size:
10K<n<100K
Tags:
speech
audio
dataset
tts
asr
merged-dataset
License:
Dataset card Files
xet
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Upload merged dataset: tr123

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README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - automatic-speech-recognition
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+ - text-to-speech
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+ language:
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+ - tr
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+ tags:
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+ - speech
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+ - audio
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+ - dataset
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+ - tts
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+ - asr
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+ - merged-dataset
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+ size_categories:
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+ - 10K<n<100K
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: "data.csv"
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+ default: true
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+ dataset_info:
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+ features:
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+ - name: audio
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+ dtype:
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+ audio:
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+ sampling_rate: 24000
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+ - name: text
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+ dtype: string
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+ - name: speaker_id
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+ dtype: string
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+ - name: emotion
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+ dtype: string
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+ - name: language
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+ dtype: string
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+ splits:
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+ - name: train
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+ num_examples: 11930
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+ config_name: default
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+ ---
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+
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+ # tr123
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+
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+ This is a merged speech dataset containing 11930 audio segments from 24 source datasets.
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+
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+ ## Dataset Information
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+
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+ - **Total Segments**: 11930
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+ - **Speakers**: 69
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+ - **Languages**: tr
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+ - **Emotions**: sad, neutral, happy, angry
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+ - **Original Datasets**: 24
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+
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+ ## Dataset Structure
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+
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+ Each example contains:
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+ - `audio`: Audio file (WAV format, 16kHz sampling rate)
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+ - `text`: Transcription of the audio
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+ - `speaker_id`: Unique speaker identifier (made unique across all merged datasets)
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+ - `emotion`: Detected emotion (neutral, happy, sad, etc.)
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+ - `language`: Language code (en, es, fr, etc.)
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+
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+ ## Usage
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+
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+ ### Loading the Dataset
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Load the dataset
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+ dataset = load_dataset("Codyfederer/tr123")
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+
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+ # Access the training split
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+ train_data = dataset["train"]
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+
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+ # Example: Get first sample
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+ sample = train_data[0]
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+ print(f"Text: {sample['text']}")
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+ print(f"Speaker: {sample['speaker_id']}")
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+ print(f"Language: {sample['language']}")
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+ print(f"Emotion: {sample['emotion']}")
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+
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+ # Play audio (requires audio libraries)
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+ # sample['audio']['array'] contains the audio data
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+ # sample['audio']['sampling_rate'] contains the sampling rate
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+ ```
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+
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+ ### Alternative: Load from CSV
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+
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+ ```python
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+ import pandas as pd
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+ from datasets import Dataset, Audio, Features, Value
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+
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+ # Load the CSV file
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+ df = pd.read_csv("data.csv")
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+
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+ # Define features
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+ features = Features({
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+ "audio": Audio(sampling_rate=24000),
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+ "text": Value("string"),
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+ "speaker_id": Value("string"),
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+ "emotion": Value("string"),
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+ "language": Value("string")
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+ })
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+
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+ # Create dataset
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+ dataset = Dataset.from_pandas(df, features=features)
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+ ```
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+
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+ ### Dataset Structure
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+
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+ The dataset includes:
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+ - `data.csv` - Main dataset file with all columns
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+ - `*.wav` - Audio files in the root directory
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+ - `load_dataset.txt` - Python script for loading the dataset (rename to .py to use)
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+
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+ CSV columns:
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+ - `audio`: Audio filename (in root directory)
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+ - `text`: Transcription of the audio
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+ - `speaker_id`: Unique speaker identifier
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+ - `emotion`: Detected emotion
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+ - `language`: Language code
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+
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+ ## Speaker ID Mapping
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+
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+ Speaker IDs have been made unique across all merged datasets to avoid conflicts.
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+ For example:
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+ - Original Dataset A: `speaker_0`, `speaker_1`
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+ - Original Dataset B: `speaker_0`, `speaker_1`
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+ - Merged Dataset: `speaker_0`, `speaker_1`, `speaker_2`, `speaker_3`
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+
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+ Original dataset information is preserved in the metadata for reference.
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+
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+ ## Data Quality
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+
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+ This dataset was created using the Vyvo Dataset Builder with:
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+ - Automatic transcription and diarization
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+ - Quality filtering for audio segments
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+ - Music and noise filtering
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+ - Emotion detection
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+ - Language identification
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+
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+ ## License
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+
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+ This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @dataset{vyvo_merged_dataset,
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+ title={tr123},
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+ author={Vyvo Dataset Builder},
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+ year={2025},
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+ url={https://huggingface.co/datasets/Codyfederer/tr123}
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+ }
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+ ```
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+
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+ This dataset was created using the Vyvo Dataset Builder tool.
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