Türkçe TTS Birleşik Veri Seti

7 farklı açık kaynak Türkçe TTS veri setinin birleşimi. ~81,500 örnek | 24kHz | SNAC uyumlu

  Kaynaklar

Veri Seti Örnek Kaynak

Mazlum Kiper 9,643 omersaidd/tts_mazlum_kiper_tur

Ahmet Deniz 11,289 omersaidd/tts_ahmet_deniz_tur

Nisan Kumru 8,042 omersaidd/tts_nisan_kumru_tur

Derya TTS v2 42 afkfatih/derya-tts-v2

Derya Karma v3 255 afkfatih/derya-tts-karma-v3

Khan Academy 25,741 ysdede/khanacademy-turkish

Common… See the full description on the dataset page: https://huggingface.co/datasets/afkfatih/turkish-tts-combined-raw.

Dataset consists of 5 emotion labels. These labels are anger, happy, distinguish, surprise and fear. There are 800 tweets in the dataset for each label. Hence, total tweet count is 4000 for dataset.

You can use the data set in many areas such as sentiment, emotion analysis and topic modeling.

Info: Hashtags and usernames was removed in the dataset. Dataset has used many studies and researches. These researches are followed as: -(please citation this article) Güven, Z. A., Diri, B., & Cąkaloglu, T. (2020). Comparison of n-stage Latent Dirichlet Allocation versus other topic modeling methods for emotion analysis. Journal of the Faculty of Engineering and Architecture of Gazi University. https://doi.org/10.17341/gazimmfd.556104 -Güven, Z. A., Diri, B., & Çakaloğlu, T. (2019). Emotion Detection with n-stage Latent Dirichlet Allocation for Turkish Tweets. Academic Platform Journal of Engineering and Science. https://doi.org/10.21541/apjes.459447 -Guven, Z. A., Diri, B., & Cakaloglu, T. (2019). Comparison Method for Emotion Detection of Twitter Users. Proceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019. https://doi.org/10.1109/ASYU48272.2019.8946435

Dataset

This dataset contains positive , negative and notr sentences from several data sources given in the references. In the most sentiment models , there are only two labels; positive and negative. However , user input can be totally notr sentence. For such cases there were no data I could find. Therefore I created this dataset with 3 class. Positive and negative sentences are listed below. Notr examples are extraced from turkish wiki dump. In addition, added some random text… See the full description on the dataset page: https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset.

NLI-TR (Turkish NLI Research)

Unleash Your NLI Research in Turkish Language!

By Huggingface Hub [source]

About this dataset

NLI-TR is a revolutionary set of two datasets that provide an unparalleled opportunity for the natural language processing and machine learning community to conduct inference research in the Turkish Language. The datasets - SNLI-TR and MNLI-TR - contain carefully curated natural language inference data that have been translated into Turkish. With NLI-TR, researchers can explore the exciting prospects of developing automated models tailored to make inferences on texts produced in this vibrant language. Moreover, they can also investigate how models trained on data from one language fare when applied in another, a valuable insight into cross-lingual generalization capabilities. NLI-TR offers both seasoned and budding researchers an unprecedented platform to further our understanding of natural language inferencing capability

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How to use the dataset

How To Use The NLI-TR Dataset to Unlock Turkish NLI Research

Welcome to the exciting world of natural language inference (NLI) research! If you’re looking for a great dataset to use for your research in this field, the NLI-TR dataset is a perfect starting point. This guide will provide an overview of how you can use the data from this dataset to uncover new insights about NLI tasks in Turkish.

The NLI-TR dataset contains two large scale datasets intended for natural language inference tasks – SNLI-TR and MNLI- TR. Both datasets offer researchers an opportunity to explore Natural Language Inference (NLI) research in the Turkish language, with examples ranging from sentence paraphrasing task and classification tasks to question answering scenarios using various NLP techniques.

Using the Data:

The data provided in this dataset includes both training and validation sets, making it easy for researchers who are just getting started with their projects. The SNLI_tr_train.csv file is used as input for training your models, while slni_tr_validation can be used as input for testing or validating model accuracy on unseen data. Additionally, multinli_tr_validation_{matched / mismatched}.csv files offer additional validation on how well your trained models perform on more complex scenarios such as sentence paraphrasing or question answering tasks using various NLP techniques.

Each record includes four columns – premise ,hypothesis ,label , (and domain). The premise column specifies what information is provided before asking a question or making an inference; think of it as context clues that explain why one statement implies another statement more directly than others might do without them . The hypothesis column provides what lies at the heart of inference --the conclusion reached after introducing facts given before it . Last but not least we have label column which denotes whether two sentences entail each other (ENTAILMENT), contradict each other(CONTRADICTION) or are unrelated(NEUTRAL). A domain label has also been assigned by some authors when necessary; this mostly applies when inferring between sentences across different semantic domains such as weather vs sports vs finance etc .

Research Ideas

• Developing an NLI-based Turkish language question answering system.
• Training a sentiment analysis algorithm to identify sentiment in text written in Turkish.
• Building a Machine Learning Chatbot that uses NLI to understand conversational context and respond accordingly for users intending to converse in the Turkish language

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: snli_tr_train.csv | Column name | Description | |:---------------|:------------------------------------------------------------------------------------------------------------------------------------------...

2,000 hours of unscripted Turkish conversation between pairs of native speakers, 200 vetted speakers in all, recorded in Turkey. Every recording is dual-channel, one speaker per channel, and comes with a time-aligned, human-verified transcript, speaker metadata (age, gender, region and recording device) and signed consent records, under a commercial license for ASR, TTS and voice AI training.

Turkish Speech Corpus (TSC)

This repository presents an open-source Turkish Speech Corpus, introduced in "Multilingual Speech Recognition for Turkic Languages". The corpus contains 218.2 hours of transcribed speech with 186,171 utterances and is the largest publicly available Turkish dataset of its kind at that time. Paper: Multilingual Speech Recognition for Turkic Languages.
GitHub Repository: https://github.com/IS2AI/TurkicASR

  Citation

@Article{info14020074… See the full description on the dataset page: https://huggingface.co/datasets/issai/Turkish_Speech_Corpus.

Introduction

This Turkish Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for Turkish -speaking travelers.

Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

Speech Data

The dataset includes 30 hours of dual-channel audio recordings between native Turkish speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

Participant Diversity:

- Speakers: 60 native Turkish contributors from our verified pool.

- Regions: Covering multiple Turkey provinces to capture accent and dialectal variation.

- Participant Profile: Balanced representation of age (18–70) and gender (60% male, 40% female).

Recording Details:

- Conversation Nature: Naturally flowing, spontaneous customer-agent calls.

- Call Duration: Between 5 and 15 minutes per session.

- Audio Format: Stereo WAV, 16-bit depth, at 8kHz and 16kHz.

- Recording Environment: Captured in controlled, noise-free, echo-free settings.

Topic Diversity

Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

Inbound Calls:

- Booking Assistance

- Destination Information

- Flight Delays or Cancellations

- Support for Disabled Passengers

- Health and Safety Travel Inquiries

- Lost or Delayed Luggage, and more

Outbound Calls:

- Promotional Travel Offers

- Customer Feedback Surveys

- Booking Confirmations

- Flight Rescheduling Alerts

- Visa Expiry Notifications, and others

These scenarios help models understand and respond to diverse traveler needs in real-time.

Transcription

Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

Transcription Includes:

- Speaker-Segmented Dialogues

- Time-Stamped Segments

- Non-speech Markers (e.g., pauses, coughs)

- High transcription accuracy by dual-layered transcription review ensures word error rate under 5%.

Metadata

Extensive metadata enriches each call and speaker for better filtering and AI training:

Participant Metadata: ID, age, gender, region, accent, and dialect.

Conversation Metadata: Topic, domain, call type, sentiment, and audio specs.

License

This travel-focused Turkish call center dataset is commercially licensed and ready for enterprise or research deployment.

This dataset combines two established sources to create a comprehensive resource for Turkish hate speech detection. The sources are:

Fawern Turkish Hate Speech Dataset Avaapm Hate Speech Dataset

By merging these two datasets, a total of 32,000 tweets are available for binary classification tasks. The dataset is designed for hate speech detection in Turkish social media texts, which is a crucial area in natural language processing (NLP) for identifying harmful or offensive content in online communication.

Each tweet in the dataset is classified into one of two categories:

Nefret (Hate Speech): Tweets that contain offensive, discriminatory, or harmful language aimed at a person or group. Hiçbiri (Non-Hate Speech): Tweets that do not contain any hate speech

This balanced dataset offers an equal distribution of both categories, making it suitable for training and evaluating machine learning models, including deep learning and transformer-based models, for Turkish text classification. The dataset can also be used to fine-tune existing pre-trained models for hate speech detection tasks.

The goal of this dataset is to facilitate the development of reliable and accurate systems capable of identifying hate speech in Turkish, contributing to safer and more inclusive online environments.

Turkish Number Plates

## Overview

Turkish Number Plates is a dataset for object detection tasks - it contains Plate annotations for 2,246 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Welcome to the Turkish Extraction Type Prompt-Response Dataset, a meticulously curated collection of 1500 prompt and response pairs. This dataset is a valuable resource for enhancing the data extraction abilities of Language Models (LMs), a critical aspect in advancing generative AI.

Dataset Content

This extraction dataset comprises a diverse set of prompts and responses where the prompt contains input text, extraction instruction, constraints, and restrictions while completion contains the most accurate extraction data for the given prompt. Both these prompts and completions are available in Turkish language.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Turkish people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This dataset encompasses various prompt types, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. Additionally, you'll find prompts and responses containing rich text elements, such as tables, code, JSON, etc., all in proper markdown format.

Prompt Diversity

To ensure diversity, this extraction dataset includes prompts with varying complexity levels, ranging from easy to medium and hard. Additionally, prompts are diverse in terms of length from short to medium and long, creating a comprehensive variety. The extraction dataset also contains prompts with constraints and persona restrictions, which makes it even more useful for LLM training.

Response Formats

To accommodate diverse learning experiences, our dataset incorporates different types of responses depending on the prompt. These formats include single-word, short phrase, single sentence, and paragraph type of response. These responses encompass text strings, numerical values, and date and time, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

Data Format and Annotation Details

This fully labeled Turkish Extraction Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, response type, and rich text presence.

Quality and Accuracy

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Turkish version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

Continuous Updates and Customization

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom extraction prompt and completion data tailored to specific needs, providing flexibility and customization options.

License

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Turkish Extraction Prompt-Completion Dataset to enhance the data extraction abilities and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

This dataset contains 1620 hours of Turkish speech collected from real-world conversational and monologue scenarios. Each audio recording includes accurate transcripts, speaker ID, gender, and additional metadata. The dataset was collected from diverse Turkish speakers with different geographic and demographic backgrounds, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Dataset Summary

This dataset is enhanced version of existing offensive language studies. Existing studies are highly imbalanced, and solving this problem is too costly. To solve this, we proposed contextual data mining method for dataset augmentation. Our method is basically prevent us from retrieving random tweets and label individually. We can directly access almost exact hate related tweets and label them directly without any further human interaction in order to solve… See the full description on the dataset page: https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection.

Turkish Multi-Domain Customer Service Conversations Dataset

This dataset includes Turkish-language dialogues between customers and representatives across various service domains, such as finance, e-commerce, technical support, and general inquiries.

Each conversation contains multiple turns and is structured with clearly labeled speaker roles (customer or representative), making it suitable for Natural Language Processing (NLP) tasks related to dialogue systems, intent detection, and chatbot development.

🔹 Dataset Structure

• conversation_id: Unique identifier for each conversation
• category: Service domain label (e.g. Finance, Technical Support)
• speaker: Role of the speaker (customer or representative)
• text: Utterance in Turkish

🔍 Use Cases

• Chatbot training
• Intent classification
• Dialogue summarization
• Speaker role detection
• Turkish NLP pretraining/finetuning

🧾 License

CC BY 4.0 — Free to use with attribution

🙋‍♂️ Creator

Prepared by Anıl as part of a research and educational NLP project.

TurkishLegalBench is a large-scale, multi-task benchmark suite specifically designed for Natural Language Processing (NLP) in the Turkish legal domain. The dataset consists of 38,009 authentic legal documents curated from Turkish high courts. It covers seven distinct legal tasks categorized under three pillars: Classification (TurkVerdict, TurkVenue, TurkCanon), Extraction (TurkChronos, TurkCite), and Reasoning (TurkCoherence, TurkAudit). This resource aims to bridge the gap in low-resource legal NLP for Turkish and provides a standardized evaluation framework for researchers.

The Turkish Offensive Language Dataset is a Turkish-language dataset collected from Twitter, designed for training models in offensive language detection, hate speech detection, and text classification tasks. Created by Gülzade Evni and Zeynep Baydemir, the dataset covers multiple subcategories of harmful content including racism, profanity, insult, and sexism. It consists of seven files and is distributed under the Apache-2.0 license, making it openly available for research and development purposes. The dataset is intended for practitioners and researchers working on natural language processing for Turkish social media content, addressing a recognized gap in low-resource language resources for content moderation applications.

About this dataset

Context

This list ranks the 22 counties in the Maryland by Turkish population, as estimated by the United States Census Bureau. It also highlights population changes in each county over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Turkish Population: This column displays the rank of county in the Maryland by their Turkish population, using the most recent ACS data available.
• County: The County for which the rank is shown in the previous column.
• Turkish Population: The Turkish population of the county is shown in this column.
• % of Total County Population: This shows what percentage of the total county population identifies as Turkish. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Maryland Turkish Population: This tells us how much of the entire Maryland Turkish population lives in that county. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Introduction

The Turkish General Domain Chat Dataset is a high-quality, text-based dataset designed to train and evaluate conversational AI, NLP models, and smart assistants in real-world Turkish usage. Collected through FutureBeeAI’s trusted crowd community, this dataset reflects natural, native-level Turkish conversations covering a broad spectrum of everyday topics.

Conversational Text Data

This dataset includes over 15000 chat transcripts, each featuring free-flowing dialogue between two native Turkish speakers. The conversations are spontaneous, context-rich, and mimic informal, real-life texting behavior.

Words per Chat: 300–700

Turns per Chat: Up to 50 dialogue turns

Contributors: 200 native Turkish speakers from the FutureBeeAI Crowd Community

Format: TXT, DOCS, JSON or CSV (customizable)

Structure: Each record contains the full chat, topic tag, and metadata block

Diversity and Domain Coverage

Conversations span a wide variety of general-domain topics to ensure comprehensive model exposure:

Music, books, and movies

Health and wellness

Children and parenting

Family life and relationships

Food and cooking

Education and studying

Festivals and traditions

Environment and daily life

Internet and tech usage

Childhood memories and casual chatting

This diversity ensures the dataset is useful across multiple NLP and language understanding applications.

Linguistic Authenticity

Chats reflect informal, native-level Turkish usage with:

Colloquial expressions and local dialect influence

Domain-relevant terminology

Language-specific grammar, phrasing, and sentence flow

Inclusion of realistic details such as names, phone numbers, email addresses, locations, dates, times, local currencies, and culturally grounded references

Representation of different writing styles and input quirks to ensure training data realism

Metadata

Every chat instance is accompanied by structured metadata, which includes:

Participant Age

Gender

Country/Region

Chat Domain

Chat Topic

Dialect

This metadata supports model filtering, demographic-specific evaluation, and more controlled fine-tuning workflows.

Data Quality Assurance

All chat records pass through a rigorous QA process to maintain consistency and accuracy:

Manual review for content completeness

Format checks for chat turns and metadata

Linguistic verification by native speakers

Removal of inappropriate or unusable samples

This ensures a clean, reliable dataset ready for high-performance AI model training.

Licensing

This dataset is developed and owned by FutureBeeAI and is available under a commercial license. Custom licensing terms can be provided for academic, research, or enterprise clients.

Turkish has 2 licensable sets in the catalogue across 1 dataset type sourced from Turkey. Conversation sets are unscripted two-party recordings, most at 16 kHz and above. All sets ship with transcripts and speaker metadata.

Evaluation of Multi-label Target Classification performance on the Turkish Dataset, measured using Precision, Recall, F1-score, and Support.

This dataset contains chat-based dialogs in the Turkish language. The dialogs are written in a particularly natural, polite and supportive style. The interactions between the user and the chatbot aim to provide information and support on different topics. This dataset is suitable for Turkish language processing (NLP) projects and can be used in areas such as chatbots, language modeling and text analysis.