Russian Speech Dataset for recognition task

Dataset comprises 338 hours of telephone dialogues in Russian, collected from 460 native speakers across various topics and domains, with an impressive 98% Word Accuracy Rate. It is designed for research in speech recognition, focusing on various recognition models, primarily aimed at meeting the requirements for automatic speech recognition (ASR) systems.

By utilizing this dataset, researchers and developers can advance their understanding and capabilities in automatic speech recognition (ASR) systems, transcribing audio, and natural language processing (NLP). - Get the data

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Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fa3f375fb273dcad3fe17403bdfccb63b%2Fssssssssss.PNG?generation=1739884059328284&alt=media" alt=""> - Audio files: High-quality recordings in WAV format - Text transcriptions: Accurate and detailed transcripts for each audio segment - Speaker information: Metadata on native speakers, including gender and etc - Topics: Diverse domains such as general conversations, business and etc

The native speakers and various topics and domains covered in the dataset make it an ideal resource for research community, allowing researchers to study spoken languages, dialects, and language patterns.

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Unidata provides a Russian Speech Recognition dataset to train AI for seamless speech-to-text conversion

This corpus comprises 59,968 entries uttered by 50 speakers (25 males and 25 females), recorded over 4 channels (desktop in quiet office). Speech samples are stored as a sequence of 16-bit 44.1kHz for a total of 25.85 hours of speech per channel.

Golos dataset

Golos is a Russian corpus suitable for speech research. The dataset mainly consists of recorded audio files manually annotated on the crowd-sourcing platform. The total duration of the audio is about 1240 hours. We have made the corpus freely available for downloading, along with the acoustic model prepared on this corpus. Also we create 3-gram KenLM language model using an open Common Crawl corpus.

  Dataset structure

Domain Train files Train hours… See the full description on the dataset page: https://huggingface.co/datasets/SberDevices/Golos.

English(Russia) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and control, in-car command and control, numbers and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(498 people in total), geographicly speaking, 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.

Introduction

This Russian Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Russian speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Russian speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

HENSOLDT ANALYTICS MediaMiningIndexer ASR - automatic speech recognition speech-to-text engine that provides transcription of audio with spoken sentences into text with timestamps and confidence scores, in a variety of languages.

Introduction

The Russian Scripted Monologue Speech Dataset for the General Domain is a carefully curated resource designed to support the development of Russian language speech recognition systems. This dataset focuses on general-purpose conversational topics and is ideal for a wide range of AI applications requiring natural, domain-agnostic Russian speech data.

Speech Data

This dataset features over 6,000 high-quality scripted monologue recordings in Russian. The prompts span diverse real-life topics commonly encountered in general conversations and are intended to help train robust and accurate speech-enabled technologies.

Topic Coverage

The dataset covers a wide variety of general conversation scenarios, including:

Contextual Features

To enhance authenticity, the prompts include:

Each prompt is designed to reflect everyday use cases, making it suitable for developing generalized NLP and ASR solutions.

Transcription

Every audio file in the dataset is accompanied by a verbatim text transcription, ensuring accurate training and evaluation of speech models.

Metadata

Rich metadata is included for detailed filtering and analysis:

Applications & Use Cases

This dataset can power a variety of Russian language AI technologies, including:

The STC Russian speech database was recorded in 1996-1998. The main purpose of the database is to investigate individual speaker variability and to validate speaker recognition algorithms. The database was recorded through a 16-bit Vibra-16 Creative Labs sound card with an 11,025 Hz sampling rate.The database contains Russian read speech of 89 different speakers (54 male, 35 female), including 70 speakers with 15 sessions or more, 10 speakers with 10 sessions or more and 9 speakers with less than 10 sessions. The speakers were recorded in Saint-Petersburg and are within the age of 18-62. All are native speakers. The corpus consists of 5 sentences. Each speaker reads carefully but fluently each sentence 15 times on different dates over the period of 1-3 months. The corpus contains a total of 6,889 utterances and of 2 volumes, total size 700 MB uncompressed data. The signal of each utterance is stored as a separate file (approx. 126 KB). Total size of data for one speaker approximates 9,500 KB. Average utterance duration is about 5 sec.A file gives information about the speakers (speaker?s age and gender). The orthography and phonetic transcription of the corpus is given in separate files which contain the prompted sentences and their transcription in IPA. The signal files are raw files without any header, 16 bit per sample, linear, 11,025 Hz sample frequency. The recording conditions were as follows:Microphone: dynamic omnidirectional high-quality microphone, distance to mouth 5-10 cmEnvironment: office roomSampling rate: 11,025 HzResolution: 16 BitSound board: Creative Labs Vibra-16Means of delivery: CD-ROM

v1.0-beta Arguably the largest public Russian STT dataset up to date: 15m utterances; 20 000 hours; 2.3 TB (in mono .wav format in int16); For more information please visit

Improve AI/ML model performance with Macgence's Russian receipt dataset. High-quality, diverse images tailored for precision and advanced analytics!

Context

Russian Open Speech To Text (STT/ASR) Dataset.

Content

Transcriptions from validation and training subsets.

Acknowledgements

https://github.com/snakers4/open_stt

This corpus comprises 19,164 entries uttered by 30 speakers (16 males and 14 females), recorded over 2 channels (desktop in quiet office). Speech samples are stored as a sequence of 16-bit 44.1kHz for a total of 4.15 hours of speech per channel.

Introduction

This Russian 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 Russian -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 Russian 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.

Topic Diversity

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

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.

Metadata

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

Usage and Applications

This dataset is ideal for a variety of AI use cases in the travel and tourism space:

Russian Open Speech To Text (STT/ASR) Dataset

Arguably the largest public Russian STT dataset up to date.

Introduction

This Russian Call Center Speech Dataset for the Retail and E-commerce industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Russian speakers. Featuring over 30 hours of real-world, unscripted audio, it provides authentic human-to-human customer service conversations vital for training robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, data scientists, and language model researchers to build high-accuracy, production-ready models across retail-focused use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Russian speakers. Captured in realistic scenarios, these conversations span diverse retail topics from product inquiries to order cancellations, providing a wide context range for model training and testing.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world scenario coverage.

Such variety enhances your model’s ability to generalize across retail-specific voice interactions.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, making model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

This granularity supports advanced analytics, dialect filtering, and fine-tuned model evaluation.

Usage and Applications

This dataset is ideal for a range of voice AI and NLP applications:

Introduction

Welcome to the Russian General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Russian speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Russian communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Russian speech models that understand and respond to authentic Russian accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Russian. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Russian speech and language AI applications:

Dusha is a bi-modal corpus suitable for speech emotion recognition (SER) tasks. The dataset consists of audio recordings with Russian speech and their emotional labels. The corpus contains approximately 350 hours of data. Four basic emotions that usually appear in a dialog with a virtual assistant were selected: Happiness (Positive), Sadness, Anger and Neutral emotion.

Introduction

This Russian Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Russian-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Russian speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

Dataset Audio Russian Annotated

This is a dataset with Russian annotated audio data, split into train for tasks like text-to-speech, speech recognition, and speaker identification.

  Features

text: Audio transcription (string). speaker_name: Speaker identifier (string). audio: Audio file. utterance_pitch_mean: The average pitch of the speech utterance (float64). utterance_pitch_std: The standard deviation of pitch, representing variability in intonation (float64) snr:… See the full description on the dataset page: https://huggingface.co/datasets/kijjjj/audio_data_russian_annotated.