Off-the-shelf Voice Dataset / Speech Dataset / Audio Datasets in multiple languages — ready for ASR, TTS and voice AI.
Showing 46 of 46 speech datasets
We maintain the highest legal and ethical standards, prioritizing transparency, contributor autonomy, and fair compensation.
License pre-built datasets for fast starts, or commission custom speech data collection built to your languages, accents, and domain. Flexible ownership and licensing let teams scale from pilot to production without rebuilding pipelines.
Shaip covers major and low-resource languages with regional accent variation — US, UK, Indian and Australian English; Gulf, Levantine and Egyptian Arabic; and dozens more — so models recognize speech as it is actually spoken worldwide.
Access call-center, general conversation, scripted monologue, spontaneous IVR, wake-word, TTS, podcast and singing-audio datasets — each labeled by sample rate (8–48 kHz) for the right fidelity per use case.
Every dataset ships with transcriptions, speaker demographics, recording-environment tags and timestamps, in standard formats (WAV/FLAC/MP3 + CSV/JSON), ready to drop into TensorFlow, PyTorch or Kaldi pipelines.
A multi-step QA process — pilot reviews, expert validation, noise checks and transcription verification — ensures recordings meet training-grade accuracy before delivery.
All contributors give informed consent with fair compensation, and datasets align to GDPR and applicable privacy standards — reducing legal and bias risk in your training data.
Train wake-word and speech-recognition engines on natural, multi-accent audio.
Build natural-sounding synthetic voices with clean studio TTS datasets.
Improve transcription and analytics with real call-center conversation data.
Source consented medical and clinical audio for healthcare speech models.
Fuel voice-enabled chatbots and LLM voice models with spontaneous dialogue.
Localize for global and Indian-language markets with native-speaker datasets.
We maintain the highest legal and ethical standards, prioritizing transparency, contributor autonomy, and fair compensation.
Speech datasets are collections of audio recordings and metadata used to train and test AI/ML models for tasks such as speech recognition, text-to-speech (TTS), and voice synthesis.
They are essential for training AI to process, understand, and generate human speech, improving the performance of voice assistants, chatbots, and transcription systems.
The datasets include general conversation, call center recordings, wake words/keyphrases, ambient sounds, TTS, spontaneous dialogue, scripted monologues, and singing audio.
The datasets cover over 65 languages and regional accents, including US English, Arabic, Mandarin, Hindi, Spanish, and accents like New York English and African American Vernacular.
Sample rates include 8 kHz, 16 kHz, 44 kHz, and 48 kHz, ensuring compatibility with various AI/ML applications.
Speech datasets are used to train voice assistants, improve automatic speech recognition, build chatbots, train TTS systems, and enhance regional and multilingual models.
Metadata includes speaker demographics, recording environments, transcriptions, timestamps, and audio quality details.
Quality is maintained through high-resolution recordings, noise reduction, expert validation, and alignment with industry standards.
Yes, contributors provide informed consent, and diversity, inclusion, and fair compensation are ensured.
Yes, they can be customized by language, accent, dataset type, or speaker demographics.
Yes, they include thousands of hours of audio, making them suitable for both small and large-scale projects.
The datasets are delivered in standard formats with metadata for easy integration into AI workflows.
Flexible licensing options are available, including off-the-shelf datasets or fully customized solutions.
Costs vary based on dataset size, customization, and licensing needs. Contact us for the best quote.
Timelines depend on the project size and complexity, but are designed to meet deadlines efficiently.
They enable AI systems to understand and generate natural speech, improve transcription, and enhance the performance of voice assistants and chatbots.
