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Nine dataset types collected to your requirements
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Audio and speechThis page
Real speech for ASR and voice AI, across accents, dialects, and noise.
Audio built to your spec
The sound files your model needs, in the conditions it will face.
Why speech models fail
A model breaks on the accents, noise, and devices missing from its training.
Performs in ideal conditions. Breaks on real accents, noise, and code-switching.
Holds up where your users actually speak.
The conditions clean audio never sees
Rwazi captures these conditions through its consumer network, so a model hears them before it ships.
What does an audio sample look like?
Each delivery arrives as clips matched to the agreed languages and conditions, in WAV, MP3, or FLAC. Every clip carries demographic metadata: age, gender, and location. Each file records who captured it, where, and when, under a consistent naming convention.
Accented and multilingual speech, in real-world noise.
Request accessCode-switching, spontaneous conversation.
Request accessStudio-grade, single or multi-speaker.
Request accessContact-center and noisy-environment audio.
Request accessWhat we capture, to your spec
You set every dimension, and we collect exactly what your model needs.
Two ways to capture, your choice
We work both ends of the spectrum. You pick the condition your model needs.
Real-world capture
For models that must hold up in production. Accents, background noise, and spontaneous speech, captured where your users actually are.
Studio-grade capture
For models that need precision. Cleaner speech, specific mics, and scripted or semi-scripted prompts, in controlled conditions.
Your users are global. Your training data should be too
When collection covers a handful of major markets, models break everywhere else. Rwazi collects across 190+ countries, in any language its contributors speak. Native speakers record in their own conditions.
- 190+ countries
- 100+ languages
- Regional accents and dialects
- Code-switching
- Real-world or studio
Why teams collect with Rwazi
Built for the voice AI you are shipping
Voice assistants, ASR, and conversational AI
Models break on non-standard accents and dialects.
Speech in any language contributors speak, with regional accents, from native speakers, for ASR and conversational AI datasets.
From your spec to your cloud, in four steps
Run it as a one-off project or a recurring refresh, weekly or monthly.
How Rwazi compares to other providers
The same data, captured in the real world. Here is how that stacks up against the alternatives.
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Rwazi collects it from the real world.
Our 5M+ consumer network captures data in real environments across 190+ countries, so your models train on what your users actually produce.
Quality you set, checked before it ships
You set the spec. A multi-stage QC team validates every file against your pass-or-reject criteria, with human review and reports. Every file carries its provenance: who recorded it, where, and when.
- 01You set the pass-or-reject spec
- 02Multi-stage checks with human review
- 03Human review
- 04Provenance recorded per file
- 05Delivered to your cloud
Contact the Rwazi AI datasets team
Send us your brief, or book a live demo. We will reply with how we would collect it and a sample to review.
Questions teams ask before they buy
What is audio and speech training data?+
Audio of real people speaking, used to train and fine-tune speech models such as ASR and voice AI. Rwazi collects it to your spec across 190+ countries, real-world or studio.
Which languages and accents can Rwazi collect?+
Rwazi collects any language its contributors speak, with regional accents, code-switching, and real-world noise or studio-clean capture. Coverage is strongest in English, French, Spanish, Chinese, and Hindi.
Do you cover code-switching and noisy environments?+
Yes. We capture mixed-language speech and real-world background noise, or studio-clean when you need it.
What formats and delivery come with each dataset?+
Rwazi delivers WAV, MP3, or FLAC files to S3, Azure Blob Storage, or GCS, or via SFTP. Every file passes quality checks and human review, with a consistent naming convention and demographic metadata. Raw bulk files are also available.
Does it include transcription or speaker labels?+
Raw audio is the core. Transcription, timestamps, and speaker labels are available as add-ons.
How fast can you deliver?+
Curated sprints run in days; larger or recurring engagements run longer. Run it one-off or as a weekly or monthly refresh.
How is it priced?+
Scoped to your use case. The variables include volume, languages and accents, exclusive versus licensed, and add-ons. Share your requirement and we will scope it.
How do you handle consent and ownership?+
Contributors collect under explicit consent, direct from Rwazi. License it or own it outright, and every file carries its provenance.
How does real speech compare to synthetic audio?+
Synthetic audio comes from a model, so it covers the conditions that the model learned. Rwazi collects speech from real people, in studio or real-world conditions, to match what a product hears in use.
What does a delivery look like?+
Delivery includes quality-checked and human-reviewed files with a consistent naming convention, the specified format, and demographic metadata delivered to your cloud. Raw bulk files are also available.
Where can a team buy a voice dataset with transcription?+
Rwazi collects voice datasets to order. A team sets the languages, accents, speakers, hours, and recording conditions. Transcription, timestamps, and speaker labels come as add-ons. The team licenses the data or owns it outright.
What makes a good AI audio dataset?+
A good AI audio dataset matches the conditions the model will face. Native speakers from the target market record it, in the accents users bring. When users talk over traffic on phone mics, the clips carry the same sounds. Contributors give explicit consent per task. Every clip carries demographic metadata, and human review checks each file against a pass-or-reject spec.