Haryanvi speech data is harder to find than Hindi data, but a sensible search strategy can uncover useful recordings and help you build a compliant dataset. The key is to distinguish between discoverable audio and usable training data: a public video or repository is not automatically licensed for machine-learning use, and a small collection of clips may not represent Haryana’s accents, ages, genders, or real-world noise conditions.
This guide focuses on practical sources, due diligence, and collection methods for builders working on automatic speech recognition (ASR), keyword spotting, voice interfaces, transcription, and language research in 2026.
Start with the right search terms
Haryanvi is frequently labelled inconsistently. A search limited to “Haryanvi dataset” will miss relevant material described as a Haryana dialect, Western Hindi, Hindi-Haryanvi, or an Indic-language corpus. Try combinations such as:
- “Haryanvi speech dataset” and “Haryanvi ASR”
- “Haryana dialect audio corpus”
- “Haryanvi Common Voice”
- “Haryanvi wav transcription” or “Haryanvi read speech”
- “Haryanvi conversational speech corpus”
- “Haryanvi automatic speech recognition GitHub”
Also search in Devanagari and Hindi-language research portals. University papers may describe a corpus even when the recordings are available only on request. For broader context on working with scarce Indic data, see this builder’s guide to low-resource Indic NLP.
Best places to look
Mozilla Common Voice
Mozilla Common Voice is the first place to check for a community-contributed Haryanvi or closely labelled dataset. Download availability, language support, and corpus size change over time, so inspect the current language catalogue rather than relying on an old article or repository link.
Before using a release, record:
- The exact corpus version and download date
- Audio format, sampling rate, and clip duration limits
- The licence applying to the recordings and metadata
- Whether speaker identifiers allow speaker-independent splits
- Validation status and the proportion of rejected or empty clips
Common Voice data can be valuable for a baseline, but it may be too small or uneven for a production model. Treat it as one component of a larger data plan.
OpenSLR and speech-research repositories
OpenSLR hosts speech and language resources, including corpora used in ASR research. Search the catalogue and linked papers for Haryanvi, Hindi dialect, and multilingual Indic resources. Do not assume that every repository listed under a research project contains Haryanvi audio, or that a paper’s dataset is openly downloadable. Confirm the licence and access conditions on the release page.
Other useful discovery paths include institutional repositories, conference supplementary files, and dataset catalogues such as Kaggle. Kaggle can surface community uploads quickly, but provenance is uneven. Prefer datasets with a README, speaker and recording protocol, transcript files, a clear licence, and a citation to the original collector.
GitHub and Indian-language projects
Use GitHub’s repository and code search for terms such as haryanvi audio, haryanvi speech, indic asr, and haryana corpus. Look for links to releases or object storage rather than assuming that files inside a repository are legally reusable. A repository may contain only scripts, sample clips, or pointers to a restricted corpus.
Indian open-source communities are also useful for finding collection tools, annotation scripts, and baseline models. If you are extending an existing project, this overview of Indian open-source AI developer projects can help you identify communities and collaboration patterns.
Universities, language researchers, and public programmes
Contact linguistics, computer-science, and speech-technology groups at universities in Haryana and nearby states. Ask a precise question: the language variety required, number of speakers, recording conditions, transcription format, intended use, and whether commercial use is needed. Researchers are more likely to respond when you propose citation, contributor acknowledgement, secure handling, and a data-sharing plan.
Government and public research organisations may have Indian-language resources, but access is not always “open” in the permissive sense. Check whether the data is downloadable, whether registration is required, and whether derivative models or commercial deployments are allowed.
When public data is insufficient: collect your own corpus
For Haryanvi, a targeted collection is often faster than waiting for a perfect dataset. Recruit speakers across districts and backgrounds rather than treating Haryanvi as a single uniform accent. Capture consent before recording and explain:
- The purpose of collection and expected model uses
- Whether recordings may be shared publicly or only used internally
- The right to withdraw, where technically feasible
- Storage, retention, and deletion procedures
- Whether voice data will be used to create a speaker profile or synthetic voice
Use short prompts that cover everyday vocabulary, names, numbers, code-switching, place names, and noisy conditions. Collect both read speech and spontaneous responses. Record lossless WAV where possible, preserve the original files, and generate compressed derivatives only for distribution or annotation.
A simple manifest should include a stable clip ID, speaker ID, transcript, language or variety label, recording device, sample rate, duration, consent status, and split assignment. Never put names, phone numbers, or other unnecessary personal data in a public manifest.
Evaluate a dataset before training
A dataset is useful only if its quality matches the task. Check:
- Coverage: speakers, districts, age groups, genders, accents, and speaking styles
- Audio quality: clipping, background noise, silence, reverberation, and inconsistent volume
- Transcripts: spelling conventions, punctuation, code-switching, numbers, and named entities
- Duplication: repeated speakers, prompts, clips, or audio copied across train and test sets
- Split integrity: no speaker should appear in both training and evaluation data
- Licence: explicit permission for research, redistribution, derivative models, and commercial use
Measure baseline word error rate or character error rate separately by speaker group and condition. For Haryanvi, character error rate can be informative when spelling and word segmentation are inconsistent, but it should not replace careful transcript normalisation.
Licensing mistakes to avoid
“Open source” is not a universal legal category for audio. A Creative Commons licence may require attribution, prohibit commercial use, or restrict derivatives. Platform terms may also limit downloading or automated reuse even when a clip is publicly visible. Keep a licence ledger containing the source URL, version, access date, file identifiers, permissions, and attribution text.
Do not scrape YouTube, social media, films, songs, or WhatsApp forwards for model training without rights clearance. Public availability does not establish consent, and copyrighted or identifiable speech can create serious risks for a deployed product.
A practical workflow for builders
1. Search Common Voice, OpenSLR, GitHub, academic repositories, and Kaggle using variant labels.
2. Download only from documented sources and preserve version information.
3. Audit licences before model training or redistribution.
4. Build a small, consented Haryanvi validation set that reflects your users.
5. Normalise transcripts and remove duplicate speakers across splits.
6. Establish a baseline with an open ASR toolkit, then test regional and noisy conditions.
7. Publish documentation, attribution, limitations, and dataset cards with any release.
Students can begin with a small evaluation pipeline using the practices in these open-source AI projects for student developers. Teams moving toward deployment should also plan reproducible preprocessing, monitoring, and model documentation; open tooling alone does not solve data governance.
Frequently asked questions
Is there a large, definitive open Haryanvi speech dataset?
There may not be a single comprehensive corpus that covers all Haryanvi varieties and permits every use. Check current releases and combine documented public data with a consented collection where necessary.
Can Hindi datasets be used for Haryanvi?
Hindi data can help with acoustic pretraining and shared Devanagari patterns, but it should not be treated as a substitute for Haryanvi evaluation data. Dialect vocabulary, pronunciation, code-switching, and speaking style can materially affect accuracy.
What format should I use?
Lossless mono WAV is a strong archival choice. Keep transcripts in UTF-8 and store metadata in CSV, JSONL, or Parquet with a documented schema. Convert formats only in reproducible scripts.
Where can Indian AI builders find support?
Share a clear data request with researchers and open-source communities, and document your contribution pathway. Builders developing language technology can also explore AI Grants India for funding and ecosystem support.