South Asia · Gurmukhi / Shahmukhi
Punjabi Speech Translation Dataset
Punjabi Speech Translation delivered to your specification. A catalog entry, not a stock listing. This page covers what is specifically hard about this pairing, and which fields your specification needs to pin down.
Why Speech Translation in Punjabi is its own problem
Speech translation out of Punjabi runs into the tone problem at the transcript stage: the transcript is text, text has no tone marks, and tone distinguishes real word pairs, so a translator working from the transcript alone will silently choose the wrong word and translate it confidently. The pairing direction also depends on the side: Indian-side Punjabi translates naturally into Hindi and English, Pakistani-side into Urdu and English, and one target pair cannot reuse the translator pool of the other. All three components (audio, source transcript, target translation), have to arrive together.
The field to pin down first: State which script the source transcript uses, require translators to work from the audio rather than the transcript alone, and fix the target-language pair per side: tone is not recoverable from any transcript.
At a glance
| Language | Punjabi |
|---|---|
| Primary region | South Asia |
| Writing system | Gurmukhi / Shahmukhi |
| Category | Speech Translation |
| Specification field to settle first | State which script the source transcript uses, require translators to work from the audio rather than the transcript alone, and fix the target-language pair per side: tone is not recoverable from any transcript. |
| Delivery | Sourced to order, pilot batch before the full run |
What Speech Translation data is
Parallel data pairing source-language audio with target-language translation, used for speech-to-text and speech-to-speech translation models.
What buyers get wrong about it
Speech translation data needs all three components: audio, source-language transcription, and target-language translation. Miss one and it cannot be trained on. Many suppliers deliver only two.
The specification field that decides the quote
Whether the translation is human or machine translation with human post-editing: cost and quality differ by an order of magnitude.
The language side: what Punjabi demands
Punjabi is the classic case of one spoken language with two scripts: Gurmukhi on the Indian side and Shahmukhi (a Perso-Arabic system) on the Pakistani side. The same passage written on either side cannot be cross-searched against the other.
What we can put in this delivery
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Collection to your specification
Scripted or unscripted, in the language, domain and conditions your task needs. Specified separately where the variants need different contributor pools.
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Labels to your convention
Orthographic, phonetic or categorical, with the guideline written before production and shared with you for review.
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Contributor metadata
The attributes that let you slice the set rather than take it whole. The ones your task turns on, not a fixed list.
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Consent documentation
Signed contributor consent covering the intended use, plus collection methodology and the annotation guideline.
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Pilot batch
A small batch first, which you can reject. Misalignments surface after a few hours rather than at delivery.
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Delivery in your format
File format, structure, segmentation and metadata schema set to your pipeline's requirements.
What arrives in a delivery
| Component | What it is |
|---|---|
| The data files | Format, structure and segmentation set to your pipeline. Named to a convention you specify. |
| Labels or transcription | Orthographic, phonetic or categorical, produced under a guideline you review before production starts. |
| Contributor metadata | The attributes that let you slice the set: age band, gender, region, dialect background, or whatever your task turns on. |
| Collection conditions | Environment, device and, where it can be measured, signal quality: recorded per file. |
| Annotation guideline | The document the annotators worked from, so you can reproduce the conventions on your own data. |
| Consent records | Signed contributor consent covering your intended use, plus cross-border transfer terms where they apply. |
| Collection methodology | How contributors were recruited, screened and scheduled. The part that tells you how biased the pool is. |
| Quality report | Pilot outcome, re-work log, and the annotator agreement figures where the task supports measuring them. |
Questions we get about Punjabi Speech Translation Dataset
How many distinct speakers can you provide for Punjabi Speech Translation Dataset?
It depends on the specification and the timeline, and we will give a real number rather than a target. For this pairing, speaker recruitment is usually the step that sets the schedule. State which script the source transcript uses, require translators to work from the audio rather than the transcript alone, and fix the target-language pair per side: tone is not recoverable from any transcript.
Can you annotate to our own guideline instead of the default?
Yes, and for this pairing we would recommend it. Speech translation out of Punjabi runs into the tone problem at the transcript stage: the transcript is text, text has no tone marks, and tone distinguishes real word pairs, so a translator working from the transcript alone will silently choose the wrong word and translate it confidently. That is exactly the kind of decision a default guideline leaves open, and it is where annotators diverge. Send us your guideline, or we will draft one and you can review it before production starts.
Do you offer a sample before we commit to a full run?
Yes. Select a free sample in the request form and describe what you need. A pilot batch is the cheapest way to establish whether the quality bar is reachable for Punjabi Speech Translation Dataset before committing to the full volume.
Is the data licensed or owned outright?
Licensing terms are set per project, so tell us how the model will be used and whether it will be distributed. Consent documentation travels with the data either way, and we do not handle medical or clinical data or recorded telephone calls.
Other datasets in Punjabi
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Punjabi Call Center Speech
Customer-service calls, simulated rather than intercepted, capturing both the agent and the caller side, used for call center QC, intent recognition, and dialogue systems.
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Punjabi Conversational Speech
Speech from natural conversation between two or more people, on open or semi-structured topics, used for conversational AI, voice assistants, and small talk models.
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Punjabi Singing Voice
Vocal recordings with melody, in both a cappella and accompanied form, used for singing voice synthesis, music information retrieval, and lyric alignment.
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Punjabi Speech Commands
Targeted recordings of short command words or phrases, usually with many speakers reading each entry several times over, used for wake words and on-device command recognition.
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Punjabi Read Speech
Recordings of speakers reading specified text, with clear pronunciation and known text, the base material for TTS and ASR.
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Punjabi Podcast Speech
Long-form podcast and interview audio, either solo monologue or two-person conversation, used for long-form speech recognition and speaker modeling.
Request Punjabi Speech Translation Dataset
Tell us the language, the hours, and what the data needs to look like. You will get a real number and a real timeline, not a range. If we cannot source it well, we will tell you that instead.
- Pilot batch before the full run, so problems surface early.
- Consent documentation delivered with the data.
- No medical or clinical data. No recorded telephone calls.
- You send the spec Language, hours, recording conditions, and the deadline.
- We reply within two business days A real number and a real timeline, not a range.
- You approve a pilot batch A small run first, so problems surface before the full order.
- Full run, then delivery with documents Audio, transcripts, and the consent record for every speaker.