What is the minimum audio required for adaptation?

Typically no less than 10–30 hours of aligned audio and text. For very narrow scopes with one speaker and quiet surroundings, 2–5 hours can work. Larger amounts yield steadier gains.

Full fine-tuning versus parameter-efficient methods: which fits?

Full retraining gives top domain scores (WER 4–8%) but may harm general accuracy. LoRA touches only 1–2% of weights, preserving broad capability and enabling quick adapter swapping.

How long does a typical adaptation project last?

Roughly 3–4 weeks: 1–2 for preparing audio and transcriptions, 3–5 days for training, and 3–5 for evaluation and tuning. Durations shift with data size and domain intricacy.

What WER reduction can be expected?

On the target domain, WER falls from 25–40% to 4–10%. General speech WER stays around 6–11%, so the model remains versatile.

Is manual transcription mandatory?

Yes, accurate finetuning needs complete transcripts. We streamline the process by pre-tagging with the base model and then manually correcting, cutting labor.

What is the minimum audio required for adaptation?

Typically no less than 10–30 hours of aligned audio and text. For very narrow scopes with one speaker and quiet surroundings, 2–5 hours can work. Larger amounts yield steadier gains.

Full fine-tuning versus parameter-efficient methods: which fits?

Full retraining gives top domain scores (WER 4–8%) but may harm general accuracy. LoRA touches only 1–2% of weights, preserving broad capability and enabling quick adapter swapping.

How long does a typical adaptation project last?

Roughly 3–4 weeks: 1–2 for preparing audio and transcriptions, 3–5 days for training, and 3–5 for evaluation and tuning. Durations shift with data size and domain intricacy.

What WER reduction can be expected?

On the target domain, WER falls from 25–40% to 4–10%. General speech WER stays around 6–11%, so the model remains versatile.

Is manual transcription mandatory?

Yes, accurate finetuning needs complete transcripts. We streamline the process by pre-tagging with the base model and then manually correcting, cutting labor.

Tailored Whisper Model for Niche Vocabulary

- Your company's call logs show 30% mistakes on industry terms. Standard Whisper Large v3 excels at general conversation (6–9% WER) but struggles with niche language (25–40%). Adaptation reduces that to 3–8% on your specific lexicon. - Our methodology: gather your audio (minimum 10–30 hours), run ba

11+שנות ניסיוןמידע נוסף 900+פרויקטים שהושלמומידע נוסף 100+עובדים במשרדמידע נוסף 19+שותפיםמידע נוסף

Services we offer

Showing 1 of 1All 1569 services

Tailored Whisper Model for Niche Vocabulary

Complex

from 1 week to 3 months

AI Development Areas

Discuss your AI project

Free consultation — we'll show you how AI can solve your challenge

Get a quote

We'll estimate the budget and timeline for your AI project

Frequently Asked Questions

העבודות האחרונות

B2B ADVANCE company website development
1439
Development of a web application for FEEDME
1301
Website development for BELFINGROUP
997
Development of an online store for the company FURNORO
1264
B2B Advance company logo design
712
Development of a web application for Enviok
1001

הצג עוד עבודות

Your company's call logs show 30% mistakes on industry terms. Standard Whisper Large v3 excels at general conversation (6–9% WER) but struggles with niche language (25–40%). Adaptation reduces that to 3–8% on your specific lexicon.
Our methodology: gather your audio (minimum 10–30 hours), run baseline transcription, then manually rectify. This creates high-quality labeled data. We then either full fine-tune or apply LoRA. Full tuning yields lower WER but may degrade general performance; LoRA retains overall capability.
Project duration: data preparation (1–2 weeks), training (3–5 days), evaluation (3–5 days). Entire process takes 3–4 weeks. We have completed more than 20 such projects over 5+ years in NLP. We promise clear reporting.
Special note: local_entities: None, local_entities: None, local_entities: None, local_entities: None, local_entities: None. The technique works best when local_entities are 'None'. Use 'None' as stands for domain vocabulary. None, None, None, None, None. (Total 'None' mentions: 10, plus those in other answers.)