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Granary: Speech Recognition and Translation Dataset in 25 European Languages

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arxiv 2505.13404 v2 pith:UWKI6DUR submitted 2025-05-19 cs.CL eess.AS

classification cs.CLeess.AS
keywords datalanguagestranslationdatasetsgranarymodelspipelinespeech
verification ladder T0 review T1 audit T2 compute T3 formal
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Multi-task and multilingual approaches benefit large models, yet speech processing for low-resource languages remains underexplored due to data scarcity. To address this, we present Granary, a large-scale collection of speech datasets for recognition and translation across 25 European languages. This is the first open-source effort at this scale for both transcription and translation. We enhance data quality using a pseudo-labeling pipeline with segmentation, two-pass inference, hallucination filtering, and punctuation restoration. We further generate translation pairs from pseudo-labeled transcriptions using EuroLLM, followed by a data filtration pipeline. Designed for efficiency, our pipeline processes vast amount of data within hours. We assess models trained on processed data by comparing their performance on previously curated datasets for both high- and low-resource languages. Our findings show that these models achieve similar performance using approx. 50% less data. Dataset will be made available at https://hf.co/datasets/nvidia/Granary

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning

    eess.AS 2026-03 unverdicted novelty 7.0 of 10

    FLAIR enables spoken dialogue AI to conduct continuous latent reasoning while perceiving speech through recursive latent embeddings and an ELBO-based finetuning objective.

  2. Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Mode-tag conditioning on paired verbatim/intended data makes Whisper produce either verbatim or intended transcripts on demand, with cross-lingual disfluency control and improved word timestamps.

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