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MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research

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arxiv 2406.18301 v1 pith:67JQNOOD submitted 2024-06-26 eess.AS cs.CLcs.SD

Song Li , Yongbin You , Xuezhi Wang , Zhengkun Tian , Ke Ding , Guanglu Wan This is my paper · ORCID
classification eess.AScs.CLcs.SD
keywords multilingualcorpusmsr-86krecognitionresearchspeechdataevolving
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Recently, multilingual artificial intelligence assistants, exemplified by ChatGPT, have gained immense popularity. As a crucial gateway to human-computer interaction, multilingual automatic speech recognition (ASR) has also garnered significant attention, as evidenced by systems like Whisper. However, the proprietary nature of the training data has impeded researchers' efforts to study multilingual ASR. This paper introduces MSR-86K, an evolving, large-scale multilingual corpus for speech recognition research. The corpus is derived from publicly accessible videos on YouTube, comprising 15 languages and a total of 86,300 hours of transcribed ASR data. We also introduce how to use the MSR-86K corpus and other open-source corpora to train a robust multilingual ASR model that is competitive with Whisper. MSR-86K will be publicly released on HuggingFace, and we believe that such a large corpus will pave new avenues for research in multilingual ASR.

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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. Weakly Supervised Data Refinement and Flexible Sequence Compression for Efficient Thai LLM-based ASR

    cs.SD 2025-05 conditional novelty 6.0 of 10

    EThai-ASR combines a self-refined Zipformer encoder with a Thai LLM and reports SOTA CER on Thai test sets plus a cosine-similarity frame pruning that gives 1.5-2.1x speedups in some modes.

  2. Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge

    eess.AS 2025-08 conditional novelty 2.0 of 10

    A frozen Whisper-large-v3 encoder plus a trainable adaptor plus LoRA-adapted Qwen2.5-7B achieves 9.83% WER/CER on 11-language conversational ASR and third place in MLC-SLM 2025 Track 1.

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