Bangla-WhisperDiar: Fine-Tuning Whisper and PyAnnote for Bangla Long-Form Speech Recognition and Speaker Diarization

• URL: http://arxiv.org/abs/2605.08214v1
• Date: Wed, 06 May 2026 10:35:58 GMT
• Title: Bangla-WhisperDiar: Fine-Tuning Whisper and PyAnnote for Bangla Long-Form Speech Recognition and Speaker Diarization
• Abstract summary: This work addresses two core tasks in Bangla spoken language understanding by developing robust systems for ASR and speaker diarization.<n>For ASR (Problem 1), we fine tune the tugstugi bengaliai regional asr whisper medium model on a custom-curated dataset of approximately 15,000 chunked and aligned Bangla audio segments.<n>For speaker diarization (Problem 2), we fine-tune the pyannote/segmentation-3.0 model using PyTorch Lightning on the competition annotated diarization dataset.
• Score: 1.1677859440338065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic Speech Recognition (ASR) and speaker diarization in Bangla remain challenging due to long form recordings, diverse acoustic conditions, and significant speaker variability. This work addresses these two core tasks in Bangla spoken language understanding by developing robust systems for long form ASR and speaker diarization. For ASR (Problem 1), we fine tune the tugstugi bengaliai regional asr whisper medium model on a custom-curated dataset of approximately 15,000 chunked and aligned Bangla audio segments, employing full weight training with extensive data augmentation including noise injection, reverb simulation, echo, clipping distortion, and pitch/time perturbation. For speaker diarization (Problem 2), we fine-tune the pyannote/segmentation-3.0 model using PyTorch Lightning on the competition annotated diarization dataset, swapping the fine-tuned segmentation backbone into the pyannote/speaker-diarization-community-1 pipeline while retaining the pretrained speaker embedding and clustering components. Our ASR system achieves a Word Error Rate (WER) of 0.2441, while our diarization system achieves a Diarization Error Rate (DER) of 0.2392, both evaluated on the test set, demonstrating notable improvements over the respective pretrained baselines. We describe our complete pipeline, including data preprocessing, text normalization, audio augmentation, training strategies, inference optimization, and post-processing for both tasks.

Related papers

• Diarization-Guided Qwen-ASR Adaptation for Multilingual Two-Speaker Conversational Speech [14.048968602801606]
  This paper describes our self-designed hallucination system for Task 1 of the MLC-SLM 2026 Challenge for multilingual two-speaker conversational speech.<n>The system combines a modular speaker diarization front end with a challenge-adapted Qwen3-ASR-1.7B recognizer.
  arXiv  Detail & Related papers  (2026-07-09T08:07:34Z)
• Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs [53.41256816230278]
  Recent Audio Large Language Models (AudioLLMs) exhibit a striking performance inversion.<n>While excelling at complex reasoning tasks, they consistently underperform on fine-grained acoustic perception.<n>We propose Unified Audio (UAS), a holistic and structured supervision framework that organizes audio information into three explicit components.
  arXiv  Detail & Related papers  (2026-04-14T09:30:12Z)
• Make It Hard to Hear, Easy to Learn: Long-Form Bengali ASR and Speaker Diarization via Extreme Augmentation and Perfect Alignment [0.0]
  We introduce Lipi-Ghor-882, a comprehensive 882-hour multi-speaker Bengali dataset.<n>For ASR, we demonstrate that raw data scaling is ineffective; instead, targeted fine-tuning utilizing perfectly aligned annotations paired with synthetic acoustic degradation.<n>For speaker diarization, we observed that global open-source state-of-the-art models performed surprisingly poorly on this complex dataset.
  arXiv  Detail & Related papers  (2026-02-26T14:59:24Z)
• DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification [55.306583814017046]
  We present a novel difficulty-aware semantic augmentation (DASA) approach for speaker verification. DASA generates diversified training samples in speaker embedding space with negligible extra computing cost. The best result achieves a 14.6% relative reduction in EER metric on CN-Celeb evaluation set.
  arXiv  Detail & Related papers  (2023-10-18T17:07:05Z)
• Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition [52.11964238935099]
  An audio-visual multi-channel speech separation, dereverberation and recognition approach is proposed in this paper. Video input is consistently demonstrated in mask-based MVDR speech separation, DNN-WPE or spectral mapping (SpecM) based speech dereverberation front-end. Experiments were conducted on the mixture overlapped and reverberant speech data constructed using simulation or replay of the Oxford LRS2 dataset.
  arXiv  Detail & Related papers  (2023-07-06T10:50:46Z)
• Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction [109.44933866397123]
  Noise robustness is essential for deploying automatic speech recognition systems in real-world environments. We employ a noise-robust representation learned by a refined self-supervised framework for noisy speech recognition. We achieve comparable performance to the best supervised approach reported with only 16% of labeled data.
  arXiv  Detail & Related papers  (2021-10-28T20:39:02Z)
• WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing [102.45426364965887]
  We propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. We scale up the training dataset from 60k hours to 94k hours of public audio data, and optimize its training procedure for better representation extraction.
  arXiv  Detail & Related papers  (2021-10-26T17:55:19Z)
• PL-EESR: Perceptual Loss Based END-TO-END Robust Speaker Representation Extraction [90.55375210094995]
  Speech enhancement aims to improve the perceptual quality of the speech signal by suppression of the background noise. We propose an end-to-end deep learning framework, dubbed PL-EESR, for robust speaker representation extraction.
  arXiv  Detail & Related papers  (2021-10-03T07:05:29Z)
• Training Speech Enhancement Systems with Noisy Speech Datasets [7.157870452667369]
  We propose two improvements to train SE systems on noisy speech data. First, we propose several modifications of the loss functions, which make them robust against noisy speech targets. We show that using our robust loss function improves PESQ by up to 0.19 compared to a system trained in the traditional way.
  arXiv  Detail & Related papers  (2021-05-26T03:32:39Z)
• Data augmentation using prosody and false starts to recognize non-native children's speech [12.911954427107977]
  This paper describes AaltoASR's speech recognition system for the INTERSPEECH 2020 shared task on Automatic Speech Recognition. The task is to recognize non-native speech from children of various age groups given a limited amount of speech.
  arXiv  Detail & Related papers  (2020-08-29T05:32:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.