whisper

The paper introduces BanglaTurn, a benchmark corpus and Whisper-based model for end-of-turn detection in Bangla speech, achieving 84.33% accuracy compared to a 69.28% baseline.

The paper introduces a method to prune encoder layers in the Whisper ASR model, reducing encoder size by 18.5% and recovering performance through unlabeled data distillation, with code and a pre-trained model released for adoption.

This paper systematically evaluates token merging for multilingual speech recognition on the Whisper model family, demonstrating improved computational efficiency with minimal accuracy loss across low-resource languages and fine-tuned models.

This paper introduces the first benchmark for automatic lyric transcription in Greek songs, demonstrating that adapting Whisper through multitask learning and two-stage training achieves a 27.2% Word Error Rate, significantly improving over zero-shot baselines.

BuzzASR is a collection of language-specialized Whisper models for automatic speech recognition in 102 languages, outperforming Whisper-large-v3 on 77 languages with significant improvements in error rates and compression efficiency.

This paper introduces SpeakPay and a Nepali financial speech dataset, showing that LoRA fine-tuning of Whisper reduces Word Error Rate by 67.2% and improves transaction success rates for low-resource language accessibility.

This study develops an Automatic Speech Recognition system for Mizo, a low-resource language, by fine-tuning Whisper and SraVaani 1.0 models, achieving a morphology-aware WER of 7.22% with Whisper-large-v3.

FlyCut Caption is a browser-based AI video editing tool that automatically generates subtitles and cuts out filler words in videos, using Whisper and FunASR models for local processing without uploading footage.

PreenCut is an open-source tool (MIT License) based on Whisper transcription and LLM analysis, allowing users to search for segments in long videos using natural language, with support for batch processing, export/merging, and a REST API.

Vocal Slice is a desktop application for audio editing that uses on-device Whisper transcription to allow users to select and export clips by highlighting text in the transcript, designed for podcasters and voice professionals.

Introducing KrillinAI, a free locally-run video translation tool that supports precise subtitles, natural voiceover, and voice cloning. It integrates Whisper, LLM, and CosyVoice, and supports Win/Mac and yt-dlp.

This paper compares context biasing methods and speech LLMs for recognizing rare and new words in automatic speech recognition, reporting trade-offs in word error rate across read and non-read speech.

This paper studies using Whisper for Persian speech emotion recognition, showing PCA-based dimensionality reduction improves performance and efficiency, while ASR fine-tuning offers only modest gains.

Room reverberation and low-frequency noise from the environment hurt speech-to-text accuracy far more than the choice of model size; front-end audio preprocessing like adaptive spectral subtraction can recover masked phonemes and reduce word error rate more effectively than upgrading the model backend.

WhisperLive is an open-source real-time transcription tool using OpenAI's Whisper, supporting multiple backends like faster-whisper and TensorRT for live speech-to-text.

Introduces a fully local AI girlfriend project made by a Bilibili developer, integrating four models: Silero VAD, Whisper, llama.cpp, and Qwen3-TTS, all packed into 15G VRAM with hot-swapping capability.

VoiceBox is an open-source desktop voice-to-text tool that captures speech, transcribes it via Whisper on Cloudflare AI, and formats output with an LLM, auto-pasting the result into the active application.

An experimental open-source framework that enables LLM agents to perceive and recognize non-speech audio events using local models (CLAP, Whisper, Silero VAD) and a growing concept memory. The system uses event-gated recognition, fingerprinting, and symbol-based reasoning, with no formal benchmarks yet.

Apple's new SpeechAnalyzer API significantly outperforms both its predecessor SFSpeechRecognizer and OpenAI's Whisper models in accuracy and speed for English on-device transcription, as benchmarked on an M2 Pro machine. The new API achieves a 2.12% word error rate on clean speech, compared to 3.74% for Whisper Small, and runs three times faster.

A user seeks advice on AI speech recognition models that can accurately understand severely impaired speech, such as that of their minimally verbal brother with Down syndrome and autism, noting that current systems like Whisper fail to recognize it.