Model Detail
whisper-large-v3
—whisper-large-v3 is an audio model with 772M parameters released by OpenAI. The model is registered under the automatic-speech-recognition pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
whisper-large-v3 ships with 772M parameters. Total weight footprint is approximately 1.5 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Downloads of whisper-large-v3 have moved +1.1% over the trailing thirty days. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
whisper-large-v3 is best fit for speech recognition, transcription, or speech synthesis depending on the task head. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
On the Interpretability of Whisper Encodings Using Sparse Autoencoders
arXiv:2605.12225v2 Announce Type: replace Abstract: While deep transformer-based models have advanced rapidly, their internal mechanisms remain largely a mystery. Recent work has prioritized understanding text-based transformer models, leaving ASR systems largely unexplored. In order to address this
Robust Assamese Speech Recognition through Controlled Fine-Tuning of Whisper Models
arXiv:2607.17164v1 Announce Type: new Abstract: Developing Automatic Speech Recognition (ASR) for morphologically rich, low-resource languages such as Assamese is challenging due to insufficient annotated speech data. The pretrained Whisper model performs poorly on Assamese speech recognition tasks.
When Audio Separation Hurts Zero-Shot ASR: Evaluating SAM-Audio with Whisper on Bengali and English Speech
arXiv:2603.04710v2 Announce Type: replace-cross Abstract: Recent advances in automatic speech recognition (ASR) and speech enhancement have strengthened the common belief that cleaner audio should lead to more accurate transcription. In this work, we examine whether this assumption holds for modern
From Dispersion to Attraction: Spectral Dynamics of Hallucination Across Whisper Model Scales
arXiv:2604.08591v2 Announce Type: replace-cross Abstract: Hallucinations in large ASR models present a critical safety risk. In this work, we propose the \textit{Spectral Sensitivity Theorem}, which predicts a phase transition in deep networks from a dispersive regime (signal decay) to an attractor
Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English
arXiv:2606.23948v1 Announce Type: new Abstract: Self-supervised and supervised speech models are increasingly used to investigate which linguistic information their internal representations encode, and at what level of abstraction they encode it. One underexplored phenomenon is consonant cluster red
Semi-Supervised Speech Confidence Detection using Pseudo-Labelling and Whisper Embeddings
arXiv:2606.16505v1 Announce Type: cross Abstract: Understanding speaker confidence is crucial in educational settings, as it can enhance personalised feedback and improve learning outcomes. This study introduces a novel framework for detecting speaker confidence by integrating human-engineered featu
