Fine-Tune W2V2-Bert for low-resource ASR with 🤗 Transformers

Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers

Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers

Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia

Scaling-up BERT Inference on CPU (Part 1)

Scaling up BERT-like model Inference on modern CPU - Part 2

Fine-Tune Wav2Vec2 for English ASR in Hugging Face with 🤗 Transformers

Pre-Train BERT with Hugging Face Transformers and Habana Gaudi

Boosting Wav2Vec2 with n-grams in 🤗 Transformers

Fine-Tune MMS Adapter Models for low-resource ASR

Generative vs. Encoder Large Language Models for ASR Evaluation: A Comparative Study

The paper compares encoder‑based and generative decoder‑based large language models for evaluating automatic speech recognition (ASR). It examines BERTScore and SemDist across various LLMs, layers, and pooling strategies, finding that both metrics can strongly correlate with human judgments when properly configured. For generative LLMs, the study explores pairwise hypothesis selection via prompting and direct error classification, showing that while encoder‑based metrics remain competitive, generative models excel in hypothesis comparison and enhance interpretability of ASR evaluation.

Finally, a Replacement for BERT: Introducing ModernBERT

Separating Representation from Reconstruction Enables Scalable Text Encoders

arXiv:2607. 04011v1 Announce Type: cross Abstract: While decoders have rapidly scaled, encoders have remained largely unchanged since BERT.