Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers

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

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

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

How to generate text: using different decoding methods for language generation with Transformers

BaltiVoice: A Speech Corpus and Fine-tuned Whisper ASR System for the Balti Language

arXiv:2606. 03504v1 Announce Type: cross Abstract: We present BaltiVoice, a 16.

BaltiVoice: A Speech Corpus and Fine-tuned Whisper ASR System for the Balti Language

We present BaltiVoice, a 16. 8-hour read-speech corpus for Balti (ISO 639-3: bft), a Tibetic language spoken in Gilgit-Baltistan, Pakistan, with no prior publicly available ASR resources.

Data Scale, Not Latency, Shapes Cross-Lingual Encoder Transfer in Streaming ASR

arXiv:2606. 24169v1 Announce Type: new Abstract: Adapting a streaming speech recognition model to a new language requires choosing between two plausible warm starts: a multilingual (ML) encoder or an English-only (EN) encoder.

Blazingly fast whisper transcriptions with Inference Endpoints

Training a language model with 🤗 Transformers using TensorFlow and TPUs

Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis

The paper examines how multilingual medical adaptation affects the internal representations of Whisper ASR models. By comparing various fine‑tuning strategies—zero‑shot decoding, English‑only, German‑only, two‑stage EN→EN+DE, and direct EN+DE fine‑tuning—it shows that fine‑tuning significantly improves performance, with the best model varying by setting. Layer‑wise encoder analysis reveals that English medical fine‑tuning drives the main representation shift, while multilingual continuation largely preserves the adapted space, and that domain and language signals remain recoverable across layers.

Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis

Medical automatic speech recognition (MedASR) requires adaptation to specialised terminology, limited annotated clinical data, and multilingual use cases. Although large-scale pretrained ASR models such as Whisper achieve strong generalisation, their behaviour after medical and multilingual adaptation remains insufficiently understood beyond word error rate (WER).

Speculative Decoding for 2x Faster Whisper Inference