# Supplemental reading and resources

本单元介绍了语音识别的实践，语音识别是音频领域最受欢迎的任务之一。
想了解更多吗？在这里，您可以找到更多资源，加深您对这个主题的理解，提升学习体验。

* [Whisper Talk](https://www.youtube.com/live/fZMiD8sDzzg?feature=share) by Jong Wook Kim: a presentation on the Whisper model, explaining the motivation, architecture, training and results, delivered by Whisper author Jong Wook Kim
* [End-to-End Speech Benchmark (ESB)](https://arxiv.org/abs/2210.13352): a paper that comprehensively argues for using the orthographic WER as opposed to the normalised WER for evaluating ASR systems and presents an accompanying benchmark
* [Fine-Tuning Whisper for Multilingual ASR](https://huggingface.co/blog/fine-tune-whisper): an in-depth blog post that explains how the Whisper model works in more detail, and the pre- and post-processing steps involved with the feature extractor and tokenizer
* [Fine-tuning MMS Adapter Models for Multi-Lingual ASR](https://huggingface.co/blog/mms_adapters): an end-to-end guide for fine-tuning Meta AI's new [MMS](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) speech recognition models, freezing the base model weights and only fine-tuning a small number of *adapter* layers
* [Boosting Wav2Vec2 with n-grams in 🤗 Transformers](https://huggingface.co/blog/wav2vec2-with-ngram): a blog post for combining CTC models with external language models (LMs) to combat spelling and punctuation errors

