While finetuning w2v-bert, the WER is not decreasing
While finetuning w2v-bert, the WER is not decreasing. I’ve fine-tuned wav2vec2, xls-r with no problem but w2v-bert has some issune while fine tuning, can anyone help?StepTraining LossValidation LossWer3004.636700inf0.99391960018.166900nan1.0000009000.000000nan1.00000012000.000000nan1.000000Below is my collab notebook.
2024-09-30T05:29:36Z
I’m not familiar with voice model training, but something doesn’t seem right. Take a look at the following page. Maybe it means there are some environments that work well.I’ve been modifying some Spaces recently, and it occurred to me that all of the voice-related libraries are very old. I think it is possible that some of the libraries that are not explicitly specified in the pip may be doing something wrong.In the context of generative AI, even functions that are 6 months old may be obsolete. Even if there is no syntax error, the content may have changed, which is tricky.github.comhuggingface/blog/blob/main/fine-tune-w2v2-bert.md#training-1---
title: "Fine-Tune W2V2-Bert for low-resource ASR with 🤗 Transformers"
thumbnail: /blog/assets/fine-tune-w2v2-bert/w2v_thumbnail.png
authors:
- user: ylacombe
---

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

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<a target="_blank" href="https://colab.research.google.com/github/ylacombe/scripts_and_notebooks/blob/main/Fine_Tune_W2V2_BERT_on_CV16_Mongolian.ipynb">
    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

***New (01/2024)***: *This blog post is strongly inspired by "[Fine-tuning XLS-R on Multi-Lingual ASR](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)" and ["Fine-tuning MMS Adapter Models for Multi-Lingual ASR"](https://huggingface.co/blog/mms_adapters)*.

## IntroductionThis file has been truncated.show original
2024-09-30T17:45:53Z
Can you manually try printing the loss and gradient norm ? With debugger maybe ?See if the train_loss/gradient norm is becoming infinite. In that case, your program might be suffering from gradient explosion problem.