![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F13889710%2F686a553e3cc6a523399be535452e413e%2FScreenshot%202023-09-14%20at%209.24.43%20PM.png?generation=1694697944203381&alt=media\")
My training code leads to negative finetuning result. The public score gets lower (wer gets higher) as I run for more epochs and on larger datasets. What I literarily do is to combine DHANKHAR's finetuned model and TANAY MEHTA's training code and feed in more data. However, this leads to a negative effect, and as a noob, I really can't sort it out. If you have some innovative methods that you'd share, please leave it in the comment
\n
I wonder whether I can get a good performance by finetuning the finetuned model, or I can optimize the model more easily by finetuning the pretrained base model (like indicwav2vec)
", "rawMarkdown": "I wonder whether I can get a good performance by finetuning the finetuned model, or I can optimize the model more easily by finetuning the pretrained base model (like indicwav2vec)", "votes": 1, "replies": [ { "id": 2445880, "postDate": "2023-09-19T06:13:29.770Z", "content": "Yes you can. I literally fine-tune on DHANKHAR's finetuned model and the score is improved to 0.428. You may refer to JOSEPH JOSIA's training notebook for fine-tuning
", "rawMarkdown": "Yes you can. I literally fine-tune on DHANKHAR's finetuned model and the score is improved to 0.428. You may refer to [JOSEPH JOSIA's training notebook](https://www.kaggle.com/code/takanashihumbert/bengali-sr-wav2vec-v1-bengali-training) for fine-tuning", "votes": 2 } ] }, { "id": 2439580, "postDate": "2023-09-15T01:58:46.473Z", "content": "just a shot in the dark here, try lowering the learning rate to power -5
", "rawMarkdown": "just a shot in the dark here, try lowering the learning rate to power -5", "votes": 1, "replies": [ { "id": 2439706, "postDate": "2023-09-15T04:29:46.707Z", "rawMarkdown": "", "isDeleted": true }, { "id": 2439709, "postDate": "2023-09-15T04:31:21.413Z", "content": "What fine-tuning method did you use? Any clue? It's totally ok if you don't want to share…
", "rawMarkdown": "What fine-tuning method did you use? Any clue? It's totally ok if you don't want to share...", "replies": [ { "id": 2439824, "postDate": "2023-09-15T06:16:31.990Z", "content": "I followed someone who has already got it working 😅 He shared the training configuration here:
\nhttps://www.kaggle.com/competitions/bengaliai-speech/discussion/433722
\n(look at his learning rate).
\nYou need to choose good training data as mentioned here :
\nhttps://www.kaggle.com/competitions/bengaliai-speech/discussion/435300
\nGood luck !
Hi I am running with the same problem with you. I am using wav2vec and the base model can reach 0.445, but after finetuning it drop to 0.833. This really is a problem. May I ask how do you solve that? Thanks.
", "rawMarkdown": "Hi I am running with the same problem with you. I am using wav2vec and the base model can reach 0.445, but after finetuning it drop to 0.833. This really is a problem. May I ask how do you solve that? Thanks. ", "replies": [ { "id": 2453496, "postDate": "2023-09-24T05:11:07.383Z", "content": "did you normalize the label? Also, look for some notebooks on EDA and external datasets, the quality of the audio is quite important. Good Luck👍
", "rawMarkdown": "did you normalize the label? Also, look for some notebooks on EDA and external datasets, the quality of the audio is quite important. Good Luck👍", "votes": 1 } ] }, { "id": 2438740, "postDate": "2023-09-14T13:25:54.560Z", "content": "My training code leads to negative finetuning result. The public score gets lower (wer gets higher) as I run for more epochs and on larger datasets. What I literarily do is to combine DHANKHAR's finetuned model and TANAY MEHTA's training code and feed in more data. However, this leads to a negative effect, and as a noob, I really can't sort it out. If you have some innovative methods that you'd share, please leave it in the comment
\n
I wonder whether I can get a good performance by finetuning the finetuned model, or I can optimize the model more easily by finetuning the pretrained base model (like indicwav2vec)
", "votes": 1, "replies": [ { "id": 2445880, "author_name": "Roy Wei", "author_url": "", "post_date": "2023-09-19T06:13:29.770000", "content": "Yes you can. I literally fine-tune on DHANKHAR's finetuned model and the score is improved to 0.428. You may refer to JOSEPH JOSIA's training notebook for fine-tuning
", "votes": 2, "replies": [] } ] }, { "id": 2439580, "author_name": "yukiya", "author_url": "", "post_date": "2023-09-15T01:58:46.473000", "content": "just a shot in the dark here, try lowering the learning rate to power -5
", "votes": 1, "replies": [ { "id": 2439706, "author_name": "", "author_url": "", "post_date": "2023-09-15T04:29:46.707000", "content": "", "votes": 0, "replies": [] }, { "id": 2439709, "author_name": "Roy Wei", "author_url": "", "post_date": "2023-09-15T04:31:21.413000", "content": "What fine-tuning method did you use? Any clue? It's totally ok if you don't want to share…
", "votes": 0, "replies": [ { "id": 2439824, "author_name": "yukiya", "author_url": "", "post_date": "2023-09-15T06:16:31.990000", "content": "I followed someone who has already got it working 😅 He shared the training configuration here:
\nhttps://www.kaggle.com/competitions/bengaliai-speech/discussion/433722
\n(look at his learning rate).
\nYou need to choose good training data as mentioned here :
\nhttps://www.kaggle.com/competitions/bengaliai-speech/discussion/435300
\nGood luck !
Hi I am running with the same problem with you. I am using wav2vec and the base model can reach 0.445, but after finetuning it drop to 0.833. This really is a problem. May I ask how do you solve that? Thanks.
", "votes": 0, "replies": [ { "id": 2453496, "author_name": "Roy Wei", "author_url": "", "post_date": "2023-09-24T05:11:07.383000", "content": "did you normalize the label? Also, look for some notebooks on EDA and external datasets, the quality of the audio is quite important. Good Luck👍
", "votes": 1, "replies": [] } ] } ], "raw_markdown_by_id": { "2445161": "I wonder whether I can get a good performance by finetuning the finetuned model, or I can optimize the model more easily by finetuning the pretrained base model (like indicwav2vec)", "2439580": "just a shot in the dark here, try lowering the learning rate to power -5", "2453375": "Hi I am running with the same problem with you. I am using wav2vec and the base model can reach 0.445, but after finetuning it drop to 0.833. This really is a problem. May I ask how do you solve that? Thanks. ", "2438740": "My training code leads to negative finetuning result. The public score gets lower (wer gets higher) as I run for more epochs and on larger datasets. What I literarily do is to combine [DHANKHAR's finetuned model](https://www.kaggle.com/code/nischaydnk/bengali-finetuning-baseline-wav2vec2-inference) and [TANAY MEHTA's training code](https://www.kaggle.com/code/heyytanay/pytorch-training-wav2vec2-for-bengaliai) and feed in more data. However, this leads to a negative effect, and as a noob, I really can't sort it out. If you have some innovative methods that you'd share, please leave it in the comment\n\n" } }