Hey, I fine-tuned a XLSR-Wav2Vec2 in Turkish.My training code can be found here:https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_🤗_Transformers.ipynbTraining details:Data: OfficialCommon VoiceTrain + Validation dataset amounting to 3478 data samplesData preprocessing: I removed all special characters like .,!?Hyperparameter tuning:Learning rate: A LR of 1e-5 didn’t work well. 3e-4 worked wellDropout: I disabled (setting to 0.0) / enabled (setting to 0.1) multiple combinations ofattention_dropout,hidden_dropout,feat_proj_dropoutLayerdrop: A rate of 0.1 worked well for memask_time_prob: I tried 0.1 and 0.05 → 0.05 seemed to work betterTime:I trained for 30 epochs and saw the WER nicely decreasing. I could have probably trained a bit longer to get better results.Training Graph:You can see my some training stats here:https://wandb.ai/patrickvonplaten/huggingface/reports/Project-Dashboard–Vmlldzo1[…]539b1pmkfbfrd96pxtvzuycfkdc13sp1cy3wl161g2s9whrcikebv20rte35o9leAny ideas on how training could be improved?
2021-03-18T09:12:02Z
Thank you very much for sharing the notebook, gonna work for my fine tuning process using your notebook.
2021-03-18T10:11:20Z
Great explanations about the process, so thank you for this effort@patrickvonplatenI started the same process for trying different (also too basic) preprocessing steps. Then, I will try different hyperparams settings if Colab let me do itEven I have Colab Pro, it generally limits my GPU connection and prevents to access it. So, if you have any suggestion about tuning hyperparameters faster or easier, I will be glad to hear.
2021-03-18T11:59:26Z
FYI: In Turkish you can omit the'character from your vocabulary.(in reference to:this)It’s usually just used as possessive pronoun(?) similar to Ceyda’s == Ceyda’nın  OR separating other type of postfixes from special named entities. Doesn’t effect pronunciationI just finished reading the blogpost+code… The end results werenot that I was expecting much with so littledata. Now I’m even more motivated to improve it ~ I have plansEdit: oops it looks like I clicked the wrong reply button@ozcangundesI know you know Turkish and didn’t need the extra explanation
2021-03-18T12:29:53Z
ceyda:evenWe are currently trying to get GPU compute power for everybody - we’ll let you know soon
2021-03-18T13:16:34Z
tiny tiny bug: a couple of places forgets to-1fromlen(list)like inhereshould be:random.randint(0, len(common_voice_train)-1)
2021-03-18T15:35:13Z
Great catch! Do you want to open a PR to fix it and tag me for review?
2021-03-18T18:31:26Z
DonehereBecause ipynb files are finicky about changes(diffs) I just modified the relevant code block
2021-03-20T08:04:19Z
Hi all! I have just completed my initial experiment with 40 epoch and 2e-3 learning rate (the other hparams are kept the same with@patrickvonplatendemo notebook). I run the code from Kaggle since Colab Pro restricted my GPU usage due to long usage timeIt tooks more than 9 hours and Kaggle also stopped workingHowever, validation loss and WER results did not change dramatically even though training loss decreases sharply, as seen below.WhatsApp Image 2021-03-20 at 10.55.321600×720 148 KBAlso, keep in mind that fine tuning CNN layers does not lead to better results as suggested by the authors and demo file.I gave it a shot but the score (50% WER in test set) was not as good as demo file, too.WhatsApp Image 2021-03-20 at 11.02.401600×720 143 KBGood luck with your experiments
2021-05-31T16:13:08Z
Hi ! Thanks for the great tutorial. I fined-tuned a pre-trained model (facebook/wav2vec2-large-xlsr-53-french) and got encouraging results. However there are 2 things I wonder (and I think could maybe help some other fellow readers)In both your notebook@patrickvonplatenand@ozcangundesscreenshots, and also from what I experienced, Validation Loss decreases during the first iterations but then starts going up while Wer keeps going down. Do you know why this is happening and can we be sure that WER is the best metric to track (for early stopping at least) especially as I use a custom LM for prediction once my model is done training?Which are the main hyperparameters to change to try to improve performance (I have limit GPU time and any insights or educated guess on where to start could help ;))  ?Thanks again!