--- base_model: masoudmzb/wav2vec2-xlsr-multilingual-53-fa metrics: - wer widget: - example_title: M22N20 src: >- https://huggingface.co/lnxdx/20_2000_1e-5_hp-mehrdad/blob/main/M16A01.wav - example_title: Common Voice sample 2978 src: >- https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3/resolve/main/sample2978.flac - example_title: Common Voice sample 5168 src: >- https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3/resolve/main/sample5168.flac model-index: - name: wav2vec2-large-xlsr-persian-shemo results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13.0 fa type: common_voice_13_0 args: fa metrics: - name: Test WER type: wer value: 19.21 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: ShEMO type: shemo args: fa metrics: - name: Test WER type: wer value: 32.85 language: - fa pipeline_tag: automatic-speech-recognition tags: - audio - speech - automatic-speech-recognition - asr --- # Wav2Vec2 Large XLSR Persian ShEMO This model is a fine-tuned version of [masoudmzb/wav2vec2-xlsr-multilingual-53-fa](https://huggingface.co/masoudmzb/wav2vec2-xlsr-multilingual-53-fa) on the [ShEMO](https://github.com/pariajm/sharif-emotional-speech-dataset) dataset for speech recognition in Persian (Farsi). When using this model, make sure that your speech input is sampled at 16 kHz. It achieves the following results: - Loss on ShEMO train set: 0.7618 - Loss on ShEMO dev set: 0.6728 - WER on ShEMO train set: 30.47 - WER on ShEMO dev set: 32.85 - WER on Common Voice 13 test set: 19.21 ## Evaluation results ๐Ÿงช | Checkpoint Name | WER on ShEMO dev set | WER on Common Voice 13 test set | Max :) | | :---------------------------------------------------------------------------------------------------------------: | :------: | :-------: | :---: | | [m3hrdadfi/wav2vec2-large-xlsr-persian-v3](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3) | 46.55 | **17.43** | 46.55 | | [m3hrdadfi/wav2vec2-large-xlsr-persian-shemo](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-shemo) | **7.42** | 33.88 | 33.88 | | [masoudmzb/wav2vec2-xlsr-multilingual-53-fa](https://huggingface.co/masoudmzb/wav2vec2-xlsr-multilingual-53-fa) | 56.54 | 24.68 | 56.54 | | This checkpoint | 32.85 | 19.21 | **32.85** | As you can see, my model performs better in maximum case :D ## Training procedure ๐Ÿ‹๏ธ #### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP You may need *gradient_accumulation* because you need more batch size. #### Training log | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.8553 | 0.62 | 100 | 1.4126 | 0.4866 | | 1.4083 | 1.25 | 200 | 1.0428 | 0.4366 | | 1.1718 | 1.88 | 300 | 0.8683 | 0.4127 | | 0.9919 | 2.5 | 400 | 0.7921 | 0.3919 | | 0.9493 | 3.12 | 500 | 0.7676 | 0.3744 | | 0.9414 | 3.75 | 600 | 0.7247 | 0.3695 | | 0.8897 | 4.38 | 700 | 0.7202 | 0.3598 | | 0.8716 | 5.0 | 800 | 0.7096 | 0.3546 | | 0.8467 | 5.62 | 900 | 0.7023 | 0.3499 | | 0.8227 | 6.25 | 1000 | 0.6994 | 0.3411 | | 0.855 | 6.88 | 1100 | 0.6883 | 0.3432 | | 0.8457 | 7.5 | 1200 | 0.6773 | 0.3426 | | 0.7614 | 8.12 | 1300 | 0.6913 | 0.3344 | | 0.8127 | 8.75 | 1400 | 0.6827 | 0.3335 | | 0.8443 | 9.38 | 1500 | 0.6725 | 0.3356 | | 0.7548 | 10.0 | 1600 | 0.6759 | 0.3318 | | 0.7839 | 10.62 | 1700 | 0.6773 | 0.3286 | | 0.7912 | 11.25 | 1800 | 0.6748 | 0.3286 | | 0.8238 | 11.88 | 1900 | 0.6735 | 0.3297 | | 0.7618 | 12.5 | 2000 | 0.6728 | 0.3286 | #### Hyperparameter tuning ๐Ÿ”ง Several models with differet hyperparameters were trained. The following figures show the training process for three of them. ![wer](wandb-wer.png) ![loss](wandb-loss.png) **20_2000_1e-5_hp-mehrdad** is the current model (lnxdx/Wav2Vec2-Large-XLSR-Persian-ShEMO) and it's hyperparameters are: ```python model = Wav2Vec2ForCTC.from_pretrained( model_name_or_path if not last_checkpoint else last_checkpoint, # hp-mehrdad: Hyperparams of 'm3hrdadfi/wav2vec2-large-xlsr-persian-v3' attention_dropout = 0.05316, hidden_dropout = 0.01941, feat_proj_dropout = 0.01249, mask_time_prob = 0.04529, layerdrop = 0.01377, ctc_loss_reduction = 'mean', ctc_zero_infinity = True, ) ``` The hyperparameters of **19_2000_1e-5_hp-base** are: ```python model = Wav2Vec2ForCTC.from_pretrained( model_name_or_path if not last_checkpoint else last_checkpoint, # hp-base: Hyperparams simmilar to ('facebook/wav2vec2-large-xlsr-53' or 'facebook/wav2vec2-xls-r-300m') attention_dropout = 0.1, hidden_dropout = 0.1, feat_proj_dropout = 0.1, mask_time_prob = 0.075, layerdrop = 0.1, ctc_loss_reduction = 'mean', ctc_zero_infinity = True, ) ``` And the hyperparameters of **22_2000_1e-5_hp-masoud** are: ```python model = Wav2Vec2ForCTC.from_pretrained( model_name_or_path if not last_checkpoint else last_checkpoint, # hp-masoud: Hyperparams of 'masoudmzb/wav2vec2-xlsr-multilingual-53-fa' attention_dropout = 0.2, hidden_dropout = 0.2, feat_proj_dropout = 0.1, mask_time_prob = 0.2, layerdrop = 0.2, ctc_loss_reduction = 'mean', ctc_zero_infinity = True, ) ``` Learning rate is 1e-5 for all three models. As you can see this model performs better with WER metric on validation(evaluation) set. The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Persian_ShEMO_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb). Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more information. #### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0 ## Contact us ๐Ÿค If you have any technical question regarding the model, pretraining, code or publication, please create an issue in the repository. This is the *best* way to reach us. ## Citation ๐Ÿ–‡ *TO DO!* **Fine-tuned with โค๏ธ without โ˜•๏ธŽ**