README.md

---
base_model: masoudmzb/wav2vec2-xlsr-multilingual-53-fa
metrics:
- wer
widget:
  - example_title: M22N20
    src: >-
      https://huggingface.co/lnxdx/Wav2Vec2-Large-XLSR-Persian-ShEMO/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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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 🧪
|                             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 🏋️

#### 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 🔧
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/lnxdx/Wav2Vec2-Large-XLSR-Persian-ASR/blob/main/Fine_Tune_Wav2Vec2_Large_XLSR_on_ShEMO_for_Persian_ASR_with_%F0%9F%A4%97_Transformers.ipynb).

📒 The script used for evaluating on ShEMO and Common Voice can be found [here](https://colab.research.google.com/github/lnxdx/Wav2Vec2-Large-XLSR-Persian-ASR/blob/main/evaluate_asr_on_shemo_and_common_voice.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 🛎
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 ↩️
*TO DO!*

**Fine-tuned with ❤️ without ☕︎**