---
base_model: masoudmzb/wav2vec2-xlsr-multilingual-53-fa
metrics:
- wer
widget:
  - example_title: Common Voice sample 1
    src: >-
      https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3/resolve/main/sample1.flac
  - 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-asr-shemo_me7494
  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: Common Voice 13.0 fa
          type: common_voice_13_0
          args: fa
        metrics:
          - name: Test WER
            type: wer
            value: 19.21
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-asr-shemo_me7494

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.
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

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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

### Training results

| 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 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0