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---
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: 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
## 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 |