Instructions to use MIbrahimAI/Wav2Vec-SER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MIbrahimAI/Wav2Vec-SER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="MIbrahimAI/Wav2Vec-SER")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("MIbrahimAI/Wav2Vec-SER") model = AutoModelForAudioClassification.from_pretrained("MIbrahimAI/Wav2Vec-SER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,549 Bytes
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library_name: transformers
license: apache-2.0
base_model: elgeish/wav2vec2-large-xlsr-53-arabic
tags:
- generated_from_trainer
model-index:
- name: Wav2Vec-SER
results: []
---
<!-- 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. -->
# Wav2Vec-SER
This model is a fine-tuned version of [elgeish/wav2vec2-large-xlsr-53-arabic](https://huggingface.co/elgeish/wav2vec2-large-xlsr-53-arabic) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.0637
- eval_accuracy: 0.5745
- eval_runtime: 329.6619
- eval_samples_per_second: 13.717
- eval_steps_per_second: 1.717
- epoch: 1.4938
- step: 3800
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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