---
license: apache-2.0
base_model: KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-130hr-v1
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: multilingual_speech_to_intent_wav2vec_xlsr
  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. -->

# multilingual_speech_to_intent_wav2vec_xlsr

This model is a fine-tuned version of [KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-130hr-v1](https://huggingface.co/KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-130hr-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1493
- Accuracy: 0.9804
- Precision: 0.9813
- Recall: 0.9804
- F1: 0.9805

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.65          | 1.0   | 219  | 0.1235          | 0.9795   | 0.9799    | 0.9795 | 0.9795 |
| 0.2315        | 2.0   | 438  | 0.1033          | 0.9851   | 0.9854    | 0.9851 | 0.9852 |
| 0.2355        | 3.0   | 657  | 0.1331          | 0.9724   | 0.9740    | 0.9724 | 0.9724 |
| 0.1943        | 4.0   | 876  | 0.2951          | 0.9250   | 0.9304    | 0.9250 | 0.9245 |
| 0.1854        | 5.0   | 1095 | 0.5676          | 0.8931   | 0.9056    | 0.8931 | 0.8925 |
| 0.1499        | 6.0   | 1314 | 0.3552          | 0.9243   | 0.9344    | 0.9243 | 0.9240 |
| 0.1461        | 7.0   | 1533 | 0.2503          | 0.9441   | 0.9492    | 0.9441 | 0.9442 |
| 0.1407        | 8.0   | 1752 | 0.2951          | 0.9214   | 0.9269    | 0.9214 | 0.9212 |
| 0.116         | 9.0   | 1971 | 0.3022          | 0.9391   | 0.9425    | 0.9391 | 0.9390 |
| 0.1142        | 10.0  | 2190 | 0.2169          | 0.9483   | 0.9526    | 0.9483 | 0.9483 |
| 0.1064        | 11.0  | 2409 | 0.5370          | 0.9115   | 0.9171    | 0.9115 | 0.9111 |
| 0.1067        | 12.0  | 2628 | 1.1525          | 0.8259   | 0.8471    | 0.8259 | 0.8266 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1