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---
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
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