metadata
library_name: transformers
license: apache-2.0
base_model: ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wav2vec2-lg-xlsr-en-speech-emotion-recognition-finetuned-babycry-v0
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value:
accuracy: 0.8043478260869565
- name: F1
type: f1
value: 0.7171293871136721
- name: Precision
type: precision
value: 0.6469754253308129
- name: Recall
type: recall
value: 0.8043478260869565
wav2vec2-lg-xlsr-en-speech-emotion-recognition-finetuned-babycry-v0
This model is a fine-tuned version of ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.8267
- Accuracy: {'accuracy': 0.8043478260869565}
- F1: 0.7171
- Precision: 0.6470
- Recall: 0.8043
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: 0.0001
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.9496 | 1.1905 | 25 | 0.7967 | {'accuracy': 0.8043478260869565} | 0.7171 | 0.6470 | 0.8043 |
| 0.6402 | 2.3810 | 50 | 0.8211 | {'accuracy': 0.8043478260869565} | 0.7171 | 0.6470 | 0.8043 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1