Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use ashdev01/my_awesome_mind_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashdev01/my_awesome_mind_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ashdev01/my_awesome_mind_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("ashdev01/my_awesome_mind_model") model = AutoModelForAudioClassification.from_pretrained("ashdev01/my_awesome_mind_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from ashdev01/my_awesome_mind_model: direct link, hf CLI and curl.
- Browser
- Download file 2.21 kB
-
https://huggingface.co/ashdev01/my_awesome_mind_model/resolve/main/README.md
- Command line
-
hf download hf://ashdev01/my_awesome_mind_model/README.md
-
curl -L -o README.md https://huggingface.co/ashdev01/my_awesome_mind_model/resolve/main/README.md
2.21 kB
metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: my_awesome_mind_model
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.02654867256637168
my_awesome_mind_model
This model is a fine-tuned version of facebook/wav2vec2-base on the minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 2.6773
- Accuracy: 0.0265
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.8 | 3 | 2.6470 | 0.0796 |
| No log | 1.8667 | 7 | 2.6552 | 0.0531 |
| 2.6346 | 2.9333 | 11 | 2.6686 | 0.0531 |
| 2.6346 | 4.0 | 15 | 2.6684 | 0.0442 |
| 2.6346 | 4.8 | 18 | 2.6727 | 0.0442 |
| 2.6189 | 5.8667 | 22 | 2.6744 | 0.0442 |
| 2.6189 | 6.9333 | 26 | 2.6765 | 0.0265 |
| 2.6137 | 8.0 | 30 | 2.6773 | 0.0265 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1