Instructions to use leahkerry/audiotutorial_model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leahkerry/audiotutorial_model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="leahkerry/audiotutorial_model2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("leahkerry/audiotutorial_model2") model = AutoModelForAudioClassification.from_pretrained("leahkerry/audiotutorial_model2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from leahkerry/audiotutorial_model2: direct link, hf CLI and curl.
- Browser
- Download file 1.81 kB
-
https://huggingface.co/leahkerry/audiotutorial_model2/resolve/main/README.md
- Command line
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hf download hf://leahkerry/audiotutorial_model2/README.md
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curl -L -o README.md https://huggingface.co/leahkerry/audiotutorial_model2/resolve/main/README.md
1.81 kB
metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: audiotutorial_model2
results: []
audiotutorial_model2
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5295
- Accuracy: 0.9111
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: 3.3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5909 | 1.0 | 23 | 0.5326 | 0.9111 |
| 1.6955 | 2.0 | 46 | 0.5965 | 0.8667 |
| 1.0633 | 3.0 | 69 | 0.6074 | 0.8667 |
| 0.5805 | 4.0 | 92 | 0.6958 | 0.8444 |
| 0.5385 | 4.8 | 110 | 0.5295 | 0.9111 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0