Instructions to use bisoye/wav2vec2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bisoye/wav2vec2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="bisoye/wav2vec2-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("bisoye/wav2vec2-base") model = AutoModelForAudioClassification.from_pretrained("bisoye/wav2vec2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from bisoye/wav2vec2-base: direct link, hf CLI and curl.
- Browser
- Download file 2.05 kB
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https://huggingface.co/bisoye/wav2vec2-base/resolve/main/README.md
- Command line
-
hf download hf://bisoye/wav2vec2-base/README.md
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curl -L -o README.md https://huggingface.co/bisoye/wav2vec2-base/resolve/main/README.md
2.05 kB
metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base
results: []
wav2vec2-base
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7306
- Accuracy: 0.8322
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 |
|---|---|---|---|---|
| 2.0418 | 0.9851 | 33 | 2.0022 | 0.3299 |
| 1.7549 | 2.0 | 67 | 1.6488 | 0.4602 |
| 1.3727 | 2.9851 | 100 | 1.4025 | 0.5970 |
| 1.1483 | 4.0 | 134 | 1.1598 | 0.6739 |
| 1.0037 | 4.9851 | 167 | 0.9707 | 0.7516 |
| 0.8469 | 6.0 | 201 | 0.8492 | 0.7994 |
| 0.8115 | 6.9851 | 234 | 0.7889 | 0.8097 |
| 0.7371 | 8.0 | 268 | 0.7440 | 0.8247 |
| 0.6778 | 8.9851 | 301 | 0.7367 | 0.8304 |
| 0.6806 | 9.8507 | 330 | 0.7306 | 0.8322 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1