Instructions to use Bedru/wave2vec-base-Am with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bedru/wave2vec-base-Am with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Bedru/wave2vec-base-Am")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Bedru/wave2vec-base-Am") model = AutoModelForCTC.from_pretrained("Bedru/wave2vec-base-Am", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Bedru/wave2vec-base-Am: direct link, hf CLI and curl.
- Browser
- Download file 1.81 kB
-
https://huggingface.co/Bedru/wave2vec-base-Am/resolve/main/README.md
- Command line
-
hf download hf://Bedru/wave2vec-base-Am/README.md
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curl -L -o README.md https://huggingface.co/Bedru/wave2vec-base-Am/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:
- wer
model-index:
- name: wave2vec-base-Am
results: []
wave2vec-base-Am
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: 17.7251
- Wer: 1.0
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.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_steps: 150
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 1.0 | 8 | 28.8111 | 1.0 |
| No log | 2.0 | 16 | 27.1709 | 1.0 |
| No log | 3.0 | 24 | 23.3666 | 1.0 |
| 82.8012 | 4.0 | 32 | 19.1785 | 1.0 |
| 82.8012 | 4.4 | 35 | 17.7251 | 1.0 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0