Instructions to use Sjdan/finetuning11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/finetuning11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/finetuning11")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/finetuning11") model = AutoModelForCTC.from_pretrained("Sjdan/finetuning11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,244 Bytes
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license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetuning11
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning11
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- 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: 0.00024
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 0.0 | 0.31 | 500 | nan | 1.0 |
| 0.0 | 0.61 | 1000 | nan | 1.0 |
| 0.0 | 0.92 | 1500 | nan | 1.0 |
| 0.0 | 1.23 | 2000 | nan | 1.0 |
| 0.0 | 1.54 | 2500 | nan | 1.0 |
| 0.0 | 1.84 | 3000 | nan | 1.0 |
| 0.0 | 2.15 | 3500 | nan | 1.0 |
| 0.0 | 2.46 | 4000 | nan | 1.0 |
| 0.0 | 2.77 | 4500 | nan | 1.0 |
| 0.0 | 3.07 | 5000 | nan | 1.0 |
| 0.0 | 3.38 | 5500 | nan | 1.0 |
| 0.0 | 3.69 | 6000 | nan | 1.0 |
| 0.0 | 4.0 | 6500 | nan | 1.0 |
| 0.0 | 4.3 | 7000 | nan | 1.0 |
| 0.0 | 4.61 | 7500 | nan | 1.0 |
| 0.0 | 4.92 | 8000 | nan | 1.0 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|