Instructions to use Sjdan/m16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/m16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/m16")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/m16") model = AutoModelForCTC.from_pretrained("Sjdan/m16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Sjdan/m16: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/Sjdan/m16/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://Sjdan/m16@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/Sjdan/m16/resolve/refs%2Fpr%2F1/README.md
1.6 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: m16
results: []
m16
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3606
- Wer: 1.3141
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 9.4771 | 1.32 | 500 | 3.1544 | 1.0 |
| 2.7212 | 2.64 | 1000 | 1.9047 | 2.2222 |
| 1.0794 | 3.96 | 1500 | 0.6606 | 1.6920 |
| 0.248 | 5.28 | 2000 | 0.3928 | 1.5015 |
| 0.1053 | 6.6 | 2500 | 0.3606 | 1.3141 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2