Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Swedish
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use Neprox/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neprox/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Neprox/model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Neprox/model") model = AutoModelForSpeechSeq2Seq.from_pretrained("Neprox/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Neprox/model: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
-
https://huggingface.co/Neprox/model/resolve/refs%2Fpr%2F1/README.md
- Command line
-
hf download hf://Neprox/model@refs/pr/1/README.md
-
curl -L -o README.md https://huggingface.co/Neprox/model/resolve/refs%2Fpr%2F1/README.md
1.57 kB
metadata
language:
- sv
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small - Swedish
results: []
Whisper Small - Swedish
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3011
- Wer: 19.8220
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1353 | 1.94 | 1500 | 0.2815 | 20.8357 |
| 0.019 | 3.89 | 3000 | 0.3011 | 19.8220 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2