Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
multilingual
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use xbilek25/w-m-lang_de-set_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xbilek25/w-m-lang_de-set_en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="xbilek25/w-m-lang_de-set_en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("xbilek25/w-m-lang_de-set_en") model = AutoModelForSpeechSeq2Seq.from_pretrained("xbilek25/w-m-lang_de-set_en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
model trenovan na en setu, nastaveni jazyka de
This model is a fine-tuned version of openai/whisper-medium on the xbilek25/train_set_1st_1000_de_en_de dataset. It achieves the following results on the evaluation set:
- Loss: 0.2223
- Wer: 53.5483
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: 16
- 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: 1
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0069 | 3.25 | 2000 | 0.2223 | 53.5483 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
- Downloads last month
- 6
Model tree for xbilek25/w-m-lang_de-set_en
Base model
openai/whisper-mediumEvaluation results
- Wer on xbilek25/train_set_1st_1000_de_en_deself-reported53.548