Instructions to use DRAGOO/whisper_Fr_Ht with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DRAGOO/whisper_Fr_Ht with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DRAGOO/whisper_Fr_Ht")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("DRAGOO/whisper_Fr_Ht") model = AutoModelForSpeechSeq2Seq.from_pretrained("DRAGOO/whisper_Fr_Ht") - Notebooks
- Google Colab
- Kaggle
whisper_Fr_Ht
This model is a fine-tuned version of qanastek/whisper-small-french-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8968
- 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: 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: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.293 | 3.95 | 1000 | 0.6567 | 1.0 |
| 0.0541 | 7.91 | 2000 | 0.7640 | 1.0 |
| 0.0063 | 11.86 | 3000 | 0.8664 | 1.0 |
| 0.0016 | 15.81 | 4000 | 0.8968 | 1.0 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
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Model tree for DRAGOO/whisper_Fr_Ht
Base model
qanastek/whisper-small-french-uncased