Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
French
whisper
whisper-event
Generated from Trainer
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use qanastek/whisper-large-french-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qanastek/whisper-large-french-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="qanastek/whisper-large-french-uncased")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("qanastek/whisper-large-french-uncased") model = AutoModelForMultimodalLM.from_pretrained("qanastek/whisper-large-french-uncased") - Notebooks
- Google Colab
- Kaggle
Whisper Large French
This model is a fine-tuned version of openai/whisper-large on the mozilla-foundation/common_voice_11_0 fr dataset. It achieves the following results on the evaluation set:
- Loss: 0.00
- Wer: 00.00
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: 32
- eval_batch_size: 16
- 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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.00 | 0.2 | 1000 | 0.00 | 00.00 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Evaluation results
- Wer on mozilla-foundation/common_voice_11_0 frtest set self-reported0.000