Automatic Speech Recognition
Transformers
Safetensors
Moroccan Arabic
whisper
Generated from Trainer
Instructions to use soufiyane/Speech_To_Text_Darija with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use soufiyane/Speech_To_Text_Darija with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="soufiyane/Speech_To_Text_Darija")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("soufiyane/Speech_To_Text_Darija") model = AutoModelForSpeechSeq2Seq.from_pretrained("soufiyane/Speech_To_Text_Darija") - Notebooks
- Google Colab
- Kaggle
Whisper small Darija
This model is a fine-tuned version of openai/whisper-small on the Darija Speech to Text dataset. It achieves the following results on the evaluation set:
- Loss: 0.33
- Wer: 27
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: 15
- 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: 300
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.33 | 10 | 500 | 0.4218 | 27.9218 |
Framework versions
- Transformers 4.45.0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for soufiyane/Speech_To_Text_Darija
Base model
openai/whisper-small