Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Arabic
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use lorenzoncina/whisper-medium-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lorenzoncina/whisper-medium-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lorenzoncina/whisper-medium-ar")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("lorenzoncina/whisper-medium-ar") model = AutoModelForSpeechSeq2Seq.from_pretrained("lorenzoncina/whisper-medium-ar") - Notebooks
- Google Colab
- Kaggle
Commit ·
f206969
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Parent(s): c3b96ed
update model card README.md
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_11_0
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metrics:
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- wer
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model-index:
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- name: openai/whisper-medium
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: common_voice_11_0
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type: common_voice_11_0
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config: ar
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split: test
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args: ar
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metrics:
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- name: Wer
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type: wer
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value: 48.75333333333333
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# openai/whisper-medium
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4711
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- Wer: 48.7533
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 4
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- training_steps: 10000
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|
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| 0.2215 | 0.1 | 1000 | 0.3361 | 49.9307 |
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| 0.1134 | 1.07 | 2000 | 0.3290 | 56.76 |
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| 0.0765 | 2.04 | 3000 | 0.3400 | 54.3947 |
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| 0.0417 | 3.01 | 4000 | 0.3599 | 52.5320 |
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| 0.0364 | 3.11 | 5000 | 0.3740 | 55.5653 |
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| 0.0094 | 4.08 | 6000 | 0.4152 | 56.4307 |
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| 0.0077 | 5.05 | 7000 | 0.4218 | 47.5307 |
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| 0.0018 | 6.02 | 8000 | 0.4556 | 50.0493 |
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| 0.0012 | 6.12 | 9000 | 0.4760 | 54.8147 |
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| 0.0009 | 7.09 | 10000 | 0.4711 | 48.7533 |
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### Framework versions
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- Transformers 4.28.0.dev0
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- Pytorch 2.0.0+cu117
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- Datasets 2.11.1.dev0
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- Tokenizers 0.13.2
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