Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Greek
whisper
hf-asr-leaderboard
whisper-medium
mozilla-foundation/common_voice_11_0
greek
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use emilios/whisper-medium-el with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emilios/whisper-medium-el with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="emilios/whisper-medium-el")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("emilios/whisper-medium-el") model = AutoModelForSpeechSeq2Seq.from_pretrained("emilios/whisper-medium-el", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Browse files- README.md +14 -33
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README.md
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license: apache-2.0
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tags:
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- whisper-
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- generated_from_trainer
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datasets:
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- mozilla-foundation/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: Whisper Medium El Greco
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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: mozilla-foundation/common_voice_11_0,google/fleurs
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type: mozilla-foundation/common_voice_11_0,google/fleurs
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config: null
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split: None
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metrics:
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- name: Wer
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type: wer
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value: 11.199851411589897
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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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# Whisper Medium El Greco
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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## Model description
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- training_steps: 5000
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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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| 0.0176 | 2.49 | 1000 | 0.2945 | 12.6114 |
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| 0.0064 | 4.98 | 2000 | 0.3423 | 12.2307 |
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| 0.0022 | 7.46 | 3000 | 0.3632 | 11.5899 |
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| 0.0014 | 9.95 | 4000 | 0.3788 | 11.2556 |
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| 0.0008 | 12.44 | 5000 | 0.3801 | 11.1999 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- el
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license: apache-2.0
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tags:
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- hf-asr-leaderboard, whisper-medium, mozilla-foundation/common_voice_11_0, greek,
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whisper-event
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_11_0
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model-index:
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- name: Whisper Medium El Greco Greek
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results: []
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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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# Whisper Medium El Greco Greek
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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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- eval_loss: 0.3924
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- eval_wer: 12.4443
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- eval_runtime: 1211.1631
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- eval_samples_per_second: 1.4
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- eval_steps_per_second: 0.088
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- epoch: 4.04
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- step: 5000
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## Model description
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- training_steps: 5000
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.26.0.dev0
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README.md.new
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---
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language:
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- el
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license: apache-2.0
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tags:
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- whisper-event
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+
- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_11_0,google/fleurs
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metrics:
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- wer
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model-index:
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- name: Whisper Medium El Greco
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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: mozilla-foundation/common_voice_11_0,google/fleurs
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type: mozilla-foundation/common_voice_11_0,google/fleurs
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config: null
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split: None
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metrics:
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- name: Wer
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type: wer
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value: 11.199851411589897
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+
---
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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. -->
|
| 31 |
+
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+
# Whisper Medium El Greco
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+
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This model is a fine-tuned version of [emilios/whisper-medium-el](https://huggingface.co/emilios/whisper-medium-el) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3801
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- Wer: 11.1999
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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: 32
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- eval_batch_size: 16
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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: 5000
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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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| 0.0176 | 2.49 | 1000 | 0.2945 | 12.6114 |
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| 0.0064 | 4.98 | 2000 | 0.3423 | 12.2307 |
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| 0.0022 | 7.46 | 3000 | 0.3632 | 11.5899 |
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| 0.0014 | 9.95 | 4000 | 0.3788 | 11.2556 |
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| 0.0008 | 12.44 | 5000 | 0.3801 | 11.1999 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.13.0+cu117
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- Datasets 2.7.1.dev0
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- Tokenizers 0.13.2
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