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README.md
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---
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license: apache-2.0
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---
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---
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language:
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- sr
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license: apache-2.0
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base_model: openai/whisper-medium
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tags:
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_13_0
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- google/fleurs
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- Sagicc/audio-lmb-ds
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metrics:
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- wer
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model-index:
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- name: Whisper Medium cmb
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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 13
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type: mozilla-foundation/common_voice_13_0
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config: sr
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split: test
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args: sr
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metrics:
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- name: Wer
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type: wer
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value: 0.0658123370981755
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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 sr v2
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium).
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It achieves the following results on the evaluation set:
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- Loss: 0.2216
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- Wer Ortho: 0.1663
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- Wer: 0.0738
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## Model description
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This is a fine tunned on merged datasets Common Voice 16 + Fleurs + [Juzne vesti (South news)](http://hdl.handle.net/11356/1679) + [LBM](https://huggingface.co/datasets/Sagicc/audio-lmb-ds)
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Rupnik, Peter and Ljubešić, Nikola, 2022,\
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ASR training dataset for Serbian JuzneVesti-SR v1.0, Slovenian language resource repository CLARIN.SI, ISSN 2820-4042,\
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http://hdl.handle.net/11356/1679.
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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: 4
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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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: 50
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- training_steps: 1500
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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 Ortho | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
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| 0.3634 | 0.40 | 500 | 0.1619 | 0.1953 | 0.0921 |
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| 0.3185 | 0.81 | 1000 | 0.1423 | 0.175 | 0.0800 |
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| 0.2216 | 1.21 | 1500 | 0.137 | 0.1663 | 0.0738 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.0.1+cu117
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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