transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer sr dataset:mozilla-foundation/common_voice_13_0 dataset:google/fleurs dataset:Sagicc/audio-lmb-ds base_model:openai/whisper-medium license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- sr
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
- google/fleurs
- Sagicc/audio-lmb-ds
metrics:
- wer
model-index:
- name: Whisper Medium cmb
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 13
      type: mozilla-foundation/common_voice_13_0
      config: sr
      split: test
      args: sr
    metrics:
    - name: Wer
      type: wer
      value: 0.0658123370981755
---

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# Whisper Medium sr v2

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium).
It achieves the following results on the evaluation set:
- Loss: 0.2216
- Wer Ortho: 0.1663
- Wer: 0.0738

## Model description

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)

Rupnik, Peter and Ljubešić, Nikola, 2022,\
  ASR training dataset for Serbian JuzneVesti-SR v1.0, Slovenian language resource repository CLARIN.SI, ISSN 2820-4042,\
  http://hdl.handle.net/11356/1679.

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.3634        | 0.40  | 500  | 0.1619          | 0.1953    | 0.0921 |
| 0.3185        | 0.81  | 1000 | 0.1423          | 0.175     | 0.0800 |
| 0.2216        | 1.21  | 1500 | 0.137           | 0.1663    | 0.0738 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1