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---
base_model: openai/whisper-large-v3
datasets:
- BembaSpeech
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-bem-fsv
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: BembaSpeech bem
type: BembaSpeech
args: bem
metrics:
- type: wer
value: 0.4033761652809272
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: BembaSpeech
type: BembaSpeech
config: en
split: test
metrics:
- type: wer
value: 47.3
name: WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v3-bem-fsv
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the BembaSpeech bem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4783
- Wer: 0.4034
## 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: 1.75e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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