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---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
metrics:
- wer
model-index:
- name: whisper-medium-pt-1000h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
default
type: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
args: default
metrics:
- name: Wer
type: wer
value: 0.1490
---
<!-- 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-small-pt-1000h
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3036
- Wer: 0.1490
## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 182000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.2594 | 1.58 | 160000 | 0.6842 | 0.1525 |
| 0.3036 | 1.77 | 180000 | 0.6491 | 0.1490 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.1.dev0
- Tokenizers 0.15.0