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---
license: apache-2.0
base_model: openai/whisper-medium
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: 0.11473958668640959
---
<!-- 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-medium-pt-1000h
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) 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: 0.6491
- Wer: 0.1147
## 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: 300000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.4574 | 0.2 | 20000 | 0.5339 | 0.1631 |
| 0.4124 | 0.39 | 40000 | 0.4512 | 0.1517 |
| 0.481 | 0.59 | 60000 | 0.4628 | 0.1466 |
| 0.3452 | 0.79 | 80000 | 0.4677 | 0.1392 |
| 0.4086 | 0.98 | 100000 | 0.4551 | 0.1364 |
| 0.1565 | 1.18 | 120000 | 0.5060 | 0.1316 |
| 0.1513 | 1.38 | 140000 | 0.5330 | 0.1286 |
| 0.1496 | 1.57 | 160000 | 0.5519 | 0.1263 |
| 0.1533 | 1.77 | 180000 | 0.5528 | 0.1234 |
| 0.1525 | 1.97 | 200000 | 0.4857 | 0.1194 |
| 0.1918 | 2.16 | 220000 | 0.5915 | 0.1189 |
| 0.1175 | 2.36 | 240000 | 0.6099 | 0.1174 |
| 0.0959 | 2.56 | 260000 | 0.6183 | 0.1157 |
| 0.0988 | 2.75 | 280000 | 0.6423 | 0.1152 |
| 0.0913 | 2.95 | 300000 | 0.6491 | 0.1147 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.1.dev0
- Tokenizers 0.15.0
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