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---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- facebook/multilingual_librispeech
metrics:
- wer
model-index:
- name: Whisper largeV2 Spanish MLS
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: facebook/multilingual_librispeech spanish
type: facebook/multilingual_librispeech
config: spanish
split: test
args: spanish
metrics:
- name: Wer
type: wer
value: 3.4677966101694913
---
<!-- 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 largeV2 Spanish MLS
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the facebook/multilingual_librispeech spanish dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0910
- Wer: 3.4678
## Model description
The model is fine-tuned for 4000 updates/steps on multilingual librispeech Spanish train data.
- Zero-shot - 4.2 (MLS Spanish test)
- Fine-tune MLS spanish train - 3.46 (MLS Spanish test) (-17.61%)
------------------------------------------------------------------------------
- Zero-shot - 6.3 (CV11 test)
- Fine-tune MLS spanish train - 8.38 (CV11 test)
When the model is fine-tuned on specific dataset, the model loose its ability to generalise across datasets.
Here the model is fine-tuned on MLS Spanish and evaluated on CV11 Spanish test. We can observe the drop in performance on CV11 test data.
## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2176 | 0.25 | 1000 | 0.1200 | 5.3932 |
| 0.1845 | 0.5 | 2000 | 0.1055 | 4.2 |
| 0.4516 | 0.75 | 3000 | 0.0977 | 3.6768 |
| 0.1549 | 1.14 | 4000 | 0.0910 | 3.4678 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|