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---
license: apache-2.0
language:
- es
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
- facebook/multilingual_librispeech
- facebook/voxpopuli
metrics:
- wer
model-index:
- name: openai/whisper-medium
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 es
type: mozilla-foundation/common_voice_11_0
config: es
split: test
args: es
metrics:
- name: Wer
type: wer
value: 6.346473676004366
- name: Cer
type: cer
value: 2.1391
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: FLEURS ASR
type: google/fleurs
config: es_419
split: test
args: es
metrics:
- name: WER
type: wer
value: 4.0266
- name: Cer
type: cer
value: 1.6631
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech
type: facebook/multilingual_librispeech
config: spanish
split: test
args:
language: es
metrics:
- name: WER
type: wer
value: 4.6644
- name: Cer
type: cer
value: 1.7056
- task:
type: Automatic Speech Recognition
name: speech-recognition
dataset:
name: VoxPopuli
type: facebook/voxpopuli
config: es
split: test
args:
language: es
metrics:
- name: WER
type: wer
value: 8.3668
- name: Cer
type: cer
value: 5.479
---
<!-- 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. -->
# openai/whisper-medium-mix-es
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0, google/fleurs, facebook/multilingual_librispeech and facebook/voxpopuli datasets.
It achieves the following results on the evaluation set:
- Loss: 0.1344
- Wer: 6.3465
Using the [evaluation script](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_eval_whisper_streaming.py) provided in the Whisper Sprint the model achieves these results on the test sets (WER):
- **google/fleurs: 4.0266 %**
(python run_eval_whisper_streaming.py --model_id="deepdml/whisper-medium-mix-es" --dataset="google/fleurs" --config="es_419" --device=0 --language="es")
- **facebook/multilingual_librispeech: 4.6644 %**
(python run_eval_whisper_streaming.py --model_id="deepdml/whisper-medium-mix-es" --dataset="facebook/multilingual_librispeech" --config="spanish" --device=0 --language="es")
- **facebook/voxpopuli: 8.3668 %**
(python run_eval_whisper_streaming.py --model_id="deepdml/whisper-medium-mix-es" --dataset="facebook/voxpopuli" --config="es" --device=0 --language="es")
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data used:
- **mozilla-foundation/common_voice_11_0:** es, train+validation
- **google/fleurs:** es_419, train
- **facebook/multilingual_librispeech:** spanish, train
- **facebook/voxpopuli:** es, train
Evaluating over test split from mozilla-foundation/common_voice_11_0 dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.266 | 0.2 | 1000 | 0.1657 | 8.0395 |
| 0.1394 | 0.4 | 2000 | 0.1539 | 7.3937 |
| 0.1316 | 0.6 | 3000 | 0.1452 | 6.9656 |
| 0.1165 | 0.8 | 4000 | 0.1392 | 6.5765 |
| 0.2816 | 1.0 | 5000 | 0.1344 | 6.3465 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|