readme: ---
  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
  