readme: ---
  language:
  - es
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  - cer
  model-index:
  - name: Whisper Large Spanish
    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: 4.673613637544826
      - name: CER
        type: cer
        value: 1.5573247819517182
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: google/fleurs es_419
        type: google/fleurs
        config: es_419
        split: test
        args: es_419
      metrics:
      - name: WER
        type: wer
        value: 5.396216546072705
      - name: CER
        type: cer
        value: 3.450427960057061
  ---
  
  # Whisper Large Spanish
  
  This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on Spanish using the train split of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0).
  
  ## Usage
  
  ```python
  
  from transformers import pipeline
  
  transcriber = pipeline(
    "automatic-speech-recognition", 
    model="jonatasgrosman/whisper-large-es-cv11"
  )
  
  transcriber.model.config.forced_decoder_ids = (
    transcriber.tokenizer.get_decoder_prompt_ids(
      language="es", 
      task="transcribe"
    )
  )
  
  transcription = transcriber("path/to/my_audio.wav")
  
  ```
  
  ## Evaluation
  
  I've performed the evaluation of the model using the test split of two datasets, the [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (same dataset used for the fine-tuning) and the [Fleurs](https://huggingface.co/datasets/google/fleurs) (dataset not seen during the fine-tuning). As Whisper can transcribe casing and punctuation, I've performed the model evaluation in 2 different scenarios, one using the raw text and the other using the normalized text (lowercase + removal of punctuations). Additionally, for the Fleurs dataset, I've evaluated the model in a scenario where there are no transcriptions of numerical values since the way these values are described in this dataset is different from how they are described in the dataset used in fine-tuning (Common Voice), so it is expected that this difference in the way of describing numerical values will affect the performance of the model for this type of transcription in Fleurs.
  
  ### Common Voice 11
  
  | | CER | WER |
  | --- | --- | --- |
  | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) | 2.43 | 8.85 |
  | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + text normalization | 1.56 | 4.67 |
  | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | 3.71 | 12.34 |
  | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization | 2.45 | 6.30 |
  
  ### Fleurs
  
  | | CER | WER |
  | --- | --- | --- |
  | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) | 3.06 | 9.11 |
  | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + text normalization | 3.45 | 5.40 |
  | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + keep only non-numeric samples | 1.83 | 7.57 |
  | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + text normalization + keep only non-numeric samples | 2.36 | 4.14 |
  | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | 2.30 | 8.50 |
  | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization | 2.76 | 4.79 |
  | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + keep only non-numeric samples | 1.93 | 7.33 |
  | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization + keep only non-numeric samples | 2.50 | 4.28 |
  