readme: ---
  language: es
  license: apache-2.0
  datasets:
  - common_voice
  - mozilla-foundation/common_voice_6_0
  metrics:
  - wer
  - cer
  tags:
  - audio
  - automatic-speech-recognition
  - es
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_6_0
  - robust-speech-event
  - speech
  - xlsr-fine-tuning-week
  model-index:
  - name: XLSR Wav2Vec2 Spanish by Jonatas Grosman
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice es
        type: common_voice
        args: es
      metrics:
      - name: Test WER
        type: wer
        value: 8.82
      - name: Test CER
        type: cer
        value: 2.58
      - name: Test WER (+LM)
        type: wer
        value: 6.27
      - name: Test CER (+LM)
        type: cer
        value: 2.06
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: es
      metrics:
      - name: Dev WER
        type: wer
        value: 30.19
      - name: Dev CER
        type: cer
        value: 13.56
      - name: Dev WER (+LM)
        type: wer
        value: 24.71
      - name: Dev CER (+LM)
        type: cer
        value: 12.61
  ---
  
  # Fine-tuned XLSR-53 large model for speech recognition in Spanish
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice).
  When using this model, make sure that your speech input is sampled at 16kHz.
  
  This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
  
  The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
  
  ## Usage
  
  The model can be used directly (without a language model) as follows...
  
  Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
  
  ```python
  from huggingsound import SpeechRecognitionModel
  
  model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
  audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
  
  transcriptions = model.transcribe(audio_paths)
  ```
  
  Writing your own inference script:
  
  ```python
  import torch
  import librosa
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  LANG_ID = "es"
  MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-spanish"
  SAMPLES = 10
  
  test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
  
  processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
  model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
      batch["speech"] = speech_array
      batch["sentence"] = batch["sentence"].upper()
      return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
  with torch.no_grad():
      logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
  
  predicted_ids = torch.argmax(logits, dim=-1)
  predicted_sentences = processor.batch_decode(predicted_ids)
  
  for i, predicted_sentence in enumerate(predicted_sentences):
      print("-" * 100)
      print("Reference:", test_dataset[i]["sentence"])
      print("Prediction:", predicted_sentence)
  ```
  
  | Reference  | Prediction |
  | ------------- | ------------- |
  | HABITA EN AGUAS POCO PROFUNDAS Y ROCOSAS. | HABITAN AGUAS POCO PROFUNDAS Y ROCOSAS |
  | OPERA PRINCIPALMENTE VUELOS DE CABOTAJE Y REGIONALES DE CARGA. | OPERA PRINCIPALMENTE VUELO DE CARBOTAJES Y REGIONALES DE CARGAN |
  | PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN. | PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN |
  | TRES | TRES |
  | REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA, PARA CONTINUAR LUEGO EN ESPAÑA. | REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA PARA CONTINUAR LUEGO EN ESPAÑA |
  | EN LOS AÑOS QUE SIGUIERON, ESTE TRABAJO ESPARTA PRODUJO DOCENAS DE BUENOS JUGADORES. | EN LOS AÑOS QUE SIGUIERON ESTE TRABAJO ESPARTA PRODUJO DOCENA DE BUENOS JUGADORES |
  | SE ESTÁ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS. | SE ESTÓ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS |
  | SÍ | SÍ |
  | "FUE ""SACADA"" DE LA SERIE EN EL EPISODIO ""LEAD"", EN QUE ALEXANDRA CABOT REGRESÓ." | FUE SACADA DE LA SERIE EN EL EPISODIO LEED EN QUE ALEXANDRA KAOT REGRESÓ |
  | SE UBICAN ESPECÍFICAMENTE EN EL VALLE DE MOKA, EN LA PROVINCIA DE BIOKO SUR. | SE UBICAN ESPECÍFICAMENTE EN EL VALLE DE MOCA EN LA PROVINCIA DE PÍOCOSUR |
  
  ## Evaluation
  
  1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test`
  
  ```bash
  python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-spanish --dataset mozilla-foundation/common_voice_6_0 --config es --split te