readme: ---
  language: fr
  datasets:
  - common_voice
  metrics:
  - wer
  - cer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Voxpopuli Wav2Vec2 French by Jonatas Grosman
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice fr
        type: common_voice
        args: fr
      metrics:
         - name: Test WER
           type: wer
           value: 17.62
         - name: Test CER
           type: cer
           value: 6.04
  ---
  
  # Fine-tuned French Voxpopuli wav2vec2 large model for speech recognition in French
  
  Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) on French 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-fr-voxpopuli-french")
  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 = "fr"
  MODEL_ID = "jonatasgrosman/wav2vec2-large-fr-voxpopuli-french"
  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 |
  | ------------- | ------------- |
  | "CE DERNIER A ÉVOLUÉ TOUT AU LONG DE L'HISTOIRE ROMAINE." | CE DERNIER A ÉVOLÉ TOUT AU LONG DE L'HISTOIRE ROMAINE |
  | CE SITE CONTIENT QUATRE TOMBEAUX DE LA DYNASTIE ACHÉMÉNIDE ET SEPT DES SASSANIDES. | CE SITE CONTIENT QUATRE TOMBEAUX DE LA DYNESTIE ACHÉMÉNIDE ET SEPT DES SACENNIDES |
  | "J'AI DIT QUE LES ACTEURS DE BOIS AVAIENT, SELON MOI, BEAUCOUP D'AVANTAGES SUR LES AUTRES." | JAI DIT QUE LES ACTEURS DE BOIS AVAIENT SELON MOI BEAUCOUP DAVANTAGE SUR LES AUTRES |
  | LES PAYS-BAS ONT REMPORTÉ TOUTES LES ÉDITIONS. | LE PAYS-BAS ON REMPORTÉ TOUTES LES ÉDITIONS |
  | IL Y A MAINTENANT UNE GARE ROUTIÈRE. | IL A MAINTENANT GULA E RETIREN |
  | HUIT | HUIT |
  | DANS L’ATTENTE DU LENDEMAIN, ILS NE POUVAIENT SE DÉFENDRE D’UNE VIVE ÉMOTION | DANS LATTENTE DU LENDEMAIN IL NE POUVAIT SE DÉFENDRE DUNE VIVE ÉMOTION |
  | LA PREMIÈRE SAISON EST COMPOSÉE DE DOUZE ÉPISODES. | LA PREMIÈRE SAISON EST COMPOSÉE DE DOUZ ÉPISODES |
  | ELLE SE TROUVE ÉGALEMENT DANS LES ÎLES BRITANNIQUES. | ELLE SE TROUVE ÉGALEMENT DANS LES ÎLES BRITANNIQUES |
  | ZÉRO | ZÉRO |
  
  ## Evaluation
  
  The model can be evaluated as follows on the French (fr) test data of Common Voice.
  
  ```python
  import torch
  import re
  import librosa
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  LANG_ID = "fr"
  MODEL_ID = "jonatasgrosman/wav2vec2-large-fr-voxpopuli-french"
  DEVICE = "cuda"
  
  CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
                     "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
                     "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
                     "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
                     "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
  
  test_dataset = load_dataset("common_voice", LANG_ID, split="test")
  
  we