readme: ---
  language: es
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Large 53 Spanish by pcuenq 
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice es
        type: common_voice
        args: es
      metrics:
         - name: Test WER
           type: wer
           value: 10.50
  ---
  
  # Wav2Vec2-Large-XLSR-53-Spanish
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset{s}.
  When using this model, make sure that your speech input is sampled at 16kHz.
  
  ## Usage
  
  The model can be used directly (without a language model) as follows:
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  test_dataset = load_dataset("common_voice", "es", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-es")
  model = Wav2Vec2ForCTC.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-es")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
  	speech_array, sampling_rate = torchaudio.load(batch["path"])
  	batch["speech"] = resampler(speech_array).squeeze().numpy()
  	return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset["speech"][:2], 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)
  
  print("Prediction:", processor.batch_decode(predicted_ids))
  print("Reference:", test_dataset["sentence"][:2])
  ```
  
  
  ## Evaluation
  
  The model can be evaluated as follows on the Spanish test data of Common Voice.
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  
  test_dataset = load_dataset("common_voice", "es", split="test")
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-es")
  model = Wav2Vec2ForCTC.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-es")
  model.to("cuda")
  
  ## Text pre-processing
  
  chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\￼\…\’\ː\'\‹\›\`\´\®\—\→]'
  chars_to_ignore_pattern = re.compile(chars_to_ignore_regex)
  
  def remove_special_characters(batch):
      batch["sentence"] = chars_to_ignore_pattern.sub('', batch["sentence"]).lower() + " "
      return batch
  
  def replace_diacritics(batch):
      sentence = batch["sentence"]
      sentence = re.sub('ì', 'í', sentence)
      sentence = re.sub('ù', 'ú', sentence)
      sentence = re.sub('ò', 'ó', sentence)
      sentence = re.sub('à', 'á', sentence)
      batch["sentence"] = sentence
      return batch
  
  def replace_additional(batch):
      sentence = batch["sentence"]
      sentence = re.sub('ã', 'a', sentence)   # Portuguese, as in São Paulo
      sentence = re.sub('ō', 'o', sentence)   # Japanese
      sentence = re.sub('ê', 'e', sentence)   # Português
      batch["sentence"] = sentence
      return batch
  
  ## Audio pre-processing
  
  # I tried to perform the resampling using a `torchaudio` `Resampler` transform,
  # but found that the process deadlocked when using multiple processes.
  # Perhaps my torchaudio is using the wrong sox library under the hood, I'm not sure.
  # Fortunately, `librosa` seems to work fine, so that's what I'll use for now.
  
  import librosa
  def speech_file_to_array_fn(batch):
      speech_array, sample_rate = torchaudio.load(batch["path"])
      batch["speech"] = librosa.resample(speech_array.squeeze().numpy(), sample_rate, 16_000)
      return batch
  
  # One-pass mapping function
  
  # Text transformation and audio resampling
  def cv_prepare(batch):
      batch = remove_special_characters(batch)
      batch = replace_diacritics(batch)
      batch = replace_additional(batch)
      batch = speech_file_to_array_fn(batch)
      return batch
  
  # Number of CPUs or None
  num_proc = 16
  
  test_dataset = test_dataset.map(cv_prepare, remove_columns=['path'], num_proc=num_proc)
  
  def evaluate(batch):
      inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
      with torch.no_grad():
          logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
  
      pred_ids = torch.argmax(logits, dim=-1)
      batch["pred_strings"] = processor.batch_decode(pred_ids)
      return batch
  
  result = test_dataset.map(evaluate, batched=True, batch_size=8)
  
  # WER Metric computation
  # `wer.compute` crashes in my computer with more than ~10000 samples.
  # Until I confirm in a different one, I created a "chunked" version of the computation.
  # It gives the same resul