readme: ---
  language: en
  datasets:
  - common_voice
  metrics:
  - wer
  - cer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Wav2Vec2 English by Jonatas Grosman
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice en
        type: common_voice
        args: en
      metrics:
         - name: Test WER
           type: wer
           value: 21.53
         - name: Test CER
           type: cer
           value: 9.66
  ---
  
  # Wav2vec2-Large-English
  
  Fine-tuned [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on English using the [Common Voice](https://huggingface.co/datasets/common_voice).
  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...
  
  Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library:
  
  ```python
  from asrecognition import ASREngine
  
  asr = ASREngine("fr", model_path="jonatasgrosman/wav2vec2-large-english")
  
  audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
  transcriptions = asr.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 = "en"
  MODEL_ID = "jonatasgrosman/wav2vec2-large-english"
  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 |
  | ------------- | ------------- |
  | "SHE'LL BE ALL RIGHT." | SHELL BE ALL RIGHT |
  | SIX | SIX |
  | "ALL'S WELL THAT ENDS WELL." | ALLAS WELL THAT ENDS WELL |
  | DO YOU MEAN IT? | W MEAN IT |
  | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE, BUT STILL CAUSES REGRESSIONS. | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE BUT STILL CAUSES REGRESTION |
  | HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOW IS MOSILLA GOING TO BANDL AND BE WHIT IS LIKE QU AND QU |
  | "I GUESS YOU MUST THINK I'M KINDA BATTY." | RUSTION AS HAME AK AN THE POT |
  | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING? | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING |
  | SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER. | SAUCE FOR THE GUCE IS SAUCE FOR THE GONDER |
  | GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. | GRAFS STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD |
  
  ## Evaluation
  
  The model can be evaluated as follows on the English (en) 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 = "en"
  MODEL_ID = "jonatasgrosman/wav2vec2-large-english"
  DEVICE = "cuda"
  
  CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
                     "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
                     "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
                     "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
                     "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
  
  test_dataset = load_dataset("common_voice", LANG_ID, split="test")
  
  wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
  cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
  
  chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
  
  processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
  model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
  model.to(DEVICE)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          speech_array, sampling_rate = librosa.load(batch["pat