readme: ---
  language: vi
  datasets:
  - common_voice
  - FOSD: https://data.mendeley.com/datasets/k9sxg2twv4/4
  - VIVOS: https://ailab.hcmus.edu.vn/vivos
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Vietnamese by Nhut
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice vi
        type: common_voice
        args: vi
      metrics:
         - name: Test WER
           type: wer
           value: 49.59
  ---
  # Wav2Vec2-Large-XLSR-53-Vietnamese
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4) and [VIVOS](https://ailab.hcmus.edu.vn/vivos).
  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
  ENCODER = {
      "ia ": "iê ",
      "ìa ": "iề ",
      "ía ": "iế ",
      "ỉa ": "iể ",
      "ĩa ": "iễ ",
      "ịa ": "iệ ",
      "ya ": "yê ",
      "ỳa ": "yề ",
      "ýa ": "yế ",
      "ỷa ": "yể ",
      "ỹa ": "yễ ",
      "ỵa ": "yệ ",
      "ua ": "uô ",
      "ùa ": "uồ ",
      "úa ": "uố ",
      "ủa ": "uổ ",
      "ũa ": "uỗ ",
      "ụa ": "uộ ",
      "ưa ": "ươ ",
      "ừa ": "ườ ",
      "ứa ": "ướ ",
      "ửa ": "ưở ",
      "ữa ": "ưỡ ",
      "ựa ": "ượ ",
      "ke": "ce",
      "kè": "cè",
      "ké": "cé",
      "kẻ": "cẻ",
      "kẽ": "cẽ",
      "kẹ": "cẹ",
      "kê": "cê",
      "kề": "cề",
      "kế": "cế",
      "kể": "cể",
      "kễ": "cễ",
      "kệ": "cệ",
      "ki": "ci",
      "kì": "cì",
      "kí": "cí",
      "kỉ": "cỉ",
      "kĩ": "cĩ",
      "kị": "cị",
      "ky": "cy",
      "kỳ": "cỳ",
      "ký": "cý",
      "kỷ": "cỷ",
      "kỹ": "cỹ",
      "kỵ": "cỵ",
      "ghe": "ge",
      "ghè": "gè",
      "ghé": "gé",
      "ghẻ": "gẻ",
      "ghẽ": "gẽ",
      "ghẹ": "gẹ",
      "ghê": "gê",
      "ghề": "gề",
      "ghế": "gế",
      "ghể": "gể",
      "ghễ": "gễ",
      "ghệ": "gệ",
      "ngh": "\x80",
      "uyê": "\x96",
      "uyề": "\x97",
      "uyế": "\x98",
      "uyể": "\x99",
      "uyễ": "\x9a",
      "uyệ": "\x9b",
      "ng": "\x81",
      "ch": "\x82",
      "gh": "\x83",
      "nh": "\x84",
      "gi": "\x85",
      "ph": "\x86",
      "kh": "\x87",
      "th": "\x88",
      "tr": "\x89",
      "uy": "\x8a",
      "uỳ": "\x8b",
      "uý": "\x8c",
      "uỷ": "\x8d",
      "uỹ": "\x8e",
      "uỵ": "\x8f",
      "iê": "\x90",
      "iề": "\x91",
      "iế": "\x92",
      "iể": "\x93",
      "iễ": "\x94",
      "iệ": "\x95",
      "uô": "\x9c",
      "uồ": "\x9d",
      "uố": "\x9e",
      "uổ": "\x9f",
      "uỗ": "\xa0",
      "uộ": "\xa1",
      "ươ": "\xa2",
      "ườ": "\xa3",
      "ướ": "\xa4",
      "ưở": "\xa5",
      "ưỡ": "\xa6",
      "ượ": "\xa7",
  }
    
  def decode_string(x):
    for k, v in list(reversed(list(ENCODER.items()))):
      x = x.replace(v, k)
    return x
  test_dataset = load_dataset("common_voice", "vi", split="test[:2%]") 
  processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
  model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  # Preprocessing the datasets.
  # We need to read the aduio 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:", [decode_string(x) for x in processor.batch_decode(predicted_ids)])
  print("Reference:", test_dataset["sentence"][:2])
  ```
  ## Evaluation
  The model can be evaluated as follows on the Vietnamese test data of Common Voice.
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  
  ENCODER = {
      "ia ": "iê ",
      "ìa ": "iề ",
      "ía ": "iế ",
      "ỉa ": "iể ",
      "ĩa ": "iễ ",
      "ịa ": "iệ ",
      "ya ": "yê ",
      "ỳa ": "yề ",
      "ýa ": "yế ",
      "ỷa ": "yể ",
      "ỹa ": "yễ ",
      "ỵa ": "yệ ",
      "ua ": "uô ",
      "ùa ": "uồ ",
      "úa ": "uố ",
      "ủa ": "uổ ",
      "ũa ": "uỗ ",
      "ụa ": "uộ ",
      "ưa ": "ươ ",
      "ừa ": "ườ ",
      "ứa ": "ướ ",
      "ửa ": "ưở ",
      "ữa ": "ưỡ ",
      "ựa ": "ượ ",
      "ke": "ce",
      "kè": "cè",
      "ké": "cé",
      "kẻ": "cẻ",
      "kẽ": "cẽ",
      "kẹ": "cẹ",
      "kê": "cê",
      "kề": "cề",
      "kế": "cế",
      "kể": "cể",
      "kễ": "cễ",
      "kệ": "cệ",
      "ki": "ci",
      "kì": "cì",
      "kí": "cí",
      "kỉ": "c