readme: ---
  language: vi
  datasets:
  - common_voice
  - FOSD: https://data.mendeley.com/datasets/k9sxg2twv4/4
  metrics:
  - wer
  tags:
  - language-modeling
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: MT5 Fix Asr Vietnamese by Ontocord
    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: 25.207182
  ---
  
  # Ontocord/mt5-fix-asr-vietnamese
  Fine-tuned mt5 to correct output of an ASR model trained on [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) which was trained on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), and [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4).
  
  ## Usage
  The model can be used directly by submitting vietnamese asr text, but is is best to use with the ontocord/wav2vec2-large-xlsr-vietnamese model.
  
  ```
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, pipelines
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  test_dataset = load_dataset("common_voice", "vi", split="test[:2%]") 
  processor = Wav2Vec2Processor.from_pretrained("ontocord/wav2vec2-large-xlsr-53-vietnamese") 
  model = Wav2Vec2ForCTC.from_pretrained("ontocord/wav2vec2-large-xlsr-53-vietnamese").to(device) 
  mt5 = pipelines.pipeline("text2text-generation","ontocord/mt5-fix-asr-vietnamese", device=0 if device == "cuda" else -1)
  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.to(device), attention_mask=inputs.attention_mask.to(device)).logits
  predicted_ids = torch.argmax(logits, dim=-1)
  print("Prediction:", [aHash['generated_text'] for aHash in mt5(processor.batch_decode(predicted_ids), max_length=100)])
  print("Reference:", test_dataset["sentence"][:2])
  ```
  
  ## Evaluation
  The model can be evaluated as follows on the Vietnamese test data of Common Voice.
  ```
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, pipelines
  import re
  test_dataset = load_dataset("common_voice", "vi", split="test")
  wer = load_metric("wer")
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  processor = Wav2Vec2Processor.from_pretrained("ontocord/wav2vec2-large-xlsr-vietnamese")
  model = Wav2Vec2ForCTC.from_pretrained("ontocord/wav2vec2-large-xlsr-vietnamese").to(device)
  mt5 = pipelines.pipeline("text2text-generation","ontocord/mt5-fix-asr-vietnamese", device=0 if device == "cuda" else -1)
  chars_to_ignore_regex = '[\\\+\@\ǀ\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
  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):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    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)
  
  # you may also want to use the  decode_string from https://huggingface.co/Nhut/wav2vec2-large-xlsr-vietnamese
  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(device), attention_mask=inputs.attention_mask.to(device)).logits
      pred_ids = torch.argmax(logits, dim=-1)
      max_length = int(pred_ids.size()[1])
      txt = [aHash['generated_text'].strip() for aHash in mt5(processor.batch_decode(pred_ids), max_length=max_length)]
      batch["pred_strings"] = txt
    return batch
  result = test_dataset.map(evaluate, batched=True, batch_size=8)
  print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
  ```
  **Test Result**: 25.207182
  ## Training
  The Common Voice train, validation, and FPT datasets were used for training.
  The script used for training can be found here # TODO