readme: ---
  
  language: km
  datasets:
  - OpenSLR
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: wav2vec2-xlsr-Khmer by Gagan Bhatia 
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: OpenSLR km 
        type: OpenSLR
        args: km
      metrics:
         - name: Test WER
           type: wer
           value: 24.96
  ---
  
  # Wav2Vec2-Large-XLSR-53-khmer 
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Khmer using the [Common Voice](https://huggingface.co/datasets/common_voice), and [OpenSLR Kh](http://www.openslr.org/42/). 
  
  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
  
  !wget https://www.openslr.org/resources/42/km_kh_male.zip
  !unzip km_kh_male.zip
  !ls km_kh_male
  
  colnames=['path','sentence'] 
  df  = pd.read_csv('/content/km_kh_male/line_index.tsv',sep='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t',header=None,names = colnames)
  df['path'] = '/content/km_kh_male/wavs/'+df['path'] +'.wav'
  
  train, test = train_test_split(df, test_size=0.1)
  
  test.to_csv('/content/km_kh_male/line_index_test.csv')
  
  test_dataset = load_dataset('csv', data_files='/content/km_kh_male/line_index_test.csv',split = 'train')
  
  processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
  model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-nepali") 
  
  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):
  \\\\\\\\\\\\\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
  \\\\\\\\\\\\\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  \\\\\\\\\\\\\\\\treturn 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():
  \\\\\\\\\\\\\\\\tlogits = 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])
  
  ```
  #### Result 
  
  Prediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
  
  Reference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
  
  ## Evaluation
  
  The model can be evaluated as follows on the {language} test data of Common Voice.  # TODO: replace #TODO: replace language with your {language}, *e.g.* French
  
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  from sklearn.model_selection import train_test_split
  import pandas as pd
  from datasets import load_dataset
  
  !wget https://www.openslr.org/resources/42/km_kh_male.zip
  !unzip km_kh_male.zip
  !ls km_kh_male
  
  colnames=['path','sentence'] 
  df  = pd.read_csv('/content/km_kh_male/line_index.tsv',sep='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t',header=None,names = colnames)
  df['path'] = '/content/km_kh_male/wavs/'+df['path'] +'.wav'
  
  train, test = train_test_split(df, test_size=0.1)
  
  test.to_csv('/content/km_kh_male/line_index_test.csv')
  
  test_dataset = load_dataset('csv', data_files='/content/km_kh_male/line_index_test.csv',split = 'train')
  wer = load_metric("wer")
  cer = load_metric("cer")
  
  
  processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-khmer")
  model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-khmer") 
  model.to("cuda")
  
  chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]'  
  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):
  \\tbatch["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower()
  \\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
  \\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  \\treturn batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def evaluate(batch):
  \\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
  \\twith torch.no_grad():
  \\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
  
  \\tpred_ids = torch.argmax(logits, dim=-1)
  \\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
  \\treturn batch
  
  cer = load_metric("cer")
  
  result = test_dataset.map(eval