readme: ---
  language: su
  datasets:
  - openslr
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Sundanese by cahya
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: OpenSLR High quality TTS data for Sundanese
        type: OpenSLR
        args: su
      metrics:
         - name: Test WER
           type: wer
           value: 6.19
  ---
  
  # Wav2Vec2-Large-XLSR-Sundanese
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
  on the [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/).
  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, load_metric, Dataset
  from datasets.utils.download_manager import DownloadManager
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  from pathlib import Path
  import pandas as pd
  
  
  def load_dataset_sundanese():
      urls = [
          "https://www.openslr.org/resources/44/su_id_female.zip",
          "https://www.openslr.org/resources/44/su_id_male.zip"
      ]
      dm = DownloadManager()
      download_dirs = dm.download_and_extract(urls)
      data_dirs = [ 
          Path(download_dirs[0])/"su_id_female/wavs",
          Path(download_dirs[1])/"su_id_male/wavs",
      ]
      filenames = [ 
          Path(download_dirs[0])/"su_id_female/line_index.tsv",
          Path(download_dirs[1])/"su_id_male/line_index.tsv",
      ]
  
      dfs = []
      
      dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"]))
      dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"]))
      
      for i, dir in enumerate(data_dirs):
          dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
      df = pd.concat(dfs)
      # df = df.sample(frac=1, random_state=1).reset_index(drop=True)
      dataset = Dataset.from_pandas(df)
      dataset = dataset.remove_columns('__index_level_0__')
      
      return dataset.train_test_split(test_size=0.1, seed=1)
      
  dataset = load_dataset_sundanese()
  test_dataset = dataset['test']
  
  processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
  model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
  
  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[:2]["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)
  
  print("Prediction:", processor.batch_decode(predicted_ids))
  print("Reference:", test_dataset[:2]["sentence"])
  ```
  
  
  ## Evaluation
  
  The model can be evaluated as follows or using the [notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb).
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric, Dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  from datasets.utils.download_manager import DownloadManager
  import re
  from pathlib import Path
  import pandas as pd
  
  
  def load_dataset_sundanese():
      urls = [
          "https://www.openslr.org/resources/44/su_id_female.zip",
          "https://www.openslr.org/resources/44/su_id_male.zip"
      ]
      dm = DownloadManager()
      download_dirs = dm.download_and_extract(urls)
      data_dirs = [ 
          Path(download_dirs[0])/"su_id_female/wavs",
          Path(download_dirs[1])/"su_id_male/wavs",
      ]
      filenames = [ 
          Path(download_dirs[0])/"su_id_female/line_index.tsv",
          Path(download_dirs[1])/"su_id_male/line_index.tsv",
      ]
  
      dfs = []
      
      dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"]))
      dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"]))
      
      for i, dir in enumerate(data_dirs):
          dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
      df = pd.concat(dfs)
      # df = df.sample(frac=1, random_state=1).reset_index(drop=True)
      dataset = Dataset.from_pandas(df)
      dataset = dataset.remove_columns('__index_level_0__')
      
      return dataset.train_test_split(test_size=0.1, seed=1)
      
  dataset = load_dataset_sundanese()
  test_dataset = dataset['test']
  
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
  model = Wav2Vec2For