readme: ---
  language: sah
  datasets:
  - common_voice 
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Anurag Singh XLSR Wav2Vec2 Large 53 Sakha
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice sah
        type: common_voice
        args: sah
      metrics:
         - name: Test WER
           type: wer
           value: 38.04
  ---
  # Wav2Vec2-Large-XLSR-53-Sakha
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Sakha 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:
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  test_dataset = load_dataset("common_voice", "sah", split="test[:2%]")
  processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-sah")
  model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-sah")
  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:", processor.batch_decode(predicted_ids))
  print("Reference:", test_dataset["sentence"][:2])
  ```
  ## Evaluation
  The model can be evaluated as follows on the Sakha test data of Common Voice.
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  test_dataset = load_dataset("common_voice", "sah", split="test")
  wer = load_metric("wer")
  processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-sah")
  model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-sah")
  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):
      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)
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  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("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
      pred_ids = torch.argmax(logits, dim=-1)
      batch["pred_strings"] = processor.batch_decode(pred_ids)
      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**: 38.04 % 
  ## Training
  The Common Voice `train` and `validation` datasets were used for training.