metadata

language: ar
datasets:
  - https://arabicspeech.org/
tags:
  - audio
  - automatic-speech-recognition
  - speech
license: apache-2.0
model-index:
  - name: XLSR Wav2Vec2 Egyptian by Zaid Alyafeai and Othmane Rifki
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: arabicspeech.org MGB-3
          type: arabicspeech.org MGB-3
          args: ar
        metrics:
          - name: Test WER
            type: wer
            value: 55.2

Test Wav2Vec2 with egyptian arabic

Fine-tuned facebook/wav2vec2-large-xlsr-53 in Egyptian using the arabicspeech.org MGB-3 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:

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
dataset = load_dataset("arabic_speech_corpus", split="test")
processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec_test")
model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec_test")
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])