--- language: arz datasets: - https://arabicspeech.org/ metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: `XLSR Wav2Vec2 Egyptian Arabic by 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 --- # Wav2Vec2-Large-XLSR-53-Egyptian-Arabic Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Egyptian using the [arabicspeech.org MGB-3](https://arabicspeech.org/mgb3-asr/) 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", "ar", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-egyptian") model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-egyptian") 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["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 Arabic 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", "ar", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-egyptian") model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-egyptian") model.to("cuda") chars_to_ignore_regex = '[\؛\—\_get\«\»\ـ\ـ\,\?\.\!\-\;\:\"\“\%\‘\”\�\#\،\☭,\؟]' 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): 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 audio 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**: 55.2 ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://github.com/othrif/xlsr-wav2vec2)