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--- |
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language: ar |
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datasets: |
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- arabic_speech_corpus |
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- mozilla-foundation/common_voice_6_1 |
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metrics: |
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- wer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- xlsr-fine-tuning-week |
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license: apache-2.0 |
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model-index: |
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- name: muzamil47-wav2vec2-large-xlsr-53-arabic |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 6.1 (Arabic) |
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type: mozilla-foundation/common_voice_6_1 |
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config: ar |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 53.54 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Arabic |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the [Common Voice](https://huggingface.co/datasets/common_voice). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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import librosa |
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import torch |
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from lang_trans.arabic import buckwalter |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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asr_model = "muzamil47/wav2vec2-large-xlsr-53-arabic-demo" |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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def load_file_to_data(file, srate=16_000): |
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batch = {} |
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speech, sampling_rate = librosa.load(file, sr=srate) |
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batch["speech"] = speech |
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batch["sampling_rate"] = sampling_rate |
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return batch |
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processor = Wav2Vec2Processor.from_pretrained(asr_model) |
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model = Wav2Vec2ForCTC.from_pretrained(asr_model).to(device) |
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def predict(data): |
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features = processor(data["speech"], sampling_rate=data["sampling_rate"], return_tensors="pt", padding=True) |
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input_values = features.input_values.to(device) |
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try: |
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attention_mask = features.attention_mask.to(device) |
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except: |
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attention_mask = None |
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with torch.no_grad(): |
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predicted = torch.argmax(model(input_values, attention_mask=attention_mask).logits, dim=-1) |
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data["predicted"] = processor.tokenizer.decode(predicted[0]) |
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print("predicted:", buckwalter.untrans(data["predicted"])) |
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return data |
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predict(load_file_to_data("common_voice_ar_19058307.mp3")) |
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``` |
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**Output Result**: |
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```shell |
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predicted: هل يمكنني التحدث مع المسؤول هنا |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the Arabic test data of Common Voice. |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset |
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from lang_trans.arabic import buckwalter |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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asr_model = "muzamil47/wav2vec2-large-xlsr-53-arabic-demo" |
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dataset = load_dataset("common_voice", "ar", split="test[:10]") |
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resamplers = { # all three sampling rates exist in test split |
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48000: torchaudio.transforms.Resample(48000, 16000), |
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44100: torchaudio.transforms.Resample(44100, 16000), |
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32000: torchaudio.transforms.Resample(32000, 16000), |
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} |
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def prepare_example(example): |
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speech, sampling_rate = torchaudio.load(example["path"]) |
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example["speech"] = resamplers[sampling_rate](speech).squeeze().numpy() |
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return example |
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dataset = dataset.map(prepare_example) |
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processor = Wav2Vec2Processor.from_pretrained(asr_model) |
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model = Wav2Vec2ForCTC.from_pretrained(asr_model).eval() |
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def predict(batch): |
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inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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predicted = torch.argmax(model(inputs.input_values).logits, dim=-1) |
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predicted[predicted == -100] = processor.tokenizer.pad_token_id # see fine-tuning script |
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batch["predicted"] = processor.tokenizer.batch_decode(predicted) |
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return batch |
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dataset = dataset.map(predict, batched=True, batch_size=1, remove_columns=["speech"]) |
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for reference, predicted in zip(dataset["sentence"], dataset["predicted"]): |
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print("reference:", reference) |
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print("predicted:", buckwalter.untrans(predicted)) |
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print("--") |
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``` |
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**Output Results**: |
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```shell |
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reference: ما أطول عودك! |
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predicted: ما اطول عودك |
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reference: ماتت عمتي منذ سنتين. |
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predicted: ما تتعمتي منذو سنتين |
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reference: الألمانية ليست لغة سهلة. |
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predicted: الالمانية ليست لغة سهلة |
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reference: طلبت منه أن يبعث الكتاب إلينا. |
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predicted: طلبت منه ان يبعث الكتاب الينا |
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reference: .السيد إيتو رجل متعلم |
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predicted: السيد ايتو رجل متعلم |
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reference: الحمد لله. |
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predicted: الحمذ لللا |
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reference: في الوقت نفسه بدأت الرماح والسهام تقع بين الغزاة |
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predicted: في الوقت نفسه ابدات الرماح و السهام تقع بين الغزاء |
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reference: لا أريد أن أكون ثقيلَ الظِّل ، أريد أن أكون رائعًا! ! |
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predicted: لا اريد ان اكون ثقيل الظل اريد ان اكون رائع |
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reference: خذ مظلة معك في حال أمطرت. |
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predicted: خذ مظلة معك في حال امطرت |
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reference: .ركب توم السيارة |
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predicted: ركب توم السيارة |
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``` |
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The model evaluation **(WER)** on the Arabic test data of Common Voice. |
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```python |
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import re |
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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import set_seed, Wav2Vec2ForCTC, Wav2Vec2Processor |
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set_seed(42) |
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test_dataset = load_dataset("common_voice", "ar", split="test") |
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processor = Wav2Vec2Processor.from_pretrained("muzamil47/wav2vec2-large-xlsr-53-arabic-demo") |
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model = Wav2Vec2ForCTC.from_pretrained("muzamil47/wav2vec2-large-xlsr-53-arabic-demo") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\,\؟\.\!\-\;\\:\'\"\☭\«\»\؛\—\ـ\_\،\“\%\‘\”\�]' |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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batch["sentence"] = re.sub('[a-z]','',batch["sentence"]) |
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batch["sentence"] = re.sub("[إأٱآا]", "ا", batch["sentence"]) |
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noise = re.compile(""" ّ | # Tashdid |
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َ | # Fatha |
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ً | # Tanwin Fath |
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ُ | # Damma |
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ٌ | # Tanwin Damm |
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ِ | # Kasra |
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ٍ | # Tanwin Kasr |
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ْ | # Sukun |
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ـ # Tatwil/Kashida |
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""", re.VERBOSE) |
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batch["sentence"] = re.sub(noise, '', batch["sentence"]) |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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def evaluate(batch): |
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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wer = load_metric("wer") |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result**: 53.54 |
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## Training |
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The Common Voice `train`, `validation` datasets were used for training. |
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The script used for training can be found [here](https://huggingface.co/kmfoda/wav2vec2-large-xlsr-arabic/tree/main) |
