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--- |
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language: ar |
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datasets: |
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- common_voice |
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- arabic_speech_corpus |
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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: Mohammed XLSR Wav2Vec2 Large 53 |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice ar |
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type: common_voice |
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args: ar |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 36.69 |
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- name: Validation WER |
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type: wer |
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value: 36.69 |
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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) |
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on Arabic using the `train` splits of [Common Voice](https://huggingface.co/datasets/common_voice) |
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and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus). |
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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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%%capture |
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!pip install datasets |
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!pip install transformers==4.4.0 |
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!pip install torchaudio |
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!pip install jiwer |
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!pip install tnkeeh |
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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 transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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test_dataset = load_dataset("common_voice", "ar", split="test[:2%]") |
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processor = Wav2Vec2Processor.from_pretrained("mohammed/ar") |
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model = Wav2Vec2ForCTC.from_pretrained("mohammed/ar") |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the audio files as arrays |
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def speech_file_to_array_fn(batch): |
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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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inputs = processor(test_dataset["speech"][:2], 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, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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print("The predicted sentence is: ", processor.batch_decode(predicted_ids)) |
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print("The original sentence is:", test_dataset["sentence"][:2]) |
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``` |
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The output is: |
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``` |
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The predicted sentence is : ['ألديك قلم', 'ليست نارك مكسافة على هذه الأرض أبعد من يوم أمس'] |
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The original sentence is: ['ألديك قلم ؟', 'ليست هناك مسافة على هذه الأرض أبعد من يوم أمس.'] |
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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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|
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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 Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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# creating a dictionary with all diacritics |
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dict = { |
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'ِ': '', |
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'ُ': '', |
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'ٓ': '', |
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'ٰ': '', |
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'ْ': '', |
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'ٌ': '', |
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'ٍ': '', |
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'ً': '', |
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'ّ': '', |
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'َ': '', |
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'~': '', |
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',': '', |
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'ـ': '', |
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'—': '', |
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'.': '', |
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'!': '', |
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'-': '', |
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';': '', |
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':': '', |
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'\'': '', |
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'"': '', |
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'☭': '', |
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'«': '', |
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'»': '', |
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'؛': '', |
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'ـ': '', |
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'_': '', |
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'،': '', |
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'“': '', |
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'%': '', |
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'‘': '', |
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'”': '', |
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'�': '', |
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'_': '', |
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',': '', |
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'?': '', |
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'#': '', |
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'‘': '', |
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'.': '', |
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'؛': '', |
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'get': '', |
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'؟': '', |
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' ': ' ', |
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'\'ۖ ': '', |
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'\'': '', |
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'\'ۚ' : '', |
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' \'': '', |
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'31': '', |
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'24': '', |
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'39': '' |
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} |
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# replacing multiple diacritics using dictionary (stackoverflow is amazing) |
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def remove_special_characters(batch): |
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# Create a regular expression from the dictionary keys |
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regex = re.compile("(%s)" % "|".join(map(re.escape, dict.keys()))) |
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# For each match, look-up corresponding value in dictionary |
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batch["sentence"] = regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], batch["sentence"]) |
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return batch |
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test_dataset = load_dataset("common_voice", "ar", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("mohammed/ar") |
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model = Wav2Vec2ForCTC.from_pretrained("mohammed/ar") |
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model.to("cuda") |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the audio files as arrays |
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def speech_file_to_array_fn(batch): |
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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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test_dataset = test_dataset.map(remove_special_characters) |
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# Preprocessing the datasets. |
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# We need to read the audio files as arrays |
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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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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result**: 36.69% |
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## Future Work |
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One can use *data augmentation*, *transliteration*, or *attention_mask* to increase the accuracy. |
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