8.31 kB
| --- | |
| language: ar | |
| datasets: | |
| - common_voice | |
| - arabic_speech_corpus | |
| metrics: | |
| - wer | |
| - cer | |
| tags: | |
| - audio | |
| - automatic-speech-recognition | |
| - speech | |
| - xlsr-fine-tuning-week | |
| license: apache-2.0 | |
| model-index: | |
| - name: XLSR Wav2Vec2 Arabic by Jonatas Grosman | |
| results: | |
| - task: | |
| name: Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Common Voice ar | |
| type: common_voice | |
| args: ar | |
| metrics: | |
| - name: Test WER | |
| type: wer | |
| value: 39.59 | |
| - name: Test CER | |
| type: cer | |
| value: 18.18 | |
| --- | |
| # Fine-tuned XLSR-53 large model for speech recognition in Arabic | |
| Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus). | |
| When using this model, make sure that your speech input is sampled at 16kHz. | |
| This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) | |
| The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint | |
| ## Usage | |
| The model can be used directly (without a language model) as follows... | |
| Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: | |
| ```python | |
| from huggingsound import SpeechRecognitionModel | |
| model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-arabic") | |
| audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] | |
| transcriptions = model.transcribe(audio_paths) | |
| ``` | |
| Writing your own inference script: | |
| ```python | |
| import torch | |
| import librosa | |
| from datasets import load_dataset | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| LANG_ID = "ar" | |
| MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic" | |
| SAMPLES = 10 | |
| test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") | |
| processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) | |
| model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) | |
| # Preprocessing the datasets. | |
| # We need to read the audio files as arrays | |
| def speech_file_to_array_fn(batch): | |
| speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) | |
| batch["speech"] = speech_array | |
| batch["sentence"] = batch["sentence"].upper() | |
| return batch | |
| test_dataset = test_dataset.map(speech_file_to_array_fn) | |
| inputs = processor(test_dataset["speech"], 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) | |
| predicted_sentences = processor.batch_decode(predicted_ids) | |
| for i, predicted_sentence in enumerate(predicted_sentences): | |
| print("-" * 100) | |
| print("Reference:", test_dataset[i]["sentence"]) | |
| print("Prediction:", predicted_sentence) | |
| ``` | |
| | Reference | Prediction | | |
| | ------------- | ------------- | | |
| | ألديك قلم ؟ | ألديك قلم | | |
| | ليست هناك مسافة على هذه الأرض أبعد من يوم أمس. | ليست نالك مسافة على هذه الأرض أبعد من يوم الأمس م | | |
| | إنك تكبر المشكلة. | إنك تكبر المشكلة | | |
| | يرغب أن يلتقي بك. | يرغب أن يلتقي بك | | |
| | إنهم لا يعرفون لماذا حتى. | إنهم لا يعرفون لماذا حتى | | |
| | سيسعدني مساعدتك أي وقت تحب. | سيسئدنيمساعدتك أي وقد تحب | | |
| | أَحَبُّ نظريّة علمية إليّ هي أن حلقات زحل مكونة بالكامل من الأمتعة المفقودة. | أحب نظرية علمية إلي هي أن حل قتزح المكوينا بالكامل من الأمت عن المفقودة | | |
| | سأشتري له قلماً. | سأشتري له قلما | | |
| | أين المشكلة ؟ | أين المشكل | | |
| | وَلِلَّهِ يَسْجُدُ مَا فِي السَّمَاوَاتِ وَمَا فِي الْأَرْضِ مِنْ دَابَّةٍ وَالْمَلَائِكَةُ وَهُمْ لَا يَسْتَكْبِرُونَ | ولله يسجد ما في السماوات وما في الأرض من دابة والملائكة وهم لا يستكبرون | | |
| ## Evaluation | |
| The model can be evaluated as follows on the Arabic test data of Common Voice. | |
| ```python | |
| import torch | |
| import re | |
| import librosa | |
| from datasets import load_dataset, load_metric | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| LANG_ID = "ar" | |
| MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic" | |
| DEVICE = "cuda" | |
| CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", | |
| "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", | |
| "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", | |
| "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", | |
| "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"] | |
| test_dataset = load_dataset("common_voice", LANG_ID, split="test") | |
| wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py | |
| cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py | |
| chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" | |
| processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) | |
| model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) | |
| model.to(DEVICE) | |
| # Preprocessing the datasets. | |
| # We need to read the audio files as arrays | |
| def speech_file_to_array_fn(batch): | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter("ignore") | |
| speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) | |
| batch["speech"] = speech_array | |
| batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper() | |
| 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(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).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) | |
| predictions = [x.upper() for x in result["pred_strings"]] | |
| references = [x.upper() for x in result["sentence"]] | |
| print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") | |
| print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") | |
| ``` | |
| **Test Result**: | |
| In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-14). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used. | |
| | Model | WER | CER | | |
| | ------------- | ------------- | ------------- | | |
| | jonatasgrosman/wav2vec2-large-xlsr-53-arabic | **39.59%** | **18.18%** | | |
| | bakrianoo/sinai-voice-ar-stt | 45.30% | 21.84% | | |
| | othrif/wav2vec2-large-xlsr-arabic | 45.93% | 20.51% | | |
| | kmfoda/wav2vec2-large-xlsr-arabic | 54.14% | 26.07% | | |
| | mohammed/wav2vec2-large-xlsr-arabic | 56.11% | 26.79% | | |
| | anas/wav2vec2-large-xlsr-arabic | 62.02% | 27.09% | | |
| | elgeish/wav2vec2-large-xlsr-53-arabic | 100.00% | 100.56% | | |
| ## Citation | |
| If you want to cite this model you can use this: | |
| ```bibtex | |
| @misc{grosman2021xlsr53-large-arabic, | |
| title={Fine-tuned {XLSR}-53 large model for speech recognition in {A}rabic}, | |
| author={Grosman, Jonatas}, | |
| howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-arabic}}, | |
| year={2021} | |
| } | |
| ``` | |