200 lines
8.1 KiB
Markdown
200 lines
8.1 KiB
Markdown
---
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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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- cer
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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: XLSR Wav2Vec2 Arabic by Jonatas Grosman
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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: 39.59
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- name: Test CER
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type: cer
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value: 18.18
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---
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# Fine-tuned XLSR-53 large model for speech recognition in Arabic
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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).
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When using this model, make sure that your speech input is sampled at 16kHz.
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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/) :)
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The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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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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Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
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```python
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from huggingsound import SpeechRecognitionModel
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model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-arabic")
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audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
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transcriptions = model.transcribe(audio_paths)
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```
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Writing your own inference script:
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```python
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import torch
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import librosa
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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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LANG_ID = "ar"
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic"
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SAMPLES = 10
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test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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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 = librosa.load(batch["path"], sr=16_000)
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batch["speech"] = speech_array
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batch["sentence"] = batch["sentence"].upper()
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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"], 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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predicted_sentences = processor.batch_decode(predicted_ids)
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for i, predicted_sentence in enumerate(predicted_sentences):
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print("-" * 100)
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print("Reference:", test_dataset[i]["sentence"])
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print("Prediction:", predicted_sentence)
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```
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| Reference | Prediction |
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| ------------- | ------------- |
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| ألديك قلم ؟ | ألديك قلم |
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| ليست هناك مسافة على هذه الأرض أبعد من يوم أمس. | ليست نالك مسافة على هذه الأرض أبعد من يوم الأمس م |
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| إنك تكبر المشكلة. | إنك تكبر المشكلة |
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| يرغب أن يلتقي بك. | يرغب أن يلتقي بك |
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| إنهم لا يعرفون لماذا حتى. | إنهم لا يعرفون لماذا حتى |
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| سيسعدني مساعدتك أي وقت تحب. | سيسئدنيمساعدتك أي وقد تحب |
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| 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 |
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| سأشتري له قلماً. | سأشتري له قلما |
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| أين المشكلة ؟ | أين المشكل |
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| 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 |
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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 re
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import librosa
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from datasets import load_dataset, load_metric
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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LANG_ID = "ar"
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic"
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DEVICE = "cuda"
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CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "<22>", "ʿ", "·", "჻", "~", "՞",
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"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
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"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
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"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
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"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"]
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test_dataset = load_dataset("common_voice", LANG_ID, split="test")
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wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
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cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
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chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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model.to(DEVICE)
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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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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
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batch["speech"] = speech_array
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batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
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return batch
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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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(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).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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predictions = [x.upper() for x in result["pred_strings"]]
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references = [x.upper() for x in result["sentence"]]
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print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
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print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
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```
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**Test Result**:
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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.
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| Model | WER | CER |
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| ------------- | ------------- | ------------- |
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| jonatasgrosman/wav2vec2-large-xlsr-53-arabic | **39.59%** | **18.18%** |
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| bakrianoo/sinai-voice-ar-stt | 45.30% | 21.84% |
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| othrif/wav2vec2-large-xlsr-arabic | 45.93% | 20.51% |
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| kmfoda/wav2vec2-large-xlsr-arabic | 54.14% | 26.07% |
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| mohammed/wav2vec2-large-xlsr-arabic | 56.11% | 26.79% |
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| anas/wav2vec2-large-xlsr-arabic | 62.02% | 27.09% |
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| elgeish/wav2vec2-large-xlsr-53-arabic | 100.00% | 100.56% |
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## Citation
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If you want to cite this model you can use this:
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```bibtex
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@misc{grosman2021xlsr53-large-arabic,
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title={Fine-tuned {XLSR}-53 large model for speech recognition in {A}rabic},
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author={Grosman, Jonatas},
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howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-arabic}},
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year={2021}
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}
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``` |