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--- |
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language: ary |
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datasets: |
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- mgb5 |
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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: XLSR Wav2Vec2 Moroccan Arabic dialect by Othmane Rifki |
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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: MGB5 from ELDA and https://arabicspeech.org/ |
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type: ELDA and https://arabicspeech.org/ |
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args: ary |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 66.45 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Moroccan |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [MGB5 Moroccan Arabic](http://www.islrn.org/resources/938-639-614-524-5/) kindly provided by [ELDA](http://www.elra.info/en/about/elda/) and [ArabicSpeech](https://arabicspeech.org/mgb5/). |
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In order to have access to MGB5, please request it from ELDA. |
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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 re |
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import torch |
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import librosa |
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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 soundfile as sf |
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dataset = load_dataset("ma_speech_corpus", split="test") |
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processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-moroccan") |
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model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-moroccan") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\'\\�]' |
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def remove_special_characters(batch): |
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batch["text"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).lower() + " " |
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return batch |
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dataset = dataset.map(remove_special_characters) |
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dataset = dataset.select(range(10)) |
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def speech_file_to_array_fn(batch): |
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start, stop = batch['segment'].split('_') |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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speech_array, sampling_rate = sf.read(batch["path"], start=int(float(start) * sampling_rate), |
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stop=int(float(stop) * sampling_rate)) |
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batch["speech"] = librosa.resample(speech_array, sampling_rate, 16_000) |
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batch["sampling_rate"] = 16_000 |
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batch["target_text"] = batch["text"] |
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return batch |
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dataset = dataset.map( |
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speech_file_to_array_fn |
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) |
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def predict(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["predicted"] = processor.batch_decode(pred_ids) |
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return batch |
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dataset = dataset.map(predict, batched=True, batch_size=32) |
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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:", predicted) |
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print("--") |
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``` |
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Here's the output: |
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``` |
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reference: عشرين ألفريال الوحده وشي خمسميه دريال |
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predicted: عشرين علف ريا لوحده وشي خمسميات ريال |
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-- |
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reference: واحد جوج تلاتة ربعه خمسة ستة |
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predicted: غيحك تويش تتبة نتاست |
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-- |
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reference: هي هاديك غتجينا تقريبا ميه وسته وعشرين ألف ريال |
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predicted: ياض كتجينا تقريبه ميه أو ستي و عشيناأفرين |
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-- |
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reference: ###والصرف ليبقا نجيب بيه الصالون فلهوندا... أهاه نديروها علاش لا؟... |
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predicted: أواصرف ليبقا نجيب يه اصالون فالهندا أه نديروها علاش لا |
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-- |
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reference: ###صافي مشات... أنا أختي معندي مندير بهاد صداع الراس... |
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predicted: صافي مشات أنا خصي معندي مندير بهاد داع راسك |
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ف |
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-- |
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reference: خلصو ليا غير لكريدي ديالي وديرو ليعجبكوم |
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predicted: خلصو ليا غير لكريدي ديالي أوديرو لي عجبكوم |
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-- |
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reference: أنا نتكلف يلاه لقى شي حاجه نشغل بيها راسي |
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predicted: أنا نتكلف يالله لقا شي حاجه نشغل بيها راسي |
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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 re |
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import torch |
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import librosa |
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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 soundfile as sf |
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eval_dataset = load_dataset("ma_speech_corpus", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec2-large-xlsr-moroccan") |
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model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec2-large-xlsr-moroccan") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\'\\�]' |
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def remove_special_characters(batch): |
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batch["text"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).lower() + " " |
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return batch |
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eval_dataset = eval_dataset.map(remove_special_characters, remove_columns=["sentence"]) |
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#eval_dataset = eval_dataset.select(range(100)) |
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def speech_file_to_array_fn(batch): |
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start, stop = batch['segment'].split('_') |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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speech_array, sampling_rate = sf.read(batch["path"], start=int(float(start) * sampling_rate), |
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stop=int(float(stop) * sampling_rate)) |
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batch["speech"] = librosa.resample(speech_array, sampling_rate, 16_000) |
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batch["sampling_rate"] = 16_000 |
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batch["target_text"] = batch["text"] |
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return batch |
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eval_dataset = eval_dataset.map( |
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speech_file_to_array_fn, |
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remove_columns=eval_dataset.column_names |
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) |
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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 = eval_dataset.map(evaluate, batched=True, batch_size=32) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["target_text"]))) |
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``` |
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**Test Result**: 66.45 |
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## Training |
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The [MGB5](http://www.islrn.org/resources/938-639-614-524-5/) `train`, `validation` datasets were used for training. |
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The script used for training can be found [here](https://github.com/othrif/xlsr-wav2vec2) |
