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--- |
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language: fa |
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datasets: |
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- common_voice |
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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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widget: |
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- example_title: Common Voice sample 687 |
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src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample687.flac |
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- example_title: Common Voice sample 1671 |
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src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample1671.flac |
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model-index: |
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- name: XLSR Wav2Vec2 Persian (Farsi) by Mehrdad Farahani |
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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 fa |
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type: common_voice |
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args: fa |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 32.20 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Persian |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [Common Voice](https://huggingface.co/datasets/common_voice). 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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**Requirements** |
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```bash |
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# requirement packages |
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!pip install git+https://github.com/huggingface/datasets.git |
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!pip install git+https://github.com/huggingface/transformers.git |
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!pip install torchaudio |
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!pip install librosa |
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!pip install jiwer |
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!pip install hazm |
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``` |
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**Prediction** |
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```python |
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import librosa |
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import torch |
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import torchaudio |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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from datasets import load_dataset |
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import numpy as np |
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import hazm |
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import re |
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import string |
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import IPython.display as ipd |
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_normalizer = hazm.Normalizer() |
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chars_to_ignore = [ |
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",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", |
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"#", "!", "؟", "?", "«", "»", "ء", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?", |
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".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„' |
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] |
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# In case of farsi |
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chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits) |
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chars_to_mapping = { |
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'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی', |
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'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی", |
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"ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع", |
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"ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", "ئ": "ی", 'ﺍ': "ا", 'ة': "ه", |
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'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش", |
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'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ", |
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"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ", |
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} |
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def multiple_replace(text, chars_to_mapping): |
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pattern = "|".join(map(re.escape, chars_to_mapping.keys())) |
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return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text)) |
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def remove_special_characters(text, chars_to_ignore_regex): |
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text = re.sub(chars_to_ignore_regex, '', text).lower() + " " |
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return text |
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def normalizer(batch, chars_to_ignore, chars_to_mapping): |
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chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]""" |
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text = batch["sentence"].lower().strip() |
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text = _normalizer.normalize(text) |
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text = multiple_replace(text, chars_to_mapping) |
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text = remove_special_characters(text, chars_to_ignore_regex) |
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batch["sentence"] = text |
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return batch |
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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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speech_array = speech_array.squeeze().numpy() |
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speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) |
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batch["speech"] = speech_array |
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return batch |
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def predict(batch): |
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features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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input_values = features.input_values.to(device) |
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attention_mask = features.attention_mask.to(device) |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["predicted"] = processor.batch_decode(pred_ids)[0] |
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return batch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian") |
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model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian").to(device) |
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dataset = load_dataset("common_voice", "fa", split="test[:1%]") |
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dataset = dataset.map( |
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normalizer, |
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fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping}, |
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remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) |
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) |
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dataset = dataset.map(speech_file_to_array_fn) |
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result = dataset.map(predict) |
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max_items = np.random.randint(0, len(result), 20).tolist() |
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for i in max_items: |
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reference, predicted = result["sentence"][i], result["predicted"][i] |
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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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**Output:** |
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```text |
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reference: اطلاعات مسری است |
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predicted: اطلاعات مسری است |
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--- |
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reference: نه منظورم اینه که وقتی که ساکته چه کاریه خودمونه بندازیم زحمت |
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predicted: نه منظورم اینه که وقتی که ساکت چی کاریه خودمونو بندازیم زحمت |
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--- |
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reference: من آب پرتقال می خورم لطفا |
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predicted: من آپ ارتغال می خورم لطفا |
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--- |
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reference: وقت آن رسیده آنها را که قدم پیش میگذارند بزرگ بداریم |
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predicted: وقت آ رسیده آنها را که قدم پیش میگذارند بزرگ بداریم |
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--- |
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reference: سیم باتری دارید |
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predicted: سیم باتری دارید |
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--- |
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reference: این بهتره تا اینکه به بهونه درس و مشق هر روز بره خونه شون |
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predicted: این بهتره تا اینکه به بهمونه درسومش خرروز بره خونه اشون |
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--- |
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reference: ژاکت تنگ است |
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predicted: ژاکت تنگ است |
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--- |
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reference: آت و اشغال های خیابان |
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predicted: آت و اشغال های خیابان |
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--- |
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reference: من به این روند اعتراض دارم |
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predicted: من به این لوند تراج دارم |
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--- |
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reference: کرایه این مکان چند است |
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predicted: کرایه این مکان چند است |
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--- |
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reference: ولی این فرصت این سهم جوانی اعطا نشده است |
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predicted: ولی این فرصت این سحم جوانی اتان نشده است |
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--- |
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reference: متوجه فاجعهای محیطی میشوم |
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predicted: متوجه فاجایهای محیطی میشوم |
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--- |
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reference: ترافیک شدیدیم بود و دیدن نور ماشینا و چراغا و لامپهای مراکز تجاری حس خوبی بهم میدادن |
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predicted: ترافیک شدید ی هم بودا دیدن نور ماشینا و چراغ لامپهای مراکز تجاری حس خولی بهم میدادن |
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--- |
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reference: این مورد عمل ها مربوط به تخصص شما می شود |
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predicted: این مورد عملها مربوط به تخصص شما میشود |
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--- |
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reference: انرژی خیلی کمی دارم |
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predicted: انرژی خیلی کمی دارم |
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--- |
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reference: زیادی خوبی کردنم تهش داستانه |
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predicted: زیادی خوبی کردنم ترش داستانه |
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--- |
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reference: بردهای که پادشاه شود |
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predicted: برده ای که پاده شاه شود |
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--- |
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reference: یونسکو |
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predicted: یونسکو |
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--- |
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reference: شما اخراج هستید |
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predicted: شما اخراج هستید |
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--- |
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reference: من سفر کردن را دوست دارم |
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predicted: من سفر کردم را دوست دارم |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the Persian (Farsi) test data of Common Voice. |
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```python |
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import librosa |
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import torch |
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import torchaudio |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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from datasets import load_dataset, load_metric |
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import numpy as np |
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import hazm |
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import re |
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import string |
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_normalizer = hazm.Normalizer() |
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chars_to_ignore = [ |
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",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", |
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"#", "!", "؟", "?", "«", "»", "ء", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?", |
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".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„' |
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] |
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# In case of farsi |
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chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits) |
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chars_to_mapping = { |
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'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی', |
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'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی", |
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"ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع", |
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"ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", "ئ": "ی", 'ﺍ': "ا", 'ة': "ه", |
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'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش", |
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'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ", |
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"\\u200c": " ", "\\u200d": " ", "\\u200e": " ", "\\u200f": " ", "\\ufeff": " ", |
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} |
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def multiple_replace(text, chars_to_mapping): |
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pattern = "|".join(map(re.escape, chars_to_mapping.keys())) |
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return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text)) |
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def remove_special_characters(text, chars_to_ignore_regex): |
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text = re.sub(chars_to_ignore_regex, '', text).lower() + " " |
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return text |
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def normalizer(batch, chars_to_ignore, chars_to_mapping): |
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chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]""" |
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text = batch["sentence"].lower().strip() |
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text = _normalizer.normalize(text) |
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text = multiple_replace(text, chars_to_mapping) |
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text = remove_special_characters(text, chars_to_ignore_regex) |
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batch["sentence"] = text |
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return batch |
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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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speech_array = speech_array.squeeze().numpy() |
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speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) |
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batch["speech"] = speech_array |
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return batch |
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def predict(batch): |
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features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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input_values = features.input_values.to(device) |
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attention_mask = features.attention_mask.to(device) |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["predicted"] = processor.batch_decode(pred_ids)[0] |
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return batch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian") |
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model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian").to(device) |
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dataset = load_dataset("common_voice", "fa", split="test") |
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dataset = dataset.map( |
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normalizer, |
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fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping}, |
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remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) |
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) |
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dataset = dataset.map(speech_file_to_array_fn) |
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result = dataset.map(predict) |
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wer = load_metric("wer") |
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print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"]))) |
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``` |
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**Test Result:** |
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- WER: 32.20% |
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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://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Persian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb) |
