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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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- label: 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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- label: 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: 32.09 |
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- name: Test CER |
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type: cer |
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value: 8.23 |
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--- |
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# Wav2Vec2-Large-XLSR-53-tw-gpt |
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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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```bash |
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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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```python |
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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 librosa |
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import pandas as pd |
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import numpy as np |
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import hazm |
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import random |
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import os |
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import string |
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import six |
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import re |
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import IPython.display as ipd |
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# Loading the datasets |
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dataset = load_dataset("common_voice", "fa", split="test[:2%]") |
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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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# Preprocessing the datasets. |
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# Normalizing the texts |
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_normalizer = hazm.Normalizer() |
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def multiple_replace(mapping, text): |
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pattern = "|".join(map(re.escape, mapping.keys())) |
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return re.sub(pattern, lambda m: mapping[m.group()], str(text)) |
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def convert_weirdos(input_str): |
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# character |
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mapping = { |
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'ك': 'ک', |
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'دِ': 'د', |
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'بِ': 'ب', |
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'زِ': 'ز', |
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'ذِ': 'ذ', |
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'شِ': 'ش', |
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'سِ': 'س', |
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'ى': 'ی', |
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'ي': 'ی', |
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'أ': 'ا', |
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'ؤ': 'و', |
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"ے": "ی", |
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"ۀ": "ه", |
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"ﭘ": "پ", |
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"ﮐ": "ک", |
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"ﯽ": "ی", |
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"ﺎ": "ا", |
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"ﺑ": "ب", |
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"ﺘ": "ت", |
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"ﺧ": "خ", |
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"ﺩ": "د", |
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"ﺱ": "س", |
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"ﻀ": "ض", |
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"ﻌ": "ع", |
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"ﻟ": "ل", |
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"ﻡ": "م", |
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"ﻢ": "م", |
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"ﻪ": "ه", |
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"ﻮ": "و", |
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"ئ": "ی", |
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'ﺍ': "ا", |
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'ة': "ه", |
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'ﯾ': "ی", |
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'ﯿ': "ی", |
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'ﺒ': "ب", |
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'ﺖ': "ت", |
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'ﺪ': "د", |
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'ﺮ': "ر", |
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'ﺴ': "س", |
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'ﺷ': "ش", |
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'ﺸ': "ش", |
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'ﻋ': "ع", |
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'ﻤ': "م", |
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'ﻥ': "ن", |
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'ﻧ': "ن", |
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'ﻭ': "و", |
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'ﺭ': "ر", |
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"ﮔ": "گ", |
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} |
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# notation |
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mapping.update(**{ |
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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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"ء": " ", |
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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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'"': " ", |
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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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return multiple_replace(mapping, input_str) |
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PERSIAN_ALPHA = "\u0621-\u0628\u062A-\u063A\u0641-\u0642\u0644-\u0648\u064E-\u0651\u0655\u067E\u0686\u0698\u06A9\u06AF\u06BE\u06CC" # noqa: E501 |
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PERSIAN_DIGIT = "\u06F0-\u06F9" |
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COMMON_ARABIC_ALPHA = "\u0629\u0643\u0649-\u064B\u064D\u06D5" |
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COMMON_ARABIC_DIGIT = "\u0660-\u0669" |
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ZWNJ = "\u200c" |
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ENGLISH = "a-z0-9\&" |
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PERSIAN = PERSIAN_ALPHA + PERSIAN_DIGIT + COMMON_ARABIC_ALPHA + COMMON_ARABIC_DIGIT + ZWNJ |
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def normalizer(text, min_ratio=1.1): |
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text = text.lower() |
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text = _normalizer.normalize(text) |
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text = text.replace("\u200c", " ") |
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text = text.replace("\u200d", " ") |
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text = text.replace("\u200e", " ") |
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text = text.replace("\u200f", " ") |
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text = text.replace("\ufeff", " ") |
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text = convert_weirdos(text) |
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words = [word.replace("آ", "ا") if "آ" in word and not word.startswith("آ") else word for word in text.split()] |
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text = " ".join(words) |
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if not text or not len(text) > 2: |
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return None |
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en_text = re.sub(r"[^" + ENGLISH + "+]", " ", six.ensure_str(text)) |
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en_text = re.sub(r"\s+", " ", en_text) |
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if len(en_text) > 1: |
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return None |
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return text |
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]' |
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def remove_special_characters(batch): |
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text = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " |
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text = normalizer(text) |
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batch["sentence"] = text |
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return batch |
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# We need to read the aduio 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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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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dataset = dataset.map(remove_special_characters) |
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dataset = dataset.map(speech_file_to_array_fn, remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))) |
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result = dataset.map(predict) |
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``` |
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## Prediction |
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```python |
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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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```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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```python |
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!mkdir cer |
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!wget -O cer/cer.py https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese/raw/main/cer.py |
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wer = load_metric("wer") |
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cer = load_metric("./cer") |
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print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"]))) |
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print("CER: {:.2f}".format(100 * cer.compute(predictions=result["predicted"], references=result["sentence"]))) |
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``` |
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**Test Result**: |
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- WER: 32.09% |
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- CER: 8.23% |
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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) |
