Initial model

README.md

@@ -0,0 +1,341 @@
+---
+language: fa
+datasets:
+- common_voice
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+widget:
+- label: Common Voice sample 687
+  src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample687.flac
+- label: Common Voice sample 1671
+  src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample1671.flac
+model-index:
+- name: XLSR Wav2Vec2 Persian (Farsi) by Mehrdad Farahani
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice fa
+      type: common_voice
+      args: fa  
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 32.09
+       - name: Test CER
+         type: cer
+         value: 8.23
+        
+---
+
+# Wav2Vec2-Large-XLSR-53-tw-gpt
+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.
+
+## Usage
+The model can be used directly (without a language model) as follows:
+
+```bash
+!pip install git+https://github.com/huggingface/datasets.git
+!pip install git+https://github.com/huggingface/transformers.git
+!pip install torchaudio
+!pip install librosa
+!pip install jiwer
+!pip install hazm
+```
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+import librosa
+
+import pandas as pd
+import numpy as np
+
+import hazm
+
+import random
+import os
+import string
+import six
+import re
+
+import IPython.display as ipd
+
+# Loading the datasets
+dataset = load_dataset("common_voice", "fa", split="test[:2%]")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian")
+model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian").to(device)
+
+
+# Preprocessing the datasets.
+# Normalizing the texts
+_normalizer = hazm.Normalizer()
+def multiple_replace(mapping, text):
+    pattern = "|".join(map(re.escape, mapping.keys()))
+    return re.sub(pattern, lambda m: mapping[m.group()], str(text))
+
+def convert_weirdos(input_str):
+    # character
+    mapping = {
+        'ك': 'ک',
+        'دِ': 'د',
+        'بِ': 'ب',
+        'زِ': 'ز',
+        'ذِ': 'ذ',
+        'شِ': 'ش',
+        'سِ': 'س',
+        'ى': 'ی',
+        'ي': 'ی',
+        'أ': 'ا',
+        'ؤ': 'و',
+        "ے": "ی",
+        "ۀ": "ه",
+        "ﭘ": "پ",
+        "ﮐ": "ک",
+        "ﯽ": "ی",
+        "ﺎ": "ا",
+        "ﺑ": "ب",
+        "ﺘ": "ت",
+        "ﺧ": "خ",
+        "ﺩ": "د",
+        "ﺱ": "س",
+        "ﻀ": "ض",
+        "ﻌ": "ع",
+        "ﻟ": "ل",
+        "ﻡ": "م",
+        "ﻢ": "م",
+        "ﻪ": "ه",
+        "ﻮ": "و",
+        "ئ": "ی",
+        'ﺍ': "ا",
+        'ة': "ه",
+        'ﯾ': "ی",
+        'ﯿ': "ی",
+        'ﺒ': "ب",
+        'ﺖ': "ت",
+        'ﺪ': "د",
+        'ﺮ': "ر",
+        'ﺴ': "س",
+        'ﺷ': "ش",
+        'ﺸ': "ش",
+        'ﻋ': "ع",
+        'ﻤ': "م",
+        'ﻥ': "ن",
+        'ﻧ': "ن",
+        'ﻭ': "و",
+        'ﺭ': "ر",
+        "ﮔ": "گ",
+    }
+
+    # notation
+    mapping.update(**{
+        "#": " ",
+        "!": " ",
+        "؟": " ",
+        "?": " ",
+        "«": " ",
+        "»": " ",
+        "ء": " ",
+        "،": " ",
+        "(": " ",
+        ")": " ",
+        "؛": " ",
+        "'ٔ": " ",
+        "٬": " ",
+        'ٔ': " ",
+        ",": " ",
+        "?": " ",
+        ".": " ",
+        "!": " ",
+        "-": " ",
+        ";": " ",
+        ":": " ",
+        '"': " ",
+        "“": " ",
+        "%": " ",
+        "‘": " ",
+        "”": " ",
+        "�": " ",
+        "–": " ",
+        "…": " ",
+        "_": " ",
+    })
+
+    return multiple_replace(mapping, input_str)
+
+
+PERSIAN_ALPHA = "\u0621-\u0628\u062A-\u063A\u0641-\u0642\u0644-\u0648\u064E-\u0651\u0655\u067E\u0686\u0698\u06A9\u06AF\u06BE\u06CC"  # noqa: E501
+PERSIAN_DIGIT = "\u06F0-\u06F9"
+
+COMMON_ARABIC_ALPHA = "\u0629\u0643\u0649-\u064B\u064D\u06D5"
+COMMON_ARABIC_DIGIT = "\u0660-\u0669"
+
+ZWNJ = "\u200c"
+
+ENGLISH = "a-z0-9\&"
+PERSIAN = PERSIAN_ALPHA + PERSIAN_DIGIT + COMMON_ARABIC_ALPHA + COMMON_ARABIC_DIGIT + ZWNJ
+
+
+def normalizer(text, min_ratio=1.1):
+    text = text.lower()
+    text = _normalizer.normalize(text)
+    text = text.replace("\u200c", " ")
+    text = text.replace("\u200d", " ")
+    text = text.replace("\u200e", " ")
+    text = text.replace("\u200f", " ")
+    text = text.replace("\ufeff", " ")
+    text = convert_weirdos(text)
+
+    words = [word.replace("آ", "ا") if "آ" in word and not word.startswith("آ") else word for word in text.split()]
+    text = " ".join(words)
+
+    if not text or not len(text) > 2:
+        return None
+
+    en_text = re.sub(r"[^" + ENGLISH + "+]", " ", six.ensure_str(text))
+    en_text = re.sub(r"\s+", " ", en_text)
+    if len(en_text) > 1:
+        return None
+
+    return text
+
+
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
+def remove_special_characters(batch):
+    text = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
+    text = normalizer(text)
+    batch["sentence"] = text
+    return batch
+
+# We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    speech_array = speech_array.squeeze().numpy()
+    speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
+
+    batch["speech"] = speech_array
+    return batch
+
+def predict(batch):
+    features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+    input_values = features.input_values.to(device)
+    attention_mask = features.attention_mask.to(device)
+
+    with torch.no_grad():
+        logits = model(input_values, attention_mask=attention_mask).logits 
+        
+    pred_ids = torch.argmax(logits, dim=-1)
+
+    batch["predicted"] = processor.batch_decode(pred_ids)[0]
+    return batch
+
+dataset = dataset.map(remove_special_characters)
+dataset = dataset.map(speech_file_to_array_fn, remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])))
+result = dataset.map(predict)
+```
+
+## Prediction
+
+```python
+max_items = np.random.randint(0, len(result), 20).tolist()
+for i in max_items:
+    reference, predicted =  result["sentence"][i], result["predicted"][i]
+    print("reference:", reference)
+    print("predicted:", predicted)
+    print('---')
+```
+
+```text
+reference: اطلاعات مسری است
+predicted: اطلاعات مسری است
+---
+reference: نه منظورم اینه که وقتی که ساکته چه کاریه خودمونه بندازیم زحمت
+predicted: نه منظورم اینه که وقتی که ساکت چی کاریه خودمونو بندازیم زحمت
+---
+reference: من آب پرتقال می خورم لطفا
+predicted: من آپ ارتغال می خورم لطفا
+---
+reference: وقت آن رسیده آنها را که قدم پیش میگذارند بزرگ بداریم
+predicted: وقت آ رسیده آنها را که قدم پیش میگذارند بزرگ بداریم
+---
+reference: سیم باتری دارید
+predicted: سیم باتری دارید
+---
+reference: این بهتره تا اینکه به بهونه درس و مشق هر روز بره خونه شون
+predicted: این بهتره تا اینکه به بهمونه درسومش خرروز بره خونه اشون
+---
+reference: ژاکت تنگ است
+predicted: ژاکت تنگ است
+---
+reference: آت و اشغال های خیابان
+predicted: آت و اشغال های خیابان
+---
+reference: من به این روند اعتراض دارم
+predicted: من به این لوند تراج دارم
+---
+reference: کرایه این مکان چند است
+predicted: کرایه این مکان چند است
+---
+reference: ولی این فرصت این سهم جوانی اعطا نشده است
+predicted: ولی این فرصت این سحم جوانی اتان نشده است
+---
+reference: متوجه فاجعهای محیطی میشوم
+predicted: متوجه فاجایهای محیطی میشوم
+---
+reference: ترافیک شدیدیم بود و دیدن نور ماشینا و چراغا و لامپهای مراکز تجاری حس خوبی بهم میدادن
+predicted: ترافیک شدید ی هم بودا دیدن نور ماشینا و چراغ لامپهای مراکز تجاری حس خولی بهم میدادن
+---
+reference: این مورد عمل ها مربوط به تخصص شما می شود
+predicted: این مورد عملها مربوط به تخصص شما میشود
+---
+reference: انرژی خیلی کمی دارم
+predicted: انرژی خیلی کمی دارم
+---
+reference: زیادی خوبی کردنم تهش داستانه
+predicted: زیادی خوبی کردنم ترش داستانه
+---
+reference: بردهای که پادشاه شود
+predicted: برده ای که پاده شاه شود
+---
+reference: یونسکو
+predicted: یونسکو
+---
+reference: شما اخراج هستید
+predicted: شما اخراج هستید
+---
+reference: من سفر کردن را دوست دارم
+predicted: من سفر کردم را دوست دارم
+```
+
+## Evaluation
+
+```python
+!mkdir cer
+!wget -O cer/cer.py https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese/raw/main/cer.py
+
+wer = load_metric("wer")
+cer = load_metric("./cer")
+
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"])))
+print("CER: {:2f}".format(100 * cer.compute(predictions=result["predicted"], references=result["sentence"])))
+```
+
+**Test Result**: 
+- WER: 32.09%
+- CER: 8.23%
+
+
+## Training
+The Common Voice `train`, `validation` datasets were used for training.
+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)

config.json

@@ -0,0 +1,75 @@
+{
+  "activation_dropout": 0.0,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": false,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.0,
+  "final_dropout": 0.0,
+  "gradient_checkpointing": true,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "mask_feature_length": 10,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.05,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2",
+  "num_attention_heads": 16,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "pad_token_id": 35,
+  "transformers_version": "4.5.0.dev0",
+  "vocab_size": 36
+}

preprocessor_config.json

@@ -0,0 +1,8 @@
+{
+  "do_normalize": true,
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c9497f2383df9550e1f3310265224e7bc7e994c0ec844aac51fca5d8e9483b4
+size 1262081431

special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]"}

tokenizer_config.json

@@ -0,0 +1 @@
+{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|"}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbcb369f506a36d7e8b81d8831323d570a47695ce0b9cb55afbf5dff6f84f5ff
+size 2351

vocab.json

@@ -0,0 +1 @@
+{"ت": 0, "گ": 1, "ب": 2, "ژ": 3, "ع": 4, "ذ": 5, "چ": 6, "ج": 7, "خ": 8, "ا": 9, "د": 10, "ن": 11, "ح": 12, "آ": 13, "غ": 14, "م": 15, "ص": 16, "ر": 17, "پ": 18, "ظ": 19, "ض": 20, "ه": 21, "ق": 23, "ک": 24, "ش": 25, "ط": 26, "ف": 27, "ی": 28, "ز": 29, "و": 30, "ل": 31, "س": 32, "ث": 33, "|": 22, "[UNK]": 34, "[PAD]": 35}