Update app.py

app.py

@@ -1,3 +1,102 @@
 import gradio as gr
 
-gr.load("models/m3hrdadfi/wav2vec2-large-xlsr-persian").launch()
+gr.load("models/m3hrdadfi/wav2vec2-large-xlsr-persian").launch()
+
+
+import librosa
+import torch
+import torchaudio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+from datasets import load_dataset
+
+import numpy as np
+import hazm
+import re
+import string
+
+import IPython.display as ipd
+
+_normalizer = hazm.Normalizer()
+chars_to_ignore = [
+    ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
+    "#", "!", "؟", "?", "«", "»", "ء", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?", 
+    ".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„'
+]
+
+# In case of farsi
+chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits)
+
+chars_to_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",
+    "\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
+}
+
+def multiple_replace(text, chars_to_mapping):
+    pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
+    return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
+
+def remove_special_characters(text, chars_to_ignore_regex):
+    text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
+    return text
+
+def normalizer(batch, chars_to_ignore, chars_to_mapping):
+    chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
+    text = batch["sentence"].lower().strip()
+    
+    text = _normalizer.normalize(text)
+    text = multiple_replace(text, chars_to_mapping)
+    text = remove_special_characters(text, chars_to_ignore_regex)
+
+    batch["sentence"] = text
+    return batch
+
+
+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
+
+
+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)
+
+dataset = load_dataset("common_voice", "fa", split="test[:1%]")
+dataset = dataset.map(
+    normalizer, 
+    fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
+    remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
+)
+
+dataset = dataset.map(speech_file_to_array_fn)
+result = dataset.map(predict)
+
+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('---')