Delete tekshur.py

tekshur.py

@@ -1,75 +0,0 @@
-import torch
-from data import SpeechDataset, SpeechDataLoader, featurelen, uyghur_latin, cer
-from GCGCResM import GCGCResM
-from uformer import UFormer
-from UDS2W2L50 import UDS2W2L50
-from UFormerCTC2 import UFormerCTC2
-
-import sys
-import os
-import glob
-from tqdm import tqdm
-
-def tekshurctc(model, hojjet, device):
-    training_set = SpeechDataset(hojjet, augumentation=False)
-    loader = SpeechDataLoader(training_set,num_workers=4, shuffle=False, batch_size=32)
-
-    line = []
-    with torch.no_grad():
-        pbar = tqdm(iter(loader), leave=True, total=len(loader))
-        for inputs, targets, input_lengths, _ , paths in pbar:
-
-            inputs  = inputs.to(device,non_blocking=True)
-            outputs, output_lengths = model(inputs, input_lengths)
-            preds   = model.greedydecode(outputs, output_lengths)
-            targets = [uyghur_latin.decode(target) for target in targets]
-            
-            for pred, src, wavename in zip(preds, targets, paths):
-                xatasani , _ = cer(pred, src)
-                if xatasani >= 1:
-                    xata = f"{wavename}\t{src}\t{xatasani}\n"
-                    #xata = f"{src}\n{pred}\n\n"
-                    line.append(xata)
-    return line
-    
-
-def tekshurs2s(model, hojjet, device):
-    training_set = SpeechDataset(hojjet, augumentation=False)
-    loader = SpeechDataLoader(training_set,num_workers=4, shuffle=False, batch_size=20)
-
-    line = []
-    with torch.no_grad():
-        pbar = tqdm(iter(loader), leave=True, total=len(loader))
-        for inputs, targets, input_lengths, _ , paths in pbar:
-
-            inputs  = inputs.to(device,non_blocking=True)
-            targets  = targets.to(device,non_blocking=True)
-            input_lengths  = input_lengths.to(device,non_blocking=True)
-
-            outputs, _ = model(inputs, input_lengths, targets)
-            preds   = model.greedydecode(outputs, 0)
-            targets = [uyghur_latin.decode(target) for target in targets]
-            
-            for pred, src, wavename in zip(preds, targets, paths):
-                xatasani , _ = cer(pred, src)
-                if xatasani >= 5:
-                    xata = f"{wavename}\t{src}\t{xatasani}\n"
-                    #xata = f"{src}\n{pred}\n\n"
-                    line.append(xata)
-    return line
-
-if __name__ == '__main__':
-    device = 'cuda'
-    #model = GCGCResM(featurelen, load_best=False)
-    #model = UFormer(featurelen, load_best=False)
-    
-    model = UDS2W2L50(featurelen, load_best=False)
-    #model = UFormerCTC2(featurelen, load_best=False)
-    model.to(device)
-    model.eval()
-
-    #'uyghur_train.csv' 'uyghur_thuyg20_train_small.csv', ''
-    #netije = tekshurs2s(model, 'uyghur_train.csv', device)
-    netije = tekshurctc(model, 'uyghur_thuyg20_test_small.csv', device)
-    with open('tek_test.csv','w',encoding='utf_8_sig') as f:
-        f.writelines(netije)