diff --git "a/gas1_indicwav2vec_MUCS_warmup500_s300shuff100_3751605.out" "b/gas1_indicwav2vec_MUCS_warmup500_s300shuff100_3751605.out"
--- "a/gas1_indicwav2vec_MUCS_warmup500_s300shuff100_3751605.out"
+++ "b/gas1_indicwav2vec_MUCS_warmup500_s300shuff100_3751605.out"
@@ -4264,4 +4264,1058 @@ last prediction string लता द्वारा अनुवादित ह
   warnings.warn(
 /scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
   with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
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  9%|▊         | 1300/15000 [36:47<2:25:22,  1.57it/s]Printing predictions for a few samples:
+Sample 1:
+  Reference: लिबर ऑफिस impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागत है
+######
+
+
+  Prediction: liber office impes में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स्वा गैै
+
+
+
+Sample 2:
+  Reference: इस tutorial में हम impress window के भागों के बारे में सीखेंगे और कैसे स्लाइड इन्सर्ट करें और कॉपी करें फॉन्ट तथा फॉन्ट को फॉर्मेट करना सीखेंगे
+######
+
+
+  Prediction: इस tutorial में हम impres window के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करेंfont तथा font को format करना सीखेंगे
+
+
+
+Sample 3:
+  Reference: यहाँ हम अपने ऑपरेटिंग सिस्टम के रूप में gnu/linux और लिबरऑफिस वर्जन 334 का उपयोग कर रहे हैं
+######
+
+
+  Prediction: यहाँ हम अपने opereting system के रूप में jn linuकस और liber office version 334 का उपयोग कर रह हैं
+
+
+
+Sample 4:
+  Reference: चलिए अपनी प्रस्तुति प्रेजैटेशन sample impress open करते हैं जिसे पिछले tutorial में बनाया था
+######
+
+
+  Prediction: चलिए अपनी प्रस्तुति sample impres open करते हैं जिसे पिछल t हैंंं
+
+
+
+Sample 5:
+  Reference: चलिए देखते हैं कि screen पर क्या क्या है
+######
+
+
+  Prediction: चलिए देखते हैं कि scren पर क्या क्या है
+
+
+
+last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद
+
+
+last prediction string लातार द्वारा अनुवादित है आई आई ट मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद
+{'eval_loss': 1.5508540868759155, 'eval_cer': 0.32895325256096924, 'eval_wer': 0.4703145356411688, 'eval_runtime': 110.3187, 'eval_samples_per_second': 28.427, 'eval_steps_per_second': 1.777, 'epoch': 0.96}
+{'loss': 5.4868, 'grad_norm': 26.70827293395996, 'learning_rate': 0.0005713241379310344, 'epoch': 0.96}
+{'loss': 2.7424, 'grad_norm': 7.779392719268799, 'learning_rate': 0.0005712827586206896, 'epoch': 0.96}
+{'loss': 1.2021, 'grad_norm': 1.7913457155227661, 'learning_rate': 0.0005712413793103447, 'epoch': 0.96}
+{'loss': 1.7324, 'grad_norm': 1.760551929473877, 'learning_rate': 0.0005711999999999999, 'epoch': 0.96}
+{'loss': 1.7695, 'grad_norm': 2.5545730590820312, 'learning_rate': 0.0005711586206896552, 'epoch': 0.96}
+{'loss': 1.2859, 'grad_norm': 3.367995262145996, 'learning_rate': 0.0005711172413793103, 'epoch': 0.96}
+{'loss': 1.1425, 'grad_norm': 2.67651104927063, 'learning_rate': 0.0005710758620689655, 'epoch': 0.97}
+{'loss': 1.2813, 'grad_norm': 1.7702245712280273, 'learning_rate': 0.0005710344827586206, 'epoch': 0.97}
+{'loss': 1.205, 'grad_norm': 1.4949688911437988, 'learning_rate': 0.0005709931034482758, 'epoch': 0.97}
+{'loss': 1.1341, 'grad_norm': 1.0344305038452148, 'learning_rate': 0.000570951724137931, 'epoch': 0.97}
+{'loss': 0.9666, 'grad_norm': 0.9068742394447327, 'learning_rate': 0.0005709103448275862, 'epoch': 0.97}
+{'loss': 1.1407, 'grad_norm': 3.2932441234588623, 'learning_rate': 0.0005708689655172413, 'epoch': 0.97}
+{'loss': 1.0087, 'grad_norm': 1.2329061031341553, 'learning_rate': 0.0005708275862068965, 'epoch': 0.97}
+{'loss': 1.1756, 'grad_norm': 1.1002187728881836, 'learning_rate': 0.0005707862068965518, 'epoch': 0.97}
+{'loss': 1.4149, 'grad_norm': 2.14863920211792, 'learning_rate': 0.0005707448275862068, 'epoch': 0.97}
+{'loss': 0.756, 'grad_norm': 0.8424788117408752, 'learning_rate': 0.0005707034482758621, 'epoch': 0.97}
+{'loss': 0.9421, 'grad_norm': 0.9140539169311523, 'learning_rate': 0.0005706620689655172, 'epoch': 0.97}
+{'loss': 1.4061, 'grad_norm': 1.0328311920166016, 'learning_rate': 0.0005706206896551724, 'epoch': 0.97}
+{'loss': 0.9327, 'grad_norm': 1.1561588048934937, 'learning_rate': 0.0005705793103448275, 'epoch': 0.98}
+{'loss': 1.0851, 'grad_norm': 0.998042106628418, 'learning_rate': 0.0005705379310344827, 'epoch': 0.98}
+{'loss': 1.156, 'grad_norm': 0.9896425604820251, 'learning_rate': 0.0005704965517241379, 'epoch': 0.98}
+{'loss': 0.8168, 'grad_norm': 1.1115268468856812, 'learning_rate': 0.0005704551724137931, 'epoch': 0.98}
+{'loss': 0.9202, 'grad_norm': 1.3038030862808228, 'learning_rate': 0.0005704137931034482, 'epoch': 0.98}
+{'loss': 1.1559, 'grad_norm': 1.2686210870742798, 'learning_rate': 0.0005703724137931034, 'epoch': 0.98}
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  9%|▉         | 1400/15000 [39:40<2:24:30,  1.57it/s]Printing predictions for a few samples:
+Sample 1:
+  Reference: लिबर ऑफिस impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागत है
+######
+
+
+  Prediction: liबer ऑfफice impls में एक प्रस्तुति documेंट बनाना और बुनियादी fॉrmating के इस spoken tutorial में आपका स्वा गहै
+
+
+
+Sample 2:
+  Reference: इस tutorial में हम impress window के भागों के बारे में सीखेंगे ���र कैसे स्लाइड इन्सर्ट करें और कॉपी करें फॉन्ट तथा फॉन्ट को फॉर्मेट करना सीखेंगे
+######
+
+
+  Prediction: इस tutorial में हम impes wiंdw के भागों के बारे में सीखेंगे और कैसे slide insert करें और copy करें fond तथा font को फॉrmaट करना सीखेंगे
+
+
+
+Sample 3:
+  Reference: यहाँ हम अपने ऑपरेटिंग सिस्टम के रूप में gnu/linux और लिबरऑफिस वर्जन 334 का उपयोग कर रहे हैं
+######
+
+
+  Prediction: यहाँ हम अपने operेating sysटेm के रूप में janu लiनकस और libरr ofफic वeरsion 34 का उपयोग कर रहंं
+
+
+
+Sample 4:
+  Reference: चलिए अपनी प्रस्तुति प्रेजैटेशन sample impress open करते हैं जिसे पिछले tutorial में बनाया था
+######
+
+
+  Prediction: चलिए अपनी प्रस्तुति sैmpl impls open करते हैं जिसे पल ं
+
+
+
+Sample 5:
+  Reference: चलिए देखते हैं कि screen पर क्या क्या है
+######
+
+
+  Prediction: चलिए देखते हैं कि सcren पर क्या क्या है
+
+
+
+last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद
+
+
+last prediction string लता द्वारा अनुवादित है आई आई टी मुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
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+
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 10%|█         | 1500/15000 [42:33<2:13:46,  1.68it/s]Printing predictions for a few samples:
+Sample 1:
+  Reference: लिबर ऑफिस impress में एक प्रस्तुति document बनाना और बुनियादी formatting के इस spoken tutorial में आपका स्वागत है
+######
+
+
+  Prediction: libr ofice imples में एक प्रस्तुति document बनाना और बुनियादी formating के इस spoken tutorial में आपका स ागहै
+
+
+
+Sample 2:
+  Reference: इस tutorial में हम impress window के भागों के बारे में सीखेंगे और कैसे स्लाइड इन्सर्ट करें और कॉपी करें फॉन्ट तथा फॉन्ट को फॉर्मेट करना सीखेंगे
+######
+
+
+  Prediction: इस tutorial में हम impres window के भागों के बारे में सीखेंगे और cैसे slide insert करें और copy करेंfont तथा font को format करना सीखेंगे
+
+
+
+Sample 3:
+  Reference: यहाँ हम अपने ऑपरेटिंग सिस्टम के रूप में gnu/linux और लिबरऑफिस वर्जन 334 का उपयोग कर रहे हैं
+######
+
+
+  Prediction: यहाँ हम अपने operating system के रूप में gnu linux और liber offic vrsion 334 का उपयोग कर रहं
+
+
+
+Sample 4:
+  Reference: चलिए अपनी प्रस्तुति प्रेजैटेशन sample impress open करते हैं जिसे पिछले tutorial में बनाया था
+######
+
+
+  Prediction: चलिए अपनी प्रस्तुति sampl implus open करते हैंजि
+
+
+
+Sample 5:
+  Reference: चलिए देखते हैं कि screen पर क्या क्या है
+######
+
+
+  Prediction: चलिए देखते हैं कि scren पर क्या क्या है
+
+
+
+last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद
+
+
+last prediction string लातार द्वारा अनुवादित है आई आई टी बुम्बई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
+{'eval_loss': 1.7250072956085205, 'eval_cer': 0.3120638552337372, 'eval_wer': 0.4628433171146739, 'eval_runtime': 110.6488, 'eval_samples_per_second': 28.342, 'eval_steps_per_second': 1.771, 'epoch': 1.12}
+{'loss': 1.7029, 'grad_norm': 2.7480571269989014, 'learning_rate': 0.0005630482758620689, 'epoch': 1.12}
+{'loss': 2.2325, 'grad_norm': 4.263364315032959, 'learning_rate': 0.000563006896551724, 'epoch': 1.12}
+{'loss': 14.8587, 'grad_norm': 49.1270637512207, 'learning_rate': 0.0005629655172413793, 'epoch': 1.12}
+{'loss': 2.4462, 'grad_norm': 4.8319411277771, 'learning_rate': 0.0005629241379310345, 'epoch': 1.12}
+{'loss': 2.0957, 'grad_norm': 3.368211507797241, 'learning_rate': 0.0005628827586206896, 'epoch': 1.12}
+{'loss': 1.3825, 'grad_norm': 2.852962017059326, 'learning_rate': 0.0005628413793103448, 'epoch': 1.12}
+{'loss': 1.2427, 'grad_norm': 2.289372444152832, 'learning_rate': 0.0005627999999999999, 'epoch': 1.13}
+{'loss': 2.1151, 'grad_norm': 1.7106167078018188, 'learning_rate': 0.0005627586206896551, 'epoch': 1.13}
+{'loss': 1.4859, 'grad_norm': 2.564164876937866, 'learning_rate': 0.0005627172413793103, 'epoch': 1.13}
+{'loss': 1.2831, 'grad_norm': 2.878242254257202, 'learning_rate': 0.0005626758620689655, 'epoch': 1.13}
+{'loss': 1.2223, 'grad_norm': 1.9603344202041626, 'learning_rate': 0.0005626344827586206, 'epoch': 1.13}
+{'loss': 1.7498, 'grad_norm': 1.272351622581482, 'learning_rate': 0.0005625931034482759, 'epoch': 1.13}
+{'loss': 1.2638, 'grad_norm': 1.4843800067901611, 'learning_rate': 0.0005625517241379309, 'epoch': 1.13}
+{'loss': 0.7714, 'grad_norm': 1.0668007135391235, 'learning_rate': 0.0005625103448275862, 'epoch': 1.13}
+{'loss': 1.0039, 'grad_norm': 1.307996153831482, 'learning_rate': 0.0005624689655172413, 'epoch': 1.13}
+{'loss': 0.9377, 'grad_norm': 1.196547508239746, 'learning_rate': 0.0005624275862068965, 'epoch': 1.13}
+{'loss': 1.0085, 'grad_norm': 2.018998861312866, 'learning_rate': 0.0005623862068965517, 'epoch': 1.13}
+{'loss': 0.6082, 'grad_norm': 1.3907991647720337, 'learning_rate': 0.0005623448275862068, 'epoch': 1.13}
+{'loss': 0.8415, 'grad_norm': 0.9655148983001709, 'learning_rate': 0.0005623034482758621, 'epoch': 1.14}
+{'loss': 0.981, 'grad_norm': 1.1644136905670166, 'learning_rate': 0.0005622620689655172, 'epoch': 1.14}
+{'loss': 1.5076, 'grad_norm': 1.1590325832366943, 'learning_rate': 0.0005622206896551724, 'epoch': 1.14}
+{'loss': 1.0958, 'grad_norm': 2.2664847373962402, 'learning_rate': 0.0005621793103448275, 'epoch': 1.14}
+{'loss': 1.2766, 'grad_norm': 1.658430814743042, 'learning_rate': 0.0005621379310344827, 'epoch': 1.14}
+{'loss': 0.9254, 'grad_norm': 1.150023341178894, 'learning_rate': 0.0005620965517241378, 'epoch': 1.14}
+{'loss': 1.1535, 'grad_norm': 1.5645912885665894, 'learning_rate': 0.0005620551724137931, 'epoch': 1.14}
+{'loss': 1.3296, 'grad_norm': 1.4625437259674072, 'learning_rate': 0.0005620137931034482, 'epoch': 1.14}
+{'loss': 0.8223, 'grad_norm': 1.4360191822052002, 'learning_rate': 0.0005619724137931034, 'epoch': 1.14}
+{'loss': 0.6758, 'grad_norm': 1.0189428329467773, 'learning_rate': 0.0005619310344827586, 'epoch': 1.14}
+{'loss': 0.7727, 'grad_norm': 1.1368669271469116, 'learning_rate': 0.0005618896551724137, 'epoch': 1.14}
+{'loss': 1.1502, 'grad_norm': 1.411299467086792, 'learning_rate': 0.000561848275862069, 'epoch': 1.14}
+{'loss': 1.1706, 'grad_norm': 1.6248334646224976, 'learning_rate': 0.000561806896551724, 'epoch': 1.14}
+{'loss': 0.7774, 'grad_norm': 1.1938365697860718, 'learning_rate': 0.0005617655172413793, 'epoch': 1.15}
+{'loss': 0.0, 'grad_norm': nan, 'learning_rate': 0.0005617655172413793, 'epoch': 1.15}
+{'loss': 1.2341, 'grad_norm': 1.5610140562057495, 'learning_rate': 0.0005617241379310344, 'epoch': 1.15}
+{'loss': 0.0, 'grad_norm': nan, 'learning_rate': 0.0005617241379310344, 'epoch': 1.15}
+{'loss': 0.0, 'grad_norm': nan, 'learning_rate': 0.0005617241379310344, 'epoch': 1.15}
+{'loss': 0.0, 'grad_norm': nan, 'learning_rate': 0.0005617241379310344, 'epoch': 1.15}
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                                                 /scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py:157: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.
+  warnings.warn(
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
+  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
+
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