End of training

README.md

@@ -15,12 +15,12 @@ should probably proofread and complete it, then remove this comment. -->
 This model was trained from scratch on an unknown dataset.
 It achieves the following results on the evaluation set:
 - eval_loss: 2.2188
-- eval_model_preparation_time: 0.0044
+- eval_model_preparation_time: 0.0045
 - eval_cer: 0.4569
 - eval_wer: 0.5264
-- eval_runtime: 39.8784
-- eval_samples_per_second: 14.344
-- eval_steps_per_second: 0.903
+- eval_runtime: 39.0212
+- eval_samples_per_second: 14.659
+- eval_steps_per_second: 0.923
 - step: 0
 
 ## Model description

all_results.json

@@ -1,10 +1,10 @@
 {
     "eval_cer": 0.45689757252812313,
     "eval_loss": 2.218759059906006,
-    "eval_model_preparation_time": 0.0044,
-    "eval_runtime": 39.8784,
+    "eval_model_preparation_time": 0.0045,
+    "eval_runtime": 39.0212,
     "eval_samples": 572,
-    "eval_samples_per_second": 14.344,
-    "eval_steps_per_second": 0.903,
+    "eval_samples_per_second": 14.659,
+    "eval_steps_per_second": 0.923,
     "eval_wer": 0.5264004680415387
 }

eval_results.json

@@ -1,10 +1,10 @@
 {
     "eval_cer": 0.45689757252812313,
     "eval_loss": 2.218759059906006,
-    "eval_model_preparation_time": 0.0044,
-    "eval_runtime": 39.8784,
+    "eval_model_preparation_time": 0.0045,
+    "eval_runtime": 39.0212,
     "eval_samples": 572,
-    "eval_samples_per_second": 14.344,
-    "eval_steps_per_second": 0.903,
+    "eval_samples_per_second": 14.659,
+    "eval_steps_per_second": 0.923,
     "eval_wer": 0.5264004680415387
 }

training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d2cbc4a2b03e9738021be3965f5083d23904411fa2d3c32ac712544fd2ddf0d6
+oid sha256:18e2f8064918f795c1ead980847a11024e3cbc4f83e27b2e86a70ddcefffdcde
 size 5496

transliteratedevalonlyhindi_indicwav2vec_MUCS_warmup500_s300shuff100_3708271.out

@@ -145,3 +145,45 @@ Last Prediction string लता द्वारा अनुवादित ह
   eval_steps_per_second       =      0.903
   eval_wer                    =     0.5264
 
+wandb: - 0.005 MB of 0.005 MB uploaded
+wandb: Run history:
+wandb:                    eval/cer ▁
+wandb:                   eval/loss ▁
+wandb: eval/model_preparation_time ▁
+wandb:                eval/runtime ▁
+wandb:     eval/samples_per_second ▁
+wandb:       eval/steps_per_second ▁
+wandb:                    eval/wer ▁
+wandb:                    eval_cer ▁
+wandb:                   eval_loss ▁
+wandb: eval_model_preparation_time ▁
+wandb:                eval_runtime ▁
+wandb:                eval_samples ▁
+wandb:     eval_samples_per_second ▁
+wandb:       eval_steps_per_second ▁
+wandb:                    eval_wer ▁
+wandb:           train/global_step ▁▁
+wandb: 
+wandb: Run summary:
+wandb:                    eval/cer 0.4569
+wandb:                   eval/loss 2.21876
+wandb: eval/model_preparation_time 0.0044
+wandb:                eval/runtime 39.8784
+wandb:     eval/samples_per_second 14.344
+wandb:       eval/steps_per_second 0.903
+wandb:                    eval/wer 0.5264
+wandb:                    eval_cer 0.4569
+wandb:                   eval_loss 2.21876
+wandb: eval_model_preparation_time 0.0044
+wandb:                eval_runtime 39.8784
+wandb:                eval_samples 572
+wandb:     eval_samples_per_second 14.344
+wandb:       eval_steps_per_second 0.903
+wandb:                    eval_wer 0.5264
+wandb:           train/global_step 0
+wandb: 
+wandb: 🚀 View run transliterated_wer_glamorous_tree_37 at: https://wandb.ai/priyanshipal/huggingface/runs/214gwh3b
+wandb: ⭐️ View project at: https://wandb.ai/priyanshipal/huggingface
+wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: ./wandb/run-20241014_232133-214gwh3b/logs
+wandb: WARNING The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require("core")`! See https://wandb.me/wandb-core for more information.

transliteratedevalonlyhindi_indicwav2vec_MUCS_warmup500_s300shuff100_3708892.out

@@ -0,0 +1,157 @@
+wandb: Currently logged in as: priyanshi-pal (priyanshipal). Use `wandb login --relogin` to force relogin
+wandb: - Waiting for wandb.init()...
+wandb:  $ pip install wandb --upgrade
+wandb: Tracking run with wandb version 0.17.6
+wandb: Run data is saved locally in /scratch/elec/t405-puhe/p/palp3/MUCS/wandb/run-20241014_233924-6ill7u88
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run transliterated_wer_glamorous_tree_37
+wandb: ⭐️ View project at https://wandb.ai/priyanshipal/huggingface
+wandb: 🚀 View run at https://wandb.ai/priyanshipal/huggingface/runs/6ill7u88
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py:991: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
+  warnings.warn(
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/feature_extraction_auto.py:331: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
+  warnings.warn(
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/accelerate/accelerator.py:488: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  self.scaler = torch.cuda.amp.GradScaler(**kwargs)
+max_steps is given, it will override any value given in num_train_epochs
+Wav2Vec2CTCTokenizer(name_or_path='', vocab_size=149, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '[UNK]', 'pad_token': '[PAD]'}, clean_up_tokenization_spaces=False),  added_tokens_decoder={
+	147: AddedToken("[UNK]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
+	148: AddedToken("[PAD]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
+	149: AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	150: AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+}
+CHECK MODEL PARAMS Wav2Vec2ForCTC(
+  (wav2vec2): Wav2Vec2Model(
+    (feature_extractor): Wav2Vec2FeatureEncoder(
+      (conv_layers): ModuleList(
+        (0): Wav2Vec2LayerNormConvLayer(
+          (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (activation): GELUActivation()
+        )
+        (1-4): 4 x Wav2Vec2LayerNormConvLayer(
+          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (activation): GELUActivation()
+        )
+        (5-6): 2 x Wav2Vec2LayerNormConvLayer(
+          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (activation): GELUActivation()
+        )
+      )
+    )
+    (feature_projection): Wav2Vec2FeatureProjection(
+      (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+      (projection): Linear(in_features=512, out_features=1024, bias=True)
+      (dropout): Dropout(p=0.3, inplace=False)
+    )
+    (encoder): Wav2Vec2EncoderStableLayerNorm(
+      (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(
+        (conv): ParametrizedConv1d(
+          1024, 1024, kernel_size=(128,), stride=(1,), padding=(64,), groups=16
+          (parametrizations): ModuleDict(
+            (weight): ParametrizationList(
+              (0): _WeightNorm()
+            )
+          )
+        )
+        (padding): Wav2Vec2SamePadLayer()
+        (activation): GELUActivation()
+      )
+      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+      (dropout): Dropout(p=0.2, inplace=False)
+      (layers): ModuleList(
+        (0-23): 24 x Wav2Vec2EncoderLayerStableLayerNorm(
+          (attention): Wav2Vec2SdpaAttention(
+            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
+            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
+            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
+            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
+          )
+          (dropout): Dropout(p=0.2, inplace=False)
+          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+          (feed_forward): Wav2Vec2FeedForward(
+            (intermediate_dropout): Dropout(p=0.0, inplace=False)
+            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)
+            (intermediate_act_fn): GELUActivation()
+            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)
+            (output_dropout): Dropout(p=0.2, inplace=False)
+          )
+          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+        )
+      )
+    )
+  )
+  (dropout): Dropout(p=0.0, inplace=False)
+  (lm_head): Linear(in_features=1024, out_features=151, bias=True)
+)
+check the eval set length 572
+10/14/2024 23:39:37 - INFO - __main__ - *** Evaluate ***
+/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(
+
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+/scratch/work/palp3/myenv/lib/python3.11/site-packages/huggingface_hub/hf_api.py:3889: UserWarning: It seems that you are about to commit a data file (json/default-b60d5edd0f197c71/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a/json-train.arrow) to a model repository. You are sure this is intended? If you are trying to upload a dataset, please set `repo_type='dataset'` or `--repo-type=dataset` in a CLI.
+  warnings.warn(
+Printing predictions for a few samples:
+Sample 1:
+  Reference (English): हम उनका उपयोग ऐसे ही कर सकते हैं या आवश्यकता अनुसार कुछ बदलाव करके उपयोग कर सकते हैं
+ True Reference: हम उनका उपयोग ऐसे ही कर सकते हैं या आवश्यकता अनुसार कुछ बदलाव करके उपयोग कर सकते हैं
+######
+  Prediction (English): हम उनका उपयोग ऐसे ही कर सकते हैं
+ True Prediction: हम उनका उपयोग ऐसे ही कर सकते हैं
+
+
+
+Sample 2:
+  Reference (English): अतः शीर्षक इस तरह से जोड़ सकते हैं
+ True Reference: अतः शीर्षक इस तरह से जोड़ सकते हैं
+######
+  Prediction (English): अतः शीर्ष है
+ True Prediction: अतः शीर्ष है
+
+
+
+Sample 3:
+  Reference (English): प्रेसेंटेशन के अंत में आपने स्लाइड की एक कॉपी बना ली है
+ True Reference: प्रेसेंटेशन के अंत में आपने स्लाइड की एक कॉपी बना ली है
+######
+  Prediction (English): presentation के अंत में आपने स ैंैं
+ True Prediction: presentation के अंत में आपने स ैंैं
+
+
+
+Sample 4:
+  Reference (English): चलिए अब फोंट्स और फोंट्स को फॉर्मेट करने के कुछ तरीके देखते हैं
+ True Reference: चलिए अब फोंट्स और फोंट्स को फॉर्मेट करने के कुछ तरीके देखते हैं
+######
+  Prediction (English): चलिए अब fonts और fonts को format करने के कुछ तरीके देेहं
+ True Prediction: चलिए अब fonts और fonts को format करने के कुछ तरीके देेहं
+
+
+
+Sample 5:
+  Reference (English): यह एक डायलॉग बॉक्स खोलेगा जिसमें हम अपनी आवश्यकतानुसार फॉन्ट स्टाइल और साइज़ सेट कर सकते हैं
+ True Reference: यह एक डायलॉग बॉक्स खोलेगा जिसमें हम अपनी आवश्यकतानुसार फॉन्ट स्टाइल और साइज़ सेट कर सकते हैं
+######
+  Prediction (English): यह एक dialog box खोलेगा जिसमें हम अपनी आवश्यकत हैहै
+ True Prediction: यह एक dialog box खोलेगा जिसमें हम अपनी आवश्यकत हैहै
+
+
+
+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 मुmबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँ हमसे जुड़ने के लिए धन्यवाद
+***** eval metrics *****
+  eval_cer                    =     0.4569
+  eval_loss                   =     2.2188
+  eval_model_preparation_time =     0.0045
+  eval_runtime                = 0:00:39.02
+  eval_samples                =        572
+  eval_samples_per_second     =     14.659
+  eval_steps_per_second       =      0.923
+  eval_wer                    =     0.5264
+