{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":35879,"status":"ok","timestamp":1704989702311,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"tFJ93uaApPwj","outputId":"61e1d790-0954-4594-f906-444b62dc984f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting datasets\n"," Downloading datasets-2.16.1-py3-none-any.whl (507 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.1)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n","Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) 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install accelerate>=0.20.1"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["56b92e8f345f473cbe66eb02c76286e7","7b814ac665ea436ca9e0b5023229cf19","da10964b9ebc4aee8703b59dece740ee","62dbd08c57214934977d420d484e489d","ad9927d02531447f8792d8f1361fb019","9de0d23860d848a597047f7e2a64b08c","ced40fb3eb9745558903a3027fab2048","b52a598169a54b778493ed51d72916a4","dab68552f50140debf4d90b5b3012765","d6e56b1f6f4e42ae92a6b642ba409fbc","cdff634d245943f09aa1be8210139c53","5ab8f7a82b0544098bf50863d6a81820","557c0f6c5d624c25ac7b19826476a701","8a97b4407add4e01a50637e4fdecd264","1b71e2c0b62c4a31afae61e8336da13a","82fcb5bd5477444d8394750a37963bba","a434411a73b143f58700868e890f7888","301a3b31fc0a4fbca085b9469f96cf59","cadd749494b4453e9f0bdb223f9fab2e","d7077fe97de34dd7971a1f7fcdb9d447","fb2ed38695404764b64f6eb4b5720413","4d8cd55dd01c4fbfb81e5c93098025a0","43a80072757449ec9dcd23f9b6bc7b7b","163714882d0e42a3be7d126d05ad1c6c","f8306479c0d643c88e4df47d743c6809","187b5e18e35d44b1a3eec7748d1205ae","f65f62d16524490ba0f96faf452df167","94ee56f77f6642009816a6cbe391ccf6","7d7f689b4f654351aa88cbaf8e92d881","b1b92c580450450a8c8ab9b2fd081899","f5fb0ee251454c2291e94b4b1cb01ea1","166fa99fe8bc4e8bb8cbeb38b12215ba"]},"executionInfo":{"elapsed":906,"status":"ok","timestamp":1704989715057,"user":{"displayName":"Atmatech 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0Olahraga yang paling saya sukai adalah bisbol.
1Saya kesusahan karena kehilangan paspor di luar negeri.
2Perbedaan utama di antara mereka adalah pengucapan beberapa kata.
3Seri tv ini terjadi di Mobius, sebuah planet yang sebagian besar dihuni oleh hewan-hewan antropomorfik.
4Ia memainkan tokoh utama dalam \"Is It Just Me?\"
5Dia melepas topi dan jubah gadis itu dan melemparkannya ke samping.
6Ada banyak air.
7Hanya Peter Craine yang berhasil.
8Karena sibuk, saya tidak bisa makan siang.
9Minami bukan nenek resmi dari cucu Watanabe.
"]},"metadata":{}}],"source":["show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)"]},{"cell_type":"markdown","source":["## Build Vocabulary From Set of Letters"],"metadata":{"id":"87-CR6L3tvSp"}},{"cell_type":"code","execution_count":8,"metadata":{"executionInfo":{"elapsed":1081,"status":"ok","timestamp":1704989910624,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"7UEenlS89pAx"},"outputs":[],"source":["import re\n","chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\'\\”\\�]'\n","\n","def remove_special_characters(batch):\n"," batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower() + \" \"\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('！ ', '')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('，', '')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('é', 'e')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('á', 'a')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('ń', 'n')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('ō', 'o')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('—', '')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('&', 'dan')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('\\\\', '')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('ł', 'l')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('[', '')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace(')', '')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('(', '')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('–', '')\n"," batch[\"sentence\"] = batch[\"sentence\"].replace('’', '')\n"," return batch"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["ff01a9a6b4384a2ea4f6eabbbdb3a16d","fa482b1b96ff48bfa952e6d8e013846b","c7dd25f2a14f4455abef6db58db875a8","c360a0a0d4e845ba9c6e2e3f911177c7","f79a7be338a24a8aadd2b2fdcc4d2d47","ccdaeddf10124cec9e9c1a55e97fa405","f11afd359a674885a4ff5877a5e97a0a","2efebd1ea4e14df8a474d7e54a3b7953","822b62e1a8d94739af7a30c0f3047acc","a163308b2d994d1cb5448022fd89eee3","188dd32598d64b339239fe35555184a9","d923cbce1ea04914a06ce6cffb5074c5","901df157b2bd4d87bb7a49487125d999","05080dc6cc5b466aa59d25f615b34f40","dae4b20db3844d73a2b60bc8ba678935","edf1c9be49af4f8abedab30b6986bb95","503eaea32d9f4a89b55f0e603a186848","9637ffdaab11400bbf5e6e44b13a7d97","01bed6980c394aa19465350ddfbb252d","1562ec4b586b40b6b3db3abd231be31a","194941096afe4656b12ce812c610848c","cb21a313472f4f3a80cd646ab7365162"]},"executionInfo":{"elapsed":784,"status":"ok","timestamp":1704989913270,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"ztvh3WPL9jGp","outputId":"808a7897-e515-40cc-c691-0970fd10037b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/8309 [00:00"],"text/html":["\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
sentence
0sekarang pukul lima
1semua orang dipersilahkan
2namun tentangan kuat dari industri minuman beralkohol dan periklanan pada akhirnya menang
3risalah ini dibagi menjadi sepuluh buku
4saya menekan tombol dan menyalakan lampu
5seekor burung terbang melewati jalan setapak
6tom membidik dan menembakkan tembakan peringatan
7selain itu blom telah memenangkan lima kejuaraan nasional dan empat kejuaraan dalam ruang nasional
8tak terasa mereka sudah berada tak jauh dari tempat tinggal
9beratnya bisa disesuaikan dengan menambah cairan yang lebih berat
"]},"metadata":{}}],"source":["show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1704989917249,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"YVdZHkns99Bw"},"outputs":[],"source":["def extract_all_chars(batch):\n"," all_text = \" \".join(batch[\"sentence\"])\n"," vocab = list(set(all_text))\n"," return {\"vocab\": [vocab], \"all_text\": [all_text]}"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["bb657a8054bf4680995a6262812faa82","88c2117ad9824677918cc966013ec520","6cca2aa152e54411bae73a5a28cc748c","185c349e270a41a084789ec45bbd8dcf","a1ebe60df51244caaf8263579aaeb460","4de95438ad9c439091cd537fc505acbf","e81ad7a756094417bb8ec59a21c4cd31","d185031fba20439b86101f2c2db18945","62abebfb17c84a298befc1b850f17d26","43913bd97fc7413780a84fd5c6f85968","63fe071a52a941b784d62ab96fff7063","74aa46b1e1ca4792a57c6b773ab5dda2","37c338a4d391474d890f26a69164cb10","8b2023b77b81458e9ed85f96aa67cc42","b2ef036992d1426386473fa2a4f6e66c","84c2e841ce3545a79320e8bbed3a9e21","ebfae83c4d444e4f81ee4aa79cb34526","b19a838aae2542f9a3cd7fd4d2ea0a5c","0f2d2b8075f442208394d5f3eeaee27c","e90eee9ec4c14b17aa5f1ad40e4f9433","1b5e7954050f46ecae13addf4c1de549","79ac070bcf2d413cb10bf9d03c513830"]},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1704989917946,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"EL41ErCi-InN","outputId":"84cef57e-c90f-4252-f2dd-e9e825dc5702"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/8309 [00:00=\n"," 7.5 (Volta).\n"," \"\"\"\n","\n"," processor: Wav2Vec2Processor\n"," padding: Union[bool, str] = True\n"," max_length: Optional[int] = None\n"," max_length_labels: Optional[int] = None\n"," pad_to_multiple_of: Optional[int] = None\n"," pad_to_multiple_of_labels: Optional[int] = None\n","\n"," def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n"," # split inputs and labels since they have to be of different lenghts and need\n"," # different padding methods\n"," input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n"," label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n","\n"," batch = self.processor.pad(\n"," input_features,\n"," padding=self.padding,\n"," max_length=self.max_length,\n"," pad_to_multiple_of=self.pad_to_multiple_of,\n"," return_tensors=\"pt\",\n"," )\n"," with self.processor.as_target_processor():\n"," labels_batch = self.processor.pad(\n"," label_features,\n"," padding=self.padding,\n"," max_length=self.max_length_labels,\n"," pad_to_multiple_of=self.pad_to_multiple_of_labels,\n"," return_tensors=\"pt\",\n"," )\n","\n"," # replace padding with -100 to ignore loss correctly\n"," labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n","\n"," batch[\"labels\"] = labels\n","\n"," return batch"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"-1HKG5XKCGFs","executionInfo":{"status":"ok","timestamp":1704990018620,"user_tz":-420,"elapsed":19,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"}}},"outputs":[],"source":["data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"]},{"cell_type":"markdown","source":["## Define Evaluation Metrics"],"metadata":{"id":"Gi37kXZ4wNFT"}},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":173,"referenced_widgets":["da497a2492e04618a9a248b88affcd16","fa6ca5915c4a4841a3afbbac00f0b880","f7de56a7e7f244f7b78e3f39af3153bd","46592443ff05469a8aac0163722d6022","6b601de19d504827814506e4998a2161","1b109de801f14df2b457bd7b970923cb","5058f33209044eac88531323aae6c24b","694a215752b849158c95d9aea777f507","152a46db899548bcb949adc4327bddaa","4958be033c584b8f9c5f48fb63289e0e","f4f78e9a0c514c748968261f46d19822"]},"executionInfo":{"elapsed":1795,"status":"ok","timestamp":1704990020397,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"ADGUlsm1CGLW","outputId":"03f4f915-2c2b-4fa1-ba00-e4648cd1c0b4"},"outputs":[{"output_type":"stream","name":"stderr","text":[":1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n"," wer_metric = load_metric(\"wer\")\n","/usr/local/lib/python3.10/dist-packages/datasets/load.py:752: FutureWarning: The repository for wer contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.16.1/metrics/wer/wer.py\n","You can avoid this message in future by passing the argument `trust_remote_code=True`.\n","Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script: 0%| | 0.00/1.90k [00:00\n"," \n"," \n"," [2942/4000 58:59 < 21:13, 0.83 it/s, Epoch 5.66/8]\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining LossValidation LossWer
10002.9088002.928160100.000000
20002.8406002.827221100.000000

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: 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.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: 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.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

\n"," \n"," \n"," [3001/4000 1:00:04 < 20:00, 0.83 it/s, Epoch 5.77/8]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining LossValidation LossWer
10002.9088002.928160100.000000
20002.8406002.827221100.000000

\n","

\n"," \n"," \n"," [101/456 00:34 < 02:01, 2.93 it/s]\n","
\n"," "]},"metadata":{}}],"source":["trainer.train()"]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[],"gpuType":"V100","authorship_tag":"ABX9TyOEMkmctFB3gnk2q/zrkDZk"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"widgets":{"application/vnd.jupyter.widget-state+json":{"56b92e8f345f473cbe66eb02c76286e7":{"model_module":"@jupyter-widgets/controls","model_name":"VBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"VBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"VBoxView","box_style":"","children":["IPY_MODEL_fb2ed38695404764b64f6eb4b5720413","IPY_MODEL_4d8cd55dd01c4fbfb81e5c93098025a0","IPY_MODEL_43a80072757449ec9dcd23f9b6bc7b7b","IPY_MODEL_163714882d0e42a3be7d126d05ad1c6c"],"layout":"IPY_MODEL_ced40fb3eb9745558903a3027fab2048"}},"7b814ac665ea436ca9e0b5023229cf19":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_b52a598169a54b778493ed51d72916a4","placeholder":"​","style":"IPY_MODEL_dab68552f50140debf4d90b5b3012765","value":"

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