diff --git "a/wav2vec2-large-xlsr-indonesian.ipynb" "b/wav2vec2-large-xlsr-indonesian.ipynb" new file mode 100644--- /dev/null +++ "b/wav2vec2-large-xlsr-indonesian.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":30446,"status":"ok","timestamp":1704952889746,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"tFJ93uaApPwj","outputId":"72e72bdf-73e8-460c-9491-c3d823703efb"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.16.1)\n","Requirement 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 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sentence
0Saya ingin memesan tempat di restoran peringkat teratas di Idana.
1Kali ini kamu harus melakukannya sendiri.
2Sekolah Komunikasi Manipal juga mempunyai radio kampus sendiri.
3Orang bilang aku adalah pendengar yang baik.
4Kota itu merupakan resor turis yang populer dan dilengkapi dengan pelabuhan feri.
5Roket diluncurkan dari Cape Canaveral pukul dua tiga puluh satu pagi.
6Beberapa lelaki kulit berwarna diterima di militer.
7Para sarjana memiliki pandangan berbeda mengenai waktu \"walima\" yang tepat.
8Tambahkan Go West Young Man ke daftar putar yang berjudul, Disco Forever.
9Cahaya itu akan terlihat jika aku terus berlari.
"]},"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":7,"metadata":{"executionInfo":{"elapsed":578,"status":"ok","timestamp":1704952976234,"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":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["0adac7216f7c4174a418ea7408f2e790","bb2e5ea2218546388406619706b5d15e","1bbb15f6186545fda4c31744c227784f","44f30650087f47eb9572fe58bda12aef","649a53cd687d470191d470dcd82fbe8b","c2298792a9ae4595b4fad9ed1d3194fb","3c1f753afc574fd796b259e36d5bc347","9df72a060b9d4359b1696ec2c232f336","92cfd1c150384c2da9b4194840e34c94","181b273b78cc4175a254a1ef1bf5a631","4de95fdc0fb14c60b0bcb857a68e0224","5cad05f42b8a4a1e982d4f16102e7694","2eae13b32d1144f1a2426903ba590b6f","6efddce729044860bb2da26680f4ddc4","2a9de152cd18436daeb7002c0d4f4add","149a7abbcce644228e77044f65857964","c7ac52f0106b4ad48cf6f5b3b5fe63d8","da0a1df3b38f4ab3b91e11a73b9059d5","40abce0bb9ac490a8766e296eaaaaaf5","53dadc7ec3474f1da73f31e698c37e03","4a409c9cc6284126af78c41d3aca58d1","d6f4739a3ed04164b4d90d5aacf015eb"]},"executionInfo":{"elapsed":1185,"status":"ok","timestamp":1704952979219,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"ztvh3WPL9jGp","outputId":"e1314084-7e3e-4c01-efa5-df7acbab0e89"},"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
0di kamar saya ada kursi kecil
1di depan rumahku ada pohon sakura
2rilis baru dan mendatang mencakup musik dari rachmaninoff aaron copland dan john williams
3margaret akhirnya berbicara ketika dia mendesak finn dan melanie untuk melarikan diri
4lokomotif sering bekerja berpasangan atau bertiga di kereta berat
5begitu mereka mendarat anggota korps harus melakukan sesi wawancara untuk melamar pekerjaan seperti orangorang lainnya
6kamu harus hatihati jangan membuatnya marah
7jembatanjembatan ini adalah bagian penting dari sejarah arsitektur amerika serikat
8hidup kana normal jika tidak buruk karena kebiasaan buruknya
9dua wanita menggoyangkan pinggulnya melakukan tarian perut
"]},"metadata":{}}],"source":["show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":589,"status":"ok","timestamp":1704952981208,"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":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["a6f0130f11494522896b1e106cb27239","8ff71eb5317f419f9bf0a89a4eb4d731","a9bdd10e9dbd4d9a8c4c6515e541ba28","49d06d643d174fc9be6cbebe24138981","056878db190344239d341032c6a8ff8d","a2e476c2dfae49cca46eb993ea312196","264c3fcc4cdc46159614db01c46f1db1","a175199d66f14238b882a4444fd180b0","c0cf0f16de104f7dad7bf0c6559bfc21","e17f93710bd34d4aa9818160d9e09536","bd00db58d9564aa1826aa4785c132d63","5544ae11f8f143bba3f25098dd2ea7d2","53a00378e0b54d13974b0a7e8bd7a8c2","345987ab3bcd41d7849ddb82acbf3d05","59c7079ac4474aabb1f7f528efe56e1d","92a97f4a33354c2c9527b1c53f7e811b","39159c76a9a14e4fb4a7a4bd291522bd","a8102ca7a3064fa5ba3bb96d53754a4b","e0550c5795a94901b4ebac5cdfbd143a","d53daab7730d4af2bb969afc8b299a74","f3447832a24340a499167da34c360cae","607a225301064b089d2a5befaa958c1b"]},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1704952981767,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"EL41ErCi-InN","outputId":"4e042eab-a223-4434-e70b-f714698ff534"},"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":30,"metadata":{"id":"-1HKG5XKCGFs","executionInfo":{"status":"ok","timestamp":1704953103017,"user_tz":-420,"elapsed":364,"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":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":173,"referenced_widgets":["e974d666e2194b6a9d8e84d67608519e","660ea02e76fb4d12823a35ec172dc98c","4efcf9a8d20f4169a132e3906b51d809","cfde7161c6c84cce963a9bae2c21040d","f2298540315e4bdd8478a1941bb05366","23d0f9ca5c4942f88f98ec05e99a4028","cd9b75037f7a45e1a31e05c022fe6ed2","b80e725cd51b4b2482e436111379564e","b63d7f3abcad4551969de5f74b8e8aeb","0bf05bd6e97445ae8bf3df991d8cc0f4","2afa12ffe96145e094a05773697ceb61"]},"executionInfo":{"elapsed":2731,"status":"ok","timestamp":1704953107607,"user":{"displayName":"Atmatech Dev","userId":"08685432457477166628"},"user_tz":-420},"id":"ADGUlsm1CGLW","outputId":"f6b1bdb3-25c7-4352-d2e7-710cf410e105"},"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"],"text/html":["\n","
\n"," \n"," \n"," [4000/4000 1:30:28, Epoch 7/8]\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"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining LossValidation LossWer
10002.9066002.926268100.000000
20002.8394002.833131100.000000
30001.9467001.59991597.738985
40000.9197000.68778668.949348

"]},"metadata":{}},{"output_type":"stream","name":"stderr","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","/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":"execute_result","data":{"text/plain":["TrainOutput(global_step=4000, training_loss=3.183463498592377, metrics={'train_runtime': 5432.8652, 'train_samples_per_second': 11.78, 'train_steps_per_second': 0.736, 'total_flos': 1.576389923539723e+19, 'train_loss': 3.183463498592377, 'epoch': 7.69})"]},"metadata":{},"execution_count":38}],"source":["trainer.train()"]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[],"gpuType":"V100","authorship_tag":"ABX9TyNQe0cXRNIj8lPDSjYP51RB"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"widgets":{"application/vnd.jupyter.widget-state+json":{"0ca5bacb50534ac9947e82a6ea5d08db":{"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_b75aa5cff8ff499a8758f678068177d9","IPY_MODEL_7536639a0449445f840f3063fe84f9c8","IPY_MODEL_7e968acba6eb4758b8b5d96807f8dec8","IPY_MODEL_e8e7f2d149974b169937693089e41be4"],"layout":"IPY_MODEL_3994e5c25ddd405c8edeeaed5545dcda"}},"7b7cccd09dad475a81ddcaed889971a7":{"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_f76126d1b3c54ad3a9184e7bb875916d","placeholder":"​","style":"IPY_MODEL_536d0bfeeca84d248748ee3e31b43fd4","value":"


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