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{ + "cell_type": "code", + "source": [ + "gpu_info = !nvidia-smi\n", + "gpu_info = '\\n'.join(gpu_info)\n", + "if gpu_info.find('failed') >= 0:\n", + " print('Not connected to a GPU')\n", + "else:\n", + " print(gpu_info)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Q6Yoet9IyrrF", + "outputId": "7f49dd58-a5ea-4395-f789-a2e9334511a2" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tue Mar 21 13:52:07 2023 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 56C P0 26W / 70W | 0MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "| No running processes found |\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%%capture\n", + "!pip install datasets==1.18.3\n", + "!pip install transformers==4.11.3\n", + "!pip install torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n", + "!pip install jiwer" + ], + "metadata": { + "id": "X_h_syh6yfJ0" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "%%capture\n", + "!apt install git-lfs" + ], + "metadata": { + "id": "3OQ9DyLwUIoL" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412, + "referenced_widgets": [ + "41c9d3e1240c4955a9f68d8c137afb5e", + "bc880f1512714e77b95a475f606d29d8", + "be7fa57e32e6460fb161d723481ac151", + "55935c3874c749d2859fdbccd581f026", + "20508aeb88164aa5b7b5c50a397b09b2", + "1bf9887c2b9b4dcc89474e27df6093b6", + "0b9a1e1be7604701a7f1cabef2fd8437", + "a70342f7fcf54e948e693738b7ecbf12", + "e30f4444542a474da09cf462f58b9f7f", + "e18cf221f06f4640a7abc9b4f6581b3d", + "cb63baa113db4105bbca13d5221b7c6a", + 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sentence
0Нансал угаасаа шударга, хэрийн юмыг халгаахгүй золиг.
1Эцсийн бүлэгт Монгол хүнд эх орноос сайхан орон гэж байдаггүй юм билээ.
2Ингэж золгон мэндчилдэг байсны ачаар монгол хүн есөн үеэ мэддэг байсан гэлцдэг.
3Үүнд л гол эмгэнэл оршиж байна.
4\"\"\"Хатуулаг ихтэй архи уувал догширно, сүүн шимийг хүртвэл тайвширна\"\" гэж үргэлж хэлдэг.\"
5Долоо хоног, сар, жилээр ажилласаар... Бүхний төгсгөл улам улам ойртож байлаа.
6Мэдээж энэ нь тодорхой шилжилтийн хугацаанд л үргэлжилнэ.
7Нансал шиг намын уриалгаар явсан хүн бол дуун дээр...
8Тэгээд бусад нь бүгд сайн дурынхан.
9Ганцхан амь өршөө хэмээн тэрээр уйлагнан хэлээд дөрвөн хөллөж мөлхлөө.
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import re\n", + "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\«\\»\\']'\n", + "\n", + "def remove_special_characters(batch):\n", + " batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n", + " return batch" + ], + "metadata": { + "id": "Kjca_XBOMRaj" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "common_voice_train = common_voice_train.map(remove_special_characters)\n", + "common_voice_test = common_voice_test.map(remove_special_characters)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118, + "referenced_widgets": [ + "7cd0e995a2a24cb28b3ae1485d96103f", + "2d982c9b02a74a8ca5d125a6eb90c4f7", + "1b074b0e54734f4ea5dbb4de9dbef265", + "3c4cea4e33b04714b25050060b296184", + "12850b698a8f4cc9842822f743d84a3c", + "6b9d1ccf82c441048f468280584723bb", + "bc5ad52e67304e6d99c928870ac7cd11", + "fec1d3044a114e968a232b7b757146a7", + "df57cfae0a584c77b2fe5094910ccc8f", + "3b272b82b6664a67b7baaae586b7b681", + "eba2f5b6278a494f83566b4cfce3e34c", + "b266ea87326a4c76aba95d890c9e3795", + "a7544ea88a534614800c5cefa5bff972", + "17ff72a685364dcc9928bd2ab17993a5", + "a0d9ae3fed4e48ac96b5904355ac7ae9", + "bea5935746364682a6fa512babdb38b5", + "16da06916f07409a8b89689910de734f", + "18e06b7921334310be53a0aa9423ccc4", + "e0d214a6c83641048b3087f419b6ad88", + "e0051794fca943a4b191a155d658791c", + "b2bbc2944a1548abb86d13fe6796254c", + "82a7470b687a4b73bc9e48202da8b6a6" + ] + }, + "id": "cMJWTgSbMmhQ", + "outputId": "4302ed39-2224-4fae-cfab-1a3efe177562" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:datasets.fingerprint:Parameter 'function'= of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "0ex [00:00, ?ex/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "7cd0e995a2a24cb28b3ae1485d96103f" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "0ex [00:00, ?ex/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "b266ea87326a4c76aba95d890c9e3795" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "chars_to_remove_regex = 'h'\n", + "\n", + "def remove_special_characters(batch):\n", + " batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n", + " return batch\n", + "common_voice_train = common_voice_train.map(remove_special_characters)\n", + "common_voice_test = common_voice_test.map(remove_special_characters)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "3c1b1e0daac14c6587cc5370e18a8118", + "18d12fcf62514e9aa4ff60e8eef89515", + "4842978450594ceeb35e9f4acceeb059", + "c4beadcfe74044739804fbf706e66418", + "1dd03e9454324149961ed959f5c1b7d9", + "f1664bdb0e8c4eb19fb33bcce193e0bc", + "e60fb9e658064384bb20a647374cd4c9", + "fdba99ade6a24bd387536674bfadb149", + "88da251879f149778e501463e805cca1", + "3646f304eb1e4cc6b96d27f30f4fb7b4", + "4c8dd0fa211949aa830da4798ef7eb67", + "c87ef5c6a28f48b2a51f8f5d95ed8557", + "20b5b16e07b6447492e3e45712d40dff", + "91f54c0e1a5d4e7a88d133aa740785ad", + "e8dfff0ddf5f4a989cf4a1fcc31f3b39", + "c9f7c54a56c043d6be8816bba3b11757", + "c4226ebdec3649538fc46cc13a9646da", + "785222d6f9034a4d8d0974264d42cb9f", + "7f4e1380e0aa4a73b78661e8c2e11144", + "7742d68a31424dd18272d534d9b022f8", + "7f246f4fb28c4445966ef18976602b61", + "d80b7bb1cb7a4282b953e4547335ef67" + ] + }, + "id": "MLh-A7tC5Mdy", + "outputId": "498dc92f-fd18-45ef-c214-970a2ce965cd" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "0ex [00:00, ?ex/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "3c1b1e0daac14c6587cc5370e18a8118" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "0ex [00:00, ?ex/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "c87ef5c6a28f48b2a51f8f5d95ed8557" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "ELg3drcwMpK6", + "outputId": "bf5d8676-478f-46cc-9e28-289e41d7d010" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "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
0бөмбөгөө хураалгахаас зайлсхийн зүгээр л ойролцоох тоглогч руу чиглүүлээд чулуудаж орхив
1цайзад бидний түүсэн мөчир байгаа гэж эд хариулав
2гидролизын урвалыг ашиглан хамгийн хурдан үйлчилдэг уургийн хэлбэрийг гарган авчээ
3энэ төсвийг жаалуудаас харамлаж жалга довны зүйлд зарлагадсан гэж хардахаас өөр арга алга
4санал хүсэлтийн утсыг олон газар ажиллуулдаг ч залгаад улам уурлаад дуусах нь элбэг тохиолддог
5байшингийн захиргааны дарга энэ ярианд сэтгэл огт ханасангүй
6аливаа зүйл хомсдохдоо ү��э цэн нь өсөж хүмүүс түүнийг улам ихээр үгүйлдэг
7харин дархан джузеппег тэр аль хэдийнээс мэддэг байлаа
8марияд эх орноо хайрлах үзэл нялх бага байхад нь л төлөвшсөн
9тэгээд гэнэт цочин ээжийг харахад дуугүйхэн нулимсаа арчиж байна
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "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]}" + ], + "metadata": { + "id": "bfcwBKamNDX8" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n", + "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "22a73456734c4195a8b664eea56060b4", + "a6fb84966d464fb084e4ff28fb819597", + "759972fc0c264e7ca12c3e9beefb687b", + "c0de6ce1aecf4f9b9f55cc0fb3dd408b", + "14634a3546bb4d05ac892a8e236b388c", + "4b498b14406545c1bed99f25a6594722", + "69f722871117402fa549647da50e76d7", + "e9f0076bedaf46eba7c84f22f3c3a516", + "e79476d487e844c1ae3ba3ac3c6b8a92", + "cc98af514295460ebfde23cd3ab74588", + "974993c7e39647ba93a6af05513c8911", + "d26ded21d3d040c78d51a0b366aade4e", + "46e7918fe0f947a9bd1579875dd1e709", + "d25e569e8bdf4130b57cbd4a8f73396a", + "e962a75632054a6991d235f3404cebbf", + "adf9836a19b34730a49b75748678d4e4", + "aacf7a1900ee4b049f742e5d71641653", + "01e1f849ead44c1c99fa749d62dc5be8", + "d5ab34c153ed4649ae42e8fcce7779c6", + "1a547a8d2a964a3a93f307036d58b45d", + "1f3b5f5f73274fc498ff04f73cd45033", + "42c73ab5ffb84f04b848f4479cc2cb25" + ] + }, + "id": "_Lz5XcXMWlRK", + "outputId": "0c20cd73-f955-4d15-c00a-0aeca25cbb69" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/1 [00:00 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", + " 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", + " 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" + ], + "metadata": { + "id": "whu0EJxOgfyy" + }, + "execution_count": 36, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" + ], + "metadata": { + "id": "Ltc2dxGlgukN" + }, + "execution_count": 37, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from datasets import load_dataset, load_metric, Audio" + ], + "metadata": { + "id": "QS5WEKd8hnhi" + }, + "execution_count": 38, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "wer_metric = load_metric(\"wer\")" + ], + "metadata": { + "id": "xR73S4hhglX_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "97b0443dfc314fcaa1f58148e7b0fe51", + "11618b57382943e4a308591cfad2e9e5", + "9a62654284a04fcdac75d5dfbab9eca5", + "8747480e6ddb4831b538d0b0b69b77a6", + "ff19a949944f48ad8a06589868ff5f1e", + "e5bab6c722a54f79b63b489ad87a1e8f", + "935ed8c2c69044979a4f4fa51b3c60c9", + "d4bc82f10abc4a2a9fc7ebb810c4a233", + "6f1886c3a50a40acb8b50d3a40782f51", + "ffdb0fd1ce574c64bb3e351e33e3c89b", + "e7b0c2f4568e4214ac4e6202eee9a8cc" + ] + }, + "outputId": "378cdcc0-5339-4478-dd1e-247d2f6ab410" + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| | 0.00/1.90k [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining LossValidation LossWer
4002.2494000.8487290.526894
8000.1430000.8295560.525183
12000.1007000.8132150.504110
16000.0700000.8332260.480113

" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", + "***** Running Evaluation *****\n", + " Num examples = 1862\n", + " Batch size = 8\n", + "Saving model checkpoint to repo_name/checkpoint-400\n", + "Configuration saved in repo_name/checkpoint-400/config.json\n", + "Model weights saved in repo_name/checkpoint-400/pytorch_model.bin\n", + "Configuration saved in repo_name/checkpoint-400/preprocessor_config.json\n", + "/usr/local/lib/python3.9/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\n", + "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", + "***** Running Evaluation *****\n", + " Num examples = 1862\n", + " Batch size = 8\n", + "Saving model checkpoint to repo_name/checkpoint-800\n", + "Configuration saved in repo_name/checkpoint-800/config.json\n", + "Model weights saved in repo_name/checkpoint-800/pytorch_model.bin\n", + "Configuration saved in repo_name/checkpoint-800/preprocessor_config.json\n", + "/usr/local/lib/python3.9/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\n", + "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", + "***** Running Evaluation *****\n", + " Num examples = 1862\n", + " Batch size = 8\n", + "Saving model checkpoint to repo_name/checkpoint-1200\n", + "Configuration saved in repo_name/checkpoint-1200/config.json\n", + "Model weights saved in repo_name/checkpoint-1200/pytorch_model.bin\n", + "Configuration saved in repo_name/checkpoint-1200/preprocessor_config.json\n", + "Deleting older checkpoint [repo_name/checkpoint-400] due to args.save_total_limit\n", + "/usr/local/lib/python3.9/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\n", + "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", + "***** Running Evaluation *****\n", + " Num examples = 1862\n", + " Batch size = 8\n", + "Saving model checkpoint to repo_name/checkpoint-1600\n", + "Configuration saved in repo_name/checkpoint-1600/config.json\n", + "Model weights saved in repo_name/checkpoint-1600/pytorch_model.bin\n", + "Configuration saved in repo_name/checkpoint-1600/preprocessor_config.json\n", + "Deleting older checkpoint [repo_name/checkpoint-800] due to args.save_total_limit\n", + "/usr/local/lib/python3.9/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\n", + "\n", + "\n", + "Training completed. Do not forget to share your model on huggingface.co/models =)\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=1800, training_loss=0.5758350520663791, metrics={'train_runtime': 8501.5757, 'train_samples_per_second': 6.772, 'train_steps_per_second': 0.212, 'total_flos': 7.124680585822395e+18, 'train_loss': 0.5758350520663791, 'epoch': 30.0})" + ] + }, + "metadata": {}, + "execution_count": 44 + } + ] + }, + { + "cell_type": "code", + "source": [ + "common_voice_test_transcription = load_dataset(\"common_voice\", \"mn\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")\n", + "\n", + "for i in range(10):\n", + " input_dict = processor(common_voice_test[i][\"input_values\"], return_tensors=\"pt\", padding=True)\n", + "\n", + " logits = model(input_dict.input_values.to(\"cuda\")).logits\n", + "\n", + " pred_ids = torch.argmax(logits, dim=-1)[0]\n", + "\n", + " print(f\"Prediction_{i}:\")\n", + " print(processor.decode(pred_ids))\n", + "\n", + " print(f\"\\nReference_{i}:\")\n", + " print(common_voice_test_transcription[i][\"sentence\"].lower())" + ], + "metadata": { + "id": "FHIIY-qT7qo4", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bdfdcb9d-01f5-4816-c9c7-7ca328805c82" + }, + "execution_count": 54, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:datasets.builder:Using custom data configuration mn-00b7e43d66b8c1a3\n", + "WARNING:datasets.builder:Reusing dataset common_voice (/root/.cache/huggingface/datasets/common_voice/mn-00b7e43d66b8c1a3/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd)\n", + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n", + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Prediction_0:\n", + "хэрэв захиа байгаагүй бол чиг андалсын тухай дурсах ч үгүй байсан\n", + "\n", + "Reference_0:\n", + "хэрэв захиа байгаагүй бол чи гэндальфын тухай дурсах ч үгүй байсан.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Prediction_1:\n", + "алтагууны ширээн дээр мэдээдллэг харагдахуулц байрнь цоог дөшүүлөн тавьжээ\n", + "\n", + "Reference_1:\n", + "гал тогооны ширээн дээр мэдээллийг харагдахуйц байранд сав түшүүлэн тавьжээ.\n", + "Prediction_2:\n", + "энэ мо та хоёр яг адилхань бага болчир шүү дээ\n", + "\n", + "Reference_2:\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n", + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "энэ мод та хоёр яг адилхан бага балчир шүү дээ.\n", + "Prediction_3:\n", + "чойллсон ч тэгж бодсон нь бий\n", + "\n", + "Reference_3:\n", + "чойбалсан ч тэгж бодсон нь бий.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Prediction_4:\n", + "мөн л овоог нэгэн насриг нь тодорхойсон өнгө бүрийн туузүй зүүжээ\n", + "\n", + "Reference_4:\n", + "мөн л овог нэр насыг нь тодорхойлсон өнгө бүрийн тууз зүүжээ.\n", + "Prediction_5:\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n", + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "үнэндээ гэхэд би нэ бутын хаань ч хаяг бүртгэлгүй явна шүү дээ\n", + "\n", + "Reference_5:\n", + "үнэндээ гэхэд би энэ хотын хаана нь ч хаяг бүртгэлгүй явна шүү дээ.\n", + "Prediction_6:\n", + "сайн ч мөрдөгч бас шилдэг дарга байсан юм шүү\n", + "\n", + "Reference_6:\n", + "сайн ч мөрдөгч бас шилдэг дарга байсан юм шүү.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Prediction_7:\n", + "эцэг эхчүүд сурагчид та бүхэн доорхаягаар урж захим хичээлэн хийгээрэй\n", + "\n", + "Reference_7:\n", + "эцэг, эхчүүд, сурагчид та бүхэн доорх хаягаар орж цахим хичээлээ хийгээрэй.\n", + "Prediction_8:\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "та шинэ жэлийн баяраар илүү чамин баярын уур амьсгал оруулах гоёмсог умьс бэлллээтээ\n", + "\n", + "Reference_8:\n", + "та шинэ жилийн баяраар илүү чамин баярын уур амьсгал оруулах гоёмсог оймс бэлэглээрэй.\n", + "Prediction_9:\n", + "машины тунслогуйн тагарвал яана сайхан хувцаа баллай л гэсэн үг\n", + "\n", + "Reference_9:\n", + "машины донсолгоонд хагарвал яана, сайхан хувцсаа баллана л гэсэн үг.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Даалгавар 1\n", + "энэхүү сургалтын WER 48% бууруулах\n", + "\n", + "# Даалгавар 2\n", + "өөрийн яриаг сургалтанд ашиглах\n", + "\n", + "# Даалгавар 3\n", + "Бусад сүүлийн үеийн модел ашиглан сургах\n", + "\n", + "# Хугацаа 4 долоо хоног" + ], + "metadata": { + "id": "0baGGHnPA7QC" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "1r0Xt6q4BvHb" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file