"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import IPython.display as ipd\n",
+ "import numpy as np\n",
+ "import random\n",
+ "\n",
+ "rand_int = random.randint(0, len(common_voice_train)-1)\n",
+ "\n",
+ "print(common_voice_train[rand_int][\"sentence\"])\n",
+ "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gY8m3vARHYTa"
+ },
+ "source": [
+ "It seems like the data is now correctly loaded and resampled. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1MaL9J2dNVtG"
+ },
+ "source": [
+ "It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus.\n",
+ "\n",
+ "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n",
+ "\n",
+ "**Note**: *You can click the following cell a couple of times to verify multiple samples.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1Po2g7YPuRTx",
+ "outputId": "63478ae9-2927-4ec1-c13c-41fb3754e18e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Target text: ülke olimpiyatları yirmi ikinci sırada tamamladı\n",
+ "Input array shape: (74880,)\n",
+ "Sampling rate: 16000\n"
+ ]
+ }
+ ],
+ "source": [
+ "rand_int = random.randint(0, len(common_voice_train)-1)\n",
+ "\n",
+ "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n",
+ "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n",
+ "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "M9teZcSwOBJ4"
+ },
+ "source": [
+ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "k3Pbn5WvOYZF"
+ },
+ "source": [
+ "Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by `Wav2Vec2ForCTC` for training. To do so let's make use of Dataset's [`map(...)`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=map#datasets.DatasetDict.map) function.\n",
+ "\n",
+ "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n",
+ "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). \n",
+ "Third, we encode the transcriptions to label ids.\n",
+ "\n",
+ "**Note**: This mapping function is a good example of how the `Wav2Vec2Processor` class should be used. In \"normal\" context, calling `processor(...)` is redirected to `Wav2Vec2FeatureExtractor`'s call method. When wrapping the processor into the `as_target_processor` context, however, the same method is redirected to `Wav2Vec2CTCTokenizer`'s call method.\n",
+ "For more information please check the [docs](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#transformers.Wav2Vec2Processor.__call__)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "eJY7I0XAwe9p"
+ },
+ "outputs": [],
+ "source": [
+ "def prepare_dataset(batch):\n",
+ " audio = batch[\"audio\"]\n",
+ "\n",
+ " # batched output is \"un-batched\"\n",
+ " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n",
+ " batch[\"input_length\"] = len(batch[\"input_values\"])\n",
+ " \n",
+ " with processor.as_target_processor():\n",
+ " batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n",
+ " return batch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "q6Pg_WR3OGAP"
+ },
+ "source": [
+ "Let's apply the data preparation function to all examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
+ "012b09b271a842d0a7f1cd1260654c82",
+ "6a40faf77bf445308534e7acada6bf0d",
+ "a62ff1ee03dc47c9834b43720df991ea",
+ "03d00b443bfd445d9286f64fd8556031",
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+ "fe156fa95ee9410daff8f7c9a8b0d3a3",
+ "a5e2e1fe3ab94cc6ae1a73cc1ab6f9a8"
+ ]
+ },
+ "id": "-np9xYK-wl8q",
+ "outputId": "00d6940a-a7bf-4128-896b-76bc289e5b7f"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "012b09b271a842d0a7f1cd1260654c82",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/3478 [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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "lbQf5GuZyQ4_"
+ },
+ "outputs": [],
+ "source": [
+ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xO-Zdj-5cxXp"
+ },
+ "source": [
+ "Next, the evaluation metric is defined. As mentioned earlier, the \n",
+ "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "7c81059d35534b799623afe872095cac",
+ "379128a308ab43ee9e785e234bb94049",
+ "ff8e746cf8ef4f988617b8d4c0dd3c9c",
+ "0204a7bc668746a68f6e5e5632753c1c",
+ "84361b363998428a87b9e50b6625cca9",
+ "c0919f3c8d594911815ce63273f7fe51",
+ "69f2a9e473534148b96b2e9bd639a087",
+ "5cd8c74ba2e845d498afdc1c25009767",
+ "3f0c587f7b764d96837c8e16254aab12",
+ "d3b5f5a0d35642e0b606ac63c38f88fa",
+ "7ce84a97d72848c0967f732734afdd68"
+ ]
+ },
+ "id": "9Xsux2gmyXso",
+ "outputId": "18ceeb9e-1a0d-4ee8-f511-a12ad3608bf1"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7c81059d35534b799623afe872095cac",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.95k [00:00> m$.\n",
+ "\n",
+ "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "1XZ-kjweyTy_"
+ },
+ "outputs": [],
+ "source": [
+ "def compute_metrics(pred):\n",
+ " pred_logits = pred.predictions\n",
+ " pred_ids = np.argmax(pred_logits, axis=-1)\n",
+ "\n",
+ " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
+ "\n",
+ " pred_str = processor.batch_decode(pred_ids)\n",
+ " # we do not want to group tokens when computing the metrics\n",
+ " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
+ "\n",
+ " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
+ "\n",
+ " return {\"wer\": wer}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Xmgrx4bRwLIH"
+ },
+ "source": [
+ "Now, we can load the pretrained checkpoint of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m). The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of `Wav2Vec2ForCTC` also CTC's *blank token* ${}^2$. To save GPU memory, we enable PyTorch's [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html) and also set the loss reduction to \"*mean*\".\n",
+ "\n",
+ "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning Facebook's [wav2vec2-xls-r-300m checkpoint](https://huggingface.co/facebook/wav2vec2-xls-r-300m) seems to require some hyper-parameter tuning. Therefore, I had to play around a bit with different values for dropout, [SpecAugment](https://arxiv.org/abs/1904.08779)'s masking dropout rate, layer dropout, and the learning rate until training seemed to be stable enough. \n",
+ "\n",
+ "**Note**: When using this notebook to train XLS-R on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 191,
+ "referenced_widgets": [
+ "1ca82f9af3ae423096661a1ffd8a165f",
+ "ce429683f48349c083e5fc3253a897d7",
+ "acc35dfdfca542828d1bb02cbde15dfe",
+ "aa92ff120d9c4dd68ae941d7a1f33a81",
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+ "771530ea22364f1ea8963e2d542fcf49",
+ "1cdac9cda666466ca1e09f32661d40ed",
+ "3ec9917890f4452883bcaeac444d056c",
+ "e9531fcd48b74929bfdb45cd3f3315e7",
+ "349c9e8b2a8846c1bff5a03d2a28066a",
+ "de1e5c31eacf4fa59cfa51926354acca"
+ ]
+ },
+ "id": "e7cqAWIayn6w",
+ "outputId": "3e6cebea-78ef-45df-87b0-f63b78ba9644"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1ca82f9af3ae423096661a1ffd8a165f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.53k [00:00 inspect -> Console tab and insert code*)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VYYAvgkW4P0m"
+ },
+ "source": [
+ "```javascript\n",
+ "function ConnectButton(){\n",
+ " console.log(\"Connect pushed\"); \n",
+ " document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n",
+ "}\n",
+ "setInterval(ConnectButton,60000);\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7bGgLV2r0yvZ"
+ },
+ "source": [
+ "Depending on what GPU was allocated to your google colab it might be possible that you are seeing an `\"out-of-memory\"` error here. In this case, it's probably best to reduce `per_device_train_batch_size` to 8 or even less and increase [`gradient_accumulation`](https://huggingface.co/transformers/master/main_classes/trainer.html#trainingarguments)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "background_save": true,
+ "base_uri": "https://localhost:8080/",
+ "height": 311
+ },
+ "id": "9fRr9TG5pGBl",
+ "outputId": "122ea040-7b24-452a-c7d4-7e72e1c46973"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running training *****\n",
+ " Num examples = 3478\n",
+ " Num Epochs = 30\n",
+ " Instantaneous batch size per device = 16\n",
+ " Total train batch size (w. parallel, distributed & accumulation) = 32\n",
+ " Gradient Accumulation steps = 2\n",
+ " Total optimization steps = 3270\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/feature_extraction_utils.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)\n",
+ " tensor = as_tensor(value)\n",
+ "/usr/local/lib/python3.7/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"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
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+ " [3270/3270 4:41:48, Epoch 30/30]\n",
+ "
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+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ " Wer | \n",
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+ " \n",
+ " \n",
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+ " 0.371600 | \n",
+ " 0.405785 | \n",
+ " 0.456337 | \n",
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+ " \n",
+ " | 2000 | \n",
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+ " \n",
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+ " 0.369830 | \n",
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+ " \n",
+ " | 3200 | \n",
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+ " \n",
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"
+ ],
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "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 = 1647\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400/preprocessor_config.json\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/preprocessor_config.json\n",
+ "/usr/local/lib/python3.7/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 = 1647\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800/preprocessor_config.json\n",
+ "/usr/local/lib/python3.7/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 = 1647\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/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 = 1647\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/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 = 1647\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/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 = 1647\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/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 = 1647\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/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 = 1647\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/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"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=3270, training_loss=0.5767540113641582, metrics={'train_runtime': 16918.4457, 'train_samples_per_second': 6.167, 'train_steps_per_second': 0.193, 'total_flos': 1.2873093995838829e+19, 'train_loss': 0.5767540113641582, 'epoch': 30.0})"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "a9q4mgMZplr_"
+ },
+ "source": [
+ "The training loss and validation WER go down nicely."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4Ya7WEy0pd13"
+ },
+ "source": [
+ "You can now upload the result of the training to the 🤗 Hub, just execute this instruction:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "background_save": true,
+ "referenced_widgets": [
+ "2d2f71d2c70d466cb9a0d3317fb3095e",
+ "2e894a5b95cb489db8b27c6617fd9533"
+ ]
+ },
+ "id": "ArG1Thf6NBWm",
+ "outputId": "62ef1c3d-786c-4e25-f9c5-4020e71aa298"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/preprocessor_config.json\n",
+ "Several commits (2) will be pushed upstream.\n",
+ "The progress bars may be unreliable.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2d2f71d2c70d466cb9a0d3317fb3095e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Upload file pytorch_model.bin: 0%| | 3.35k/1.18G [00:00 main\n",
+ "\n",
+ "Dropping the following result as it does not have all the necessary field:\n",
+ "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'tr'}}\n",
+ "To https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-turkish-colab\n",
+ " fe76946..5f0d67b main -> main\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-turkish-colab/commit/fe769461e4e2fb9534740e6c278a0cfabf268474'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.push_to_hub()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RHIVc44_fY2N"
+ },
+ "source": [
+ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5lWWIKyBpx1h"
+ },
+ "source": [
+ "```python\n",
+ "from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
+ "\n",
+ "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab\")\n",
+ "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab\")\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pmi1cX0fRBit"
+ },
+ "source": [
+ "For more examples of how XLS-R can be fine-tuned, please take a look at the [official speech recognition examples](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#examples)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L8b8Qkoy3KyS"
+ },
+ "source": [
+ "### Evaluation\n",
+ "\n",
+ "As a final check, let's load the model and verify that it indeed has learned to transcribe Turkish speech.\n",
+ "\n",
+ "Let's first load the pretrained checkpoint."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "background_save": true
+ },
+ "id": "R351I9IQp_9D",
+ "outputId": "f2a2ee99-7db6-4962-e140-0107054102d3"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "loading configuration file wav2vec2-large-xls-r-300m-turkish-colab/config.json\n",
+ "Model config Wav2Vec2Config {\n",
+ " \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n",
+ " \"activation_dropout\": 0.0,\n",
+ " \"apply_spec_augment\": true,\n",
+ " \"architectures\": [\n",
+ " \"Wav2Vec2ForCTC\"\n",
+ " ],\n",
+ " \"attention_dropout\": 0.0,\n",
+ " \"bos_token_id\": 1,\n",
+ " \"classifier_proj_size\": 256,\n",
+ " \"codevector_dim\": 768,\n",
+ " \"contrastive_logits_temperature\": 0.1,\n",
+ " \"conv_bias\": true,\n",
+ " \"conv_dim\": [\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512\n",
+ " ],\n",
+ " \"conv_kernel\": [\n",
+ " 10,\n",
+ " 3,\n",
+ " 3,\n",
+ " 3,\n",
+ " 3,\n",
+ " 2,\n",
+ " 2\n",
+ " ],\n",
+ " \"conv_stride\": [\n",
+ " 5,\n",
+ " 2,\n",
+ " 2,\n",
+ " 2,\n",
+ " 2,\n",
+ " 2,\n",
+ " 2\n",
+ " ],\n",
+ " \"ctc_loss_reduction\": \"mean\",\n",
+ " \"ctc_zero_infinity\": false,\n",
+ " \"diversity_loss_weight\": 0.1,\n",
+ " \"do_stable_layer_norm\": true,\n",
+ " \"eos_token_id\": 2,\n",
+ " \"feat_extract_activation\": \"gelu\",\n",
+ " \"feat_extract_dropout\": 0.0,\n",
+ " \"feat_extract_norm\": \"layer\",\n",
+ " \"feat_proj_dropout\": 0.0,\n",
+ " \"feat_quantizer_dropout\": 0.0,\n",
+ " \"final_dropout\": 0.0,\n",
+ " \"gradient_checkpointing\": false,\n",
+ " \"hidden_act\": \"gelu\",\n",
+ " \"hidden_dropout\": 0.0,\n",
+ " \"hidden_size\": 1024,\n",
+ " \"initializer_range\": 0.02,\n",
+ " \"intermediate_size\": 4096,\n",
+ " \"layer_norm_eps\": 1e-05,\n",
+ " \"layerdrop\": 0.0,\n",
+ " \"mask_feature_length\": 10,\n",
+ " \"mask_feature_prob\": 0.0,\n",
+ " \"mask_time_length\": 10,\n",
+ " \"mask_time_prob\": 0.05,\n",
+ " \"model_type\": \"wav2vec2\",\n",
+ " \"num_attention_heads\": 16,\n",
+ " \"num_codevector_groups\": 2,\n",
+ " \"num_codevectors_per_group\": 320,\n",
+ " \"num_conv_pos_embedding_groups\": 16,\n",
+ " \"num_conv_pos_embeddings\": 128,\n",
+ " \"num_feat_extract_layers\": 7,\n",
+ " \"num_hidden_layers\": 24,\n",
+ " \"num_negatives\": 100,\n",
+ " \"pad_token_id\": 36,\n",
+ " \"proj_codevector_dim\": 768,\n",
+ " \"torch_dtype\": \"float32\",\n",
+ " \"transformers_version\": \"4.11.3\",\n",
+ " \"use_weighted_layer_sum\": false,\n",
+ " \"vocab_size\": 39\n",
+ "}\n",
+ "\n",
+ "loading weights file wav2vec2-large-xls-r-300m-turkish-colab/pytorch_model.bin\n",
+ "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n",
+ "\n",
+ "All the weights of Wav2Vec2ForCTC were initialized from the model checkpoint at wav2vec2-large-xls-r-300m-turkish-colab.\n",
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n",
+ "loading feature extractor configuration file wav2vec2-large-xls-r-300m-turkish-colab/preprocessor_config.json\n",
+ "Feature extractor Wav2Vec2FeatureExtractor {\n",
+ " \"do_normalize\": true,\n",
+ " \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n",
+ " \"feature_size\": 1,\n",
+ " \"padding_side\": \"right\",\n",
+ " \"padding_value\": 0.0,\n",
+ " \"return_attention_mask\": true,\n",
+ " \"sampling_rate\": 16000\n",
+ "}\n",
+ "\n",
+ "Didn't find file wav2vec2-large-xls-r-300m-turkish-colab/tokenizer.json. We won't load it.\n",
+ "loading file wav2vec2-large-xls-r-300m-turkish-colab/vocab.json\n",
+ "loading file wav2vec2-large-xls-r-300m-turkish-colab/tokenizer_config.json\n",
+ "loading file wav2vec2-large-xls-r-300m-turkish-colab/added_tokens.json\n",
+ "loading file wav2vec2-large-xls-r-300m-turkish-colab/special_tokens_map.json\n",
+ "loading file None\n",
+ "Adding to the vocabulary\n",
+ "Adding to the vocabulary\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = Wav2Vec2ForCTC.from_pretrained(repo_name).to(\"cuda\")\n",
+ "processor = Wav2Vec2Processor.from_pretrained(repo_name)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jD7TZ1YS3S_K"
+ },
+ "source": [
+ "\n",
+ "Now, we will just take the first example of the test set, run it through the model and take the `argmax(...)` of the logits to retrieve the predicted token ids."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "background_save": true
+ },
+ "id": "pax07TnL3WZn",
+ "outputId": "867787ff-0cb7-41e9-f926-96f7b53e7134"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "input_dict = processor(common_voice_test[0][\"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]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7nkzSQu53Zs2"
+ },
+ "source": [
+ "We adapted `common_voice_test` quite a bit so that the dataset instance does not contain the original sentence label anymore. Thus, we re-use the original dataset to get the label of the first example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "background_save": true,
+ "referenced_widgets": [
+ "54097a6c744849128d7cc3da8aad6609",
+ "b8827ecbec9e44f09216f62c1ee12840",
+ "8728bb32478240b7abe576375b01e640",
+ "19b8a530352a423794395979e6415bea",
+ "5fc8ec04870e4bb7b63b20844c355ab6"
+ ]
+ },
+ "id": "fe2AE-2xqKHx",
+ "outputId": "1d8321b3-4f41-4d71-e74e-f33f32a7b261"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using custom data configuration tr-ad9f7b76efa9f3a0\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading and preparing dataset common_voice/tr (download: 592.09 MiB, generated: 2.89 MiB, post-processed: Unknown size, total: 594.98 MiB) to /root/.cache/huggingface/datasets/common_voice/tr-ad9f7b76efa9f3a0/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9...\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "54097a6c744849128d7cc3da8aad6609",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b8827ecbec9e44f09216f62c1ee12840",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8728bb32478240b7abe576375b01e640",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "19b8a530352a423794395979e6415bea",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5fc8ec04870e4bb7b63b20844c355ab6",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/common_voice/tr-ad9f7b76efa9f3a0/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9. Subsequent calls will reuse this data.\n"
+ ]
+ }
+ ],
+ "source": [
+ "common_voice_test_transcription = load_dataset(\"common_voice\", \"tr\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "epu8kCQZ3h70"
+ },
+ "source": [
+ "\n",
+ "Finally, we can decode the example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "background_save": true
+ },
+ "id": "K4xWqmk_qMn0",
+ "outputId": "d9e40b3c-f02a-48a1-d081-6d7e8b37dcaf"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prediction:\n",
+ "ha ta küçük şeyleri için bir büyük biş şeylir koğoluyor ve yeneküçük şeyler için bir birmizi incilkiyoruz\n",
+ "\n",
+ "Reference:\n",
+ "hayatta küçük şeyleri kovalıyor ve yine küçük şeyler için birbirimizi incitiyoruz.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Prediction:\")\n",
+ "print(processor.decode(pred_ids))\n",
+ "\n",
+ "print(\"\\nReference:\")\n",
+ "print(common_voice_test_transcription[0][\"sentence\"].lower())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HwhyoMml3oOT"
+ },
+ "source": [
+ "Alright! The transcription can definitely be recognized from our prediction, but it is not perfect yet. Training the model a bit longer, spending more time on the data preprocessing, and especially using a language model for decoding would certainly improve the model's overall performance.\n",
+ "\n",
+ "For a demonstration model on a low-resource language, the results are quite acceptable however 🤗."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uA4FF5fTZCK_"
+ },
+ "source": [
+ "## Decoding with an n-gram language model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "W2jMmdD6-54j",
+ "outputId": "42155c88-e3fc-4baf-9a43-d18ee288e476"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/content/drive/MyDrive/xls-r-ngram\n"
+ ]
+ }
+ ],
+ "source": [
+ "%cd /content/drive/MyDrive/xls-r-ngram/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "RZZmQONlQQAV",
+ "outputId": "ce7c8c35-b567-4b5d-be65-503b0075325c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2022-02-07 23:45:19-- https://kheafield.com/code/kenlm.tar.gz\n",
+ "Resolving kheafield.com (kheafield.com)... 35.196.63.85\n",
+ "Connecting to kheafield.com (kheafield.com)|35.196.63.85|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 491090 (480K) [application/x-gzip]\n",
+ "Saving to: ‘STDOUT’\n",
+ "\n",
+ "- 100%[===================>] 479.58K 269KB/s in 1.8s \n",
+ "\n",
+ "2022-02-07 23:45:21 (269 KB/s) - written to stdout [491090/491090]\n",
+ "\n",
+ "-- The C compiler identification is GNU 7.5.0\n",
+ "-- The CXX compiler identification is GNU 7.5.0\n",
+ "-- Check for working C compiler: /usr/bin/cc\n",
+ "-- Check for working C compiler: /usr/bin/cc -- works\n",
+ "-- Detecting C compiler ABI info\n",
+ "-- Detecting C compiler ABI info - done\n",
+ "-- Detecting C compile features\n",
+ "-- Detecting C compile features - done\n",
+ "-- Check for working CXX compiler: /usr/bin/c++\n",
+ "-- Check for working CXX compiler: /usr/bin/c++ -- works\n",
+ "-- Detecting CXX compiler ABI info\n",
+ "-- Detecting CXX compiler ABI info - done\n",
+ "-- Detecting CXX compile features\n",
+ "-- Detecting CXX compile features - done\n",
+ "-- Could NOT find Eigen3 (missing: Eigen3_DIR)\n",
+ "-- Looking for pthread.h\n",
+ "-- Looking for pthread.h - found\n",
+ "-- Looking for pthread_create\n",
+ "-- Looking for pthread_create - not found\n",
+ "-- Looking for pthread_create in pthreads\n",
+ "-- Looking for pthread_create in pthreads - not found\n",
+ "-- Looking for pthread_create in pthread\n",
+ "-- Looking for pthread_create in pthread - found\n",
+ "-- Found Threads: TRUE \n",
+ "-- Boost version: 1.65.1\n",
+ "-- Found the following Boost libraries:\n",
+ "-- program_options\n",
+ "-- system\n",
+ "-- thread\n",
+ "-- unit_test_framework\n",
+ "-- chrono\n",
+ "-- date_time\n",
+ "-- atomic\n",
+ "-- Check if compiler accepts -pthread\n",
+ "-- Check if compiler accepts -pthread - yes\n",
+ "-- Found ZLIB: /usr/lib/x86_64-linux-gnu/libz.so (found version \"1.2.11\") \n",
+ "-- Found BZip2: /usr/lib/x86_64-linux-gnu/libbz2.so (found version \"1.0.6\") \n",
+ "-- Looking for BZ2_bzCompressInit\n",
+ "-- Looking for BZ2_bzCompressInit - found\n",
+ "-- Looking for lzma_auto_decoder in /usr/lib/x86_64-linux-gnu/liblzma.so\n",
+ "-- Looking for lzma_auto_decoder in /usr/lib/x86_64-linux-gnu/liblzma.so - found\n",
+ "-- Looking for lzma_easy_encoder in /usr/lib/x86_64-linux-gnu/liblzma.so\n",
+ "-- Looking for lzma_easy_encoder in /usr/lib/x86_64-linux-gnu/liblzma.so - found\n",
+ "-- Looking for lzma_lzma_preset in /usr/lib/x86_64-linux-gnu/liblzma.so\n",
+ "-- Looking for lzma_lzma_preset in /usr/lib/x86_64-linux-gnu/liblzma.so - found\n",
+ "-- Found LibLZMA: /usr/include (found version \"5.2.2\") \n",
+ "-- Configuring done\n",
+ "-- Generating done\n",
+ "-- Build files have been written to: /content/drive/MyDrive/xls-r-ngram/kenlm/build\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target kenlm_util\u001b[0m\n",
+ "[ 0%] \u001b[32mBuilding CXX object util/CMakeFiles/kenlm_util.dir/double-conversion/bignum.cc.o\u001b[0m\n",
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+ "[ 36%] \u001b[32m\u001b[1mLinking CXX static library ../lib/libkenlm_util.a\u001b[0m\n",
+ "[ 36%] Built target kenlm_util\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target probing_hash_table_benchmark\u001b[0m\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target kenlm\u001b[0m\n",
+ "[ 37%] \u001b[32mBuilding CXX object util/CMakeFiles/probing_hash_table_benchmark.dir/probing_hash_table_benchmark_main.cc.o\u001b[0m\n",
+ "[ 38%] \u001b[32mBuilding CXX object lm/CMakeFiles/kenlm.dir/bhiksha.cc.o\u001b[0m\n",
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+ "[ 46%] Built target probing_hash_table_benchmark\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target kenlm_filter\u001b[0m\n",
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+ "[ 53%] \u001b[32mBuilding CXX object lm/filter/CMakeFiles/kenlm_filter.dir/vocab.cc.o\u001b[0m\n",
+ "[ 55%] \u001b[32m\u001b[1mLinking CXX static library ../../lib/libkenlm_filter.a\u001b[0m\n",
+ "[ 55%] Built target kenlm_filter\n",
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+ "[ 60%] \u001b[32mBuilding CXX object lm/CMakeFiles/kenlm.dir/value_build.cc.o\u001b[0m\n",
+ "[ 61%] \u001b[32mBuilding CXX object lm/CMakeFiles/kenlm.dir/virtual_interface.cc.o\u001b[0m\n",
+ "[ 62%] \u001b[32mBuilding CXX object lm/CMakeFiles/kenlm.dir/vocab.cc.o\u001b[0m\n",
+ "[ 63%] \u001b[32mBuilding CXX object lm/CMakeFiles/kenlm.dir/common/model_buffer.cc.o\u001b[0m\n",
+ "[ 65%] \u001b[32mBuilding CXX object lm/CMakeFiles/kenlm.dir/common/print.cc.o\u001b[0m\n",
+ "[ 66%] \u001b[32mBuilding CXX object lm/CMakeFiles/kenlm.dir/common/renumber.cc.o\u001b[0m\n",
+ "[ 67%] \u001b[32mBuilding CXX object lm/CMakeFiles/kenlm.dir/common/size_option.cc.o\u001b[0m\n",
+ "[ 68%] \u001b[32m\u001b[1mLinking CXX static library ../lib/libkenlm.a\u001b[0m\n",
+ "[ 68%] Built target kenlm\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target build_binary\u001b[0m\n",
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+ "[ 73%] Built target fragment\n",
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+ "[ 73%] Built target build_binary\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target query\u001b[0m\n",
+ "[ 75%] \u001b[32mBuilding CXX object lm/CMakeFiles/kenlm_benchmark.dir/kenlm_benchmark_main.cc.o\u001b[0m\n",
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+ "[ 77%] Built target query\n",
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+ "[ 81%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/kenlm_builder.dir/initial_probabilities.cc.o\u001b[0m\n",
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+ "[ 83%] Built target kenlm_benchmark\n",
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+ "[ 86%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/kenlm_builder.dir/output.cc.o\u001b[0m\n",
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+ "[ 87%] Built target phrase_table_vocab\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target filter\u001b[0m\n",
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+ "[ 91%] \u001b[32m\u001b[1mLinking CXX static library ../../lib/libkenlm_builder.a\u001b[0m\n",
+ "[ 91%] Built target kenlm_builder\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target count_ngrams\u001b[0m\n",
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+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "!wget -O - https://kheafield.com/code/kenlm.tar.gz | tar xz\n",
+ "!mkdir kenlm/build && cd kenlm/build && cmake .. && make -j2\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "uME_zJUaQrlw",
+ "outputId": "e2d23914-2260-47cf-902a-cabd80f3f734"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "build_binary filter\tkenlm_benchmark phrase_table_vocab\t query\n",
+ "count_ngrams fragment\tlmplz\t\t probing_hash_table_benchmark\n"
+ ]
+ }
+ ],
+ "source": [
+ "!ls kenlm/build/bin\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "npRCvP_sQyjF"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import AutoFeatureExtractor, AutoTokenizer, pipeline\n",
+ "from datasets import Audio, Dataset, DatasetDict, load_dataset, load_metric\n",
+ "\n",
+ "import re\n",
+ "import string\n",
+ "import unidecode"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
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+ "d8f8781216a4472a9a2051f008f5b0d5",
+ "06b325f08a6e4998931a00ef3dcb9d05",
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+ "2bfc7e79dc704b29a548b4c0d9241c40",
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+ "f1d8af549afa4bb7b24d1de284460b81",
+ "e26769ea23414403b41a2de5fd9574b4",
+ "10273a7d4aae4c05a36b7ed9a8a2ba4c",
+ "66bc73d5cab943d38870ad5e797d4755",
+ "56ec604d97864db9bf88d075184b50c4",
+ "2387b655b23c49788309cdac13146839",
+ "0e08c58a3bc44c489967a502a18f02f6",
+ "fa2fdca1923146c38968020f2dcd31af",
+ "5d1529a6bb1c489492e10fb9cdc45cc3",
+ "eb7701cf79274057b198cae0a2e1a345",
+ "ce454b556ca34befa512e6878324367a",
+ "bb62bc622b3d427cafa2c2c7180ab56a",
+ "65c4c0af846644d89d8f8b318f40aa0a",
+ "249f1ff433db46b68a7e72fc8ee5f43f",
+ "3bfbc26c9f8d41f9a638a093ed6ef10c",
+ "421e7c39be7849f586caea0edc750d3d",
+ "7018f0deca5d4b878b93bef5eb69d865",
+ "c004c6e6d15c4c93af70bb6c60eae484",
+ "8616fe21e3e0430d9e654eb499ccd5e9",
+ "26d566914b2b40e99c3d29797e1bbb86",
+ "00cd2e770d1e43048e7f41bcf6a5f961",
+ "2aba7b884caa4963baa0931fc64a9ab5",
+ "3e5a97b65b3741cb924af45b6628ca6d",
+ "377b283f33434ccaa5e64926c6bff6e3",
+ "f93d52a5e3be4c9a87f3b8e29cfaa4bc"
+ ]
+ },
+ "id": "M1YRPnTZaFHW",
+ "outputId": "414a50f9-7183-4537-a600-b2d57b361299"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "00476100c826427eb1f7263479c5016c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/10.1k [00:00‘\", # after o/g indicate modified char\n",
+ " unidecode.unidecode(batch[\"sentence\"]).lower()\n",
+ " )\n",
+ " )\n",
+ " ) + \" \"\n",
+ " return batch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "a4f265ea99fa432a879b949520d37b3f",
+ "5d84de8fa34d4636b1801669a179dba5",
+ "208e960fe7fd4f269b1c6ede3c3f09f3",
+ "3f219581737d4148982607d28672bc90",
+ "a184146a75c74e2c85ef500b10da05d7",
+ "aaa27c8a6db34928aa54c43fb0cb2c3f",
+ "1d66f0adcfc64857ba64bf0eead85e1a",
+ "35acd5d105754454a989c49e3cf54ab3",
+ "4f306a5c4e034fa5b7469da0ae8888a4",
+ "9552e2c379a5427fa821e71eecd02495",
+ "445bbdb812d545adb779ed1933f8278d"
+ ]
+ },
+ "id": "4qTL2Fc0cbMq",
+ "outputId": "ab387ea4-5cd7-4f32-b9e6-cdb12de40d8c"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a4f265ea99fa432a879b949520d37b3f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dataset_train = dataset_dict[\"train\"].map(remove_special_characters, remove_columns=dataset_dict[\"train\"].column_names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "bd4bae3547204defbf78cfa2945b5e22",
+ "112242cfa09b440182720e34e65195f5",
+ "b6248483c38b42e4addcaea467adbc04",
+ "cc3eb3f99cc84814a46e2997022ab1fc",
+ "306d398e3247444fbcbc039b1507b964",
+ "e5221d40d2ba4b1cb44c3278bccd4e5b",
+ "ffb2b52590f446aca3b9a721405dde4b",
+ "2e3522d2bd084ee6991c38ca338d08ea",
+ "a34dcc92c9874068852812dc5060b91d",
+ "3642769698e7435ebd25d3e4ff1498ac",
+ "ed2a911a02ad40bcab9b947e5302a45a"
+ ]
+ },
+ "id": "-NrwpeLz-SF7",
+ "outputId": "6a307267-d7b3-4bb9-e7b6-dad3bfe163bc"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "bd4bae3547204defbf78cfa2945b5e22",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dataset_other = dataset_dict[\"other\"].map(remove_special_characters, remove_columns=dataset_dict[\"other\"].column_names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "sX1xsY8ccWOv",
+ "outputId": "8968734b-d4d5-4b22-e157-fff1ad1933ef"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 0 1013740 8446974 xls-r-uzbek-cv8/uz_cv8_text.txt\n"
+ ]
+ }
+ ],
+ "source": [
+ "text_data = \"xls-r-uzbek-cv8/uz_cv8_text.txt\"\n",
+ "with open(text_data, \"w\") as fs:\n",
+ " fs.write(\" \".join(dataset_train[\"text\"]) + \" \")\n",
+ "\n",
+ "with open(text_data, \"a\") as fs:\n",
+ " fs.write(\" \".join(dataset_other[\"text\"]))\n",
+ "\n",
+ "\n",
+ "!wc $text_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "be17oucOcp8_",
+ "outputId": "588be248-fed1-4031-a19b-31edb7804137"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=== 1/5 Counting and sorting n-grams ===\n",
+ "Reading /content/drive/MyDrive/xls-r-ngram/xls-r-uzbek-cv8/uz_cv8_text.txt\n",
+ "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
+ "tcmalloc: large alloc 3854925824 bytes == 0x5625c9612000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7f7c51e 0x5625c7f5b2eb 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n",
+ "tcmalloc: large alloc 17989623808 bytes == 0x5626af26a000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5b308 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n",
+ "****************************************************************************************************\n",
+ "Unigram tokens 1013740 types 98005\n",
+ "=== 2/5 Calculating and sorting adjusted counts ===\n",
+ "Chain sizes: 1:1176060 2:2133515264 3:4000341248 4:6400545792 5:9334129664\n",
+ "tcmalloc: large alloc 9334136832 bytes == 0x5625c9612000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5b8d7 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n",
+ "tcmalloc: large alloc 2133516288 bytes == 0x5627f5cf4000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5bcdd 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n",
+ "tcmalloc: large alloc 4000342016 bytes == 0x562874fa2000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5bcdd 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n",
+ "tcmalloc: large alloc 6400548864 bytes == 0x562ae0750000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5bcdd 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n",
+ "Statistics:\n",
+ "1 98004 D1=0.62768 D2=1.18989 D3+=1.46871\n",
+ "2 539023 D1=0.794198 D2=1.50474 D3+=1.7039\n",
+ "3 719386 D1=0.858088 D2=1.76379 D3+=2.1921\n",
+ "4 793206 D1=0.885229 D2=1.81888 D3+=2.47486\n",
+ "5 850589 D1=0.711001 D2=1.87963 D3+=2.58459\n",
+ "Memory estimate for binary LM:\n",
+ "type MB\n",
+ "probing 63 assuming -p 1.5\n",
+ "probing 76 assuming -r models -p 1.5\n",
+ "trie 31 without quantization\n",
+ "trie 17 assuming -q 8 -b 8 quantization \n",
+ "trie 28 assuming -a 22 array pointer compression\n",
+ "trie 14 assuming -a 22 -q 8 -b 8 array pointer compression and quantization\n",
+ "=== 3/5 Calculating and sorting initial probabilities ===\n",
+ "Chain sizes: 1:1176048 2:8624368 3:14387720 4:19036944 5:23816492\n",
+ "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
+ "####################################################################################################\n",
+ "=== 4/5 Calculating and writing order-interpolated probabilities ===\n",
+ "Chain sizes: 1:1176048 2:8624368 3:14387720 4:19036944 5:23816492\n",
+ "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
+ "####################################################################################################\n",
+ "=== 5/5 Writing ARPA model ===\n",
+ "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
+ "****************************************************************************************************\n",
+ "Name:lmplz\tVmPeak:28049656 kB\tVmRSS:3944028 kB\tRSSMax:3944184 kB\tuser:3.28185\tsys:3.47537\tCPU:6.75725\treal:8.10671\n"
+ ]
+ }
+ ],
+ "source": [
+ "ngram_data = \"xls-r-uzbek-cv8/5gram.arpa\"\n",
+ "!kenlm/build/bin/lmplz -o 5 < $text_data > $ngram_data "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "po4zWZRjdOGc",
+ "outputId": "8b93829b-d871-4fde-d2a3-5c602313e5a6"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\\data\\\n",
+ "ngram 1=98004\n",
+ "ngram 2=539023\n",
+ "ngram 3=719386\n",
+ "ngram 4=793206\n",
+ "ngram 5=850589\n",
+ "\n",
+ "\\1-grams:\n",
+ "-5.7507653\t\t0\n",
+ "0\t\t-0.10007139\n",
+ "-5.608028\tshartmidi\t-0.10007139\n",
+ "-3.97965\tdeyman\t-0.12756462\n",
+ "-3.4230683\tkeyingi\t-0.20062704\n",
+ "-4.135141\tvoqealar\t-0.17204677\n",
+ "-2.7541177\tbiz\t-0.29683134\n",
+ "-3.5234666\tshunchaki\t-0.12191775\n",
+ "-3.4540253\tqo‘l\t-0.18548343\n",
+ "-3.53991\tberib\t-0.12553446\n",
+ "-4.464714\tketadigan\t-0.10406738\n",
+ "-2.817535\temas\t-0.23384908\n"
+ ]
+ }
+ ],
+ "source": [
+ "!head -20 $ngram_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "id": "55U0j6JCdbT8"
+ },
+ "outputs": [],
+ "source": [
+ "corrected_data = \"xls-r-uzbek-cv8/5gram_correct.arpa\"\n",
+ "with open(ngram_data, \"r\") as read_file, open(corrected_data, \"w\") as write_file:\n",
+ " has_added_eos = False\n",
+ " for line in read_file:\n",
+ " if not has_added_eos and \"ngram 1=\" in line:\n",
+ " count=line.strip().split(\"=\")[-1]\n",
+ " write_file.write(line.replace(f\"{count}\", f\"{int(count)+1}\"))\n",
+ " elif not has_added_eos and \"\" in line:\n",
+ " write_file.write(line)\n",
+ " write_file.write(line.replace(\"\", \"\"))\n",
+ " has_added_eos = True\n",
+ " else:\n",
+ " write_file.write(line)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gVUKO_KDearL",
+ "outputId": "914eae8a-ae79-468e-d378-f9d929cc1ebc"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\\data\\\n",
+ "ngram 1=98005\n",
+ "ngram 2=539023\n",
+ "ngram 3=719386\n",
+ "ngram 4=793206\n",
+ "ngram 5=850589\n",
+ "\n",
+ "\\1-grams:\n",
+ "-5.7507653\t\t0\n",
+ "0\t\t-0.10007139\n",
+ "0\t\t-0.10007139\n",
+ "-5.608028\tshartmidi\t-0.10007139\n",
+ "-3.97965\tdeyman\t-0.12756462\n",
+ "-3.4230683\tkeyingi\t-0.20062704\n",
+ "-4.135141\tvoqealar\t-0.17204677\n",
+ "-2.7541177\tbiz\t-0.29683134\n",
+ "-3.5234666\tshunchaki\t-0.12191775\n",
+ "-3.4540253\tqo‘l\t-0.18548343\n",
+ "-3.53991\tberib\t-0.12553446\n",
+ "-4.464714\tketadigan\t-0.10406738\n"
+ ]
+ }
+ ],
+ "source": [
+ "!head -20 $corrected_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "sSpNeQ_bel36"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import AutoProcessor\n",
+ "\n",
+ "processor = AutoProcessor.from_pretrained(\"./xls-r-uzbek-cv8/\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ms020VOvlmYr",
+ "outputId": "d1a35aee-83ed-4acf-ca39-0af66f22168d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['|',\n",
+ " 'a',\n",
+ " 'b',\n",
+ " 'c',\n",
+ " 'd',\n",
+ " 'e',\n",
+ " 'f',\n",
+ " 'g',\n",
+ " 'h',\n",
+ " 'i',\n",
+ " 'j',\n",
+ " 'k',\n",
+ " 'l',\n",
+ " 'm',\n",
+ " 'n',\n",
+ " 'o',\n",
+ " 'p',\n",
+ " 'q',\n",
+ " 'r',\n",
+ " 's',\n",
+ " 't',\n",
+ " 'u',\n",
+ " 'v',\n",
+ " 'w',\n",
+ " 'x',\n",
+ " 'y',\n",
+ " 'z',\n",
+ " '‘',\n",
+ " '’',\n",
+ " '[UNK]',\n",
+ " '[PAD]',\n",
+ " '',\n",
+ " '']"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vocab_dict =processor.tokenizer.get_vocab()\n",
+ "labels = [k for k, v in sorted(vocab_dict.items(), key=lambda x: x[1])]\n",
+ "labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zUZ8MQ2rmFT6",
+ "outputId": "190b0f71-e1ac-40c5-c797-fd60764b3acc"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Found entries of length > 1 in alphabet. This is unusual unless style is BPE, but the alphabet was not recognized as BPE type. Is this correct?\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pyctcdecode import build_ctcdecoder\n",
+ "\n",
+ "decoder = build_ctcdecoder(labels=labels, kenlm_model_path=corrected_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "id": "PTNuGmy9nFRr"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import Wav2Vec2ProcessorWithLM\n",
+ "\n",
+ "processor_with_lm = Wav2Vec2ProcessorWithLM(\n",
+ " feature_extractor=processor.feature_extractor,\n",
+ " tokenizer=processor.tokenizer,\n",
+ " decoder = decoder,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "2GuhuK11nlBe"
+ },
+ "outputs": [],
+ "source": [
+ "processor_with_lm.save_pretrained(\"xls-r-uzbek-cv8\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jrV4hXy3oEJI",
+ "outputId": "c886e32b-6712-4c9f-8d7a-1a4aaccda2bd"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "total 144M\n",
+ "-rw------- 1 root root 143M Feb 7 23:59 5gram_correct.arpa\n",
+ "-rw------- 1 root root 78 Feb 8 00:01 attrs.json\n",
+ "-rw------- 1 root root 1.1M Feb 8 00:01 unigrams.txt\n"
+ ]
+ }
+ ],
+ "source": [
+ "#!rm -r xls-r-uzbek-cv8/language_model/\n",
+ "!ls -alh xls-r-uzbek-cv8/language_model/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "GxIaO9JLAk8A",
+ "outputId": "7a0104e7-0864-4014-a602-69a98d28f417"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Reading xls-r-uzbek-cv8/language_model/5gram_correct.arpa\n",
+ "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
+ "****************************************************************************************************\n",
+ "SUCCESS\n",
+ "total 67M\n",
+ "-rw------- 1 root root 66M Feb 8 00:01 5gram.bin\n",
+ "-rw------- 1 root root 78 Feb 8 00:01 attrs.json\n",
+ "-rw------- 1 root root 1.1M Feb 8 00:01 unigrams.txt\n"
+ ]
+ }
+ ],
+ "source": [
+ "!kenlm/build/bin/build_binary xls-r-uzbek-cv8/language_model/5gram_correct.arpa xls-r-uzbek-cv8/language_model/5gram.bin\n",
+ "!rm xls-r-uzbek-cv8/language_model/5gram_correct.arpa\n",
+ "!ls -alh xls-r-uzbek-cv8/language_model/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WigwTkyWoJgg",
+ "outputId": "9f72faed-3c5c-49c7-a02c-90e1e3f9c433"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/uz/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n",
+ "Traceback (most recent call last):\n",
+ " File \"./eval.py\", line 229, in \n",
+ " main(args)\n",
+ " File \"./eval.py\", line 161, in main\n",
+ " asr = pipeline(\"automatic-speech-recognition\", model=args.model_id)\n",
+ " File \"/usr/local/lib/python3.7/dist-packages/transformers/pipelines/__init__.py\", line 628, in pipeline\n",
+ " decoder = BeamSearchDecoderCTC.load_from_hf_hub(model_name, allow_regex=allow_regex)\n",
+ " File \"/usr/local/lib/python3.7/dist-packages/pyctcdecode/decoder.py\", line 757, in load_from_hf_hub\n",
+ " cached_directory = snapshot_download(model_id, cache_dir=cache_dir, **kwargs)\n",
+ " File \"/usr/local/lib/python3.7/dist-packages/huggingface_hub/snapshot_download.py\", line 144, in snapshot_download\n",
+ " model_info = _api.model_info(repo_id=repo_id, revision=revision, token=token)\n",
+ " File \"/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py\", line 868, in model_info\n",
+ " r.raise_for_status()\n",
+ " File \"/usr/local/lib/python3.7/dist-packages/requests/models.py\", line 941, in raise_for_status\n",
+ " raise HTTPError(http_error_msg, response=self)\n",
+ "requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/models/./revision/main\n"
+ ]
+ }
+ ],
+ "source": [
+ "!python ./eval.py --model_id . --dataset mozilla-foundation/common_voice_8_0 --config uz --split \"test\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "29B3FYPhyHST",
+ "outputId": "5650c5b0-8295-47df-87fb-6e80e57841b0"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/content/drive/MyDrive/xls-r-ngram/xls-r-uzbek-cv8\n"
+ ]
+ }
+ ],
+ "source": [
+ "%cd xls-r-uzbek-cv8/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "W8DN9SZ-Errc"
+ },
+ "outputs": [],
+ "source": [
+ "!git add eval.py preprocessor_config.json tokenizer_config.json language_model/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "sLIBafqmEyq6"
+ },
+ "outputs": [],
+ "source": [
+ "!git add alphabet.json "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WnQl8yyuFg8u",
+ "outputId": "df213bf0-8839-4e23-845e-8117ed4cf2e3"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "fatal: cannot exec '.git/hooks/post-commit': Permission denied\n",
+ "[with_ngram_lm 9c389f7] add ngram LM from train+other\n",
+ " 7 files changed, 98037 insertions(+), 7 deletions(-)\n",
+ " create mode 100644 alphabet.json\n",
+ " mode change 100755 => 100644 eval.py\n",
+ " create mode 100644 language_model/5gram.bin\n",
+ " create mode 100644 language_model/attrs.json\n",
+ " create mode 100644 language_model/unigrams.txt\n"
+ ]
+ }
+ ],
+ "source": [
+ "!git commit -m \"add ngram LM from train+other\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "igL2FAM7F4gL"
+ },
+ "outputs": [],
+ "source": [
+ "!git config --global user.email \"lucien@discurs.us\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "R5x8nIucGJK3"
+ },
+ "outputs": [],
+ "source": [
+ "!git config --global user.name \"Lucio\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YBb6acjCGMSt",
+ "outputId": "a2bae291-ecd2-424b-fad5-1a4be282a5c5"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "fatal: cannot exec '.git/hooks/pre-push': Permission denied\n",
+ "^C\n"
+ ]
+ }
+ ],
+ "source": [
+ "!git push origin with_ngram_lm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
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+ "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:718: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
+ " FutureWarning,\n",
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+ "\n",
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