"
]
},
"execution_count": 25,
"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))\n",
"\n",
"ipd.Audio(data=np.asarray(common_voice_train[rand_int][\"speech\"]), autoplay=True, rate=16000)"
]
},
{
"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": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:06:50.779682Z",
"start_time": "2021-03-14T10:06:50.745322Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1Po2g7YPuRTx",
"outputId": "96b0b82c-a5df-4ae6-d17b-9c7d4f710b42"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Target text: ξεπούλησε τα κτήματα μας \n",
"Input array shape: (47232,)\n",
"Sampling rate: 16000\n"
]
}
],
"source": [
"rand_int = random.randint(0, len(common_voice_train))\n",
"\n",
"print(\"Target text:\", common_voice_train[rand_int][\"target_text\"])\n",
"print(\"Input array shape:\", np.asarray(common_voice_train[rand_int][\"speech\"]).shape)\n",
"print(\"Sampling rate:\", common_voice_train[rand_int][\"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 process the dataset to the format expected by the model for training. We will again make use of the `map(...)` function.\n",
"\n",
"First, we check that the data samples have the same sampling rate of 16kHz.\n",
"Second, we extract the `input_values` from the loaded audio file. In our case, this includes only normalization, but for other speech models, this step could correspond to extracting, *e.g.* [Log-Mel features](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": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:06:51.188480Z",
"start_time": "2021-03-14T10:06:51.185722Z"
},
"id": "eJY7I0XAwe9p"
},
"outputs": [],
"source": [
"def prepare_dataset(batch):\n",
" # check that all files have the correct sampling rate\n",
" assert (\n",
" len(set(batch[\"sampling_rate\"])) == 1\n",
" ), f\"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.\"\n",
"\n",
" batch[\"input_values\"] = processor(batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]).input_values\n",
" \n",
" with processor.as_target_processor():\n",
" batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\n",
" return batch"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:07:12.818162Z",
"start_time": "2021-03-14T10:06:51.733417Z"
},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 509,
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"text": [
"/home/earendil/anaconda3/envs/cuda110/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
" return array(a, dtype, copy=False, order=order)\n"
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"\n",
"\n",
"\n",
"\n",
"\n",
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" "
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"text": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names, batch_size=8, num_proc=8, batched=True)\n",
"common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, batch_size=8, num_proc=8, batched=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gYlQkKVoRUos"
},
"source": [
"## Training\n",
"\n",
"The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n",
"\n",
"- Define a data collator. In contrast to most NLP models, XLSR-Wav2Vec2 has a much larger input length than output length. *E.g.*, a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning XLSR-Wav2Vec2 requires a special padding data collator, which we will define below\n",
"\n",
"- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n",
"\n",
"- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n",
"\n",
"- Define the training configuration.\n",
"\n",
"After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Slk403unUS91"
},
"source": [
"### Set-up Trainer\n",
"\n",
"Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/9a06b6b11bdfc42eea08fa91d0c737d1863c99e3/examples/research_projects/wav2vec2/run_asr.py#L81).\n",
"\n",
"Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them (again making use of XLSR-Wav2Vec2's context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n",
"Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:07:12.850867Z",
"start_time": "2021-03-14T10:07:12.844816Z"
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"source": [
"import torch\n",
"\n",
"from dataclasses import dataclass, field\n",
"from typing import Any, Dict, List, Optional, Union\n",
"\n",
"@dataclass\n",
"class DataCollatorCTCWithPadding:\n",
" \"\"\"\n",
" Data collator that will dynamically pad the inputs received.\n",
" Args:\n",
" processor (:class:`~transformers.Wav2Vec2Processor`)\n",
" The processor used for proccessing the data.\n",
" padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
" Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
" among:\n",
" * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
" sequence if provided).\n",
" * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
" maximum acceptable input length for the model if that argument is not provided.\n",
" * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
" different lengths).\n",
" max_length (:obj:`int`, `optional`):\n",
" Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).\n",
" max_length_labels (:obj:`int`, `optional`):\n",
" Maximum length of the ``labels`` returned list and optionally padding length (see above).\n",
" pad_to_multiple_of (:obj:`int`, `optional`):\n",
" If set will pad the sequence to a multiple of the provided value.\n",
" This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n",
" 7.5 (Volta).\n",
" \"\"\"\n",
"\n",
" processor: Wav2Vec2Processor\n",
" padding: Union[bool, str] = True\n",
" max_length: Optional[int] = None\n",
" max_length_labels: Optional[int] = None\n",
" pad_to_multiple_of: Optional[int] = None\n",
" pad_to_multiple_of_labels: Optional[int] = None\n",
"\n",
" def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
" # split inputs and labels since they have to be of different lenghts and need\n",
" # different padding methods\n",
" input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
" label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
"\n",
" batch = self.processor.pad(\n",
" input_features,\n",
" padding=self.padding,\n",
" max_length=self.max_length,\n",
" pad_to_multiple_of=self.pad_to_multiple_of,\n",
" return_tensors=\"pt\",\n",
" )\n",
" with self.processor.as_target_processor():\n",
" labels_batch = self.processor.pad(\n",
" label_features,\n",
" padding=self.padding,\n",
" max_length=self.max_length_labels,\n",
" pad_to_multiple_of=self.pad_to_multiple_of_labels,\n",
" return_tensors=\"pt\",\n",
" )\n",
"\n",
" # replace padding with -100 to ignore loss correctly\n",
" labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
"\n",
" batch[\"labels\"] = labels\n",
"\n",
" return batch"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:07:12.883379Z",
"start_time": "2021-03-14T10:07:12.881910Z"
},
"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": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:07:15.225831Z",
"start_time": "2021-03-14T10:07:12.909977Z"
},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 67,
"referenced_widgets": [
"d5fb02debe4347e781543a996ce39be3",
"da12267c52144292adb9896249d61a6a",
"8e1a85a4a6214a16b7431da23a301ada",
"57198e3250374e1fa64c5a4be255861e",
"a3c04c75ec9743feb795f69e7c4dff4f",
"d453bd4a35e54bfba20dac1fe86c60c1",
"969080b52ad44ee997829986d198c505",
"8f737725708c4097a8caaf4da6229636"
]
},
"id": "9Xsux2gmyXso",
"outputId": "58e0e6f5-6131-4147-bccb-6c42223833db"
},
"outputs": [],
"source": [
"wer_metric = load_metric(\"wer\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E1qZU5p-deqB"
},
"source": [
"The model will return a sequence of logit vectors:\n",
"$\\mathbf{y}_1, \\ldots, \\mathbf{y}_m$ with $\\mathbf{y}_1 = f_{\\theta}(x_1, \\ldots, x_n)[0]$ and $n >> m$.\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": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:07:15.267780Z",
"start_time": "2021-03-14T10:07:15.265135Z"
},
"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 `XLSR-Wav2Vec2` checkpoint. 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-large-xlsr-53 checkpoint](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) 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 XLSR-Wav2Vec2 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": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:07:23.145790Z",
"start_time": "2021-03-14T10:07:15.296367Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e7cqAWIayn6w",
"outputId": "0a5ab559-6c38-47c6-b4f5-64480ed1df65"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-large-xlsr-53 and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"from transformers import Wav2Vec2ForCTC\n",
"\n",
"model = Wav2Vec2ForCTC.from_pretrained(\n",
" \"facebook/wav2vec2-large-xlsr-53\", \n",
" attention_dropout=0.1,\n",
" hidden_dropout=0.1,\n",
" feat_proj_dropout=0.0,\n",
" mask_time_prob=0.05,\n",
" layerdrop=0.1,\n",
" gradient_checkpointing=True, \n",
" ctc_loss_reduction=\"mean\", \n",
" pad_token_id=processor.tokenizer.pad_token_id,\n",
" vocab_size=len(processor.tokenizer)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1DwR3XLSzGDD"
},
"source": [
"NOTE: Since Greek is not one of the 53 languages that XLSR-Wav2Vec2 had been pretrained on, we may or may not follow the below suggestion of freezing the CNN layers. \n",
"\n",
"Original text: The first component of XLSR-Wav2Vec2 consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the [paper](https://arxiv.org/pdf/2006.13979.pdf) does not need to be fine-tuned anymore. \n",
"Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:07:23.193215Z",
"start_time": "2021-03-14T10:07:23.191472Z"
},
"id": "oGI8zObtZ3V0"
},
"outputs": [],
"source": [
"model.freeze_feature_extractor()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lD4aGhQM0K-D"
},
"source": [
"In a final step, we define all parameters related to training. \n",
"To give more explanation on some of the parameters:\n",
"- `group_by_length` makes training more efficient by grouping training samples of similar input length into one batch. This can significantly speed up training time by heavily reducing the overall number of useless padding tokens that are passed through the model\n",
"- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Timit dataset and might be suboptimal for other speech datasets.\n",
"\n",
"For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n",
"\n",
"**Note**: If one wants to save the trained models in his/her google drive the commented-out `output_dir` can be used instead."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:07:23.256610Z",
"start_time": "2021-03-14T10:07:23.234660Z"
},
"id": "KbeKSV7uzGPP"
},
"outputs": [],
"source": [
"from transformers import TrainingArguments\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=\"wav2vec2-large-xlsr-greek\",\n",
" # output_dir=\"./wav2vec2-large-xlsr-Greek-demo\",\n",
" group_by_length=True,\n",
" per_device_train_batch_size=6,\n",
" gradient_accumulation_steps=2,\n",
" evaluation_strategy=\"steps\",\n",
" num_train_epochs=60,\n",
" fp16=True,\n",
" save_steps=400,\n",
" eval_steps=400,\n",
" logging_steps=400,\n",
" learning_rate=3e-4,\n",
" warmup_steps=500,\n",
" save_total_limit=2,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OsW-WZcL1ZtN"
},
"source": [
"Now, all instances can be passed to Trainer and we are ready to start training!"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T10:07:24.993889Z",
"start_time": "2021-03-14T10:07:23.305173Z"
},
"id": "rY7vBmFCPFgC"
},
"outputs": [],
"source": [
"from transformers import Trainer\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" data_collator=data_collator,\n",
" args=training_args,\n",
" compute_metrics=compute_metrics,\n",
" train_dataset=common_voice_train,\n",
" eval_dataset=common_voice_test,\n",
" tokenizer=processor.feature_extractor,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UoXBx1JAA0DX"
},
"source": [
"\n",
"\n",
"---\n",
"\n",
"${}^1$ To allow models to become independent of the speaker rate, in CTC, consecutive tokens that are identical are simply grouped as a single token. However, the encoded labels should not be grouped when decoding since they don't correspond to the predicted tokens of the model, which is why the `group_tokens=False` parameter has to be passed. If we wouldn't pass this parameter a word like `\"hello\"` would incorrectly be encoded, and decoded as `\"helo\"`.\n",
"\n",
"${}^2$ The blank token allows the model to predict a word, such as `\"hello\"` by forcing it to insert the blank token between the two l's. A CTC-conform prediction of `\"hello\"` of our model would be `[PAD] [PAD] \"h\" \"e\" \"e\" \"l\" \"l\" [PAD] \"l\" \"o\" \"o\" [PAD]`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rpvZHM1xReIW"
},
"source": [
"### Training"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j-3oKSzZ1hGq"
},
"source": [
"Training will take between 180 and 240 minutes depending on the GPU allocated to this notebook. While the trained model yields somewhat satisfying results on *Common Voice*'s test data of Greek, it is by no means an optimally fine-tuned model. The purpose of this notebook is to demonstrate how XLSR-Wav2Vec2's [checkpoint](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) can be fine-tuned on a low-resource ASR dataset.\n",
"\n",
"In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (*right mouse click -> 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": "code",
"execution_count": 37,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-14T17:43:05.019454Z",
"start_time": "2021-03-14T10:07:25.037347Z"
},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 348
},
"id": "_UEjJqGsQw24",
"outputId": "2e23b190-ca76-48ad-8117-376d1d7c058e",
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/earendil/anaconda3/envs/cuda110/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:131: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
" warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
]
},
{
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" [18600/18600 7:32:53, Epoch 60/60]\n",
"
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" Step | \n",
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"
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" 12.110000 | \n",
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" \n",
" 18400 | \n",
" 0.028700 | \n",
" 0.606192 | \n",
" 0.428687 | \n",
" 124.510200 | \n",
" 12.224000 | \n",
"
\n",
" \n",
"
"
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""
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"TrainOutput(global_step=18600, training_loss=0.34002172352165305, metrics={'train_runtime': 27175.8808, 'train_samples_per_second': 0.684, 'total_flos': 2.8460932280012886e+19, 'epoch': 60.0, 'init_mem_cpu_alloc_delta': 8143405, 'init_mem_gpu_alloc_delta': 1261972480, 'init_mem_cpu_peaked_delta': 18258, 'init_mem_gpu_peaked_delta': 0, 'train_mem_cpu_alloc_delta': 1358069, 'train_mem_gpu_alloc_delta': 3779249152, 'train_mem_cpu_peaked_delta': 183666894, 'train_mem_gpu_peaked_delta': 1681072128})"
]
},
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"trainer.train()"
]
},
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"source": [
"The training loss goes down and we can see that the WER on the test set also improves nicely. Because this notebook is just for demonstration purposes, we can stop here."
]
},
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"cell_type": "code",
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"model = Wav2Vec2ForCTC.from_pretrained(\"wav2vec2-large-xlsr-greek/checkpoint-18400/\").to(\"cuda\")\n",
"processor = Wav2Vec2Processor.from_pretrained(\"wav2vec2-large-xlsr-greek/\")"
]
},
{
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"Now, we will just take a random 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": 61,
"metadata": {
"ExecuteTime": {
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"input_dict = processor(common_voice_test[\"input_values\"][345], return_tensors=\"pt\", sampling_rate=16_000, padding=True)\n",
"\n",
"logits = model(input_dict.input_values.to(\"cuda\")).logits\n",
"\n",
"pred_ids = torch.argmax(logits, dim=-1)"
]
},
{
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"metadata": {
"id": "1JDyVCMDvlYd"
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"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": 51,
"metadata": {
"ExecuteTime": {
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"id": "8dPE2GRIgtx-",
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"name": "stderr",
"output_type": "stream",
"text": [
"Using custom data configuration el-ac779bf2c9f7c09b\n",
"Reusing dataset common_voice (/home/earendil/.cache/huggingface/datasets/common_voice/el-ac779bf2c9f7c09b/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564)\n"
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]
},
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"Finally, we can decode the example."
]
},
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"Prediction:\n",
"και τι να δούμε\n",
"\n",
"Reference:\n",
"και τι να δούμε;\n"
]
}
],
"source": [
"print(\"Prediction:\")\n",
"print(processor.decode(pred_ids[0]))\n",
"\n",
"print(\"\\nReference:\")\n",
"print(common_voice_test_transcription[\"sentence\"][345].lower())\n"
]
},
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"Alright! The transcription can definitely be recognized from our prediction, but it is far from being perfect. 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 acceptable, however 🤗."
]
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