"
]
},
"execution_count": 23,
"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(timit[\"train\"]))\n",
"\n",
"print(timit[\"train\"][rand_int][\"text\"])\n",
"ipd.Audio(data=np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]), 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, etc. Overall, the recordings sound relatively clear though, which is to be expected from a 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": "3b0417f4-9e15-44d0-f95f-2de657016c41"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Target text: she had your dark suit in greasy wash water all year \n",
"Input array shape: (57242,)\n",
"Sampling rate: 16000\n"
]
}
],
"source": [
"rand_int = random.randint(0, len(timit[\"train\"]))\n",
"\n",
"print(\"Target text:\", timit[\"train\"][rand_int][\"text\"])\n",
"print(\"Input array shape:\", np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]).shape)\n",
"print(\"Sampling rate:\", timit[\"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.\n",
"\n",
"Next, we should process the data with the model's feature extractor. Let's load the feature extractor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FFQCC3A9vz2r"
},
"outputs": [],
"source": [
"from transformers import AutoFeatureExtractor\n",
"\n",
"feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BpJDkK8Pv4cM"
},
"source": [
"and wrap it into a Wav2Vec2Processor together with the tokenizer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "d-EuXD_rv7FP"
},
"outputs": [],
"source": [
"from transformers import Wav2Vec2Processor\n",
"\n",
"processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HfD3cVnSv9Tp"
},
"source": [
"Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by the model 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."
]
},
{
"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\" to ensure mapping is correct\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[\"text\"]).input_ids\n",
" return batch"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hVMZhH4-nP8-"
},
"source": [
"Let's apply the data preparation function to all examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-np9xYK-wl8q"
},
"outputs": [],
"source": [
"timit = timit.map(prepare_dataset, remove_columns=timit.column_names[\"train\"], num_proc=4)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p_MuJSH8nTuQ"
},
"source": [
"**Note**: Currently `datasets` make use of [`torchaudio`](https://pytorch.org/audio/stable/index.html) and [`librosa`](https://librosa.org/doc/latest/index.html) for audio loading and resampling. If you wish to implement your own costumized data loading/sampling, feel free to just make use of the `\"path\"` column instead and disregard the `\"audio\"` column."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M4J0bU1WsvAg"
},
"source": [
"Long input sequences require a lot of memory. Since `Wav2Vec2` is based on `self-attention` the memory requirement scales quadratically with the input length for long input sequences (*cf.* with [this](https://www.reddit.com/r/MachineLearning/comments/genjvb/d_why_is_the_maximum_input_sequence_length_of/) reddit post). For this demo, let's filter all sequences that are longer than 4 seconds out of the training dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
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"id": "nqGobEPUvG3v",
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"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d896231bf5824211a55d494d48626fc6",
"version_major": 2,
"version_minor": 0
},
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" 0%| | 0/5 [00:00, ?ba/s]"
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],
"source": [
"max_input_length_in_sec = 4.0\n",
"timit[\"train\"] = timit[\"train\"].filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "25Genil2v_Br"
},
"source": [
"Awesome, now we are ready to start training!"
]
},
{
"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 đ¤'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, speech models usually have a much larger input length than output length. *E.g.*, a sample of input length 50000 for Wav2Vec2 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 speech models 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. 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": null,
"metadata": {
"id": "tborvC9hx88e"
},
"outputs": [],
"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": 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": [
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"id": "9Xsux2gmyXso",
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"outputs": [
{
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],
"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": 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 `Wav2Vec2` checkpoint. The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of a CTC speech model also CTC's *blank token* ${}^2$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e7cqAWIayn6w",
"outputId": "937bae2c-2922-4f76-80f1-1986a4084cec"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"loading configuration file https://huggingface.co/facebook/wav2vec2-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/c7746642f045322fd01afa31271dd490e677ea11999e68660a92619ec7c892b4.02212753c42f07ecd65bbe35175ac4866badb735f9dae5bf2ae455c57db4dbb7\n",
"/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py:337: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
" \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\n",
"Model config Wav2Vec2Config {\n",
" \"activation_dropout\": 0.0,\n",
" \"apply_spec_augment\": true,\n",
" \"architectures\": [\n",
" \"Wav2Vec2ForPreTraining\"\n",
" ],\n",
" \"attention_dropout\": 0.1,\n",
" \"bos_token_id\": 1,\n",
" \"classifier_proj_size\": 256,\n",
" \"codevector_dim\": 256,\n",
" \"contrastive_logits_temperature\": 0.1,\n",
" \"conv_bias\": false,\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\": false,\n",
" \"eos_token_id\": 2,\n",
" \"feat_extract_activation\": \"gelu\",\n",
" \"feat_extract_norm\": \"group\",\n",
" \"feat_proj_dropout\": 0.1,\n",
" \"feat_quantizer_dropout\": 0.0,\n",
" \"final_dropout\": 0.0,\n",
" \"freeze_feat_extract_train\": true,\n",
" \"gradient_checkpointing\": true,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"layer_norm_eps\": 1e-05,\n",
" \"layerdrop\": 0.05,\n",
" \"mask_channel_length\": 10,\n",
" \"mask_channel_min_space\": 1,\n",
" \"mask_channel_other\": 0.0,\n",
" \"mask_channel_prob\": 0.0,\n",
" \"mask_channel_selection\": \"static\",\n",
" \"mask_feature_length\": 10,\n",
" \"mask_feature_prob\": 0.0,\n",
" \"mask_time_length\": 10,\n",
" \"mask_time_min_space\": 1,\n",
" \"mask_time_other\": 0.0,\n",
" \"mask_time_prob\": 0.05,\n",
" \"mask_time_selection\": \"static\",\n",
" \"model_type\": \"wav2vec2\",\n",
" \"no_mask_channel_overlap\": false,\n",
" \"no_mask_time_overlap\": false,\n",
" \"num_attention_heads\": 12,\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\": 12,\n",
" \"num_negatives\": 100,\n",
" \"pad_token_id\": 29,\n",
" \"proj_codevector_dim\": 256,\n",
" \"transformers_version\": \"4.11.3\",\n",
" \"use_weighted_layer_sum\": false,\n",
" \"vocab_size\": 32\n",
"}\n",
"\n",
"loading weights file https://huggingface.co/facebook/wav2vec2-base/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/ef45231897ce572a660ebc5a63d3702f1a6041c4c5fb78cbec330708531939b3.fcae05302a685f7904c551c8ea571e8bc2a2c4a1777ea81ad66e47f7883a650a\n",
"Some weights of the model checkpoint at facebook/wav2vec2-base were not used when initializing Wav2Vec2ForCTC: ['quantizer.codevectors', 'project_hid.weight', 'quantizer.weight_proj.bias', 'project_q.weight', 'project_q.bias', 'project_hid.bias', 'quantizer.weight_proj.weight']\n",
"- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base and are newly initialized: ['lm_head.bias', 'lm_head.weight']\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 AutoModelForCTC\n",
"\n",
"model = AutoModelForCTC.from_pretrained(\n",
" model_checkpoint, \n",
" ctc_loss_reduction=\"mean\", \n",
" pad_token_id=processor.tokenizer.pad_token_id,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2PDAoxYzwb-U"
},
"source": [
"The first component of most transformer-based speech models 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": "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",
"During training, a checkpoint will be uploaded asynchronously to the hub every 400 training steps. It allows you to also play around with the demo widget even while your model is still training.\n",
"\n",
"**Note**: If one does not want to upload the model checkpoints to the hub, simply set `push_to_hub=False`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KbeKSV7uzGPP",
"outputId": "4dcce61c-fc4b-44bb-a774-70ff20971031"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"PyTorch: setting up devices\n",
"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
]
}
],
"source": [
"from transformers import TrainingArguments\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=repo_name,\n",
" group_by_length=True,\n",
" per_device_train_batch_size=32,\n",
" evaluation_strategy=\"steps\",\n",
" num_train_epochs=30,\n",
" fp16=True,\n",
" gradient_checkpointing=True,\n",
" save_steps=500,\n",
" eval_steps=500,\n",
" logging_steps=500,\n",
" learning_rate=1e-4,\n",
" weight_decay=0.005,\n",
" warmup_steps=1000,\n",
" save_total_limit=2,\n",
" push_to_hub=True,\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": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rY7vBmFCPFgC",
"outputId": "191b231d-70c1-4315-bdd6-ea2a5d106537"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/content/wav2vec2-base-timit-demo-colab is already a clone of https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-colab. Make sure you pull the latest changes with `repo.git_pull()`.\n",
"Using amp fp16 backend\n"
]
}
],
"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=timit[\"train\"],\n",
" eval_dataset=timit[\"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 a couple of hours depending on the GPU allocated to this notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "_UEjJqGsQw24",
"outputId": "e76688f4-e32d-4de2-e3cb-fa1b942a93e6"
},
"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 = 3978\n",
" Num Epochs = 30\n",
" Instantaneous batch size per device = 32\n",
" Total train batch size (w. parallel, distributed & accumulation) = 32\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 3750\n"
]
},
{
"data": {
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"\n",
" \n",
" \n",
"
\n",
" [3750/3750 1:46:22, Epoch 30/30]\n",
"
\n",
" \n",
" \n",
" \n",
" Step | \n",
" Training Loss | \n",
" Validation Loss | \n",
" Wer | \n",
"
\n",
" \n",
" \n",
" \n",
" 500 | \n",
" 0.113400 | \n",
" 0.425030 | \n",
" 0.362621 | \n",
"
\n",
" \n",
" 1000 | \n",
" 0.103500 | \n",
" 0.497975 | \n",
" 0.364965 | \n",
"
\n",
" \n",
" 1500 | \n",
" 0.080100 | \n",
" 0.556286 | \n",
" 0.363242 | \n",
"
\n",
" \n",
" 2000 | \n",
" 0.059200 | \n",
" 0.622184 | \n",
" 0.360692 | \n",
"
\n",
" \n",
" 2500 | \n",
" 0.056300 | \n",
" 0.476272 | \n",
" 0.345738 | \n",
"
\n",
" \n",
" 3000 | \n",
" 0.061100 | \n",
" 0.493821 | \n",
" 0.348908 | \n",
"
\n",
" \n",
" 3500 | \n",
" 0.047500 | \n",
" 0.488801 | \n",
" 0.339191 | \n",
"
\n",
" \n",
"
"
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"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1357: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n",
" args.max_grad_norm,\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 = 1680\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-500\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-500/config.json\n",
"Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-500/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-500/preprocessor_config.json\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/preprocessor_config.json\n",
"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1357: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n",
" args.max_grad_norm,\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 = 1680\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-1000\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-1000/config.json\n",
"Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-1000/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-1000/preprocessor_config.json\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 = 1680\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-1500\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-1500/config.json\n",
"Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-1500/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-1500/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-500] due to args.save_total_limit\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 = 1680\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-2000\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-2000/config.json\n",
"Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-2000/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-2000/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-1000] due to args.save_total_limit\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 = 1680\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-2500\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-2500/config.json\n",
"Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-2500/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-2500/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-1500] due to args.save_total_limit\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 = 1680\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-3000\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-3000/config.json\n",
"Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-3000/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-3000/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-2000] due to args.save_total_limit\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 = 1680\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-3500\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-3500/config.json\n",
"Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-3500/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-3500/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-2500] due to args.save_total_limit\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=3750, training_loss=0.0719044921875, metrics={'train_runtime': 6384.5833, 'train_samples_per_second': 18.692, 'train_steps_per_second': 0.587, 'total_flos': 3.098741829539622e+18, 'train_loss': 0.0719044921875, 'epoch': 30.0})"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UCyp-v3n4Zlt"
},
"source": [
"The final WER should be around 0.3 which is reasonable given that state-of-the-art phoneme error rates (PER) are just below 0.1 (see [leaderboard](https://paperswithcode.com/sota/speech-recognition-on-timit)) and that WER is usually worse than PER.\n",
"\n",
"You can now upload the result of the training to the Hub, just execute this instruction:"
]
},
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{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving model checkpoint to wav2vec2-base-timit-demo-colab\n",
"Configuration saved in wav2vec2-base-timit-demo-colab/config.json\n",
"Model weights saved in wav2vec2-base-timit-demo-colab/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-timit-demo-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": "853be7d9a3d24adfbdc9a07d181425cc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Upload file pytorch_model.bin: 0%| | 3.36k/360M [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"Upload file runs/Oct19_15-22-32_c16c65cbdbe5/events.out.tfevents.1634657020.c16c65cbdbe5.77.2: 42%|####2 âŚ"
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"name": "stderr",
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"text": [
"To https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-colab\n",
" 870c48e..2998ea6 main -> main\n",
"\n",
"Dropping the following result as it does not have all the necessary field:\n",
"{}\n",
"To https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-colab\n",
" 2998ea6..431ef82 main -> main\n",
"\n"
]
},
{
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"type": "string"
},
"text/plain": [
"'https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-colab/commit/2998ea690ec1ba32370f856fb558cf22dcb0e119'"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.push_to_hub()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "djzwS5WeNu16"
},
"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": "Adm0LngNNxq7"
},
"source": [
"```python\n",
"from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
"\n",
"model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-colab\")\n",
"processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-colab\")\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Cw0_ygXSw_MT"
},
"source": [
"To fine-tune larger models on larger datasets using CTC loss, one should take a look at the official speech-recognition examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#connectionist-temporal-classification-without-language-model-ctc-wo-lm) đ¤."
]
}
],
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