=2.7.3\n",
"\n",
"from datasets import Audio, interleave_datasets, IterableDataset, load_dataset\n",
"from typing import List, Optional\n",
"\n",
"dataset_names = [\"mozilla-foundation/common_voice_11_0\", \"google/fleurs\"]\n",
"dataset_config_names = [\"el\", \"el_gr\"]\n",
"text_column_names = [\"sentence\", \"transcription\"]\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "674429c5-0ab4-4adf-975b-621bb69eca38",
"metadata": {
"id": "674429c5-0ab4-4adf-975b-621bb69eca38"
},
"source": [
"We'll train our system on the Spanish split of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). We can see how much training data we have by viewing the [language page](https://commonvoice.mozilla.org/en/datasets) on the Common Voice website. The Spanish split has over 400 hours of labelled training data - that's enourmous! More than we could ever fit on a Google Colab or a standard workstation. But with streaming mode, we'll only download data as and when we need it, making training on this dataset possible!\n",
"\n",
"Since Spanish is relatively high-resource, we'll only use the `train` split for training and the `test` split for evaluation. If you're training on a low-resource language, such as the Hindi split of Common Voice 11, it's worth combining the `train` and `validation` splits to give a larger training set. You can achieve this by setting: `split=\"train+validation\"` for the training split.\n",
"\n",
"If you're using a gated dataset, like Common Voice 11, ensure you have accepted the terms of use on the Hugging Face Hub: [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). Once you have accepted the terms, you will have full access to the dataset and be able to load the data locally."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
"metadata": {
"id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
"tags": []
},
"outputs": [],
"source": [
"def load_multiple_streaming_datasets(\n",
" dataset_names: List,\n",
" dataset_config_names: List,\n",
" splits: Optional[List] = None,\n",
" text_column_names: Optional[List] = None,\n",
" sampling_rate: Optional[int] = 16000,\n",
" stopping_strategy: Optional[str] = \"all_exhausted\",\n",
" **kwargs\n",
") -> IterableDataset:\n",
"\n",
" if len(dataset_names) != len(dataset_config_names):\n",
" raise ValueError(\n",
" f\"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and\"\n",
" f\" {len(dataset_config_names)} configs.\"\n",
" )\n",
"\n",
" if splits is not None and len(splits) != len(dataset_names):\n",
" raise ValueError(\n",
" f\"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits.\"\n",
" )\n",
"\n",
" if text_column_names is not None and len(text_column_names) != len(dataset_names):\n",
" raise ValueError(\n",
" f\"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and\"\n",
" f\" {len(text_column_names)} text column names.\"\n",
" )\n",
"\n",
" splits = splits if splits is not None else [\"train\" for i in range(len(dataset_names))]\n",
" text_column_names = (\n",
" text_column_names if text_column_names is not None else [\"text\" for i in range(len(dataset_names))]\n",
" )\n",
"\n",
" all_datasets = []\n",
" # iterate over the datasets we want to interleave\n",
" for i, dataset_name in enumerate(dataset_names):\n",
" dataset = load_dataset(dataset_name, dataset_config_names[i], split=splits[i], streaming=False, **kwargs)\n",
" # resample to specified sampling rate\n",
" dataset = dataset.cast_column(\"audio\", Audio(sampling_rate))\n",
" # normalise columns to [\"audio\", \"sentence\"]\n",
" if text_column_names[i] != \"sentence\":\n",
" dataset = dataset.rename_column(text_column_names[i], \"sentence\")\n",
" dataset = dataset.remove_columns(set(dataset.features.keys()) - set([\"audio\", \"sentence\"]))\n",
" all_datasets.append(dataset)\n",
"\n",
" interleaved_dataset = interleave_datasets(all_datasets, stopping_strategy=stopping_strategy)\n",
" return interleaved_dataset\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "qOwlctMhNmCG",
"metadata": {
"id": "qOwlctMhNmCG",
"tags": []
},
"outputs": [],
"source": [
"ds = load_multiple_streaming_datasets(dataset_names, dataset_config_names=dataset_config_names, text_column_names=text_column_names, use_auth_token=True)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
"metadata": {
"id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
},
"source": [
"## Prepare Processor and Pre-Process Data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "imRHJOpm4V_j",
"metadata": {
"id": "imRHJOpm4V_j"
},
"outputs": [],
"source": [
"from datasets import IterableDatasetDict\n",
"raw_datasets = IterableDatasetDict()\n",
"\n",
"raw_datasets[\"train\"] = load_multiple_streaming_datasets(dataset_names, dataset_config_names=dataset_config_names, text_column_names=text_column_names, use_auth_token=True)\n",
"raw_datasets[\"test\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"el\", split=\"test\", streaming=False, use_auth_token=True)\n",
"\n",
"\n",
"#raw_datasets = raw_datasets.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"])\n",
"\n",
"#raw_datasets[\"train\"] = load_multiple_streaming_datasets(dataset_names, \"el\", split=\"\", use_auth_token=True)\n",
"#raw_datasets[\"test\"] = load_multiple_streaming_datasets(dataset_names, \"el\", split=\"\", use_auth_token=True)\n",
"\n",
"# dataset_config_names = [\"el\", \"el_gr\"]\n",
"\n",
"#raw_datasets[\"train\"] = load_multiple_streaming_datasets(dataset_names, \"el\", use_auth_token=True)\n",
"#raw_datasets[\"train\"] = load_multiple_streaming_datasets(dataset_names, \"el\", splits=[\"train\", \"train\"], use_auth_token=True)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "601c3099-1026-439e-93e2-5635b3ba5a73",
"metadata": {
"id": "601c3099-1026-439e-93e2-5635b3ba5a73"
},
"source": [
"The ASR pipeline can be de-composed into three stages: \n",
"1) A feature extractor which pre-processes the raw audio-inputs\n",
"2) The model which performs the sequence-to-sequence mapping \n",
"3) A tokenizer which post-processes the model outputs to text format\n",
"\n",
"In đ¤ Transformers, the Whisper model has an associated feature extractor and tokenizer, \n",
"called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)\n",
"and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer) \n",
"respectively. To make our lives simple, these two objects are wrapped under a single class, called the [WhisperProcessor](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). We can call the WhisperProcessor to perform \n",
"both the audio pre-processing and the text token post-processing. In doing so, we only need to keep track of two objects during training: \n",
"the `processor` and the `model`.\n",
"\n",
"If using a multilingual checkpoint, you should set the `\"language\"` to your target text language. You should also set the task to `\"transcribe\"` for speech recogntition and `\"translate\"` for speech translation. These arguments modify the behaviour of the tokenizer - they should be set correctly to ensure the target labels are encoded properly. These arguments should be omitted for English-only fine-tuning."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
"metadata": {
"id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
},
"outputs": [],
"source": [
"from transformers import WhisperProcessor\n",
"\n",
"processor = WhisperProcessor.from_pretrained(\"emilios/whisper-medium-el\", language=\"Greek\", task=\"transcribe\")\n",
"#processor = WhisperProcessor.from_pretrained(\"farsipal/whisper-small-el\", language=\"Greek\", task=\"transcribe\")\n"
]
},
{
"cell_type": "markdown",
"id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
"metadata": {
"id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
},
"source": [
"### Pre-Process Data"
]
},
{
"cell_type": "markdown",
"id": "bf10cd3e-924e-44fc-8790-46e413de7b3d",
"metadata": {
"id": "bf10cd3e-924e-44fc-8790-46e413de7b3d"
},
"source": [
"Let's have a look at the dataset features. Pay particular attention to the `\"audio\"` column - this details the sampling rate of our audio inputs:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ab5a13b4-9bd4-4aa0-aef2-b3de9b762988",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ab5a13b4-9bd4-4aa0-aef2-b3de9b762988",
"outputId": "10050f16-519c-4211-9e80-0f4928a3559d"
},
"outputs": [
{
"data": {
"text/plain": [
"{'audio': Audio(sampling_rate=16000, mono=True, decode=True, id=None),\n",
" 'sentence': Value(dtype='string', id=None)}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_datasets[\"train\"].features"
]
},
{
"cell_type": "markdown",
"id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd",
"metadata": {
"id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
},
"source": [
"Since our input audio is sampled at 48kHz, we need to _downsample_ it to\n",
"16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n",
"\n",
"We'll set the audio inputs to the correct sampling rate using dataset's \n",
"[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n",
"method. This operation does not change the audio in-place, \n",
"but rather signals to `datasets` to resample audio samples _on the fly_ the \n",
"first time that they are loaded:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3ab6a724-3d1e-478b-a9e9-d2f85feb6c39",
"metadata": {
"id": "3ab6a724-3d1e-478b-a9e9-d2f85feb6c39"
},
"outputs": [],
"source": [
"from datasets import Audio\n",
"\n",
"raw_datasets = raw_datasets.cast_column(\"audio\", Audio(sampling_rate=16000))"
]
},
{
"cell_type": "markdown",
"id": "161322c2-94f3-4d26-9e1d-d9d5202ca3cf",
"metadata": {
"id": "161322c2-94f3-4d26-9e1d-d9d5202ca3cf"
},
"source": [
"We'll define our pre-processing strategy. We advise that you **do not** lower-case the transcriptions or remove punctuation unless mixing different datasets. This will enable you to fine-tune Whisper models that can predict punctuation and casing. Later, you will see how we can evaluate the predictions without punctuation or casing, so that the models benefit from the WER improvement obtained by normalising the transcriptions while still predicting fully formatted transcriptions."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d041650e-1c48-4439-87b3-5b6f4a514107",
"metadata": {
"id": "d041650e-1c48-4439-87b3-5b6f4a514107"
},
"outputs": [],
"source": [
"import string\n",
"import re\n",
"\n",
"do_lower_case = True\n",
"do_remove_punctuation = False\n",
"\n",
"#normalizer = BasicTextNormalizer()\n",
"\n",
"punctuation_to_remove = string.punctuation.replace(\"'\", \"\") # don't remove apostrophes\n",
"punctuation_to_remove_regex = f\"[{''.join(punctuation_to_remove)}]\"\n",
"\n",
"if do_remove_punctuation:\n",
" print(\"Removing punctuation: \", punctuation_to_remove)"
]
},
{
"cell_type": "markdown",
"id": "bfaa935b-a11d-497c-88c1-0c4d1bb3247b",
"metadata": {
"id": "bfaa935b-a11d-497c-88c1-0c4d1bb3247b"
},
"source": [
"Now we can write a function to prepare our data ready for the model:\n",
"1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, đ¤ Datasets performs any necessary resampling operations on the fly.\n",
"2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
"3. We perform any optional pre-processing (lower-case or remove punctuation).\n",
"4. We encode the transcriptions to label ids through the use of the tokenizer."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c085911c-a10a-41ef-8874-306e0503e9bb",
"metadata": {
"id": "c085911c-a10a-41ef-8874-306e0503e9bb"
},
"outputs": [],
"source": [
"def prepare_dataset(batch):\n",
" # load and (possibly) resample audio datato 16kHz\n",
" audio = batch[\"audio\"]\n",
"\n",
" # compute log-Mel input features from input audio array \n",
" batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
" # compute input length of audio sample in seconds\n",
" batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
" \n",
" # optional pre-processing steps\n",
" transcription = batch[\"sentence\"]\n",
" #transcription = batch[\"transcription\"]\n",
" if do_lower_case:\n",
" transcription = transcription.lower()\n",
" if do_remove_punctuation:\n",
" transcription = re.sub(punctuation_to_remove_regex, \" \", transcription).strip()\n",
" \n",
" # encode target text to label ids\n",
" batch[\"labels\"] = processor.tokenizer(transcription).input_ids\n",
" return batch"
]
},
{
"cell_type": "markdown",
"id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13",
"metadata": {
"id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
},
"source": [
"We can apply the data preparation function to all of our training examples using đ¤ Datasets' `.map` method. We'll remove all of the columns from the raw training data, leaving just the `input_features` and `labels` defined in the `prepare_dataset` function:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "a37a7cdb-9013-427f-8de9-6a8d0e9dc684",
"metadata": {
"id": "a37a7cdb-9013-427f-8de9-6a8d0e9dc684"
},
"outputs": [],
"source": [
"vectorized_datasets = raw_datasets.map(prepare_dataset, remove_columns=list(next(iter(raw_datasets.values())).features)).with_format(\"torch\")"
]
},
{
"cell_type": "markdown",
"id": "3d59b37e-4950-47ec-9e3e-2cf2ec7fc750",
"metadata": {
"id": "3d59b37e-4950-47ec-9e3e-2cf2ec7fc750"
},
"source": [
"We can now define how we shuffle the data in the train split. The size of the subset we load is set by the variable `buffer_size`. You can increase or decrease this depending on your memory constraints. In this example, the `buffer_size` is set to 500, meaning 500 samples are loaded before shuffling across the subset. The larger we set this value, the closer to True offline shuffling. The `seed` is set for reproducibility:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1b145699-acfc-4b1d-93a2-a2ad3d62674c",
"metadata": {
"id": "1b145699-acfc-4b1d-93a2-a2ad3d62674c"
},
"outputs": [],
"source": [
"vectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].shuffle(\n",
" #buffer_size=500,\n",
" seed=0,\n",
")\n",
"\n",
"#print(vectorized_datasets[\"train\"][0])\n",
"#print(len(vectorized_datasets))"
]
},
{
"cell_type": "markdown",
"id": "666b9ef0-7909-4e1e-a419-87604d233e29",
"metadata": {
"id": "666b9ef0-7909-4e1e-a419-87604d233e29"
},
"source": [
"Finally, we filter any training data with audio samples longer than 30s. These samples would otherwise be truncated by the Whisper feature-extractor which could affect the stability of training. We define a function that returns `True` for samples that are less than 30s, and `False` for those that are longer:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "01cb25ef-4bb0-4325-9461-f59198acadf6",
"metadata": {
"id": "01cb25ef-4bb0-4325-9461-f59198acadf6"
},
"outputs": [],
"source": [
"max_input_length = 30.0\n",
"\n",
"def is_audio_in_length_range(length):\n",
" return length < max_input_length"
]
},
{
"cell_type": "markdown",
"id": "28e37ac3-b1c5-465b-8586-7cfd8d76b0f1",
"metadata": {
"id": "28e37ac3-b1c5-465b-8586-7cfd8d76b0f1"
},
"source": [
"We apply our filter function to all samples of our training dataset through đ¤ Datasets' `.filter` method:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "333f7f6e-6053-4d3b-8924-c733c79b82ac",
"metadata": {
"id": "333f7f6e-6053-4d3b-8924-c733c79b82ac"
},
"outputs": [],
"source": [
"vectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].filter(\n",
" is_audio_in_length_range,\n",
" input_columns=[\"input_length\"],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
"metadata": {
"id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
},
"source": [
"## Training and Evaluation"
]
},
{
"cell_type": "markdown",
"id": "a693e768-c5a6-453f-89a1-b601dcf7daf7",
"metadata": {
"id": "a693e768-c5a6-453f-89a1-b601dcf7daf7"
},
"source": [
"Now that we've prepared our data, we're ready to dive into the training pipeline. \n",
"The [đ¤ Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer)\n",
"will do much of the heavy lifting for us. All we have to do is:\n",
"\n",
"- Define a data collator: the data collator takes our pre-processed data and prepares PyTorch tensors ready for the model.\n",
"\n",
"- Evaluation metrics: during evaluation, we want to evaluate the model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric. We need to define a `compute_metrics` function that handles this computation.\n",
"\n",
"- Load a pre-trained checkpoint: we need to load a pre-trained checkpoint and configure it correctly for training.\n",
"\n",
"- Define the training configuration: this will be used by the đ¤ Trainer to define the training schedule."
]
},
{
"cell_type": "markdown",
"id": "8d230e6d-624c-400a-bbf5-fa660881df25",
"metadata": {
"id": "8d230e6d-624c-400a-bbf5-fa660881df25"
},
"source": [
"### Define a Data Collator"
]
},
{
"cell_type": "markdown",
"id": "04def221-0637-4a69-b242-d3f0c1d0ee78",
"metadata": {
"id": "04def221-0637-4a69-b242-d3f0c1d0ee78"
},
"source": [
"The data collator for a sequence-to-sequence speech model is unique in the sense that it \n",
"treats the `input_features` and `labels` independently: the `input_features` must be \n",
"handled by the feature extractor and the `labels` by the tokenizer.\n",
"\n",
"The `input_features` are already padded to 30s and converted to a log-Mel spectrogram \n",
"of fixed dimension by action of the feature extractor, so all we have to do is convert the `input_features`\n",
"to batched PyTorch tensors. We do this using the feature extractor's `.pad` method with `return_tensors=pt`.\n",
"\n",
"The `labels` on the other hand are un-padded. We first pad the sequences\n",
"to the maximum length in the batch using the tokenizer's `.pad` method. The padding tokens \n",
"are then replaced by `-100` so that these tokens are **not** taken into account when \n",
"computing the loss. We then cut the BOS token from the start of the label sequence as we \n",
"append it later during training.\n",
"\n",
"We can leverage the `WhisperProcessor` we defined earlier to perform both the \n",
"feature extractor and the tokenizer operations:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
"metadata": {
"id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
},
"outputs": [],
"source": [
"import torch\n",
"\n",
"from dataclasses import dataclass\n",
"from typing import Any, Dict, List, Union\n",
"\n",
"@dataclass\n",
"class DataCollatorSpeechSeq2SeqWithPadding:\n",
" processor: Any\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 lengths and need different padding methods\n",
" # first treat the audio inputs by simply returning torch tensors\n",
" input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
" batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
"\n",
" # get the tokenized label sequences\n",
" label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
" # pad the labels to max length\n",
" labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\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",
" # if bos token is appended in previous tokenization step,\n",
" # cut bos token here as it's append later anyways\n",
" if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
" labels = labels[:, 1:]\n",
"\n",
" batch[\"labels\"] = labels\n",
"\n",
" return batch"
]
},
{
"cell_type": "markdown",
"id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86",
"metadata": {
"id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86"
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"source": [
"Let's initialise the data collator we've just defined:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
"metadata": {
"id": "fc834702-c0d3-4a96-b101-7b87be32bf42"
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"source": [
"data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
]
},
{
"cell_type": "markdown",
"id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
"metadata": {
"id": "d62bb2ab-750a-45e7-82e9-61d6f4805698"
},
"source": [
"### Evaluation Metrics"
]
},
{
"cell_type": "markdown",
"id": "66fee1a7-a44c-461e-b047-c3917221572e",
"metadata": {
"id": "66fee1a7-a44c-461e-b047-c3917221572e"
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"source": [
"We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n",
"ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from đ¤ Evaluate:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b22b4011-f31f-4b57-b684-c52332f92890",
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"base_uri": "https://localhost:8080/",
"height": 49,
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"source": [
"import evaluate\n",
"\n",
"metric = evaluate.load(\"wer\")"
]
},
{
"cell_type": "markdown",
"id": "509f96d7-3f11-4f37-add9-f74a0c44f3fc",
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"source": [
"We then simply have to define a function that takes our model \n",
"predictions and returns the WER metric. This function, called\n",
"`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
"in the `label_ids` (undoing the step we applied in the \n",
"data collator to ignore padded tokens correctly in the loss).\n",
"It then decodes the predicted and label ids to strings. Finally,\n",
"it computes the WER between the predictions and reference labels. \n",
"Here, we have the option of evaluating with the 'normalised' transcriptions \n",
"and predictions. We recommend you set this to `True` to benefit from the WER \n",
"improvement obtained by normalising the transcriptions."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "a11d1bfc-9e28-460f-a287-72d8f7bc1acb",
"metadata": {
"id": "a11d1bfc-9e28-460f-a287-72d8f7bc1acb"
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"source": [
"#Â evaluate with the 'normalised' WER\n",
"do_normalize_eval = True\n",
"\n",
"def compute_metrics(pred):\n",
" pred_ids = pred.predictions\n",
" label_ids = pred.label_ids\n",
"\n",
" # replace -100 with the pad_token_id\n",
" label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
"\n",
" # we do not want to group tokens when computing the metrics\n",
" pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True, normalize=do_normalize_eval)\n",
" label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True, normalize=do_normalize_eval)\n",
"\n",
" wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
"\n",
" return {\"wer\": wer}"
]
},
{
"cell_type": "markdown",
"id": "daf2a825-6d9f-4a23-b145-c37c0039075b",
"metadata": {
"id": "daf2a825-6d9f-4a23-b145-c37c0039075b"
},
"source": [
"###Â Load a Pre-Trained Checkpoint"
]
},
{
"cell_type": "markdown",
"id": "437a97fa-4864-476b-8abc-f28b8166cfa5",
"metadata": {
"id": "437a97fa-4864-476b-8abc-f28b8166cfa5"
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"source": [
"Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n",
"is trivial through use of đ¤ Transformers!"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
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"Downloading: 0%| | 0.00/3.06G [00:00 _inspect_ -> _Console tab_ -> _insert code_)."
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"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",
"```"
]
},
{
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"id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112",
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"source": [
"The peak GPU memory for the given training configuration is approximately 36GB. \n",
"Depending on your GPU, it is possible that you will encounter a CUDA `\"out-of-memory\"` error when you launch training. \n",
"In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 \n",
"and employ [`gradient_accumulation_steps`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps)\n",
"to compensate.\n",
"\n",
"To launch training, simply execute:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
"metadata": {
"id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading model from ./whisper-medium-el/checkpoint-4000.\n",
"/home/ubuntu/.local/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n",
"***** Running training *****\n",
" Num examples = 160000\n",
" Num Epochs = 9223372036854775807\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 = 5000\n",
" Number of trainable parameters = 763857920\n",
" Continuing training from checkpoint, will skip to saved global_step\n",
" Continuing training from epoch 0\n",
" Continuing training from global step 4000\n",
"Reading metadata...: 1914it [00:00, 4074.06it/s]\n",
"The following columns in the training set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message.\n"
]
},
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"data": {
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"\n",
" \n",
" \n",
"
\n",
" [4012/5000 01:11 < 1:57:29, 0.14 it/s, Epoch 0.00/9223372036854775807]\n",
"
\n",
" \n",
" \n",
" | Step | \n",
" Training Loss | \n",
" Validation Loss | \n",
"
\n",
" \n",
" \n",
" \n",
"
"
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""
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],
"source": [
"#trainer.train()\n",
"trainer.train(resume_from_checkpoint = True)\n"
]
},
{
"cell_type": "markdown",
"id": "747c6a6e",
"metadata": {
"id": "747c6a6e",
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},
"source": [
"(note that training may take some time to commence as we load the first training data samples with streaming mode)"
]
},
{
"cell_type": "markdown",
"id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3",
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"source": [
"We can label our checkpoint with the `whisper-event` tag on push by setting the appropriate key-word arguments (kwargs):"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6dd0e310-9b07-4133-ac14-2ed2d7524e22",
"metadata": {
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"source": [
"kwargs = {\n",
" \"dataset_tags\": \"mozilla-foundation/common_voice_11_0\",\n",
" #\"dataset_tags\": \"google/fleurs\",\n",
" \"dataset\": \"Common Voice 11.0\", # a 'pretty' name for the training dataset\n",
" #\"dataset\": \"Google FLEURS\", # a 'pretty' name for the training dataset\n",
" \"language\": \"el\",\n",
" \"model_name\": \"Whisper Medium El Greco\", # a 'pretty' name for your model\n",
" \"finetuned_from\": \"openai/whisper-medium\",\n",
" \"tasks\": \"automatic-speech-recognition\",\n",
" \"tags\": \"hf-asr-leaderboard, whisper-medium, mozilla-foundation/common_voice_11_0, greek, whisper-event\",\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "090d676a-f944-4297-a938-a40eda0b2b68",
"metadata": {
"id": "090d676a-f944-4297-a938-a40eda0b2b68"
},
"source": [
"The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "95737cda-c5dd-4887-a4d0-dfcb0d61d977",
"metadata": {
"id": "95737cda-c5dd-4887-a4d0-dfcb0d61d977"
},
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"source": [
"trainer.push_to_hub(**kwargs)"
]
},
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