"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import random\n",
"from IPython.display import Audio, display\n",
"\n",
"for _ in range(5):\n",
" rand_idx = random.randint(0, len(dataset[\"train\"])-1)\n",
" example = dataset[\"train\"][rand_idx]\n",
" audio = example[\"audio\"]\n",
"\n",
" print(f'Label: {id2label[str(example[\"label\"])]}')\n",
" print(f'Shape: {audio[\"array\"].shape}, sampling rate: {audio[\"sampling_rate\"]}')\n",
" display(Audio(audio[\"array\"], rate=audio[\"sampling_rate\"]))\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "37K_0FAQoNRM"
},
"source": [
"If you run the cell a couple of times, you'll see that despite slight variations in length, most of the samples are about 1 second long (`duration = audio_length / sampling_rate`). So we can safely truncate and pad the samples to `16000`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4zxoikSOjs0K"
},
"source": [
"### Preprocessing the data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WTupOU88p1lK"
},
"source": [
"Before we can feed those audio clips to our model, we need to preprocess them. This is done by a 🤗 Transformers `FeatureExtractor` which will normalize the inputs and put them in a format the model expects, as well as generate the other inputs that the model requires.\n",
"\n",
"To do all of this, we instantiate our feature extractor with the `AutoFeatureExtractor.from_pretrained` method, which will ensure that we get a preprocessor that corresponds to the model architecture we want to use."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 288,
"referenced_widgets": [
"2fafd1b4c6c5483f9922bef1395dba65",
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]
},
"id": "G1bX4lGAO_d9",
"outputId": "ddaa2ff9-c185-4f0b-8897-66fe01eaaa0c"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2fafd1b4c6c5483f9922bef1395dba65",
"version_major": 2,
"version_minor": 0
},
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"Downloading: 0%| | 0.00/1.80k [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5ff829f89e50495fbbfd95231a2af8cc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/159 [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"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\": false,\n",
" \"sampling_rate\": 16000\n",
"}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from transformers import AutoFeatureExtractor\n",
"\n",
"feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)\n",
"feature_extractor"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-zYhYOuDL9XP"
},
"source": [
"As we've noticed earlier, the samples are roughly 1 second long, so let's set it here:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "wX35xSLx-ghC"
},
"outputs": [],
"source": [
"max_duration = 1.0 # seconds"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qUtxmoMvqml1"
},
"source": [
"We can then write the function that will preprocess our samples. We just feed them to the `feature_extractor` with the argument `truncation=True`, as well as the maximum sample length. This will ensure that very long inputs like the ones in the `_silence_` class can be safely batched."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "4O_p3WrpRyej"
},
"outputs": [],
"source": [
"def preprocess_function(examples):\n",
" audio_arrays = [x[\"array\"] for x in examples[\"audio\"]]\n",
" inputs = feature_extractor(\n",
" audio_arrays, \n",
" sampling_rate=feature_extractor.sampling_rate, \n",
" max_length=int(feature_extractor.sampling_rate * max_duration), \n",
" truncation=True, \n",
" )\n",
" return inputs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OlbYCcVZ4hHu"
},
"source": [
"The feature extractor will return a list of numpy arays for each example:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qGUvcAlAqKy-",
"outputId": "73052908-db3e-48e7-f7a9-cac4ae016535"
},
"outputs": [
{
"data": {
"text/plain": [
"{'input_values': [array([-9.1631009e-05, -9.1631009e-05, -9.1631009e-05, ...,\n",
" -4.6719767e-02, -8.0353022e-01, -1.3182331e+00], dtype=float32), array([0.01049979, 0.01049979, 0.01049979, ..., 0.6454253 , 0.43378347,\n",
" 0.25741526], dtype=float32), array([ 9.0340059e-04, 9.0340059e-04, 9.0340059e-04, ...,\n",
" -1.7281245e-01, 2.2313449e-01, 1.9931581e+00], dtype=float32), array([ 1.5586768 , 0.3870289 , 0.74101615, ..., -0.8897349 ,\n",
" -0.7703889 , -0.09471782], dtype=float32), array([-0.01518929, -0.01518929, -0.01518929, ..., -0.84138 ,\n",
" 0.22227868, -0.02409434], dtype=float32)]}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preprocess_function(dataset['train'][:5])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a1Nw4FwN5wCS"
},
"source": [
"To apply this function on all utterances in our dataset, we just use the `map` method of our `dataset` object we created earlier. This will apply the function on all the elements of all the splits in `dataset`, so our training, validation and testing data will be preprocessed in one single command."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 405,
"referenced_widgets": [
"7e4fb53a3bd24f7f82ca87cdd578419d",
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]
},
"id": "FfxFgJ_6qPPy",
"outputId": "aedec9cc-a9c1-403e-f052-90efea5fb14c"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7e4fb53a3bd24f7f82ca87cdd578419d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/52 [00:00, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-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"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e423a8f9cbe94a2db9be2315ec331ed6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/7 [00:00, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e564387d8dcd469b8dea5014ce62fe67",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/4 [00:00, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['input_values', 'label'],\n",
" num_rows: 51094\n",
" })\n",
" validation: Dataset({\n",
" features: ['input_values', 'label'],\n",
" num_rows: 6798\n",
" })\n",
" test: Dataset({\n",
" features: ['input_values', 'label'],\n",
" num_rows: 3081\n",
" })\n",
"})"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"encoded_dataset = dataset.map(preprocess_function, remove_columns=[\"audio\", \"file\"], batched=True)\n",
"encoded_dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c0OwX3Es580y"
},
"source": [
"Even better, the results are automatically cached by the 🤗 Datasets library to avoid spending time on this step the next time you run your notebook. The 🤗 Datasets library is normally smart enough to detect when the function you pass to map has changed (and thus requires to not use the cache data). 🤗 Datasets warns you when it uses cached files, you can pass `load_from_cache_file=False` in the call to `map` to not use the cached files and force the preprocessing to be applied again."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HOXmyPQ76Qv9"
},
"source": [
"### Training the model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0a-2YT7O6ayC"
},
"source": [
"Now that our data is ready, we can download the pretrained model and fine-tune it. For classification we use the `AutoModelForAudioClassification` class. Like with the feature extractor, the `from_pretrained` method will download and cache the model for us. As the label ids and the number of labels are dataset dependent, we pass `num_labels`, `label2id`, and `id2label` alongside the `model_checkpoint` here:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 188,
"referenced_widgets": [
"facdfa8c9711409dbc854ba83c77f81f",
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]
},
"id": "X9DDujL0q1ac",
"outputId": "a3575a2d-1c4f-4f28-b3d7-6d59812f045f"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "facdfa8c9711409dbc854ba83c77f81f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/363M [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at facebook/wav2vec2-base were not used when initializing Wav2Vec2ForSequenceClassification: ['project_q.weight', 'quantizer.weight_proj.bias', 'project_hid.weight', 'project_q.bias', 'project_hid.bias', 'quantizer.codevectors', 'quantizer.weight_proj.weight']\n",
"- This IS expected if you are initializing Wav2Vec2ForSequenceClassification 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 Wav2Vec2ForSequenceClassification 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 Wav2Vec2ForSequenceClassification were not initialized from the model checkpoint at facebook/wav2vec2-base and are newly initialized: ['projector.bias', 'classifier.weight', 'projector.weight', 'classifier.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 AutoModelForAudioClassification, TrainingArguments, Trainer\n",
"\n",
"num_labels = len(id2label)\n",
"model = AutoModelForAudioClassification.from_pretrained(\n",
" model_checkpoint, \n",
" num_labels=num_labels,\n",
" label2id=label2id,\n",
" id2label=id2label,\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "U8EmET_f6458"
},
"source": [
"The warning is telling us we are throwing away some weights (the `quantizer` and `project_q` layers) and randomly initializing some other (the `projector` and `classifier` layers). This is expected in this case, because we are removing the head used to pretrain the model on an unsupervised Vector Quantization objective and replacing it with a new head for which we don't have pretrained weights, so the library warns us we should fine-tune this model before using it for inference, which is exactly what we are going to do."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FEfyuq1U8hDT"
},
"source": [
"To instantiate a `Trainer`, we will need to define the training configuration and the evaluation metric. The most important is the [`TrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments), which is a class that contains all the attributes to customize the training. It requires one folder name, which will be used to save the checkpoints of the model, and all other arguments are optional:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "xc_MTm0Ks3DF"
},
"outputs": [],
"source": [
"model_name = model_checkpoint.split(\"/\")[-1]\n",
"\n",
"args = TrainingArguments(\n",
" f\"{model_name}-finetuned-ks\",\n",
" evaluation_strategy = \"epoch\",\n",
" save_strategy = \"epoch\",\n",
" learning_rate=3e-5,\n",
" per_device_train_batch_size=batch_size,\n",
" gradient_accumulation_steps=4,\n",
" per_device_eval_batch_size=batch_size,\n",
" num_train_epochs=5,\n",
" warmup_ratio=0.1,\n",
" logging_steps=10,\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"accuracy\",\n",
" push_to_hub=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xi6JYNYs8lJO"
},
"source": [
"Here we set the evaluation to be done at the end of each epoch, tweak the learning rate, use the `batch_size` defined at the top of the notebook and customize the number of epochs for training, as well as the weight decay. Since the best model might not be the one at the end of training, we ask the `Trainer` to load the best model it saved (according to `metric_name`) at the end of training.\n",
"\n",
"The last argument `push_to_hub` allows the Trainer to push the model to the [Hub](https://huggingface.co/models) regularly during training. Remove it if you didn't follow the installation steps at the top of the notebook. If you want to save your model locally with a name that is different from the name of the repository, or if you want to push your model under an organization and not your name space, use the `hub_model_id` argument to set the repo name (it needs to be the full name, including your namespace: for instance `\"anton-l/wav2vec2-finetuned-ks\"` or `\"huggingface/anton-l/wav2vec2-finetuned-ks\"`)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2VE_HSha9RZk"
},
"source": [
"Next, we need to define a function for how to compute the metrics from the predictions, which will just use the `metric` we loaded earlier. The only preprocessing we have to do is to take the argmax of our predicted logits:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "EVWfiBuv2uCS"
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"def compute_metrics(eval_pred):\n",
" \"\"\"Computes accuracy on a batch of predictions\"\"\"\n",
" predictions = np.argmax(eval_pred.predictions, axis=1)\n",
" return metric.compute(predictions=predictions, references=eval_pred.label_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yTF0dWw49fB9"
},
"source": [
"Then we just need to pass all of this along with our datasets to the `Trainer`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "McVoaCPr3Cj-"
},
"outputs": [],
"source": [
"trainer = Trainer(\n",
" model,\n",
" args,\n",
" train_dataset=encoded_dataset[\"train\"],\n",
" eval_dataset=encoded_dataset[\"validation\"],\n",
" tokenizer=feature_extractor,\n",
" compute_metrics=compute_metrics\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ltokP9mO9pjI"
},
"source": [
"You might wonder why we pass along the `feature_extractor` as a tokenizer when we already preprocessed our data. This is because we will use it once last time to make all the samples we gather the same length by applying padding, which requires knowing the model's preferences regarding padding (to the left or right? with which token?). The `feature_extractor` has a pad method that will do all of this for us, and the `Trainer` will use it. You can customize this part by defining and passing your own `data_collator` which will receive the samples like the dictionaries seen above and will need to return a dictionary of tensors."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9j6VNsGP97LG"
},
"source": [
"Now we can finetune our model by calling the `train` method:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "Pps61vF_4QaH",
"outputId": "fc32c7ba-c478-4725-d06a-fc8ba54ddff0"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"***** Running training *****\n",
" Num examples = 51094\n",
" Num Epochs = 5\n",
" Instantaneous batch size per device = 32\n",
" Total train batch size (w. parallel, distributed & accumulation) = 128\n",
" Gradient Accumulation steps = 4\n",
" Total optimization steps = 1995\n"
]
},
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"text": [
"***** Running Evaluation *****\n",
" Num examples = 6798\n",
" Batch size = 32\n",
"Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-399\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-399/config.json\n",
"Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-399/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-399/preprocessor_config.json\n",
"Configuration saved in wav2vec2-base-finetuned-ks/preprocessor_config.json\n",
"***** Running Evaluation *****\n",
" Num examples = 6798\n",
" Batch size = 32\n",
"Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-798\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-798/config.json\n",
"Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-798/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-798/preprocessor_config.json\n",
"***** Running Evaluation *****\n",
" Num examples = 6798\n",
" Batch size = 32\n",
"Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-1197\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1197/config.json\n",
"Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-1197/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1197/preprocessor_config.json\n",
"***** Running Evaluation *****\n",
" Num examples = 6798\n",
" Batch size = 32\n",
"Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-1596\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1596/config.json\n",
"Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-1596/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1596/preprocessor_config.json\n",
"***** Running Evaluation *****\n",
" Num examples = 6798\n",
" Batch size = 32\n",
"Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-1995\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1995/config.json\n",
"Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-1995/pytorch_model.bin\n",
"Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1995/preprocessor_config.json\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
"Loading best model from wav2vec2-base-finetuned-ks/checkpoint-1995 (score: 0.9823477493380406).\n"
]
},
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"data": {
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"TrainOutput(global_step=1995, training_loss=0.5248636143846919, metrics={'train_runtime': 4281.3853, 'train_samples_per_second': 59.67, 'train_steps_per_second': 0.466, 'total_flos': 2.31918157475328e+18, 'train_loss': 0.5248636143846919, 'epoch': 5.0})"
]
},
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],
"source": [
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]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Vyb-58x_-A0e"
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"source": [
"We can check with the `evaluate` method that our `Trainer` did reload the best model properly (if it was not the last one):"
]
},
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"cell_type": "code",
"execution_count": 24,
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 190
},
"id": "niniUAnb5IrR",
"outputId": "306c07b3-e482-4c62-a147-93fb6e16e4da"
},
"outputs": [
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"name": "stderr",
"output_type": "stream",
"text": [
"***** Running Evaluation *****\n",
" Num examples = 6798\n",
" Batch size = 32\n"
]
},
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"{'epoch': 5.0,\n",
" 'eval_accuracy': 0.9823477493380406,\n",
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"source": [
"You can now upload the result of the training to the Hub, just execute this instruction:"
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"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:\n",
"\n",
"```python\n",
"from transformers import AutoModelForAudioClassification, AutoFeatureExtractor\n",
"\n",
"feature_extractor = AutoFeatureExtractor.from_pretrained(\"anton-l/my-awesome-model\")\n",
"model = AutoModelForAudioClassification.from_pretrained(\"anton-l/my-awesome-model\")\n",
"\n",
"```"
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