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Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Before we begin, we need to install torchaudio to have access to the dataset.

  1. Import all necessary libraries for loading our data
  2. Access the data in the dataset
  3. Loading the data
  4. Iterate over the data
  5. [Optional] Visualize the data

1. Import necessary libraries for loading our data¶

For this recipe, we will use torch and torchaudio . Depending on what built-in datasets you use, you can also install and import torchvision or torchtext .

2. Access the data in the dataset¶

The Yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for YesNo.

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the Yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data¶

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader . The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

4. Iterate over the data¶

Our data is now iterable using the data_loader . This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

5. [Optional] Visualize the data¶

You can optionally visualize your data to further understand the output from your DataLoader .

Congratulations! You have successfully loaded data in PyTorch.


Watch the video: Με προειδοποίησε να μην μπω σε αυτό το εγκαταλελειμμένο σπίτι - Τι βρήκα μέσα μου;


Comments:

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  2. Rich

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  3. Anwealda

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  4. Naldo

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