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Version: v1.5.0

Prediction settings: Audio regression

Overview​

To score (predict) new data through the H2O Hydrogen Torch UI (with a built model), you need to specify certain settings refer as prediction settings (which are comprised of certain dataset, prediction, and environment settings similar to those utilized when creating an experiment). Below observe the prediction settings for an audio regression model.

General settings​

Experiment​

This setting defines the model (experiment) H2O Hydrogen Torch utilizes to score new data.

Prediction name​

This setting defines the name of the prediction.

Dataset settings​

Dataset​

This setting specifies the dataset to score.

Test dataframe​

This setting defines the file containing the test dataset that H2O Hydrogen Torch scores.

note
  • Image regression | 3D image regression | Image classification | 3D image classification | Image metric learning | Text regression | Text classification | Text sequence to sequence | Text span prediction | Text token classification | Text metric learning | Audio regression | Audio classification | Graph node classification | Graph node regression
    • Defines a CSV or Parquet file containing the test dataset that H2O Hydrogen Torch utilizes for scoring.
    note

    The test dataset should have the same format as the train dataset but does not require label columns.

  • Image object detection | Image semantic segmentation | 3D image semantic segmentation | Image instance segmentation
    • Defines a Parquet file containing the test dataset that H2O Hydrogen Torch utilizes for scoring.
      :::

Data folder test​

Defines the folder location of the assets (for example, images or audios) H2O Hydrogen Torch utilizes for scoring. H2O Hydrogen Torch loads assets from this folder during scoring.

Audio column​

Specifies the dataframe column storing the names of audios that H2O Hydrogen Torch loads from the Data folder test during scoring.

Prediction settings​

Metric​

This setting defines the evaluation metric in which H2O Hydrogen Torch evaluates the model's accuracy on generated predictions.

Batch Size Inference​

This setting defines the batch size of examples to utilize for inference.

note

Selecting 0 will set the Batch size inference to the same value used for the Batch size setting (utilized during training).

Inference chunk method​

Defines the inference chunk method H2O Hydrogen Torch uses for predictions.

Details
Options

Details
Audio classification | Audio regression

  • Fix
    • Forces the same chunk size for all audio clips, using zero padding for shorter clips and truncating for longer clips.
    note

    Fix as the model's inference chunk method enables batch processing with more efficient GPU usage.

  • Varying
    • H2O Hydrogen Torch reads shorter clips without padding.

Max inference chunk seconds​

Defines the maximum chunk size in seconds that H2O Hydrogen Torch uses from the audio. Shorter audio clips are used as-is.

Setting dependency

The Max inference chunk seconds setting is only available if you select Varying in the Inference chunk method setting.

Inference chunk seconds​

Defines the exact chunk size in seconds that H2O Hydrogen Torch uses to load the audio for predictions. Shorter audio clips are padded with zeros.

Setting dependency

The Inference chunk seconds setting is only available if you select Fix in the Inference chunk method setting.

Environment settings​

GPUs​

This setting specifies the list of GPUs H2O Hydrogen Torch can use for scoring. GPUs are listed by name, referring to their system ID (starting from 1). If no GPUs are selected, H2O Hydrogen Torch utilizes CPUs for model scoring.

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