page_type: reference description: Model good at detecting speech commands, using Browser FFT spectrum.

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tflite_model_maker.audio_classifier.BrowserFftSpec

<!-- Insert buttons and diff --> <table class="tfo-notebook-buttons tfo-api nocontent" align="left"> <td> <a target="_blank" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/model_spec/audio_spec.py#L216-L408"> </a> </td> </table>

Model good at detecting speech commands, using Browser FFT spectrum.

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>tflite_model_maker.audio_classifier.BrowserFftSpec( model_dir=None, strategy=None ) </code></pre> <h3>Used in the notebooks</h3> <table class="vertical-rules"> <thead> <tr> <th>Used in the tutorials</th> </tr> </thead> <tbody> <tr> <td> <ul> <li><a href="https://www.tensorflow.org/lite/models/modify/model_maker/speech_recognition">Retrain a speech recognition model with TensorFlow Lite Model Maker</a></li> </ul> </td> </tr> </tbody> </table> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Args</h2></th></tr> <tr> <td> `model_dir`<a id="model_dir"></a> </td> <td> The location to save the model checkpoint files. </td> </tr><tr> <td> `strategy`<a id="strategy"></a> </td> <td> An instance of TF distribute strategy. If none, it will use the default strategy (either SingleDeviceStrategy or the current scoped strategy. </td> </tr> </table> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Attributes</h2></th></tr> <tr> <td> `target_sample_rate`<a id="target_sample_rate"></a> </td> <td> </td> </tr> </table>

Methods

<h3 id="create_model"><code>create_model</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/model_spec/audio_spec.py#L308-L324">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>create_model( num_classes, train_whole_model=False ) </code></pre> <h3 id="create_serving_model"><code>create_serving_model</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/model_spec/audio_spec.py#L334-L341">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>create_serving_model( training_model ) </code></pre>

Create a model for serving.

<h3 id="export_tflite"><code>export_tflite</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/model_spec/audio_spec.py#L368-L408">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>export_tflite( model, tflite_filepath, with_metadata=True, export_metadata_json_file=True, index_to_label=None, quantization_config=None ) </code></pre>

Converts the retrained model to tflite format and saves it.

This method overrides the default CustomModel._export_tflite method, and include the pre-processing in the exported TFLite library since support library can't handle audio tasks yet.

<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `model` </td> <td> An instance of the keras classification model to be exported. </td> </tr><tr> <td> `tflite_filepath` </td> <td> File path to save tflite model. </td> </tr><tr> <td> `with_metadata` </td> <td> Whether the output tflite model contains metadata. </td> </tr><tr> <td> `export_metadata_json_file` </td> <td> Whether to export metadata in json file. If True, export the metadata in the same directory as tflite model.Used only if `with_metadata` is True. </td> </tr><tr> <td> `index_to_label` </td> <td> A list that map from index to label class name. </td> </tr><tr> <td> `quantization_config` </td> <td> Configuration for post-training quantization. </td> </tr> </table> <h3 id="get_default_quantization_config"><code>get_default_quantization_config</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/model_spec/audio_spec.py#L169-L171">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>get_default_quantization_config() </code></pre>

Gets the default quantization configuration.

<h3 id="preprocess_ds"><code>preprocess_ds</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/model_spec/audio_spec.py#L297-L306">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>preprocess_ds( ds, is_training=False, cache_fn=None ) </code></pre>

Returns a preprocessed dataset.

<h3 id="run_classifier"><code>run_classifier</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/model_spec/audio_spec.py#L326-L332">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>run_classifier( model, epochs, train_ds, validation_ds, **kwargs ) </code></pre> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Class Variables</h2></th></tr> <tr> <td> EXPECTED_WAVEFORM_LENGTH<a id="EXPECTED_WAVEFORM_LENGTH"></a> </td> <td> `44032` </td> </tr> </table>