def metrics(deepspeech: DeepSpeech, layer_outputs: List[np.ndarray],
batch: Tuple[np.ndarray, np.ndarray]) -> Iterable[Metric]:
X, y = batch
y_hat = layer_outputs[-1]
predict_sentences = deepspeech.decode(y_hat)
true_sentences = deepspeech.get_transcripts(y)
return get_metrics(sources=predict_sentences, destinations=true_sentences)
def evaluate_batch(deepspeech: DeepSpeech, X: np.ndarray, y: np.ndarray, store: h5py.File,
references: pd.DataFrame, save_activations: bool, get_activations: Callable) -> List[Metric]:
if save_activations:
*activations, y_hat = get_activations([X, 0]) # Learning phase is `test=0`
else:
activations = []
y_hat = deepspeech.predict(X)
predict_sentences = deepspeech.decode(y_hat)
true_sentences = deepspeech.get_transcripts(y)
metrics = list(get_metrics(sources=predict_sentences, destinations=true_sentences))
save_in(store, [X, *activations, y_hat], metrics, references)
return metrics
