def check_encoder_decoder_model_generate(self,
config,
decoder_config,
input_values=None,
input_features=None,
**kwargs):
encoder_model, decoder_model = self.get_encoder_decoder_model(
config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model,
decoder=decoder_model)
enc_dec_model.to(torch_device)
# make sure EOS token is set to None to prevent early stopping of generation
enc_dec_model.config.eos_token_id = None
if hasattr(enc_dec_model.config, "decoder") and hasattr(
enc_dec_model.config.decoder, "eos_token_id"):
enc_dec_model.config.decoder.eos_token_id = None
inputs = input_values if input_features is None else input_features
# Bert does not have a bos token id, so use pad_token_id instead
generated_output = enc_dec_model.generate(
inputs,
decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id)
self.assertEqual(generated_output.shape,
(inputs.shape[0], ) + (decoder_config.max_length, ))
def check_encoder_decoder_model_generate(
self, config, decoder_config, input_values=None, input_features=None, **kwargs
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
inputs = input_values if input_features is None else input_features
# Bert does not have a bos token id, so use pad_token_id instead
generated_output = enc_dec_model.generate(
inputs, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id
)
self.assertEqual(generated_output.shape, (inputs.shape[0],) + (decoder_config.max_length,))
