Fix dtype of input_features in docstring (#18258)

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py

@@ -135,7 +135,7 @@ SPEECH_ENCODER_DECODER_INPUTS_DOCSTRING = r"""
             into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
             soundfile*). To prepare the array into *input_values*, the [`Wav2Vec2Processor`] should be used for padding
             and conversion into a tensor of type *torch.FloatTensor*. See [`Wav2Vec2Processor.__call__`] for details.
-        input_features (`torch.LongTensor` of shape `(batch_size, sequence_length, feature_size)`, *optional*):
+        input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, feature_size)`, *optional*):
             Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained
             by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.*
             via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the

src/transformers/models/speech_to_text/modeling_speech_to_text.py

@@ -599,7 +599,7 @@ SPEECH_TO_TEXT_START_DOCSTRING = r"""
 
 SPEECH_TO_TEXT_INPUTS_DOCSTRING = r"""
     Args:
-        input_features (`torch.LongTensor` of shape `(batch_size, sequence_length, feature_size)`):
+        input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, feature_size)`):
             Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained
             by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.*
             via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the