diff --git a/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py b/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py
index 736d67b1a2ad..d90324ef990a 100644
--- a/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py
+++ b/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py
@@ -347,9 +347,15 @@ def forward(
     ):
         r"""
         Args:
-            attention_mask (`torch.Tensor`)`, *optional*):
-                Qwen2Audio does not support masking of the `input_features`, this argument is preserved for compatibility,
-                but it is not used. By default the silence in the input log mel spectrogram are ignored.
+            input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
+                Float values of mel 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]`, a
+                `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec library (`pip install torchcodec`) or
+                the soundfile library (`pip install soundfile`). To prepare the array into
+                `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
+                and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`), *optional*):
+                attention mask used in the encoder stack (after the convolutional layers).
             output_attentions (`bool`, *optional*):
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more detail.
@@ -765,7 +771,7 @@ def forward(
                     feature_attention_mask.sum(-1)
                 )
                 batch_size, _, max_mel_seq_len = input_features.shape
-                max_seq_len = (max_mel_seq_len - 2) // 2 + 1
+                max_seq_len = (max_mel_seq_len - 1) // 2 + 1
                 # Create a sequence tensor of shape (batch_size, max_seq_len)
                 seq_range = (
                     torch.arange(0, max_seq_len, dtype=audio_feat_lengths.dtype, device=audio_feat_lengths.device)