diff --git a/docs/source/en/model_doc/speecht5.md b/docs/source/en/model_doc/speecht5.md
index 915fe2a9c4..4d5e2098a5 100644
--- a/docs/source/en/model_doc/speecht5.md
+++ b/docs/source/en/model_doc/speecht5.md
@@ -71,7 +71,7 @@ This model was contributed by [Matthijs](https://huggingface.co/Matthijs). The o
 
 [[autodoc]] SpeechT5ForTextToSpeech
     - forward
-    - generate_speech
+    - generate
 
 ## SpeechT5ForSpeechToSpeech
 
diff --git a/src/transformers/models/speecht5/modeling_speecht5.py b/src/transformers/models/speecht5/modeling_speecht5.py
index 5a3f117adb..f4e0e8052c 100644
--- a/src/transformers/models/speecht5/modeling_speecht5.py
+++ b/src/transformers/models/speecht5/modeling_speecht5.py
@@ -2717,7 +2717,7 @@ class SpeechT5ForTextToSpeech(SpeechT5PreTrainedModel):
         >>> set_seed(555)  # make deterministic
 
         >>> # generate speech
-        >>> speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+        >>> speech = model.generate(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
         >>> speech.shape
         torch.Size([15872])
         ```
@@ -2783,6 +2783,65 @@ class SpeechT5ForTextToSpeech(SpeechT5PreTrainedModel):
             encoder_attentions=outputs.encoder_attentions,
         )
 
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.LongTensor,
+        speaker_embeddings: Optional[torch.FloatTensor] = None,
+        threshold: float = 0.5,
+        minlenratio: float = 0.0,
+        maxlenratio: float = 20.0,
+        vocoder: Optional[nn.Module] = None,
+        output_cross_attentions: bool = False,
+        **kwargs,
+    ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor]]:
+        r"""
+        Converts a sequence of input tokens into a sequence of mel spectrograms, which are subsequently turned into a
+        speech waveform using a vocoder.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. The `batch_size` should be 1 currently.
+
+                Indices can be obtained using [`SpeechT5Tokenizer`]. See [`~PreTrainedTokenizer.encode`] and
+                [`~PreTrainedTokenizer.__call__`] for details.
+
+                [What are input IDs?](../glossary#input-ids)
+            speaker_embeddings (`torch.FloatTensor` of shape `(batch_size, config.speaker_embedding_dim)`, *optional*):
+                Tensor containing the speaker embeddings.
+            threshold (`float`, *optional*, defaults to 0.5):
+                The generated sequence ends when the predicted stop token probability exceeds this value.
+            minlenratio (`float`, *optional*, defaults to 0.0):
+                Used to calculate the minimum required length for the output sequence.
+            maxlenratio (`float`, *optional*, defaults to 20.0):
+                Used to calculate the maximum allowed length for the output sequence.
+            vocoder (`nn.Module`, *optional*):
+                The vocoder that converts the mel spectrogram into a speech waveform. If `None`, the output is the mel
+                spectrogram.
+            output_cross_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of the decoder's cross-attention layers.
+
+        Returns:
+            `tuple(torch.FloatTensor)` comprising various elements depending on the inputs:
+            - **spectrogram** (*optional*, returned when no `vocoder` is provided) `torch.FloatTensor` of shape
+              `(output_sequence_length, config.num_mel_bins)` -- The predicted log-mel spectrogram.
+            - **waveform** (*optional*, returned when a `vocoder` is provided) `torch.FloatTensor` of shape
+              `(num_frames,)` -- The predicted speech waveform.
+            - **cross_attentions** (*optional*, returned when `output_cross_attentions` is `True`) `torch.FloatTensor`
+              of shape `(config.decoder_layers, config.decoder_attention_heads, output_sequence_length,
+              input_sequence_length)` -- The outputs of the decoder's cross-attention layers.
+        """
+        return _generate_speech(
+            self,
+            input_ids,
+            speaker_embeddings,
+            threshold,
+            minlenratio,
+            maxlenratio,
+            vocoder,
+            output_cross_attentions,
+        )
+
     @torch.no_grad()
     def generate_speech(
         self,
diff --git a/tests/models/speecht5/test_modeling_speecht5.py b/tests/models/speecht5/test_modeling_speecht5.py
index 9324996ffe..eaec854914 100644
--- a/tests/models/speecht5/test_modeling_speecht5.py
+++ b/tests/models/speecht5/test_modeling_speecht5.py
@@ -1020,6 +1020,10 @@ class SpeechT5ForTextToSpeechIntegrationTests(unittest.TestCase):
         generated_speech = model.generate_speech(input_ids)
         self.assertEqual(generated_speech.shape, (1820, model.config.num_mel_bins))
 
+        # test model.generate, same method than generate_speech but with additional kwargs to absorb kwargs such as attention_mask
+        generated_speech_with_generate = model.generate(input_ids, attention_mask=None)
+        self.assertEqual(generated_speech_with_generate.shape, (1820, model.config.num_mel_bins))
+
 
 @require_torch
 class SpeechT5ForSpeechToSpeechTester: