🚨 🚨 Raise error when no speaker embeddings in speecht5._generate_speech (#26418)

* add warning when no speaker embeddings in speecht5._generate_speech

* modify warning to error

* adapt generation test

src/transformers/models/speecht5/modeling_speecht5.py

@@ -2550,6 +2550,14 @@ def _generate_speech(
     vocoder: Optional[nn.Module] = None,
     output_cross_attentions: bool = False,
 ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor]]:
+    if speaker_embeddings is None:
+        raise ValueError(
+            """`speaker_embeddings` must be specified. For example, you can use a speaker embeddings by following
+                    the code snippet provided in this link:
+                    https://huggingface.co/datasets/Matthijs/cmu-arctic-xvectors
+                    """
+        )
+
     encoder_attention_mask = torch.ones_like(input_values)
 
     encoder_out = model.speecht5.encoder(

tests/models/speecht5/test_modeling_speecht5.py

@@ -1015,15 +1015,21 @@ class SpeechT5ForTextToSpeechIntegrationTests(unittest.TestCase):
 
         set_seed(555)  # make deterministic
 
+        speaker_embeddings = torch.zeros((1, 512)).to(torch_device)
+
         input_text = "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
         input_ids = processor(text=input_text, return_tensors="pt").input_ids.to(torch_device)
 
-        generated_speech = model.generate_speech(input_ids)
-        self.assertEqual(generated_speech.shape, (1820, model.config.num_mel_bins))
+        generated_speech = model.generate_speech(input_ids, speaker_embeddings=speaker_embeddings)
+        self.assertEqual(generated_speech.shape, (228, model.config.num_mel_bins))
+
+        set_seed(555)  # make deterministic
 
         # 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))
+        generated_speech_with_generate = model.generate(
+            input_ids, attention_mask=None, speaker_embeddings=speaker_embeddings
+        )
+        self.assertEqual(generated_speech_with_generate.shape, (228, model.config.num_mel_bins))
 
 
 @require_torch