[SpeechT5HifiGan] Handle batched inputs (#21702)

* [SpeechT5HifiGan] Handle batched inputs

* fix docstring

* rebase and new ruff style

src/transformers/models/speecht5/modeling_speecht5.py

@@ -3030,19 +3030,27 @@ class SpeechT5HifiGan(PreTrainedModel):
 
     def forward(self, spectrogram):
         r"""
-        Converts a single log-mel spectogram into a speech waveform.
+        Converts a log-mel spectrogram into a speech waveform. Passing a batch of log-mel spectrograms returns a batch
+        of speech waveforms. Passing a single, un-batched log-mel spectrogram returns a single, un-batched speech
+        waveform.
 
         Args:
-            spectrogram (`torch.FloatTensor` of shape `(sequence_length, config.model_in_dim)`):
-                Tensor containing the log-mel spectrogram.
+            spectrogram (`torch.FloatTensor`):
+                Tensor containing the log-mel spectrograms. Can be batched and of shape `(batch_size, sequence_length,
+                config.model_in_dim)`, or un-batched and of shape `(sequence_length, config.model_in_dim)`.
 
         Returns:
-            `torch.FloatTensor`: Tensor of shape `(num_frames,)` containing the speech waveform.
+            `torch.FloatTensor`: Tensor containing the speech waveform. If the input spectrogram is batched, will be of
+            shape `(batch_size, num_frames,)`. If un-batched, will be of shape `(num_frames,)`.
         """
         if self.config.normalize_before:
             spectrogram = (spectrogram - self.mean) / self.scale
 
-        hidden_states = spectrogram.transpose(1, 0).unsqueeze(0)
+        is_batched = spectrogram.dim() == 3
+        if not is_batched:
+            spectrogram = spectrogram.unsqueeze(0)
+
+        hidden_states = spectrogram.transpose(2, 1)
 
         hidden_states = self.conv_pre(hidden_states)
         for i in range(self.num_upsamples):
@@ -3058,5 +3066,11 @@ class SpeechT5HifiGan(PreTrainedModel):
         hidden_states = self.conv_post(hidden_states)
         hidden_states = torch.tanh(hidden_states)
 
-        waveform = hidden_states.squeeze(0).transpose(1, 0).view(-1)
+        if not is_batched:
+            # remove batch dim and collapse tensor to 1-d audio waveform
+            waveform = hidden_states.squeeze(0).transpose(1, 0).view(-1)
+        else:
+            # remove seq-len dim since this collapses to 1
+            waveform = hidden_states.squeeze(1)
+
         return waveform

tests/models/speecht5/test_modeling_speecht5.py

@@ -1545,3 +1545,23 @@ class SpeechT5HifiGanTest(ModelTesterMixin, unittest.TestCase):
     # skip because it fails on automapping of SpeechT5HifiGanConfig
     def test_save_load_fast_init_to_base(self):
         pass
+
+    def test_batched_inputs_outputs(self):
+        config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            batched_inputs = inputs["spectrogram"].unsqueeze(0).repeat(2, 1, 1)
+
+            batched_outputs = model(batched_inputs)
+            self.assertEqual(
+                batched_inputs.shape[0], batched_outputs.shape[0], msg="Got different batch dims for input and output"
+            )
+
+    def test_unbatched_inputs_outputs(self):
+        config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            outputs = model(inputs["spectrogram"])
+            self.assertTrue(outputs.dim() == 1, msg="Got un-batched inputs but batched output")