Enrich TTS pipeline parameters naming (#26473)

* enrich TTS pipeline docstring for clearer forward_params use

* change token leghts

* update Pipeline parameters

* correct docstring and make style

* fix tests

* make style

* change music prompt

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* Apply suggestions from code review

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* raise errors if generate_kwargs with forward-only models

* make style

---------

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

tests/pipelines/test_pipelines_text_to_audio.py

@@ -30,6 +30,7 @@ from transformers.testing_utils import (
     slow,
     torch_device,
 )
+from transformers.trainer_utils import set_seed
 
 from .test_pipelines_common import ANY
 
@@ -174,6 +175,60 @@ class TextToAudioPipelineTests(unittest.TestCase):
         outputs = speech_generator(["This is a test", "This is a second test"], batch_size=2)
         self.assertEqual(ANY(np.ndarray), outputs[0]["audio"])
 
+    @slow
+    @require_torch
+    def test_forward_model_kwargs(self):
+        # use vits - a forward model
+        speech_generator = pipeline(task="text-to-audio", model="kakao-enterprise/vits-vctk", framework="pt")
+
+        # for reproducibility
+        set_seed(555)
+        outputs = speech_generator("This is a test", forward_params={"speaker_id": 5})
+        audio = outputs["audio"]
+
+        with self.assertRaises(TypeError):
+            # assert error if generate parameter
+            outputs = speech_generator("This is a test", forward_params={"speaker_id": 5, "do_sample": True})
+
+        forward_params = {"speaker_id": 5}
+        generate_kwargs = {"do_sample": True}
+
+        with self.assertRaises(ValueError):
+            # assert error if generate_kwargs with forward-only models
+            outputs = speech_generator(
+                "This is a test", forward_params=forward_params, generate_kwargs=generate_kwargs
+            )
+        self.assertTrue(np.abs(outputs["audio"] - audio).max() < 1e-5)
+
+    @slow
+    @require_torch
+    def test_generative_model_kwargs(self):
+        # use musicgen - a generative model
+        music_generator = pipeline(task="text-to-audio", model="facebook/musicgen-small", framework="pt")
+
+        forward_params = {
+            "do_sample": True,
+            "max_new_tokens": 250,
+        }
+
+        # for reproducibility
+        set_seed(555)
+        outputs = music_generator("This is a test", forward_params=forward_params)
+        audio = outputs["audio"]
+        self.assertEqual(ANY(np.ndarray), audio)
+
+        # make sure generate kwargs get priority over forward params
+        forward_params = {
+            "do_sample": False,
+            "max_new_tokens": 250,
+        }
+        generate_kwargs = {"do_sample": True}
+
+        # for reproducibility
+        set_seed(555)
+        outputs = music_generator("This is a test", forward_params=forward_params, generate_kwargs=generate_kwargs)
+        self.assertListEqual(outputs["audio"].tolist(), audio.tolist())
+
     def get_test_pipeline(self, model, tokenizer, processor):
         speech_generator = TextToAudioPipeline(model=model, tokenizer=tokenizer)
         return speech_generator, ["This is a test", "Another test"]