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>

src/transformers/pipelines/text_to_audio.py

@@ -43,6 +43,29 @@ class TextToAudioPipeline(Pipeline):
 
     Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
 
+    <Tip>
+
+    You can specify parameters passed to the model by using [`TextToAudioPipeline.__call__.forward_params`] or
+    [`TextToAudioPipeline.__call__.generate_kwargs`].
+
+    Example:
+
+    ```python
+    >>> from transformers import pipeline
+
+    >>> music_generator = pipeline(task="text-to-audio", model="facebook/musicgen-small", framework="pt")
+
+    >>> # diversify the music generation by adding randomness with a high temperature and set a maximum music length
+    >>> generate_kwargs = {
+    ...     "do_sample": True,
+    ...     "temperature": 0.7,
+    ...     "max_new_tokens": 35,
+    ... }
+
+    >>> outputs = music_generator("Techno music with high melodic riffs", generate_kwargs=generate_kwargs)
+    ```
+
+    </Tip>
 
     This pipeline can currently be loaded from [`pipeline`] using the following task identifiers: `"text-to-speech"` or
     `"text-to-audio"`.
@@ -107,11 +130,26 @@ class TextToAudioPipeline(Pipeline):
     def _forward(self, model_inputs, **kwargs):
         # we expect some kwargs to be additional tensors which need to be on the right device
         kwargs = self._ensure_tensor_on_device(kwargs, device=self.device)
+        forward_params = kwargs["forward_params"]
+        generate_kwargs = kwargs["generate_kwargs"]
 
         if self.model.can_generate():
-            output = self.model.generate(**model_inputs, **kwargs)
+            # we expect some kwargs to be additional tensors which need to be on the right device
+            generate_kwargs = self._ensure_tensor_on_device(generate_kwargs, device=self.device)
+
+            # generate_kwargs get priority over forward_params
+            forward_params.update(generate_kwargs)
+
+            output = self.model.generate(**model_inputs, **forward_params)
         else:
-            output = self.model(**model_inputs, **kwargs)[0]
+            if len(generate_kwargs):
+                raise ValueError(
+                    f"""You're using the `TextToAudioPipeline` with a forward-only model, but `generate_kwargs` is non empty.
+                                 For forward-only TTA models, please use `forward_params` instead of of
+                                 `generate_kwargs`. For reference, here are the `generate_kwargs` used here:
+                                 {generate_kwargs.keys()}"""
+                )
+            output = self.model(**model_inputs, **forward_params)[0]
 
         if self.vocoder is not None:
             # in that case, the output is a spectrogram that needs to be converted into a waveform
@@ -126,8 +164,14 @@ class TextToAudioPipeline(Pipeline):
         Args:
             text_inputs (`str` or `List[str]`):
                 The text(s) to generate.
-            forward_params (*optional*):
+            forward_params (`dict`, *optional*):
-                Parameters passed to the model generation/forward method.
+                Parameters passed to the model generation/forward method. `forward_params` are always passed to the
+                underlying model.
+            generate_kwargs (`dict`, *optional*):
+                The dictionary of ad-hoc parametrization of `generate_config` to be used for the generation call. For a
+                complete overview of generate, check the [following
+                guide](https://huggingface.co/docs/transformers/en/main_classes/text_generation). `generate_kwargs` are
+                only passed to the underlying model if the latter is a generative model.
 
         Return:
             A `dict` or a list of `dict`: The dictionaries have two keys:
@@ -141,14 +185,18 @@ class TextToAudioPipeline(Pipeline):
         self,
         preprocess_params=None,
         forward_params=None,
+        generate_kwargs=None,
     ):
+        params = {
+            "forward_params": forward_params if forward_params else {},
+            "generate_kwargs": generate_kwargs if generate_kwargs else {},
+        }
+
         if preprocess_params is None:
             preprocess_params = {}
-        if forward_params is None:
-            forward_params = {}
         postprocess_params = {}
 
-        return preprocess_params, forward_params, postprocess_params
+        return preprocess_params, params, postprocess_params
 
     def postprocess(self, waveform):
         output_dict = {}

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"]