Update Bark generation configs and tests (#25409)

* update bark generation configs for more coherent parameter

* make style

* update bark hub repo

src/transformers/models/bark/generation_configuration_bark.py

@@ -36,8 +36,8 @@ class BarkSemanticGenerationConfig(GenerationConfig):
         return_dict_in_generate=False,
         output_hidden_states=False,
         output_attentions=False,
-        temperature=0.7,
-        do_sample=True,
+        temperature=1.0,
+        do_sample=False,
         text_encoding_offset=10_048,
         text_pad_token=129_595,
         semantic_infer_token=129_599,
@@ -70,9 +70,9 @@ class BarkSemanticGenerationConfig(GenerationConfig):
             output_attentions (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more details.
-            temperature (`float`, *optional*, defaults to 0.7):
+            temperature (`float`, *optional*, defaults to 1.0):
                 The value used to modulate the next token probabilities.
-            do_sample (`bool`, *optional*, defaults to `True`):
+            do_sample (`bool`, *optional*, defaults to `False`):
                 Whether or not to use sampling ; use greedy decoding otherwise.
             text_encoding_offset (`int`, *optional*, defaults to 10_048):
                 Text encoding offset.
@@ -119,8 +119,8 @@ class BarkCoarseGenerationConfig(GenerationConfig):
         return_dict_in_generate=False,
         output_hidden_states=False,
         output_attentions=False,
-        temperature=0.7,
-        do_sample=True,
+        temperature=1.0,
+        do_sample=False,
         coarse_semantic_pad_token=12_048,
         coarse_rate_hz=75,
         n_coarse_codebooks=2,
@@ -150,9 +150,9 @@ class BarkCoarseGenerationConfig(GenerationConfig):
             output_attentions (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more details.
-            temperature (`float`, *optional*, defaults to 0.7):
+            temperature (`float`, *optional*, defaults to 1.0):
                 The value used to modulate the next token probabilities.
-            do_sample (`bool`, *optional*, defaults to `True`):
+            do_sample (`bool`, *optional*, defaults to `False`):
                 Whether or not to use sampling ; use greedy decoding otherwise.
             coarse_semantic_pad_token (`int`, *optional*, defaults to 12_048):
                 Coarse semantic pad token.
@@ -194,7 +194,7 @@ class BarkFineGenerationConfig(GenerationConfig):
 
     def __init__(
         self,
-        temperature=0.5,
+        temperature=1.0,
         max_fine_history_length=512,
         max_fine_input_length=1024,
         n_fine_codebooks=8,
@@ -209,7 +209,7 @@ class BarkFineGenerationConfig(GenerationConfig):
         documentation from [`GenerationConfig`] for more information.
 
         Args:
-            temperature (`float`, *optional*, defaults to 0.5):
+            temperature (`float`, *optional*):
                 The value used to modulate the next token probabilities.
             max_fine_history_length (`int`, *optional*, defaults to 512):
                 Max length of the fine history vector.
@@ -224,6 +224,13 @@ class BarkFineGenerationConfig(GenerationConfig):
         self.max_fine_input_length = max_fine_input_length
         self.n_fine_codebooks = n_fine_codebooks
 
+    def validate(self):
+        """
+        Overrides GenerationConfig.validate because BarkFineGenerationConfig don't use any parameters outside
+        temperature.
+        """
+        pass
+
 
 class BarkGenerationConfig(GenerationConfig):
     model_type = "bark"

src/transformers/models/bark/modeling_bark.py

@@ -1336,7 +1336,7 @@ class BarkFineModel(BarkPreTrainedModel):
             input_buffer = fine_input[:, start_idx : start_idx + max_fine_input_length, :]
             for n_inner in range(n_coarse, fine_generation_config.n_fine_codebooks):
                 logits = self.forward(n_inner, input_buffer).logits
-                if temperature is None:
+                if temperature is None or temperature == 1.0:
                     relevant_logits = logits[:, rel_start_fill_idx:, :codebook_size]
                     codebook_preds = torch.argmax(relevant_logits, -1)
                 else:
@@ -1499,8 +1499,8 @@ class BarkModel(BarkPreTrainedModel):
         ```python
         >>> from transformers import AutoProcessor, BarkModel
 
-        >>> processor = AutoProcessor.from_pretrained("ylacombe/bark-small")
-        >>> model = BarkModel.from_pretrained("ylacombe/bark-small")
+        >>> processor = AutoProcessor.from_pretrained("suno/bark-small")
+        >>> model = BarkModel.from_pretrained("suno/bark-small")
 
         >>> # To add a voice preset, you can pass `voice_preset` to `BarkProcessor.__call__(...)`
         >>> voice_preset = "v2/en_speaker_6"

tests/models/bark/test_modeling_bark.py

@@ -894,11 +894,11 @@ class BarkFineModelTest(ModelTesterMixin, unittest.TestCase):
 class BarkModelIntegrationTests(unittest.TestCase):
     @cached_property
     def model(self):
-        return BarkModel.from_pretrained("ylacombe/bark-large").to(torch_device)
+        return BarkModel.from_pretrained("suno/bark").to(torch_device)
 
     @cached_property
     def processor(self):
-        return BarkProcessor.from_pretrained("ylacombe/bark-large")
+        return BarkProcessor.from_pretrained("suno/bark")
 
     @cached_property
     def inputs(self):
@@ -937,6 +937,7 @@ class BarkModelIntegrationTests(unittest.TestCase):
             output_ids = self.model.semantic.generate(
                 **input_ids,
                 do_sample=False,
+                temperature=1.0,
                 semantic_generation_config=self.semantic_generation_config,
             )
 
@@ -957,6 +958,7 @@ class BarkModelIntegrationTests(unittest.TestCase):
             output_ids = self.model.semantic.generate(
                 **input_ids,
                 do_sample=False,
+                temperature=1.0,
                 semantic_generation_config=self.semantic_generation_config,
             )
 
@@ -964,6 +966,7 @@ class BarkModelIntegrationTests(unittest.TestCase):
                 output_ids,
                 history_prompt=history_prompt,
                 do_sample=False,
+                temperature=1.0,
                 semantic_generation_config=self.semantic_generation_config,
                 coarse_generation_config=self.coarse_generation_config,
                 codebook_size=self.model.generation_config.codebook_size,
@@ -994,6 +997,7 @@ class BarkModelIntegrationTests(unittest.TestCase):
             output_ids = self.model.semantic.generate(
                 **input_ids,
                 do_sample=False,
+                temperature=1.0,
                 semantic_generation_config=self.semantic_generation_config,
             )
 
@@ -1001,6 +1005,7 @@ class BarkModelIntegrationTests(unittest.TestCase):
                 output_ids,
                 history_prompt=history_prompt,
                 do_sample=False,
+                temperature=1.0,
                 semantic_generation_config=self.semantic_generation_config,
                 coarse_generation_config=self.coarse_generation_config,
                 codebook_size=self.model.generation_config.codebook_size,
@@ -1040,9 +1045,16 @@ class BarkModelIntegrationTests(unittest.TestCase):
         input_ids = self.inputs
 
         with torch.no_grad():
-            self.model.generate(**input_ids, do_sample=False, coarse_do_sample=True, coarse_temperature=0.7)
             self.model.generate(
-                **input_ids, do_sample=False, coarse_do_sample=True, coarse_temperature=0.7, fine_temperature=0.3
+                **input_ids, do_sample=False, temperature=1.0, coarse_do_sample=True, coarse_temperature=0.7
+            )
+            self.model.generate(
+                **input_ids,
+                do_sample=False,
+                temperature=1.0,
+                coarse_do_sample=True,
+                coarse_temperature=0.7,
+                fine_temperature=0.3,
             )
             self.model.generate(
                 **input_ids,
@@ -1061,7 +1073,7 @@ class BarkModelIntegrationTests(unittest.TestCase):
 
         with torch.no_grad():
             # standard generation
-            output_with_no_offload = self.model.generate(**input_ids, do_sample=False, fine_temperature=None)
+            output_with_no_offload = self.model.generate(**input_ids, do_sample=False, temperature=1.0)
 
             torch.cuda.empty_cache()
 
@@ -1088,7 +1100,7 @@ class BarkModelIntegrationTests(unittest.TestCase):
             self.assertTrue(hasattr(self.model.semantic, "_hf_hook"))
 
             # output with cpu offload
-            output_with_offload = self.model.generate(**input_ids, do_sample=False, fine_temperature=None)
+            output_with_offload = self.model.generate(**input_ids, do_sample=False, temperature=1.0)
 
         # checks if same output
         self.assertListEqual(output_with_no_offload.tolist(), output_with_offload.tolist())

tests/models/bark/test_processor_bark.py

@@ -26,7 +26,7 @@ from transformers.testing_utils import require_torch, slow
 @require_torch
 class BarkProcessorTest(unittest.TestCase):
     def setUp(self):
-        self.checkpoint = "ylacombe/bark-small"
+        self.checkpoint = "suno/bark-small"
         self.tmpdirname = tempfile.mkdtemp()
         self.voice_preset = "en_speaker_1"
         self.input_string = "This is a test string"