Fix XTTS streaming for transformers update

TTS/tts/layers/xtts/stream_generator.py

@@ -4,7 +4,7 @@
 import inspect
 import random
 import warnings
-from typing import Callable, List, Optional, Union
+from typing import Callable, Optional, Union
 
 import numpy as np
 import torch
@@ -21,10 +21,11 @@
     PreTrainedModel,
     StoppingCriteriaList,
 )
+from transformers.generation.stopping_criteria import validate_stopping_criteria
 from transformers.generation.utils import GenerateOutput, SampleOutput, logger
 
 
-def setup_seed(seed):
+def setup_seed(seed: int) -> None:
     if seed == -1:
         return
     torch.manual_seed(seed)
@@ -49,9 +50,9 @@ def generate(  # noqa: PLR0911
         generation_config: Optional[StreamGenerationConfig] = None,
         logits_processor: Optional[LogitsProcessorList] = None,
         stopping_criteria: Optional[StoppingCriteriaList] = None,
-        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
+        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], list[int]]] = None,
         synced_gpus: Optional[bool] = False,
-        seed=0,
+        seed: int = 0,
         **kwargs,
     ) -> Union[GenerateOutput, torch.LongTensor]:
         r"""
@@ -90,7 +91,7 @@ def generate(  # noqa: PLR0911
                 Custom stopping criteria that complement the default stopping criteria built from arguments and a
                 generation config. If a stopping criteria is passed that is already created with the arguments or a
                 generation config an error is thrown. This feature is intended for advanced users.
-            prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*):
+            prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], list[int]]`, *optional*):
                 If provided, this function constraints the beam search to allowed tokens only at each step. If not
                 provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
                 `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
@@ -151,18 +152,7 @@ def generate(  # noqa: PLR0911
         # 2. Set generation parameters if not already defined
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-
-        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
-            if model_kwargs.get("attention_mask", None) is None:
-                logger.warning(
-                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
-                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
-                )
-            eos_token_id = generation_config.eos_token_id
-            if isinstance(eos_token_id, list):
-                eos_token_id = eos_token_id[0]
-            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
-            generation_config.pad_token_id = eos_token_id
+        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
 
         # 3. Define model inputs
         # inputs_tensor has to be defined
@@ -174,6 +164,9 @@ def generate(  # noqa: PLR0911
         )
         batch_size = inputs_tensor.shape[0]
 
+        device = inputs_tensor.device
+        self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)
+
         # 4. Define other model kwargs
         model_kwargs["output_attentions"] = generation_config.output_attentions
         model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
@@ -182,7 +175,7 @@ def generate(  # noqa: PLR0911
         accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
         requires_attention_mask = "encoder_outputs" not in model_kwargs
 
-        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
+        if not kwargs_has_attention_mask and requires_attention_mask and accepts_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
                 inputs_tensor,
                 generation_config.pad_token_id,
@@ -209,16 +202,15 @@ def generate(  # noqa: PLR0911
 
         # 5. Prepare `input_ids` which will be used for auto-regressive generation
         if self.config.is_encoder_decoder:
-            input_ids = self._prepare_decoder_input_ids_for_generation(
-                batch_size,
-                decoder_start_token_id=generation_config.decoder_start_token_id,
-                bos_token_id=generation_config.bos_token_id,
+            input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
+                batch_size=batch_size,
+                model_input_name=model_input_name,
                 model_kwargs=model_kwargs,
+                decoder_start_token_id=generation_config.decoder_start_token_id,
                 device=inputs_tensor.device,
             )
         else:
-            # if decoder-only then inputs_tensor has to be `input_ids`
-            input_ids = inputs_tensor
+            input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
 
         # 6. Prepare `max_length` depending on other stopping criteria.
         input_ids_seq_length = input_ids.shape[-1]
@@ -577,7 +569,7 @@ def generate(  # noqa: PLR0911
 
                 def typeerror():
                     raise ValueError(
-                        "`force_words_ids` has to either be a `List[List[List[int]]]` or `List[List[int]]`"
+                        "`force_words_ids` has to either be a `list[list[list[int]]]` or `list[list[int]]`"
                         f"of positive integers, but is {generation_config.force_words_ids}."
                     )
 
@@ -649,7 +641,7 @@ def sample_stream(
         logits_warper: Optional[LogitsProcessorList] = None,
         max_length: Optional[int] = None,
         pad_token_id: Optional[int] = None,
-        eos_token_id: Optional[Union[int, List[int]]] = None,
+        eos_token_id: Optional[Union[int, list[int]]] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         output_scores: Optional[bool] = None,

pyproject.toml

@@ -69,7 +69,7 @@ dependencies = [
     "gruut[de,es,fr]==2.2.3",
     # Tortoise
     "einops>=0.6.0",
-    "transformers>=4.33.0,<4.41.0",
+    "transformers>=4.41.1",
     # Bark
     "encodec>=0.1.1",
     # XTTS