适配新版本transformers

indextts/gpt/model.py

@@ -388,7 +388,7 @@ class UnifiedVoice(nn.Module):
     def post_init_gpt2_config(self, use_deepspeed=False, kv_cache=False, half=False):
         seq_length = self.max_mel_tokens + self.max_text_tokens + 2
         gpt_config = GPT2Config(
-            vocab_size=self.max_mel_tokens,
+            vocab_size=self.number_mel_codes,
             n_positions=seq_length,
             n_ctx=seq_length,
             n_embd=self.model_dim,
@@ -687,7 +687,13 @@ class UnifiedVoice(nn.Module):
             inputs = torch.cat([input_ids, input_tokens], dim=1)
             attention_mask = F.pad(attention_mask, (0, input_tokens.shape[1]), value=1)
         trunc_index = inputs.shape[1]
-        logits_processor = LogitsProcessorList([TypicalLogitsWarper(mass=typical_mass)]) if typical_sampling else LogitsProcessorList()
+        logits_processor = LogitsProcessorList()
+        if typical_sampling:
+            # employ custom typical sampling
+            if not (typical_mass > 0.0 and typical_mass < 1.0):
+                raise ValueError(f"`typical_mass` has to be a float > 0 and < 1, but is {typical_mass}")
+            min_tokens_to_keep = 2 if hf_generate_kwargs.get("num_beams", 1) > 1 else 1
+            logits_processor.append(TypicalLogitsWarper(mass=typical_mass, min_tokens_to_keep=min_tokens_to_keep))
         max_length = (trunc_index + self.max_mel_tokens - 1) if max_generate_length is None else trunc_index + max_generate_length
         output = self.inference_model.generate(inputs, 
                                             bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token,

indextts/utils/typical_sampling.py

@@ -1,12 +1,9 @@
 import torch
-from transformers import LogitsWarper
+from transformers import TypicalLogitsWarper as BaseTypicalLogitsWarper
 
-
-class TypicalLogitsWarper(LogitsWarper):
+class TypicalLogitsWarper(BaseTypicalLogitsWarper):
     def __init__(self, mass: float = 0.9, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
-        self.filter_value = filter_value
-        self.mass = mass
-        self.min_tokens_to_keep = min_tokens_to_keep
+        super().__init__(mass=mass, filter_value=filter_value, min_tokens_to_keep=min_tokens_to_keep)
 
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
         # calculate entropy