TTS Warnings and Fixes

Below is a breakdown of each warning you’re seeing and possible steps to address (or safely ignore) them. In many cases, these warnings won’t break your code immediately, but they foreshadow future deprecations or potential bugs.

---

1. "gpu will be deprecated. Please use tts.to(device) instead."

Why You See This

Coqui TTS used to have a gpu parameter (e.g. TTS(gpu=True)), but it is being phased out in favor of the more generic PyTorch way of placing a model on a device (e.g. tts.to("cuda")).

How to Fix

• Remove any usage of gpu=True or similar in your code.
• After creating the TTS object, do:

  python
  tts = TTS(model_path=...)
  tts.to("cuda")  # or "cpu", depending on your environment

• Alternatively, if there’s a direct argument in TTS for the device, make sure you use that instead of gpu.

---

2. "torch.load with weights_only=False" FutureWarning

Why You See This

PyTorch is warning that loading entire pickled model objects can be risky if the source is untrusted. In future versions, weights_only=True will be the default for torch.load, which means it will only load the weights rather than any other pickled Python objects.

How to Fix / What to Do

• If you trust the model source (e.g., it’s your local file or an official model from a reputable organization), you can ignore the warning for now.
• If you want to be proactive:

  python
  # Example usage with weights_only=True
  checkpoint = torch.load(model_file, map_location="cpu", weights_only=True)

  Make sure your code (and the library code that calls torch.load) supports loading weights only. Otherwise, you might have to wait until Coqui TTS (or any library you rely on) officially supports this option.

---

3. "GPT2InferenceModel has generative capabilities but does NOT inherit from GenerationMixin..."

Why You See This

Hugging Face Transformers is alerting you that models deriving from PreTrainedModel will soon lose built-in .generate() functionality unless they explicitly inherit GenerationMixin. This is part of Transformers’ internal design changes post v4.50.

How to Fix

1. If you own the custom model code (i.e., the GPT2InferenceModel class):
   • Update the class definition:

     python
     from transformers.generation_utils import GenerationMixin  # or correct import path
     
     class GPT2InferenceModel(PreTrainedModel, GenerationMixin):
         ...

   • This ensures that you retain .generate() and related methods.
2. If you do not own the model code:
   • You can typically ignore the warning unless you rely on .generate().
   • If you do rely on .generate(), consider loading the model with trust_remote_code=True and use an AutoModel class if the model host supports it:

     python
     from transformers import AutoModelForCausalLM
     
     model = AutoModelForCausalLM.from_pretrained(
         "path_or_id_to_model",
         trust_remote_code=True
     )

   • Otherwise, contact the model’s authors to update their class definition.

---

4. "The attention mask is not set... pad token is same as eos token..."

Why You See This

This occurs when a Transformer-based model (often GPT-2 style) sees a pad_token_id that’s the same as the eos_token_id. Because the library cannot infer where actual padding vs. actual end-of-sequence occurs, you might get unexpected generation or attention behavior.

How to Fix

• If you’re passing text or tokens to the model yourself, explicitly provide an attention_mask. For example:

  python
  inputs = tokenizer("some text here", padding=True, return_tensors="pt")
  # returns something like {'input_ids':..., 'attention_mask':...}
  
  # Then feed these into the model
  outputs = model(**inputs)

• If you can’t change the tokenizer or underlying code, you can often ignore the warning, but be mindful that generation or text encodings might behave oddly for very long sequences or sequences with actual padding.

---

Summary of Recommendations

1. Remove the usage of gpu=True (or gpu arg) in Coqui TTS and use tts.to("cuda") (or "cpu") instead.
2. Ignore or adopt the new torch.load(weights_only=True) flow, depending on whether you trust your model source and if the library is prepared to handle weight-only loading.
3. Ignore the GPT-2 warning if you don’t use .generate(), or modify your custom model class to inherit GenerationMixin if you rely on generation capabilities.
4. Optionally provide an explicit attention_mask if the model usage and code path allows for it, which can prevent confusion between padding and the end-of-sequence token.

Generally, these are all warnings—none should completely break your code immediately. They do indicate future deprecations or possibly unexpected behavior, so it’s a good idea to address them sooner rather than later.