Do I Need To Install Nvidia CUDA To Solve This error: Microsoft Visual C++ 14.0 or greater is required"

[SRafi007]

Error: "error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/ [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for TTS Failed to build TTS ERROR: ERROR: Failed to build installable wheels for some pyproject.toml based projects (TTS)"

I Was looking for the solution almost all over the internet, mostly i saw This common answer " install a c++ build tools package from visual studio installer ", But in a youtube video a creator installed Nvidia cuda as a requirements. Look, I'm trying to be cautious installing these packages maybe because i don't have much knowledge about these tools, If you Have any experience .....Please Share

[KoljaB]

"error: Microsoft Visual C++ 14.0 or greater is required"

Did not experience this so far, so sadly can't really help here. It's coqui tts related and out of scope of this library. Which python version are you running on? I'd definitely recommend to install CUDA because otherwise you can't use the GPU for a lot of models. Can't tell if it solves the error. I'd just install the build tools, I guess.

[SRafi007]

@KoljaB I was finally able to create my Coqui tts project after downloading and installing Microsoft build tools and espeak-ng. Now struggling with fine-tuning and personalizing the model. I hope I learn that soon. Thanks

[KoljaB]

Some thoughts on XTTS finetuning:

• Recommended Tools:

  • Use xtts-webui over alltalk_tts. Danil's WebUI is more flexible and produces better models in my experience.

• Curate Your Dataset:

  • Check every sentence carefully. Trim audio clips to remove any noise, breaths, or silence at the end.
  • Ensure no artificial noises are present; even one can degrade model performance.
  • Avoid sentences longer than XTTS can process (max 250 characters for English, I recommed to cut off at 240 chars).

• Sample Length:

  • Use shorter sample lengths (around 11 seconds) for better performance. Longer samples can complicate training.

• Audio Preprocessing:

  • Convert audio to WAV signed 16-bit PCM, MONO, 22050 or 44100 Hz (whisper will downsample anyways). Use mono to ensure consistency.

• Avoid Overtraining:

  • Training beyond too many epochs can degrade model quality. Always go for at least 10 grads if you are targeting more then 12 epochs.

• Data Quality:

  • More data generally yields better results. Aim for large, high-quality dataset to improve model performance.