Fix performance issue when using CUDA provider

I encountered a huge performance issue when running the tts module on my RTX 3090 gpu instead of i9-14900k cpu.

I used some simple code to run single executions of the tts module for testing.

The time elapsed for gpu was over 30 seconds while cpu took only 0.09 - 0.11 seconds.

Based on an article I found, the solution is to change the cudnn_conv_algo_search options from "EXHAUSTIVE" (default) to "HEURISTIC" or "DEFAULT". I went with "HEURISTIC" as the article explains that leads to behavior similar to pyTorch's default. Additionally, both "DEFAULT" and "HEURISTIC" produced the same results. (https://medium.com/neuml/debug-onnx-gpu-performance-c9290fe07459)

Now, my gpu time elapsed time has significantly decreased to taking 0.15 - 0.18 seconds. The gpu is expected to be slightly slower than cpu in this case as there is data being copied to the gpu at the start of inference, and data being copied back to the cpu after inference, which adds some overhead.

There was a page I read that explains adding CPUExecutionProvider (appearing after CUDAExecutionProvider) in the providers list will grant onnxruntime explicit permission to fallback to cpu for operations unsupported in CUDA (all the yellow warning messages). This behavior occurs by default anyways but it is better to explicitly allow this behavior.

Code used for testing


import sounddevice as sd

model_path = "models/glados.onnx"
text = "All neural network modules are now loaded."
rate = 22050

_tts = tts.Synthesizer(model_path=model_path, use_cuda=True)

audio = _tts.generate_speech_audio(text)

sd.play(audio, samplerate=rate)
sd.wait()

print("done")

Inside glados/tts.py I set the start_time at the beginning of the generate_speech_audio function and printed the elapsed time before returning the concatenated audio data.

def generate_speech_audio(self, text: str) -> np.ndarray:
        start_time = time.time()
        phonemes = self._phonemizer(text)
        audio = []
        for sentence in phonemes:
            audio_chunk = self._say_phonemes(sentence)
            audio.append(audio_chunk)
        if audio:
            print("returning audio", time.time() - start_time)
            return np.concatenate(audio, axis=1).T
        return np.array([])

I also added an instruction in the README for using python 3.10 instead of python 3.12.

Summary by CodeRabbit

New Features
- Improved performance with CUDA execution support for text-to-speech when use_cuda is enabled.
Documentation
- Updated installation instructions to recommend Python 3.12 from the Microsoft Store.
- Added guidance for using Python 3.10 with necessary adjustments.

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Commits
Files that changed from the base of the PR and between 43034b1cba1c8a5bf8c39fe2560b1ae419343915 and f81b9208bd3df03e4114b5c85a1dfd37d58441bb.

Walkthrough

The recent update centers on enhancing the Text-to-Speech (TTS) capabilities in glados/tts.py, focusing on optimizing CUDA execution when use_cuda is enabled. The README.md also guides users to install Python 3.12 from the Microsoft Store and provides instructions for using Python 3.10 in glados/llama.py.

Changes

File	Change Summary
`glados/tts.py`	Modified provider settings in `_initialize_session` for improved CUDA execution
`README.md`	Updated Python installation instructions and added guidance for using Python 3.10 with `typing_extensions` in `glados/llama.py`

🐇 In code we trust, our voices clear,
With CUDA's might, we persevere.
ONNX now swift, like a hare in flight,
Bringing speech to life, day and night.
🎶 Hopping through data, with joy and delight!

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dnhkng / GlaDOS