mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-12-16 09:16:59 +08:00
d3b8f7e09e
This commit migrates the project from using NVIDIA CUDA to Intel XPU for GPU acceleration, based on the PyTorch 2.9 release. Key changes include: - Replaced `torch.cuda` with `torch.xpu` for device checks, memory management, and distributed training. - Updated device strings from "cuda" to "xpu" across the codebase. - Switched the distributed training backend from "nccl" to "ccl" for Intel GPUs. - Disabled custom CUDA kernels in the `BigVGAN` module by setting `use_cuda_kernel=False`. - Updated `requirements.txt` to include `torch==2.9` and `intel-extension-for-pytorch`. - Modified CI/CD pipelines and build scripts to remove CUDA dependencies and build for an XPU target.
103 lines
2.9 KiB
Python
103 lines
2.9 KiB
Python
# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
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# LICENSE is in incl_licenses directory.
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from __future__ import absolute_import, division, print_function, unicode_literals
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import glob
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import os
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import numpy as np
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import argparse
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import json
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import torch
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from scipy.io.wavfile import write
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from env import AttrDict
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from meldataset import MAX_WAV_VALUE
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from bigvgan import BigVGAN as Generator
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h = None
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device = None
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torch.backends.cudnn.benchmark = False
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def load_checkpoint(filepath, device):
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assert os.path.isfile(filepath)
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print(f"Loading '{filepath}'")
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checkpoint_dict = torch.load(filepath, map_location=device)
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print("Complete.")
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return checkpoint_dict
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def scan_checkpoint(cp_dir, prefix):
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pattern = os.path.join(cp_dir, prefix + "*")
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cp_list = glob.glob(pattern)
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if len(cp_list) == 0:
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return ""
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return sorted(cp_list)[-1]
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def inference(a, h):
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generator = Generator(h, use_cuda_kernel=a.use_cuda_kernel).to(device)
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state_dict_g = load_checkpoint(a.checkpoint_file, device)
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generator.load_state_dict(state_dict_g["generator"])
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filelist = os.listdir(a.input_mels_dir)
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os.makedirs(a.output_dir, exist_ok=True)
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generator.eval()
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generator.remove_weight_norm()
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with torch.no_grad():
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for i, filname in enumerate(filelist):
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# Load the mel spectrogram in .npy format
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x = np.load(os.path.join(a.input_mels_dir, filname))
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x = torch.FloatTensor(x).to(device)
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if len(x.shape) == 2:
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x = x.unsqueeze(0)
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y_g_hat = generator(x)
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audio = y_g_hat.squeeze()
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audio = audio * MAX_WAV_VALUE
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audio = audio.cpu().numpy().astype("int16")
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output_file = os.path.join(a.output_dir, os.path.splitext(filname)[0] + "_generated_e2e.wav")
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write(output_file, h.sampling_rate, audio)
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print(output_file)
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def main():
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print("Initializing Inference Process..")
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parser = argparse.ArgumentParser()
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parser.add_argument("--input_mels_dir", default="test_mel_files")
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parser.add_argument("--output_dir", default="generated_files_from_mel")
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parser.add_argument("--checkpoint_file", required=True)
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# --use_cuda_kernel argument is removed to disable custom CUDA kernels.
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# parser.add_argument("--use_cuda_kernel", action="store_true", default=False)
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a = parser.parse_args()
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a.use_cuda_kernel = False
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config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json")
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with open(config_file) as f:
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data = f.read()
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global h
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json_config = json.loads(data)
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h = AttrDict(json_config)
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torch.manual_seed(h.seed)
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global device
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if torch.xpu.is_available():
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torch.xpu.manual_seed(h.seed)
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device = torch.device("xpu")
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else:
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device = torch.device("cpu")
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inference(a, h)
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if __name__ == "__main__":
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main()
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