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995e681 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import torch
import torch.nn as nn
from conv_stft import STFT
from huggingface_hub import hf_hub_download
from vocos import Vocos
opset_version = 17
def get_args():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"--vocoder",
type=str,
default="vocos",
choices=["vocos", "bigvgan"],
help="Vocoder to export",
)
parser.add_argument(
"--output-path",
type=str,
default="./vocos_vocoder.onnx",
help="Output path",
)
return parser.parse_args()
class ISTFTHead(nn.Module):
def __init__(self, n_fft: int, hop_length: int):
super().__init__()
self.out = None
self.stft = STFT(fft_len=n_fft, win_hop=hop_length, win_len=n_fft)
def forward(self, x: torch.Tensor):
x = self.out(x).transpose(1, 2)
mag, p = x.chunk(2, dim=1)
mag = torch.exp(mag)
mag = torch.clip(mag, max=1e2)
real = mag * torch.cos(p)
imag = mag * torch.sin(p)
audio = self.stft.inverse(input1=real, input2=imag, input_type="realimag")
return audio
class VocosVocoder(nn.Module):
def __init__(self, vocos_vocoder):
super(VocosVocoder, self).__init__()
self.vocos_vocoder = vocos_vocoder
istft_head_out = self.vocos_vocoder.head.out
n_fft = self.vocos_vocoder.head.istft.n_fft
hop_length = self.vocos_vocoder.head.istft.hop_length
istft_head_for_export = ISTFTHead(n_fft, hop_length)
istft_head_for_export.out = istft_head_out
self.vocos_vocoder.head = istft_head_for_export
def forward(self, mel):
waveform = self.vocos_vocoder.decode(mel)
return waveform
def export_VocosVocoder(vocos_vocoder, output_path, verbose):
vocos_vocoder = VocosVocoder(vocos_vocoder).cuda()
vocos_vocoder.eval()
dummy_batch_size = 8
dummy_input_length = 500
dummy_mel = torch.randn(dummy_batch_size, 100, dummy_input_length).cuda()
with torch.no_grad():
dummy_waveform = vocos_vocoder(mel=dummy_mel)
print(dummy_waveform.shape)
dummy_input = dummy_mel
torch.onnx.export(
vocos_vocoder,
dummy_input,
output_path,
opset_version=opset_version,
do_constant_folding=True,
input_names=["mel"],
output_names=["waveform"],
dynamic_axes={
"mel": {0: "batch_size", 2: "input_length"},
"waveform": {0: "batch_size", 1: "output_length"},
},
verbose=verbose,
)
print("Exported to {}".format(output_path))
def load_vocoder(vocoder_name="vocos", is_local=False, local_path="", device="cpu", hf_cache_dir=None):
if vocoder_name == "vocos":
# vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz").to(device)
if is_local:
print(f"Load vocos from local path {local_path}")
config_path = f"{local_path}/config.yaml"
model_path = f"{local_path}/pytorch_model.bin"
else:
print("Download Vocos from huggingface charactr/vocos-mel-24khz")
repo_id = "charactr/vocos-mel-24khz"
config_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="config.yaml")
model_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="pytorch_model.bin")
vocoder = Vocos.from_hparams(config_path)
state_dict = torch.load(model_path, map_location="cpu", weights_only=True)
vocoder.load_state_dict(state_dict)
vocoder = vocoder.eval().to(device)
elif vocoder_name == "bigvgan":
raise NotImplementedError("BigVGAN is not supported yet")
vocoder.remove_weight_norm()
vocoder = vocoder.eval().to(device)
return vocoder
if __name__ == "__main__":
args = get_args()
vocoder = load_vocoder(vocoder_name=args.vocoder, device="cpu", hf_cache_dir=None)
if args.vocoder == "vocos":
export_VocosVocoder(vocoder, args.output_path, verbose=False) |