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import onnxruntime
import torch
import torch.nn.functional as F
from fish_speech.conversation import CODEBOOK_PAD_TOKEN_ID
from tools.vqgan.extract_vq import get_model
PAD_TOKEN_ID = torch.LongTensor([CODEBOOK_PAD_TOKEN_ID])
class Encoder(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
self.model.spec_transform.spectrogram.return_complex = False
def forward(self, audios):
mels = self.model.spec_transform(audios)
encoded_features = self.model.backbone(mels)
z = self.model.quantizer.downsample(encoded_features)
_, indices = self.model.quantizer.residual_fsq(z.transpose(-2, -1))
_, b, l, _ = indices.shape
return indices.permute(1, 0, 3, 2).long().view(b, -1, l)
class Decoder(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
self.model.head.training = False
self.model.head.checkpointing = False
def get_codes_from_indices(self, cur_index, indices):
_, quantize_dim, _ = indices.shape
d_dim = self.model.quantizer.residual_fsq.rvqs[cur_index].codebooks.shape[2]
if (
quantize_dim
< self.model.quantizer.residual_fsq.rvqs[cur_index].num_quantizers
):
assert (
self.model.quantizer.residual_fsq.rvqs[cur_index].quantize_dropout > 0.0
), "quantize dropout must be greater than 0 if you wish to reconstruct from a signal with less fine quantizations"
indices = F.pad(
indices,
(
0,
self.model.quantizer.residual_fsq.rvqs[cur_index].num_quantizers
- quantize_dim,
),
value=-1,
)
mask = indices == -1
indices = indices.masked_fill(mask, 0)
all_codes = torch.gather(
self.model.quantizer.residual_fsq.rvqs[cur_index].codebooks.unsqueeze(1),
dim=2,
index=indices.permute(2, 0, 1).unsqueeze(-1).repeat(1, 1, 1, d_dim),
)
all_codes = all_codes.masked_fill(mask.permute(2, 0, 1).unsqueeze(-1), 0.0)
scales = (
self.model.quantizer.residual_fsq.rvqs[cur_index]
.scales.unsqueeze(1)
.unsqueeze(1)
)
all_codes = all_codes * scales
return all_codes
def get_output_from_indices(self, cur_index, indices):
codes = self.get_codes_from_indices(cur_index, indices)
codes_summed = codes.sum(dim=0)
return self.model.quantizer.residual_fsq.rvqs[cur_index].project_out(
codes_summed
)
def forward(self, indices) -> torch.Tensor:
batch_size, _, length = indices.shape
dims = self.model.quantizer.residual_fsq.dim
groups = self.model.quantizer.residual_fsq.groups
dim_per_group = dims // groups
# indices = rearrange(indices, "b (g r) l -> g b l r", g=groups)
indices = indices.view(batch_size, groups, -1, length).permute(1, 0, 3, 2)
# z_q = self.model.quantizer.residual_fsq.get_output_from_indices(indices)
z_q = torch.empty((batch_size, length, dims))
for i in range(groups):
z_q[:, :, i * dim_per_group : (i + 1) * dim_per_group] = (
self.get_output_from_indices(i, indices[i])
)
z = self.model.quantizer.upsample(z_q.transpose(1, 2))
x = self.model.head(z)
return x
def main(firefly_gan_vq_path, llama_path, export_prefix):
GanModel = get_model("firefly_gan_vq", firefly_gan_vq_path, device="cpu")
enc = Encoder(GanModel)
dec = Decoder(GanModel)
audio_example = torch.randn(1, 1, 96000)
indices = enc(audio_example)
torch.onnx.export(
enc,
audio_example,
f"{export_prefix}encoder.onnx",
dynamic_axes={
"audio": {0: "batch_size", 2: "audio_length"},
},
do_constant_folding=False,
opset_version=18,
verbose=False,
input_names=["audio"],
output_names=["prompt"],
)
torch.onnx.export(
dec,
indices,
f"{export_prefix}decoder.onnx",
dynamic_axes={
"prompt": {0: "batch_size", 2: "frame_count"},
},
do_constant_folding=False,
opset_version=18,
verbose=False,
input_names=["prompt"],
output_names=["audio"],
)
test_example = torch.randn(1, 1, 96000 * 5)
encoder_session = onnxruntime.InferenceSession(f"{export_prefix}encoder.onnx")
decoder_session = onnxruntime.InferenceSession(f"{export_prefix}decoder.onnx")
# check graph has no error
onnx_enc_out = encoder_session.run(["prompt"], {"audio": test_example.numpy()})[0]
torch_enc_out = enc(test_example)
onnx_dec_out = decoder_session.run(["audio"], {"prompt": onnx_enc_out})[0]
torch_dec_out = dec(torch_enc_out)
if __name__ == "__main__":
main("checkpoints/pre/firefly-gan-vq-fsq-8x1024-21hz-generator.pth", None, "test_")
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