Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update Onnx Export #438

Merged
merged 6 commits into from
Feb 8, 2024
Merged

Update Onnx Export #438

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
8c01e27
Add files via upload
NaruseMioShirakana Feb 8, 2024
08aed05
Add files via upload
NaruseMioShirakana Feb 8, 2024
0b2e376
Add files via upload
NaruseMioShirakana Feb 8, 2024
d1c3cf7
Add files via upload
NaruseMioShirakana Feb 8, 2024
ffa49a7
Add files via upload
NaruseMioShirakana Feb 8, 2024
dd1186c
Add files via upload
NaruseMioShirakana Feb 8, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion GPT_SoVITS/AR/models/t2s_model_onnx.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ def logits_to_probs(
logits = logits / max(temperature, 1e-5)

if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
v, _ = torch.topk(logits, top_k)
pivot = v.select(-1, -1).unsqueeze(-1)
logits = torch.where(logits < pivot, inf_tensor_value, logits)

Expand Down
47 changes: 18 additions & 29 deletions GPT_SoVITS/module/attentions_onnx.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -188,38 +188,27 @@ def attention(self, query, key, value, mask=None):
query = query.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3)
key = key.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3)
value = value.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3)

scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))

if self.window_size is not None:
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
rel_logits = self._matmul_with_relative_keys(
query / math.sqrt(self.k_channels), key_relative_embeddings
)
rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings)
scores_local = self._relative_position_to_absolute_position(rel_logits)
scores = scores + scores_local

if mask is not None:
scores = scores.masked_fill(mask == 0, -1e4)
if self.block_length is not None:
block_mask = (
torch.ones_like(scores)
.triu(-self.block_length)
.tril(self.block_length)
)
scores = scores.masked_fill(block_mask == 0, -1e4)

p_attn = F.softmax(scores, dim=-1)
p_attn = self.drop(p_attn)
output = torch.matmul(p_attn, value)

if self.window_size is not None:
relative_weights = self._absolute_position_to_relative_position(p_attn)
value_relative_embeddings = self._get_relative_embeddings(
self.emb_rel_v, t_s
)
output = output + self._matmul_with_relative_values(
relative_weights, value_relative_embeddings
)
output = (
output.transpose(2, 3).contiguous().view(b, d, -1)
)
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)

output = (output.transpose(2, 3).contiguous().view(b, d, -1))
return output, p_attn

def _matmul_with_relative_values(self, x, y):
Expand All @@ -243,16 +232,16 @@ def _matmul_with_relative_keys(self, x, y):
def _get_relative_embeddings(self, relative_embeddings, length):
max_relative_position = 2 * self.window_size + 1
# Pad first before slice to avoid using cond ops.
pad_length = max(length - (self.window_size + 1), 0)
slice_start_position = max((self.window_size + 1) - length, 0)
pad_l = torch.zeros((1), dtype = torch.int64) + length - (self.window_size + 1)
pad_s = torch.zeros((1), dtype = torch.int64) + (self.window_size + 1) - length
pad_length = torch.max(pad_l, other=torch.zeros((1), dtype = torch.int64))
slice_start_position = torch.max(pad_s, other=torch.zeros((1), dtype = torch.int64))

slice_end_position = slice_start_position + 2 * length - 1
if pad_length > 0:
padded_relative_embeddings = F.pad(
relative_embeddings,
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
)
else:
padded_relative_embeddings = relative_embeddings
padded_relative_embeddings = F.pad(
relative_embeddings,
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
)
used_relative_embeddings = padded_relative_embeddings[
:, slice_start_position:slice_end_position
]
Expand Down
4 changes: 1 addition & 3 deletions GPT_SoVITS/module/models_onnx.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -896,9 +896,6 @@ def forward(self, codes, text, refer):
refer_mask = torch.ones_like(refer[:1,:1,:])
ge = self.ref_enc(refer * refer_mask, refer_mask)

y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)

quantized = self.quantizer.decode(codes)
if self.semantic_frame_rate == "25hz":
dquantized = torch.cat([quantized, quantized]).permute(1, 2, 0)
Expand All @@ -907,6 +904,7 @@ def forward(self, codes, text, refer):
x, m_p, logs_p, y_mask = self.enc_p(
quantized, text, ge
)

z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p)

z = self.flow(z_p, y_mask, g=ge, reverse=True)
Expand Down
72 changes: 46 additions & 26 deletions GPT_SoVITS/onnx_export.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -140,34 +140,35 @@ def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_na
)
onnx_encoder_export_output.save(f"onnx/{project_name}/{project_name}_t2s_encoder.onnx")
return

torch.onnx.export(
self.onnx_encoder,
(ref_seq, text_seq, ref_bert, text_bert, ssl_content),
f"onnx/{project_name}/{project_name}_t2s_encoder.onnx",
input_names=["ref_seq", "text_seq", "ref_bert", "text_bert", "ssl_content"],
output_names=["x", "prompts"],
dynamic_axes={
"ref_seq": [1],
"text_seq": [1],
"ref_bert": [0],
"text_bert": [0],
"ssl_content": [2],
"ref_seq": {1 : "ref_length"},
"text_seq": {1 : "text_length"},
"ref_bert": {0 : "ref_length"},
"text_bert": {0 : "text_length"},
"ssl_content": {2 : "ssl_length"},
},
opset_version=16
)
x, prompts = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
torch.exp

torch.onnx.export(
self.first_stage_decoder,
(x, prompts),
f"onnx/{project_name}/{project_name}_t2s_fsdec.onnx",
input_names=["x", "prompts"],
output_names=["y", "k", "v", "y_emb", "x_example"],
dynamic_axes={
"x": [1],
"prompts": [1],
"x": {1 : "x_length"},
"prompts": {1 : "prompts_length"},
},
verbose=True,
verbose=False,
opset_version=16
)
y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
Expand All @@ -179,13 +180,13 @@ def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_na
input_names=["iy", "ik", "iv", "iy_emb", "ix_example"],
output_names=["y", "k", "v", "y_emb", "logits", "samples"],
dynamic_axes={
"iy": [1],
"ik": [1],
"iv": [1],
"iy_emb": [1],
"ix_example": [1],
"iy": {1 : "iy_length"},
"ik": {1 : "ik_length"},
"iv": {1 : "iv_length"},
"iy_emb": {1 : "iy_emb_length"},
"ix_example": {1 : "ix_example_length"},
},
verbose=True,
verbose=False,
opset_version=16
)

Expand Down Expand Up @@ -224,9 +225,19 @@ def __init__(self, vits, t2s):
self.vits = vits
self.t2s = t2s

def forward(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content):
def forward(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content, debug=False):
pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
return self.vits(text_seq, pred_semantic, ref_audio)
audio = self.vits(text_seq, pred_semantic, ref_audio)
if debug:
import onnxruntime
sess = onnxruntime.InferenceSession("onnx/koharu/koharu_vits.onnx", providers=["CPU"])
audio1 = sess.run(None, {
"text_seq" : text_seq.detach().cpu().numpy(),
"pred_semantic" : pred_semantic.detach().cpu().numpy(),
"ref_audio" : ref_audio.detach().cpu().numpy()
})
return audio, audio1
return audio

def export(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content, project_name):
self.t2s.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name)
Expand All @@ -238,11 +249,12 @@ def export(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content,
input_names=["text_seq", "pred_semantic", "ref_audio"],
output_names=["audio"],
dynamic_axes={
"text_seq": [1],
"pred_semantic": [2],
"ref_audio": [1],
"text_seq": {1 : "text_length"},
"pred_semantic": {2 : "pred_length"},
"ref_audio": {1 : "audio_length"},
},
opset_version=17
opset_version=17,
verbose=False
)


Expand All @@ -261,7 +273,7 @@ def export(vits_path, gpt_path, project_name):
gpt_sovits = GptSoVits(vits, gpt)
ssl = SSLModel()
ref_seq = torch.LongTensor([cleaned_text_to_sequence(["n", "i2", "h", "ao3", ",", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"])])
text_seq = torch.LongTensor([cleaned_text_to_sequence(["w", "o3", "sh", "i4", "b", "ai2", "y", "e4"])])
text_seq = torch.LongTensor([cleaned_text_to_sequence(["w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"])])
ref_bert = torch.randn((ref_seq.shape[1], 1024)).float()
text_bert = torch.randn((text_seq.shape[1], 1024)).float()
ref_audio = torch.randn((1, 48000 * 5)).float()
Expand All @@ -275,10 +287,18 @@ def export(vits_path, gpt_path, project_name):
pass

ssl_content = ssl(ref_audio_16k).float()

debug = False

if debug:
a, b = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, debug=debug)
soundfile.write("out1.wav", a.cpu().detach().numpy(), vits.hps.data.sampling_rate)
soundfile.write("out2.wav", b[0], vits.hps.data.sampling_rate)
return

a = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content).detach().cpu().numpy()

# soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
soundfile.write("out.wav", a, vits.hps.data.sampling_rate)

gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, project_name)

Expand Down Expand Up @@ -306,9 +326,9 @@ def export(vits_path, gpt_path, project_name):
except:
pass

gpt_path = "pt_model/koharu-e20.ckpt"
vits_path = "pt_model/koharu_e20_s4960.pth"
exp_path = "koharu"
gpt_path = "GPT_weights/nahida-e25.ckpt"
vits_path = "SoVITS_weights/nahida_e30_s3930.pth"
exp_path = "nahida"
export(vits_path, gpt_path, exp_path)

# soundfile.write("out.wav", a, vits.hps.data.sampling_rate)