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import numpy as np
import onnxruntime as ort
def convert_pad_shape(pad_shape):
layer = pad_shape[::-1]
pad_shape = [item for sublist in layer for item in sublist]
return pad_shape
def sequence_mask(length, max_length=None):
if max_length is None:
max_length = length.max()
x = np.arange(max_length, dtype=length.dtype)
return np.expand_dims(x, 0) < np.expand_dims(length, 1)
def generate_path(duration, mask):
"""
duration: [b, 1, t_x]
mask: [b, 1, t_y, t_x]
"""
b, _, t_y, t_x = mask.shape
cum_duration = np.cumsum(duration, -1)
cum_duration_flat = cum_duration.reshape(b * t_x)
path = sequence_mask(cum_duration_flat, t_y)
path = path.reshape(b, t_x, t_y)
path = path ^ np.pad(path, ((0, 0), (1, 0), (0, 0)))[:, :-1]
path = np.expand_dims(path, 1).transpose(0, 1, 3, 2)
return path
class OnnxInferenceSession:
def __init__(self, path, Providers=["CPUExecutionProvider"]):
self.enc = ort.InferenceSession(path["enc"], providers=Providers)
self.emb_g = ort.InferenceSession(path["emb_g"], providers=Providers)
self.dp = ort.InferenceSession(path["dp"], providers=Providers)
self.sdp = ort.InferenceSession(path["sdp"], providers=Providers)
self.flow = ort.InferenceSession(path["flow"], providers=Providers)
self.dec = ort.InferenceSession(path["dec"], providers=Providers)
def __call__(
self,
seq,
tone,
language,
bert_zh,
bert_jp,
bert_en,
sid,
seed=114514,
seq_noise_scale=0.8,
sdp_noise_scale=0.6,
length_scale=1.0,
sdp_ratio=0.0,
):
if seq.ndim == 1:
seq = np.expand_dims(seq, 0)
if tone.ndim == 1:
tone = np.expand_dims(tone, 0)
if language.ndim == 1:
language = np.expand_dims(language, 0)
assert (seq.ndim == 2, tone.ndim == 2, language.ndim == 2)
g = self.emb_g.run(
None,
{
"sid": sid.astype(np.int64),
},
)[0]
g = np.expand_dims(g, -1)
enc_rtn = self.enc.run(
None,
{
"x": seq.astype(np.int64),
"t": tone.astype(np.int64),
"language": language.astype(np.int64),
"bert_0": bert_zh.astype(np.float32),
"bert_1": bert_jp.astype(np.float32),
"bert_2": bert_en.astype(np.float32),
"g": g.astype(np.float32),
},
)
x, m_p, logs_p, x_mask = enc_rtn[0], enc_rtn[1], enc_rtn[2], enc_rtn[3]
np.random.seed(seed)
zinput = np.random.randn(x.shape[0], 2, x.shape[2]) * sdp_noise_scale
logw = self.sdp.run(
None, {"x": x, "x_mask": x_mask, "zin": zinput.astype(np.float32), "g": g}
)[0] * (sdp_ratio) + self.dp.run(None, {"x": x, "x_mask": x_mask, "g": g})[
0
] * (
1 - sdp_ratio
)
w = np.exp(logw) * x_mask * length_scale
w_ceil = np.ceil(w)
y_lengths = np.clip(np.sum(w_ceil, (1, 2)), a_min=1.0, a_max=100000).astype(
np.int64
)
y_mask = np.expand_dims(sequence_mask(y_lengths, None), 1)
attn_mask = np.expand_dims(x_mask, 2) * np.expand_dims(y_mask, -1)
attn = generate_path(w_ceil, attn_mask)
m_p = np.matmul(attn.squeeze(1), m_p.transpose(0, 2, 1)).transpose(
0, 2, 1
) # [b, t', t], [b, t, d] -> [b, d, t']
logs_p = np.matmul(attn.squeeze(1), logs_p.transpose(0, 2, 1)).transpose(
0, 2, 1
) # [b, t', t], [b, t, d] -> [b, d, t']
z_p = (
m_p
+ np.random.randn(m_p.shape[0], m_p.shape[1], m_p.shape[2])
* np.exp(logs_p)
* seq_noise_scale
)
z = self.flow.run(
None,
{
"z_p": z_p.astype(np.float32),
"y_mask": y_mask.astype(np.float32),
"g": g,
},
)[0]
return self.dec.run(None, {"z_in": z.astype(np.float32), "g": g})[0]
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