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