| import numpy as np |
| import onnxruntime as ort |
| from text import cantonese, english, cleaned_text_to_sequence |
|
|
| language_module_map = {"EN": english, "YUE": cantonese} |
|
|
| def clean_text(text, language): |
| language_module = language_module_map[language] |
| norm_text = language_module.text_normalize(text) |
| phones, tones, word2ph = language_module.g2p(norm_text) |
| return norm_text, phones, tones, word2ph |
|
|
|
|
| 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_en, |
| bert_yue, |
| sid, |
| seed=114514, |
| seq_noise_scale=0.8, |
| sdp_noise_scale=0.6, |
| length_scale=1.0, |
| sdp_ratio=0.8, |
| ): |
| 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_en.astype(np.float32), |
| "bert_1": bert_yue.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 |
| ) |
| logs_p = np.matmul(attn.squeeze(1), logs_p.transpose(0, 2, 1)).transpose( |
| 0, 2, 1 |
| ) |
|
|
| 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] |
|
|
