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79f89ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | import onnxruntime as ort
import numpy as np
from typing import List, Optional
import threading
from ..Audio.ReferenceAudio import ReferenceAudio
from ..GetPhonesAndBert import get_phones_and_bert
MAX_T2S_LEN = 1000
def stretch_semantic_tokens(tokens: np.ndarray, speed: float) -> np.ndarray:
"""
语义 Token 插值（最近邻），用于实现语速调节。
借鉴自 AstraTTS 的 StretchSemanticTokens 算法。
Args:
tokens: 原始 semantic tokens [1, 1, T]
speed: 语速系数，>1 加速，<1 减速
Returns:
插值后的 tokens
"""
if tokens is None or tokens.size == 0:
return tokens
if abs(speed - 1.0) < 0.01:
return tokens
# 提取原始 token 序列（去除批次维度）
original = tokens.flatten()
original_len = len(original)
# 计算新长度
new_len = int(round(original_len / speed))
if new_len < 1:
new_len = 1
# 最近邻插值
result = np.zeros(new_len, dtype=original.dtype)
for i in range(new_len):
old_idx = int(i * speed)
if old_idx >= original_len:
old_idx = original_len - 1
result[i] = original[old_idx]
# 恢复原始形状 [1, 1, new_len]
return result.reshape(1, 1, -1)
class GENIE:
def __init__(self):
self.stop_event: threading.Event = threading.Event()
def tts(
self,
text: str,
prompt_audio: ReferenceAudio,
encoder: ort.InferenceSession,
first_stage_decoder: ort.InferenceSession,
stage_decoder: ort.InferenceSession,
vocoder: ort.InferenceSession,
prompt_encoder: Optional[ort.InferenceSession],
language: str = 'japanese',
text_language: str = None,
speed: float = 1.0, # 语速调节
) -> Optional[np.ndarray]:
# 如果未指定 text_language，则使用参考音频的语言
actual_text_language = text_language if text_language else language
text = '。' + text # 防止漏第一句。
text_seq, text_bert = get_phones_and_bert(text, language=actual_text_language)
semantic_tokens: np.ndarray = self.t2s_cpu(
ref_seq=prompt_audio.phonemes_seq,
ref_bert=prompt_audio.text_bert,
text_seq=text_seq,
text_bert=text_bert,
ssl_content=prompt_audio.ssl_content,
encoder=encoder,
first_stage_decoder=first_stage_decoder,
stage_decoder=stage_decoder,
)
eos_indices = np.where(semantic_tokens >= 1024) # 剔除不合法的元素，例如 EOS Token。
if len(eos_indices[0]) > 0:
first_eos_index = eos_indices[-1][0]
semantic_tokens = semantic_tokens[..., :first_eos_index]
# 🔥 语速调节：在 vocoder 前对 semantic tokens 进行插值
semantic_tokens = stretch_semantic_tokens(semantic_tokens, speed)
if prompt_encoder is None:
return vocoder.run(None, {
"text_seq": text_seq,
"pred_semantic": semantic_tokens,
"ref_audio": prompt_audio.audio_32k
})[0]
else:
# V2ProPlus 新增。
prompt_audio.update_global_emb(prompt_encoder=prompt_encoder)
audio_chunk = vocoder.run(None, {
"text_seq": text_seq,
"pred_semantic": semantic_tokens,
"ge": prompt_audio.global_emb,
"ge_advanced": prompt_audio.global_emb_advanced,
})[0]
return audio_chunk
def t2s_cpu(
self,
ref_seq: np.ndarray,
ref_bert: np.ndarray,
text_seq: np.ndarray,
text_bert: np.ndarray,
ssl_content: np.ndarray,
encoder: ort.InferenceSession,
first_stage_decoder: ort.InferenceSession,
stage_decoder: ort.InferenceSession,
) -> Optional[np.ndarray]:
"""在CPU上运行T2S模型"""
# Encoder
x, prompts = encoder.run(
None,
{
"ref_seq": ref_seq,
"text_seq": text_seq,
"ref_bert": ref_bert,
"text_bert": text_bert,
"ssl_content": ssl_content,
},
)
# First Stage Decoder
y, y_emb, *present_key_values = first_stage_decoder.run(
None, {"x": x, "prompts": prompts}
)
# Stage Decoder
input_names: List[str] = [inp.name for inp in stage_decoder.get_inputs()]
idx: int = 0
for idx in range(0, 500):
if self.stop_event.is_set():
return None
input_feed = {
name: data
for name, data in zip(input_names, [y, y_emb, *present_key_values])
}
outputs = stage_decoder.run(None, input_feed)
y, y_emb, stop_condition_tensor, *present_key_values = outputs
if stop_condition_tensor:
break
y[0, -1] = 0
return np.expand_dims(y[:, -idx:], axis=0)
tts_client: GENIE = GENIE()
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