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import lzma
from typing import List, Optional, Union
import pybase16384 as b14
import numpy as np
import torch
import torch.nn.functional as F
class Speaker:
def __init__(self, dim: int, spk_cfg: str, device=torch.device("cpu")) -> None:
spk_stat = torch.from_numpy(
np.frombuffer(b14.decode_from_string(spk_cfg), dtype=np.float16).copy()
).to(device=device)
self.std, self.mean = spk_stat.requires_grad_(False).chunk(2)
self.dim = dim
def sample_random(self) -> str:
return self._encode(self._sample_random())
@torch.inference_mode()
def apply(
self,
emb: torch.Tensor,
spk_emb: Union[str, torch.Tensor],
input_ids: torch.Tensor,
spk_emb_ids: int,
device: torch.device,
inplace: bool = True,
) -> torch.Tensor:
if isinstance(spk_emb, str):
spk_emb_tensor = torch.from_numpy(self._decode(spk_emb))
else:
spk_emb_tensor = spk_emb
n = (
F.normalize(
spk_emb_tensor,
p=2.0,
dim=0,
eps=1e-12,
)
.to(device)
.unsqueeze_(0)
.expand(emb.size(0), -1)
.unsqueeze_(1)
.expand(emb.shape)
)
cond = input_ids.narrow(-1, 0, 1).eq(spk_emb_ids).expand(emb.shape)
out = torch.where(cond, n, emb, out=emb if inplace else None)
if inplace:
del cond, n
return out
@staticmethod
@torch.no_grad()
def decorate_code_prompts(
text: List[str],
prompt: str,
txt_smp: Optional[str],
spk_emb: Optional[str],
) -> List[str]:
for i, t in enumerate(text):
text[i] = (
t.replace("[Stts]", "")
.replace("[spk_emb]", "")
.replace("[empty_spk]", "")
.strip()
)
"""
see https://github.com/2noise/ChatTTS/issues/459
"""
if prompt:
text = [prompt + i for i in text]
txt_smp = "" if txt_smp is None else txt_smp
if spk_emb is not None:
text = [f"[Stts][spk_emb]{txt_smp}{i}[Ptts]" for i in text]
else:
text = [f"[Stts][empty_spk]{txt_smp}{i}[Ptts]" for i in text]
return text
@staticmethod
@torch.no_grad()
def decorate_text_prompts(text: List[str], prompt: str) -> List[str]:
return [f"[Sbreak]{i}[Pbreak]{prompt}" for i in text]
@staticmethod
@torch.no_grad()
def encode_prompt(prompt: torch.Tensor) -> str:
arr: np.ndarray = prompt.cpu().numpy().astype(np.uint16)
shp = arr.shape
assert len(shp) == 2, "prompt must be a 2D tensor"
s = b14.encode_to_string(
np.array(shp, dtype="<u2").tobytes()
+ lzma.compress(
arr.astype("<u2").tobytes(),
format=lzma.FORMAT_RAW,
filters=[{"id": lzma.FILTER_LZMA2, "preset": 9 | lzma.PRESET_EXTREME}],
),
)
del arr
return s
@staticmethod
@torch.no_grad()
def decode_prompt(prompt: str) -> torch.Tensor:
dec = b14.decode_from_string(prompt)
shp = np.frombuffer(dec[:4], dtype="<u2")
p = np.frombuffer(
lzma.decompress(
dec[4:],
format=lzma.FORMAT_RAW,
filters=[{"id": lzma.FILTER_LZMA2, "preset": 9 | lzma.PRESET_EXTREME}],
),
dtype="<u2",
).copy()
del dec
return torch.from_numpy(p.astype(np.int32)).view(*shp)
@torch.no_grad()
def _sample_random(self) -> torch.Tensor:
spk = (
torch.randn(self.dim, device=self.std.device, dtype=self.std.dtype)
.mul_(self.std)
.add_(self.mean)
)
return spk
@staticmethod
@torch.no_grad()
def _encode(spk_emb: torch.Tensor) -> str:
arr: np.ndarray = spk_emb.to(dtype=torch.float16, device="cpu").numpy()
s = b14.encode_to_string(
lzma.compress(
arr.tobytes(),
format=lzma.FORMAT_RAW,
filters=[{"id": lzma.FILTER_LZMA2, "preset": 9 | lzma.PRESET_EXTREME}],
),
)
del arr
return s
@staticmethod
def _decode(spk_emb: str) -> np.ndarray:
return np.frombuffer(
lzma.decompress(
b14.decode_from_string(spk_emb),
format=lzma.FORMAT_RAW,
filters=[{"id": lzma.FILTER_LZMA2, "preset": 9 | lzma.PRESET_EXTREME}],
),
dtype=np.float16,
).copy()
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