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optimize: separate speaker from tokenizer
- move `spk_stat.pt` into config - move `sample_audio_speaker` to DVAE - update rvcmd to `v0.2.7`
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,5 @@ | ||
| from .dvae import DVAE | ||
| from .gpt import GPT | ||
| from .processors import gen_logits | ||
| from .speaker import Speaker | ||
| from .tokenizer import Tokenizer |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,146 @@ | ||
| 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.no_grad() | ||
| def apply( | ||
| self, | ||
| emb: torch.Tensor, | ||
| spk_emb: str, | ||
| input_ids: torch.Tensor, | ||
| spk_emb_ids: int, | ||
| device: torch.device, | ||
| ): | ||
| n = ( | ||
| F.normalize( | ||
| torch.from_numpy( | ||
| self._decode(spk_emb), | ||
| ), | ||
| 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) | ||
| torch.where(cond, n, emb, out=emb) | ||
| del cond, n | ||
|
|
||
| @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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