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| # modified from https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/model/cfm.py | |
| """ | |
| ein notation: | |
| b - batch | |
| n - sequence | |
| nt - text sequence | |
| nw - raw wave length | |
| d - dimension | |
| """ | |
| from __future__ import annotations | |
| from typing import Callable | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| from torch.nn.utils.rnn import pad_sequence | |
| from torchdiffeq import odeint | |
| from .modules import MelSpec | |
| from .utils import ( | |
| default, | |
| exists, | |
| lens_to_mask, | |
| list_str_to_idx, | |
| list_str_to_tensor, | |
| ) | |
| class CFM(nn.Module): | |
| def __init__( | |
| self, | |
| transformer: nn.Module, | |
| sigma=0.0, | |
| odeint_kwargs: dict = dict( | |
| method="euler" # 'midpoint' | |
| ), | |
| num_channels=None, | |
| mel_spec_module: nn.Module | None = None, | |
| mel_spec_kwargs: dict = dict(), | |
| frac_lengths_mask: tuple[float, float] = (0.7, 1.0), | |
| vocab_char_map: dict[str:int] | None = None, | |
| controlnet: nn.Module | None = None, | |
| ): | |
| super().__init__() | |
| self.frac_lengths_mask = frac_lengths_mask | |
| self.mel_spec = default(mel_spec_module, MelSpec(**mel_spec_kwargs)) | |
| num_channels = default(num_channels, self.mel_spec.n_mel_channels) | |
| self.num_channels = num_channels | |
| self.transformer = transformer | |
| dim = transformer.dim | |
| self.dim = dim | |
| self.sigma = sigma | |
| self.odeint_kwargs = odeint_kwargs | |
| self.vocab_char_map = vocab_char_map | |
| self.controlnet = controlnet | |
| def device(self): | |
| return next(self.parameters()).device | |
| def sample( | |
| self, | |
| cond: float["b n d"] | float["b nw"], # noqa: F722 | |
| text: int["b nt"] | list[str], # noqa: F722 | |
| clip: float["b n d"], # noqa: F722 | |
| duration: int | int["b"], # noqa: F821 | |
| *, | |
| caption_emb: float["b n d"] | None = None, # noqa: F722 | |
| spk_emb: float["b n d"] | None = None, # noqa: F722 | |
| lens: int["b"] | None = None, # noqa: F821 | |
| steps=32, | |
| cfg_strength=1.0, | |
| sway_sampling_coef=None, | |
| seed: int | None = None, | |
| max_duration=4096, | |
| vocoder: Callable[[float["b d n"]], float["b nw"]] | None = None, # noqa: F722 | |
| no_ref_audio=False, | |
| duplicate_test=False, | |
| t_inter=0.1, | |
| edit_mask=None, | |
| ): | |
| self.eval() | |
| if cond.ndim == 2: | |
| cond = self.mel_spec(cond) | |
| cond = cond.permute(0, 2, 1) | |
| assert cond.shape[-1] == self.num_channels | |
| cond = cond.to(next(self.parameters()).dtype) | |
| batch, cond_seq_len, device = *cond.shape[:2], cond.device | |
| if not exists(lens): | |
| lens = torch.full((batch,), cond_seq_len, device=device, dtype=torch.long) | |
| if isinstance(text, list): | |
| if exists(self.vocab_char_map): | |
| text = list_str_to_idx(text, self.vocab_char_map).to(device) | |
| else: | |
| text = list_str_to_tensor(text).to(device) | |
| assert text.shape[0] == batch | |
| if exists(text): | |
| text_lens = (text != -1).sum(dim=-1) | |
| lens = torch.maximum(text_lens, lens) | |
| cond_mask = lens_to_mask(lens) | |
| if edit_mask is not None: | |
| cond_mask = cond_mask & edit_mask | |
| if isinstance(duration, int): | |
| duration = torch.full((batch,), duration, device=device, dtype=torch.long) | |
| # duration = torch.maximum(lens + 1, duration) | |
| duration = duration.clamp(max=max_duration) | |
| max_duration = duration.amax() | |
| if duplicate_test: | |
| test_cond = F.pad(cond, (0, 0, cond_seq_len, max_duration - 2 * cond_seq_len), value=0.0) | |
| cond = F.pad(cond, (0, 0, 0, max_duration - cond_seq_len), value=0.0) | |
| cond_mask = F.pad(cond_mask, (0, max_duration - cond_mask.shape[-1]), value=False) | |
| cond_mask = cond_mask.unsqueeze(-1) | |
| step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond)) | |
| if batch > 1: | |
| mask = lens_to_mask(duration) | |
| else: | |
| mask = None | |
| if no_ref_audio: | |
| cond = torch.zeros_like(cond) | |
| def fn(t, x): | |
| step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond)) | |
| controlnet_embeds = self.controlnet( | |
| x=x, | |
| text=text, | |
| clip=clip, | |
| spk_emb=spk_emb, | |
| caption=caption_emb, | |
| time=t, | |
| ) | |
| cond_pred = self.transformer( | |
| x=x, | |
| cond=step_cond, | |
| text=text, | |
| time=t, | |
| mask=mask, | |
| drop_audio_cond=[False], | |
| drop_text=[False], | |
| controlnet_embeds=controlnet_embeds, | |
| ) | |
| null_pred = self.transformer( | |
| x=x, | |
| cond=step_cond, | |
| text=text, | |
| time=t, | |
| mask=mask, | |
| drop_audio_cond=[True], | |
| drop_text=[True], | |
| controlnet_embeds=None, | |
| ) | |
| return null_pred + (cond_pred - null_pred) * 2 | |
| y0 = [] | |
| for dur in duration: | |
| if exists(seed): | |
| torch.manual_seed(seed) | |
| y0.append(torch.randn(dur, self.num_channels, device=self.device, dtype=step_cond.dtype)) | |
| y0 = pad_sequence(y0, padding_value=0, batch_first=True) | |
| t_start = 0 | |
| if duplicate_test: | |
| t_start = t_inter | |
| y0 = (1 - t_start) * y0 + t_start * test_cond | |
| steps = int(steps * (1 - t_start)) | |
| t = torch.linspace(t_start, 1, steps + 1, device=self.device, dtype=step_cond.dtype) | |
| if sway_sampling_coef is not None: | |
| t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t) | |
| trajectory = odeint(fn, y0, t, **self.odeint_kwargs) | |
| sampled = trajectory[-1] | |
| out = sampled | |
| out = torch.where(cond_mask, cond, out) | |
| if exists(vocoder): | |
| out = out.permute(0, 2, 1) | |
| out = vocoder(out) | |
| return out, trajectory | |