173 lines
5.8 KiB
Python
Executable File
173 lines
5.8 KiB
Python
Executable File
from ..config import cfg
|
||
from .base import Base, list_to_tensor, Categorical
|
||
|
||
import torch
|
||
|
||
from einops import rearrange
|
||
from torch import Tensor
|
||
from tqdm import trange
|
||
|
||
class AR(Base):
|
||
@property
|
||
def n_resp_levels(self) -> int:
|
||
return cfg.models.ar.resp_levels
|
||
|
||
@property
|
||
def causal(self):
|
||
return True
|
||
|
||
@property
|
||
def use_stop_token(self):
|
||
return True
|
||
|
||
@property
|
||
def norm_type(self):
|
||
return "ln"
|
||
|
||
@property
|
||
def arch_type(self) -> bool:
|
||
return cfg.models.ar.arch_type
|
||
|
||
@property
|
||
def n_prom_levels(self) -> int:
|
||
return cfg.models.prom_levels
|
||
|
||
@property
|
||
def resp_loss_only(self):
|
||
return False
|
||
|
||
def _prune(self, l: Tensor):
|
||
indices = (l == self.stop_token).nonzero()
|
||
if len(indices) == 0:
|
||
return l
|
||
return l[: indices.min().item()]
|
||
|
||
@staticmethod
|
||
def _unsqueeze_list(x_list, axis=-1):
|
||
return [x.unsqueeze(dim=axis) for x in x_list]
|
||
|
||
def forward(
|
||
self,
|
||
text_list: list[Tensor],
|
||
proms_list: list[Tensor],
|
||
resps_list: list[Tensor] | None = None,
|
||
max_steps: int = 1000,
|
||
sampling_temperature: float = 1.0,
|
||
|
||
naive: bool = True,
|
||
):
|
||
if resps_list is not None:
|
||
resps_list = [r[..., 0] for r in resps_list] # guarantees we only have the first levels
|
||
|
||
return super().forward(
|
||
text_list=text_list,
|
||
proms_list=proms_list,
|
||
resps_list=self._unsqueeze_list(resps_list),
|
||
targ_list=resps_list,
|
||
quant_levels=None,
|
||
shift_targ_list=True,
|
||
return_all_resp=False,
|
||
)
|
||
|
||
device = text_list[0].device
|
||
resps_list: list[Tensor] = [
|
||
torch.zeros(0, device=device).to(torch.int16) for _ in text_list
|
||
]
|
||
stopped = torch.zeros(len(text_list), device=device).bool()
|
||
|
||
chunk_size = 1 # don't really know what to do about this desu
|
||
|
||
state = None
|
||
start = 0
|
||
|
||
for n in trange(max_steps // chunk_size):
|
||
# get next in sequence
|
||
|
||
r, state = super().forward(
|
||
text_list,
|
||
proms_list,
|
||
self._unsqueeze_list(resps_list),
|
||
sampling_temperature=sampling_temperature,
|
||
state=state if not naive else None,
|
||
)
|
||
|
||
# append outputted token
|
||
for i, ri in enumerate(r):
|
||
resps_list[i] = torch.cat([resps_list[i], ri[None]])
|
||
|
||
# stop token found
|
||
stopped |= r == self.stop_token
|
||
if stopped.all().item():
|
||
break
|
||
|
||
pruned = [self._prune(r) for r in resps_list]
|
||
return pruned
|
||
|
||
|
||
def example_usage():
|
||
cfg.trainer.backend = "local"
|
||
from functools import partial
|
||
|
||
from einops import repeat
|
||
|
||
from ..emb.qnt import decode_to_file
|
||
from ..engines import Engine
|
||
from tqdm import tqdm
|
||
|
||
device = "cuda"
|
||
x8 = partial(repeat, pattern="t -> t l", l=2)
|
||
symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
|
||
def tokenize(content, lang_marker="en"):
|
||
split = content.split(" ")
|
||
phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
|
||
return torch.tensor([*map(symmap.get, phones)]).to()
|
||
|
||
qnt = torch.load("data/qnt.pt")[0].t()[:, :2].to(device)
|
||
|
||
text_list = [
|
||
#torch.tensor([1, 2, 3], device=device),
|
||
tokenize("ˈ a ɪ w ɪ l nˌ ɑː t ˈ æ s k ɐ sˈ ɛ k ə n d tˈ a ɪ m").to(device),
|
||
]
|
||
proms_list = [
|
||
x8(torch.tensor([1, 2, 3], device=device)),
|
||
#qnt.to(device),
|
||
]
|
||
resps_list = [
|
||
qnt.to(device),
|
||
]
|
||
|
||
text_list = text_list[:1]
|
||
proms_list = proms_list[:1]
|
||
resps_list = resps_list[:1]
|
||
|
||
kwargs = {
|
||
'n_tokens': 1024,
|
||
'd_model': 1024,
|
||
'n_heads': 16,
|
||
'n_layers': 12,
|
||
}
|
||
model = AR(**kwargs).to(device)
|
||
engine = Engine(model=model, optimizer=torch.optim.AdamW(model.parameters(), lr=1e-4))
|
||
|
||
def sample( name, steps=400 ):
|
||
engine.eval()
|
||
out = engine(text_list, proms_list, max_steps=steps)
|
||
for i, o in enumerate(out):
|
||
wav, sr = decode_to_file(o, f"data/ar.{i}.{name}.wav", device=device)
|
||
|
||
def train():
|
||
engine.train()
|
||
t = trange(60)
|
||
for i in t:
|
||
stats = {"step": i}
|
||
stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
|
||
|
||
t.set_description(f"{stats}")
|
||
|
||
sample("init", 75)
|
||
train()
|
||
sample("final")
|
||
|
||
if __name__ == "__main__":
|
||
example_usage()
|