513 lines
16 KiB
Python
Executable File
513 lines
16 KiB
Python
Executable File
import math
|
||
import torch
|
||
import torch.nn.functional as F
|
||
import traceback
|
||
|
||
from typing import Literal, overload
|
||
from functools import partial
|
||
from einops import rearrange
|
||
|
||
from torch import Tensor, einsum, nn
|
||
from torch.distributions import Categorical
|
||
from torch.nn.utils.rnn import pad_sequence
|
||
from torch.utils.checkpoint import checkpoint
|
||
from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, MulticlassPrecision
|
||
|
||
from .retnet import RetNetDecoder, RetNetConfig
|
||
from .transformer import SinusoidalEmbedding, Block as TransformerBlock
|
||
|
||
from ..utils import wrapper as ml
|
||
|
||
def _create_mask(l, device):
|
||
"""1 is valid region and 0 is invalid."""
|
||
seq = torch.arange(max(l), device=device).unsqueeze(0) # (1 t)
|
||
stop = torch.tensor(l, device=device).unsqueeze(1) # (b 1)
|
||
return (seq < stop).float() # (b t)
|
||
|
||
def _join(x: tuple[Tensor], sep: Tensor):
|
||
"""
|
||
Args:
|
||
x: (k t d)
|
||
sep: (d)
|
||
"""
|
||
ret = x[0]
|
||
for i in range(1, len(x)):
|
||
ret = torch.cat((ret, sep[None], x[i]), dim=0)
|
||
return ret
|
||
|
||
def list_to_tensor(x_list: list[Tensor], pattern="t b c -> b t c"):
|
||
"""
|
||
Args:
|
||
x_list: [(t d)]
|
||
Returns:
|
||
x: (? ? ?)
|
||
m: (? ? ?), same as x
|
||
"""
|
||
l = list(map(len, x_list))
|
||
x = rearrange(pad_sequence(x_list), pattern)
|
||
m = _create_mask(l, x_list[0].device)
|
||
m = m.t().unsqueeze(-1) # (t b 1)
|
||
m = rearrange(m, pattern)
|
||
m = m.to(x)
|
||
return x, m
|
||
|
||
class Embedding(nn.Embedding):
|
||
def forward(self, x_list: list[Tensor]) -> list[Tensor]:
|
||
if len(x_list) == 0:
|
||
return []
|
||
|
||
return super().forward(torch.cat(x_list)).split([*map(len, x_list)])
|
||
|
||
|
||
class MultiEmbedding(nn.Embedding):
|
||
"""
|
||
This embedding sums embeddings on different levels.
|
||
"""
|
||
|
||
def __init__(self, max_n_levels, n_tokens, token_dim):
|
||
super().__init__(max_n_levels, token_dim)
|
||
self.max_n_levels = max_n_levels
|
||
self.n_tokens = n_tokens
|
||
self.weight = nn.Parameter(torch.randn(max_n_levels, n_tokens, token_dim))
|
||
|
||
def forward(self, x_list: list[Tensor]) -> list[Tensor]:
|
||
if len(x_list) == 0:
|
||
return []
|
||
|
||
w = self.weight
|
||
|
||
padded_x_list = []
|
||
|
||
for xi in x_list:
|
||
xi = F.one_hot(xi.to(torch.int64), num_classes=self.n_tokens) # t l' k
|
||
xi = F.pad(xi, (0, 0, 0, w.shape[0] - xi.shape[1])) # t l k
|
||
padded_x_list.append(xi.to(w))
|
||
|
||
x = torch.cat(padded_x_list) # n l k
|
||
x = einsum("l k d, n l k -> n d", w, x)
|
||
|
||
x_list = x.split([*map(len, x_list)])
|
||
|
||
return x_list
|
||
|
||
|
||
class Base(nn.Module):
|
||
@property
|
||
def causal(self) -> bool:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def n_resp_levels(self) -> int:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def use_stop_token(self) -> bool:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def arch_type(self) -> str:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def norm_type(self):
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def n_prom_levels(self) -> int:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def resp_loss_only(self):
|
||
raise NotImplementedError
|
||
|
||
def __init__(
|
||
self,
|
||
n_tokens: int,
|
||
d_model: int = 512,
|
||
n_heads: int = 8,
|
||
n_layers: int = 12,
|
||
p_dropout: float = 0.1,
|
||
):
|
||
super().__init__()
|
||
self.n_tokens = n_tokens
|
||
self.d_model = d_model
|
||
self.n_heads = n_heads
|
||
self.n_layers = n_layers
|
||
|
||
causal = self.causal
|
||
|
||
# +1 to include the stop token
|
||
n_stop_tokens = 1 if self.use_stop_token else 0
|
||
n_resp_tokens = n_tokens + n_stop_tokens
|
||
|
||
self.text_emb = Embedding(n_tokens, d_model)
|
||
|
||
# Here I simply use all prom levels
|
||
self.proms_emb = MultiEmbedding(self.n_prom_levels, n_tokens, d_model)
|
||
self.resps_emb = MultiEmbedding(self.n_resp_levels, n_resp_tokens, d_model)
|
||
|
||
self.sep = nn.Parameter(torch.randn(d_model))
|
||
|
||
if self.arch_type == "transformer":
|
||
self.sin_emb = SinusoidalEmbedding(d_model)
|
||
self.blocks = nn.ModuleList([TransformerBlock(
|
||
d_model=d_model,
|
||
n_heads=n_heads,
|
||
p_dropout=p_dropout,
|
||
causal=causal,
|
||
norm_type=self.norm_type,
|
||
n_levels=self.n_resp_levels,
|
||
#tention="retention" if self.use_retnet else "attention"
|
||
) for _ in range(n_layers) ])
|
||
|
||
elif self.arch_type == "retnet":
|
||
self.retnet_config = RetNetConfig(
|
||
vocab_size=n_tokens,
|
||
decoder_embed_dim=d_model,
|
||
decoder_retention_heads=n_heads,
|
||
decoder_ffn_embed_dim=d_model * 4,
|
||
decoder_layers=n_layers,
|
||
dropout=p_dropout,
|
||
checkpoint_activations=True,
|
||
|
||
chunkwise_recurrent=self.causal,
|
||
recurrent_chunkwise_size=128,
|
||
no_output_layer=True,
|
||
decoder_normalize_before=True,
|
||
)
|
||
self.retnet = RetNetDecoder(
|
||
self.retnet_config
|
||
)
|
||
elif self.arch_type == "retnet/local":
|
||
self.retnet = RetNet(
|
||
layers=n_layers,
|
||
hidden_dim=d_model,
|
||
ffn_size=d_model * 4,
|
||
heads=n_heads,
|
||
dropout=p_dropout,
|
||
norm_type=self.norm_type,
|
||
n_levels=self.n_resp_levels,
|
||
double_v_dim=True
|
||
)
|
||
self.classifier = nn.Linear(d_model, n_resp_tokens)
|
||
|
||
self.accuracy_metric = MulticlassAccuracy(
|
||
n_resp_tokens,
|
||
top_k=10,
|
||
average="micro",
|
||
multidim_average="global",
|
||
ignore_index=self.ignore_index,
|
||
)
|
||
|
||
self.precision_metric = MulticlassPrecision(
|
||
n_resp_tokens,
|
||
top_k=10,
|
||
average="micro",
|
||
multidim_average="global",
|
||
ignore_index=self.ignore_index,
|
||
)
|
||
|
||
@property
|
||
def stop_token(self):
|
||
if not self.use_stop_token:
|
||
raise ValueError("Not using stop token!")
|
||
return self.n_tokens
|
||
|
||
@property
|
||
def ignore_index(self):
|
||
return -100
|
||
|
||
@staticmethod
|
||
def _samplewise_merge_tensors(*l, sep: Tensor | None):
|
||
if sep is None:
|
||
cat = torch.cat
|
||
else:
|
||
cat = partial(_join, sep=sep)
|
||
return [*map(cat, zip(*l))]
|
||
|
||
@overload
|
||
def forward(
|
||
self,
|
||
text_list: list[Tensor],
|
||
proms_list: list[Tensor],
|
||
resps_list: list[Tensor],
|
||
targ_list: list[Tensor] | None = None,
|
||
quant_levels: Tensor | None = None,
|
||
shift_targ_list: bool = False,
|
||
return_all: Literal[False] = False,
|
||
return_all_resp: Literal[False] = False,
|
||
sampling_temperature: float = 1.0,
|
||
) -> Tensor:
|
||
...
|
||
|
||
@overload
|
||
def forward(
|
||
self,
|
||
text_list: list[Tensor],
|
||
proms_list: list[Tensor],
|
||
resps_list: list[Tensor],
|
||
targ_list: list[Tensor] | None = None,
|
||
quant_levels: Tensor | None = None,
|
||
shift_targ_list: bool = False,
|
||
return_all: Literal[True] = True,
|
||
return_all_resp: Literal[True] = True,
|
||
sampling_temperature: float = 1.0,
|
||
) -> list[Tensor]:
|
||
...
|
||
|
||
def forward(
|
||
self,
|
||
text_list: list[Tensor],
|
||
proms_list: list[Tensor],
|
||
resps_list: list[Tensor],
|
||
targ_list: list[Tensor] | None = None,
|
||
quant_levels: Tensor | None = None,
|
||
shift_targ_list: bool = False,
|
||
return_all: bool = False,
|
||
return_all_resp: bool = False,
|
||
sampling_temperature: float = 1.0,
|
||
|
||
state: list | None = None,
|
||
):
|
||
"""
|
||
Args:
|
||
text_list: [t] * b
|
||
proms_list: [t' l] * b, l quantization levels.
|
||
resps_list: [t'' l] * b, l quantization levels.
|
||
targ_list: [t''] * b, one quantization level only, when given, loss will be computed
|
||
quant_levels: specify which quant_levels to feed forward, used in NAR mode.
|
||
shift_targ_list: whether to shift target list when computing loss. True if AR.
|
||
return_all_resp: True if NAR.
|
||
sampling_temperature: a lower temperature makes the result more robust but less diverse.
|
||
Returns:
|
||
y: sampled tokens
|
||
"""
|
||
|
||
batch_size = len(text_list)
|
||
x_list = self._samplewise_merge_tensors(
|
||
self.text_emb(text_list),
|
||
self.proms_emb(proms_list),
|
||
self.resps_emb(resps_list),
|
||
sep=self.sep,
|
||
)
|
||
|
||
x, m = list_to_tensor(x_list)
|
||
|
||
if self.arch_type == "transformer":
|
||
x = self.sin_emb.add_pe(x)
|
||
for block in self.blocks:
|
||
x = block(x, m, quant_levels)
|
||
elif self.arch_type == "retnet":
|
||
x, _ = self.retnet(x, incremental_state=state, token_embeddings=x, features_only=True)
|
||
state = self.retnet.get_incremental_state( state, 'prev_state' )
|
||
elif self.arch_type == "retnet/local":
|
||
# recurrent inferencing
|
||
if self.causal and state is not None:
|
||
last = x.shape[1]
|
||
x, state = self.retnet.forward_recurrent(
|
||
x[:, last-1:last, :], # nasty way to grab the last embedding to forward
|
||
state,
|
||
last
|
||
)
|
||
else:
|
||
x = self.retnet( x, quant_levels )
|
||
|
||
x = self.classifier(x) * m
|
||
|
||
# Remove padding
|
||
h_list = [hi[:li] for hi, li in zip(x, map(len, x_list))]
|
||
|
||
|
||
# compute loss if the target is given
|
||
if targ_list is not None:
|
||
if any([l == 0 for l in map(len, targ_list)]):
|
||
raise ValueError("Cannot compute loss given empty targ_list.")
|
||
|
||
ignore_sep = torch.tensor(self.ignore_index, device=x.device)
|
||
|
||
# ignore the prompt when computing loss
|
||
prom_list = [
|
||
torch.full_like(t[..., 0], self.ignore_index) for t in proms_list
|
||
]
|
||
# remake input with ignored input prompt
|
||
text_prom_list = self._samplewise_merge_tensors(
|
||
text_list, prom_list, sep=ignore_sep
|
||
)
|
||
|
||
for i in range(len(text_prom_list)):
|
||
# ignore computing loss against text/prompt portion of input
|
||
# the NAR doesn't need to compute the loss for it
|
||
if self.resp_loss_only:
|
||
text_prom_list[i][:] = self.ignore_index
|
||
# roll the text/prompt for loss computing
|
||
# the AR benefits from this
|
||
else:
|
||
text_prom_list[i] = text_prom_list[i].roll(-1, dims=0)
|
||
text_prom_list[i][-1] = self.ignore_index
|
||
|
||
# necessary to roll the target if recurrently/causally/autoregressively generating, or it won't be able to work
|
||
if shift_targ_list:
|
||
targ_list = [*targ_list]
|
||
for i in range(len(targ_list)):
|
||
targ_list[i] = targ_list[i].roll(-1, dims=0)
|
||
targ_list[i][-1] = self.stop_token
|
||
|
||
# generate the sequence
|
||
y_list = self._samplewise_merge_tensors( text_prom_list, targ_list, sep=ignore_sep )
|
||
|
||
self.loss = dict(
|
||
nll=F.cross_entropy(
|
||
torch.cat(h_list), # input / predicted logits
|
||
torch.cat(y_list), # target / ground truth
|
||
ignore_index=self.ignore_index,
|
||
)
|
||
)
|
||
self.loss['acc'] = self.accuracy_metric( torch.cat(h_list), torch.cat(y_list) )
|
||
self.loss['precision'] = self.precision_metric( torch.cat(h_list), torch.cat(y_list) )
|
||
|
||
del targ_list
|
||
del prom_list
|
||
del text_prom_list
|
||
del y_list
|
||
|
||
# return the entire generated token string
|
||
if return_all:
|
||
logits = [hi[:] for hi, li in zip(h_list, map(len, resps_list))]
|
||
ret = [Categorical(logits=hi / sampling_temperature).sample() for hi in logits]
|
||
# return the entire generated response
|
||
elif return_all_resp:
|
||
logits = [hi[-li:] for hi, li in zip(h_list, map(len, resps_list))]
|
||
ret = [ Categorical(logits=hi / sampling_temperature).sample() for hi in logits ]
|
||
# return just the last code
|
||
else:
|
||
logits = torch.stack([hi[-1] for hi in h_list])
|
||
ret = Categorical(logits=logits / sampling_temperature).sample()
|
||
|
||
del x_list
|
||
del h_list
|
||
|
||
return ret, state
|
||
|
||
def example_usage():
|
||
from functools import partial
|
||
|
||
from einops import repeat
|
||
from tqdm import trange
|
||
|
||
from ..utils import gather_attribute
|
||
from ..emb.qnt import decode_to_file
|
||
from .ar import AR
|
||
from .nar import NAR
|
||
|
||
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()
|
||
|
||
device = "cpu"
|
||
|
||
kwargs = {
|
||
'n_tokens': 1024,
|
||
'd_model': 1024,
|
||
'n_heads': 16,
|
||
'n_layers': 12,
|
||
}
|
||
model_ar = AR(**kwargs).to(device)
|
||
model_nar = NAR(**kwargs).to(device)
|
||
|
||
train = True
|
||
|
||
if train:
|
||
qnt = torch.load("data/qnt.pt").to(device)
|
||
text_list = [
|
||
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),
|
||
#tokenize("ˌ ɔ n ɡˌ o ʊ ɪ ŋ hˈ o ʊ m ð ə tˈ uː f ɹˈ ɛ n d z fˈ a ʊ n d ɐ lˈ ɛ ɾ ɚ f ɹ ʌ m ˈ æ θ o ʊ z , hˌ uː d ɪ zˈ a ɪ ɚ d ðˌ ɛ m t ə mˈ iː t hˌ ɪ m æ t ð ə ɡ ɹˈ æ n d t ʃˈ ɑː ɹ l ɪ mˌ æ ɡ n i ɔ n ð ə fˈ ɑː l o ʊ ɪ ŋ dˈ e ɪ .").to(device),
|
||
]
|
||
|
||
x8 = partial(repeat, pattern="t -> t l", l=2)
|
||
proms_list = [
|
||
qnt[0][:2,:].t().to(device),
|
||
#x8(torch.tensor([1, 2, 3], device=device)),
|
||
# x8(torch.tensor([2, 3], device=device)),
|
||
]
|
||
|
||
resp_list_ar = [
|
||
qnt[0,0].to(device),
|
||
# qnt[0,0].to(device),
|
||
]
|
||
|
||
resp_list_nar = [
|
||
qnt[0][:2,:].t().to(device),
|
||
# qnt[0][:2,:].t().to(device),
|
||
]
|
||
|
||
model_ar.train()
|
||
optimizer = torch.optim.AdamW(model_ar.parameters(), lr=1e-4)
|
||
for i in trange(60):
|
||
optimizer.zero_grad()
|
||
_ = model_ar(text_list, proms_list, resp_list_ar)
|
||
|
||
losses = gather_attribute(model_ar, "loss")
|
||
loss = sum(losses.values())
|
||
loss.backward()
|
||
optimizer.step()
|
||
|
||
if i % 20 == 0:
|
||
print(f"iter={i}, {losses}.")
|
||
|
||
model_nar.train()
|
||
optimizer = torch.optim.AdamW(model_nar.parameters(), lr=1e-4)
|
||
for i in trange(60):
|
||
optimizer.zero_grad()
|
||
|
||
_ = model_nar(text_list, proms_list, resps_list=resp_list_nar)
|
||
|
||
losses = gather_attribute(model_nar, "loss")
|
||
loss = sum(losses.values())
|
||
loss.backward()
|
||
optimizer.step()
|
||
|
||
if i % 20 == 0:
|
||
stats = {k: v.item() for k, v in losses.items()}
|
||
stats["loss"] = loss.item()
|
||
print(f"iter={i}, {stats}.")
|
||
else:
|
||
qnt = torch.load("data/test/test.qnt.pt")[0][:2,:].t().to(device)
|
||
text_list = [
|
||
#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),
|
||
tokenize("ˌ ɔ n ɡˌ o ʊ ɪ ŋ hˈ o ʊ m ð ə tˈ uː f ɹˈ ɛ n d z fˈ a ʊ n d ɐ lˈ ɛ ɾ ɚ f ɹ ʌ m ˈ æ θ o ʊ z , hˌ uː d ɪ zˈ a ɪ ɚ d ðˌ ɛ m t ə mˈ iː t hˌ ɪ m æ t ð ə ɡ ɹˈ æ n d t ʃˈ ɑː ɹ l ɪ mˌ æ ɡ n i ɔ n ð ə fˈ ɑː l o ʊ ɪ ŋ dˈ e ɪ .").to(device),
|
||
]
|
||
proms_list = [
|
||
qnt.to(device),
|
||
]
|
||
model_ar.load_state_dict(torch.load("data/test/ar.pth"))
|
||
model_nar.load_state_dict(torch.load("data/test/nar.pth"))
|
||
|
||
model_ar.eval()
|
||
resp_list = model_ar(text_list, proms_list, max_steps=300, sampling_temperature=1.0)
|
||
resps_list = [r.unsqueeze(-1) for r in resp_list]
|
||
|
||
print("qnt:", qnt.shape, qnt)
|
||
print("out:", resp_list[0].shape, resp_list[0])
|
||
wav, sr = decode_to_file(resp_list[0], "data/test/test.ar.init.wav", device=device)
|
||
print(wav, sr)
|
||
|
||
model_nar.eval()
|
||
codes = model_nar(
|
||
text_list,
|
||
proms_list,
|
||
resps_list=resps_list,
|
||
sampling_temperature=1.0,
|
||
)[0]
|
||
|
||
|
||
print("qnt:", qnt.shape, qnt)
|
||
print("codes:", codes.shape, codes)
|
||
|
||
wav, sr = decode_to_file(codes, "data/test/test.ar+nar.init.wav", device=device)
|
||
print(wav, sr)
|
||
|
||
if __name__ == "__main__":
|
||
example_usage()
|