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from bark.model_fine import FineGPT, FineGPTConfig
from bark.model import GPT, GPTConfig
from huggingface_hub import hf_hub_download
from typing import Type, TypeVar
from transformers import BertTokenizer
from scipy.special import softmax
from encodec import EncodecModel
import logging
import torch
import os
import torch.nn as nn
import torch.nn.functional as F
import re
import numpy as np
import contextlib
import funcy
import tqdm
import numpy as np
logger = logging.getLogger('bark_model_load')
def set_bark_model_load_logger(l):
global logger
logger = l
if (
torch.cuda.is_available() and
hasattr(torch.cuda, "amp") and
hasattr(torch.cuda.amp, "autocast") and
hasattr(torch.cuda, "is_bf16_supported") and
torch.cuda.is_bf16_supported()
):
autocast = funcy.partial(torch.cuda.amp.autocast, dtype=torch.bfloat16)
else:
@contextlib.contextmanager
def autocast():
yield
class InferenceContext:
def __init__(self, benchmark=False):
# we can't expect inputs to be the same length, so disable benchmarking by default
self._chosen_cudnn_benchmark = benchmark
self._cudnn_benchmark = None
def __enter__(self):
self._cudnn_benchmark = torch.backends.cudnn.benchmark
torch.backends.cudnn.benchmark = self._chosen_cudnn_benchmark
def __exit__(self, exc_type, exc_value, exc_traceback):
torch.backends.cudnn.benchmark = self._cudnn_benchmark
@contextlib.contextmanager
def _inference_mode():
with InferenceContext(), torch.inference_mode(), torch.no_grad(), autocast():
yield
# 定义一个泛型类型变量
G = TypeVar('G', bound='GPT')
C = TypeVar('C', bound='GPTConfig')
class ModelType:
def __init__(self, model_type: str, model_class: Type[G], model_config: Type[C]):
"""
模型类型适配
:param model_type:
:param model_class:
:param model_config:
"""
self.model_type = model_type
self.model_class = model_class
self.model_config = model_config
CONTEXT_WINDOW_SIZE = 1024
SEMANTIC_RATE_HZ = 49.9
SEMANTIC_VOCAB_SIZE = 10_000
CODEBOOK_SIZE = 1024
N_COARSE_CODEBOOKS = 2
N_FINE_CODEBOOKS = 8
COARSE_RATE_HZ = 75
SAMPLE_RATE = 24_000
REMOTE_MODEL_PATHS = {
"text": {
"repo_id": "suno/bark",
"file_name": "text_2.pt",
},
"coarse": {
"repo_id": "suno/bark",
"file_name": "coarse_2.pt",
},
"fine": {
"repo_id": "suno/bark",
"file_name": "fine_2.pt",
},
}
SUPPORTED_LANGS = [
("English", "en"),
("German", "de"),
("Spanish", "es"),
("French", "fr"),
("Hindi", "hi"),
("Italian", "it"),
("Japanese", "ja"),
("Korean", "ko"),
("Polish", "pl"),
("Portuguese", "pt"),
("Russian", "ru"),
("Turkish", "tr"),
("Chinese", "zh"),
]
# init prompt_speaker_np
ALLOWED_PROMPTS = {"announcer"}
for _, lang in SUPPORTED_LANGS:
for prefix in ("", f"v2{os.path.sep}"):
for n in range(10):
ALLOWED_PROMPTS.add(f"{prefix}{lang}_speaker_{n}")
TEXT_ENCODING_OFFSET = 10_048
SEMANTIC_PAD_TOKEN = 10_000
TEXT_PAD_TOKEN = 129_595
SEMANTIC_INFER_TOKEN = 129_599
COARSE_SEMANTIC_PAD_TOKEN = 12_048
COARSE_INFER_TOKEN = 12_050
def _download(self, from_hf_path, file_name, local_dir):
os.makedirs(local_dir, exist_ok=True)
hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=local_dir)
def _load_codec_model(device):
model = EncodecModel.encodec_model_24khz()
model.set_target_bandwidth(6.0)
model.eval()
model.to(device)
return model
def _normalize_whitespace(text):
return re.sub(r"\s+", " ", text).strip()
def _load_history_prompt(history_prompt_dir: str, history_prompt_input: str):
# make sure this works on non-ubuntu
history_prompt_input = os.path.join(*history_prompt_input.split("/"))
if history_prompt_input not in ALLOWED_PROMPTS:
raise ValueError("history prompt not found")
history_prompt = np.load(
os.path.join(history_prompt_dir, "assets", "prompts", f"{history_prompt_input}.npz")
)
return history_prompt
def _flatten_codebooks(arr, offset_size=CODEBOOK_SIZE):
assert len(arr.shape) == 2
arr = arr.copy()
if offset_size is not None:
for n in range(1, arr.shape[0]):
arr[n, :] += offset_size * n
flat_arr = arr.ravel("F")
return flat_arr
class ModelCheckPointInfo:
_model_type: ModelType
_model: nn.Module
_model_path: str
def __init__(self, model_type: ModelType):
self._model_type = model_type
@property
def model_type(self) -> ModelType:
return self._model_type
@property
def model(self) -> nn.Module:
return self._model
@property
def model_path(self) -> str:
return self._model_path
@model.setter
def model(self, value):
self._model = value
@model_path.setter
def model_path(self, value):
self._model_path = value
class BarkModelLoader:
_text_model: ModelCheckPointInfo = ModelCheckPointInfo(
model_type=ModelType(model_type="text_model", model_class=GPT, model_config=GPTConfig))
_coarse_model: ModelCheckPointInfo = ModelCheckPointInfo(
model_type=ModelType(model_type="coarse_model", model_class=GPT, model_config=GPTConfig))
_fine_model: ModelCheckPointInfo = ModelCheckPointInfo(
model_type=ModelType(model_type="fine_model", model_class=FineGPT, model_config=FineGPTConfig))
_tokenizer: BertTokenizer
_tokenizer_path: str = "bert-base-multilingual-cased"
_encodec: EncodecModel
def __init__(self, tokenizer_path: str, text_path: str, coarse_path: str, fine_path: str, device: str):
if tokenizer_path:
self._tokenizer_path = tokenizer_path
logger.info(f"BertTokenizer load.")
self._tokenizer = BertTokenizer.from_pretrained(self._tokenizer_path)
logger.info(f"BertTokenizer loaded")
self._text_model.model_path = text_path
self._coarse_model.model_path = coarse_path
self._fine_model.model_path = fine_path
# if not os.path.exists(self._text_model.model_path):
# model_info = REMOTE_MODEL_PATHS['text']
# logger.info(f"text model not found, downloading into `{self._text_model.model_path}`.")
#
# _download(model_info["repo_id"], model_info["file_name"], self._text_model.model_path)
# if not os.path.exists(self._coarse_model.model_path):
# model_info = REMOTE_MODEL_PATHS['coarse']
# logger.info(f"coarse model not found, downloading into `{self._coarse_model.model_path}`.")
# _download(model_info["repo_id"], model_info["file_name"], self._coarse_model.model_path)
# if not os.path.exists(self._fine_model.model_path):
# model_info = REMOTE_MODEL_PATHS['fine']
# logger.info(f"fine model not found, downloading into `{self._fine_model.model_path}`.")
# _download(model_info["repo_id"], model_info["file_name"], self._fine_model.model_path)
self._load_moad(model_type=self._text_model.model_type, ckpt_path=self._text_model.model_path, device=device)
self._load_moad(model_type=self._coarse_model.model_type, ckpt_path=self._coarse_model.model_path, device=device)
self._load_moad(model_type=self._fine_model.model_type, ckpt_path=self._fine_model.model_path, device=device)
def _load_moad(self, model_type: ModelType, ckpt_path: str, device: str):
if not os.path.exists(self._fine_model.model_path):
raise RuntimeError("loader model path is not exists")
device = torch.device(device)
checkpoint = torch.load(ckpt_path, map_location=device)
# this is a hack
model_args = checkpoint["model_args"]
if "input_vocab_size" not in model_args:
model_args["input_vocab_size"] = model_args["vocab_size"]
model_args["output_vocab_size"] = model_args["vocab_size"]
del model_args["vocab_size"]
gptconf = model_type.model_config(**checkpoint["model_args"])
model = model_type.model_class(gptconf)
state_dict = checkpoint["model"]
# fixup checkpoint
unwanted_prefix = "_orig_mod."
for k, v in list(state_dict.items()):
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
extra_keys = set(state_dict.keys()) - set(model.state_dict().keys())
extra_keys = set([k for k in extra_keys if not k.endswith(".attn.bias")])
missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
missing_keys = set([k for k in missing_keys if not k.endswith(".attn.bias")])
if len(extra_keys) != 0:
raise ValueError(f"extra keys found: {extra_keys}")
if len(missing_keys) != 0:
raise ValueError(f"missing keys: {missing_keys}")
model.load_state_dict(state_dict, strict=False)
n_params = model.get_num_params()
val_loss = checkpoint["best_val_loss"].item()
logger.info(
f"model {model_type.model_type} loaded: {round(n_params / 1e6, 1)}M params, {round(val_loss, 3)} loss")
model.eval()
model.to(device)
del checkpoint, state_dict
if model_type.model_type == "text_model":
self._text_model.model = model
elif model_type.model_type == "coarse_model":
self._coarse_model.model = model
elif model_type.model_type == "fine_model":
self._fine_model.model = model
self._encodec = _load_codec_model(device)
def generate_text_semantic(
self,
text,
history_prompt=None,
history_prompt_dir=None,
temp=0.7,
top_k=None,
top_p=None,
silent=False,
min_eos_p=0.2,
max_gen_duration_s=None,
allow_early_stop=True,
use_kv_caching=False,
):
"""Generate semantic tokens from text."""
assert isinstance(text, str)
text = _normalize_whitespace(text)
assert len(text.strip()) > 0
if history_prompt is not None:
history_prompt = _load_history_prompt(history_prompt_dir=history_prompt_dir, history_prompt_input=history_prompt)
semantic_history = history_prompt["semantic_prompt"]
assert (
isinstance(semantic_history, np.ndarray)
and len(semantic_history.shape) == 1
and len(semantic_history) > 0
and semantic_history.min() >= 0
and semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1
)
else:
semantic_history = None
model = self._text_model.model
tokenizer = self._tokenizer
encoded_text = np.array(tokenizer.encode(text, add_special_tokens=False)) + TEXT_ENCODING_OFFSET
# if OFFLOAD_CPU:
# model.to(models_devices["text"])
device = next(model.parameters()).device
if len(encoded_text) > 256:
p = round((len(encoded_text) - 256) / len(encoded_text) * 100, 1)
logger.warning(f"warning, text too long, lopping of last {p}%")
encoded_text = encoded_text[:256]
encoded_text = np.pad(
encoded_text,
(0, 256 - len(encoded_text)),
constant_values=TEXT_PAD_TOKEN,
mode="constant",
)
if semantic_history is not None:
semantic_history = semantic_history.astype(np.int64)
# lop off if history is too long, pad if needed
semantic_history = semantic_history[-256:]
semantic_history = np.pad(
semantic_history,
(0, 256 - len(semantic_history)),
constant_values=SEMANTIC_PAD_TOKEN,
mode="constant",
)
else:
semantic_history = np.array([SEMANTIC_PAD_TOKEN] * 256)
x = torch.from_numpy(
np.hstack([
encoded_text, semantic_history, np.array([SEMANTIC_INFER_TOKEN])
]).astype(np.int64)
)[None]
assert x.shape[1] == 256 + 256 + 1
with _inference_mode():
x = x.to(device)
n_tot_steps = 768
# custom tqdm updates since we don't know when eos will occur
pbar = tqdm.tqdm(disable=silent, total=n_tot_steps)
pbar_state = 0
tot_generated_duration_s = 0
kv_cache = None
for n in range(n_tot_steps):
if use_kv_caching and kv_cache is not None:
x_input = x[:, [-1]]
else:
x_input = x
logits, kv_cache = model(
x_input, merge_context=True, use_cache=use_kv_caching, past_kv=kv_cache
)
relevant_logits = logits[0, 0, :SEMANTIC_VOCAB_SIZE]
if allow_early_stop:
relevant_logits = torch.hstack(
(relevant_logits, logits[0, 0, [SEMANTIC_PAD_TOKEN]]) # eos
)
if top_p is not None:
# faster to convert to numpy
original_device = relevant_logits.device
relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
sorted_indices = np.argsort(relevant_logits)[::-1]
sorted_logits = relevant_logits[sorted_indices]
cumulative_probs = np.cumsum(softmax(sorted_logits))
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy()
sorted_indices_to_remove[0] = False
relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
relevant_logits = torch.from_numpy(relevant_logits)
relevant_logits = relevant_logits.to(original_device)
if top_k is not None:
v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
relevant_logits[relevant_logits < v[-1]] = -float("Inf")
probs = F.softmax(relevant_logits / temp, dim=-1)
item_next = torch.multinomial(probs, num_samples=1).to(torch.int32)
if allow_early_stop and (
item_next == SEMANTIC_VOCAB_SIZE
or (min_eos_p is not None and probs[-1] >= min_eos_p)
):
# eos found, so break
pbar.update(n - pbar_state)
break
x = torch.cat((x, item_next[None]), dim=1)
tot_generated_duration_s += 1 / SEMANTIC_RATE_HZ
if max_gen_duration_s is not None and tot_generated_duration_s > max_gen_duration_s:
pbar.update(n - pbar_state)
break
if n == n_tot_steps - 1:
pbar.update(n - pbar_state)
break
del logits, relevant_logits, probs, item_next
if n > pbar_state:
if n > pbar.total:
pbar.total = n
pbar.update(n - pbar_state)
pbar_state = n
pbar.total = n
pbar.refresh()
pbar.close()
out = x.detach().cpu().numpy().squeeze()[256 + 256 + 1:]
assert all(0 <= out) and all(out < SEMANTIC_VOCAB_SIZE)
return out
def generate_coarse(
self,
x_semantic,
history_prompt=None,
history_prompt_dir=None,
temp=0.7,
top_k=None,
top_p=None,
silent=False,
max_coarse_history=630, # min 60 (faster), max 630 (more context)
sliding_window_len=60,
use_kv_caching=False,
):
"""Generate coarse audio codes from semantic tokens."""
assert (
isinstance(x_semantic, np.ndarray)
and len(x_semantic.shape) == 1
and len(x_semantic) > 0
and x_semantic.min() >= 0
and x_semantic.max() <= SEMANTIC_VOCAB_SIZE - 1
)
assert 60 <= max_coarse_history <= 630
assert max_coarse_history + sliding_window_len <= 1024 - 256
semantic_to_coarse_ratio = COARSE_RATE_HZ / SEMANTIC_RATE_HZ * N_COARSE_CODEBOOKS
max_semantic_history = int(np.floor(max_coarse_history / semantic_to_coarse_ratio))
if history_prompt is not None:
history_prompt = _load_history_prompt(history_prompt_dir=history_prompt_dir,history_prompt_input=history_prompt)
x_semantic_history = history_prompt["semantic_prompt"]
x_coarse_history = history_prompt["coarse_prompt"]
assert (
isinstance(x_semantic_history, np.ndarray)
and len(x_semantic_history.shape) == 1
and len(x_semantic_history) > 0
and x_semantic_history.min() >= 0
and x_semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1
and isinstance(x_coarse_history, np.ndarray)
and len(x_coarse_history.shape) == 2
and x_coarse_history.shape[0] == N_COARSE_CODEBOOKS
and x_coarse_history.shape[-1] >= 0
and x_coarse_history.min() >= 0
and x_coarse_history.max() <= CODEBOOK_SIZE - 1
and (
round(x_coarse_history.shape[-1] / len(x_semantic_history), 1)
== round(semantic_to_coarse_ratio / N_COARSE_CODEBOOKS, 1)
)
)
x_coarse_history = _flatten_codebooks(x_coarse_history) + SEMANTIC_VOCAB_SIZE
# trim histories correctly
n_semantic_hist_provided = np.min(
[
max_semantic_history,
len(x_semantic_history) - len(x_semantic_history) % 2,
int(np.floor(len(x_coarse_history) / semantic_to_coarse_ratio)),
]
)
n_coarse_hist_provided = int(round(n_semantic_hist_provided * semantic_to_coarse_ratio))
x_semantic_history = x_semantic_history[-n_semantic_hist_provided:].astype(np.int32)
x_coarse_history = x_coarse_history[-n_coarse_hist_provided:].astype(np.int32)
# TODO: bit of a hack for time alignment (sounds better)
x_coarse_history = x_coarse_history[:-2]
else:
x_semantic_history = np.array([], dtype=np.int32)
x_coarse_history = np.array([], dtype=np.int32)
model = self._coarse_model.model
# if OFFLOAD_CPU:
# model.to(models_devices["coarse"])
device = next(model.parameters()).device
# start loop
n_steps = int(
round(
np.floor(len(x_semantic) * semantic_to_coarse_ratio / N_COARSE_CODEBOOKS)
* N_COARSE_CODEBOOKS
)
)
assert n_steps > 0 and n_steps % N_COARSE_CODEBOOKS == 0
x_semantic = np.hstack([x_semantic_history, x_semantic]).astype(np.int32)
x_coarse = x_coarse_history.astype(np.int32)
base_semantic_idx = len(x_semantic_history)
with _inference_mode():
x_semantic_in = torch.from_numpy(x_semantic)[None].to(device)
x_coarse_in = torch.from_numpy(x_coarse)[None].to(device)
n_window_steps = int(np.ceil(n_steps / sliding_window_len))
n_step = 0
for _ in tqdm.tqdm(range(n_window_steps), total=n_window_steps, disable=silent):
semantic_idx = base_semantic_idx + int(round(n_step / semantic_to_coarse_ratio))
# pad from right side
x_in = x_semantic_in[:, np.max([0, semantic_idx - max_semantic_history]):]
x_in = x_in[:, :256]
x_in = F.pad(
x_in,
(0, 256 - x_in.shape[-1]),
"constant",
COARSE_SEMANTIC_PAD_TOKEN,
)
x_in = torch.hstack(
[
x_in,
torch.tensor([COARSE_INFER_TOKEN])[None].to(device),
x_coarse_in[:, -max_coarse_history:],
]
)
kv_cache = None
for _ in range(sliding_window_len):
if n_step >= n_steps:
continue
is_major_step = n_step % N_COARSE_CODEBOOKS == 0
if use_kv_caching and kv_cache is not None:
x_input = x_in[:, [-1]]
else:
x_input = x_in
logits, kv_cache = model(x_input, use_cache=use_kv_caching, past_kv=kv_cache)
logit_start_idx = (
SEMANTIC_VOCAB_SIZE + (1 - int(is_major_step)) * CODEBOOK_SIZE
)
logit_end_idx = (
SEMANTIC_VOCAB_SIZE + (2 - int(is_major_step)) * CODEBOOK_SIZE
)
relevant_logits = logits[0, 0, logit_start_idx:logit_end_idx]
if top_p is not None:
# faster to convert to numpy
original_device = relevant_logits.device
relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
sorted_indices = np.argsort(relevant_logits)[::-1]
sorted_logits = relevant_logits[sorted_indices]
cumulative_probs = np.cumsum(softmax(sorted_logits))
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy()
sorted_indices_to_remove[0] = False
relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
relevant_logits = torch.from_numpy(relevant_logits)
relevant_logits = relevant_logits.to(original_device)
if top_k is not None:
v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
relevant_logits[relevant_logits < v[-1]] = -float("Inf")
probs = F.softmax(relevant_logits / temp, dim=-1)
item_next = torch.multinomial(probs, num_samples=1).to(torch.int32)
item_next += logit_start_idx
x_coarse_in = torch.cat((x_coarse_in, item_next[None]), dim=1)
x_in = torch.cat((x_in, item_next[None]), dim=1)
del logits, relevant_logits, probs, item_next
n_step += 1
del x_in
del x_semantic_in
# if OFFLOAD_CPU:
# model.to("cpu")
gen_coarse_arr = x_coarse_in.detach().cpu().numpy().squeeze()[len(x_coarse_history):]
del x_coarse_in
assert len(gen_coarse_arr) == n_steps
gen_coarse_audio_arr = gen_coarse_arr.reshape(-1, N_COARSE_CODEBOOKS).T - SEMANTIC_VOCAB_SIZE
for n in range(1, N_COARSE_CODEBOOKS):
gen_coarse_audio_arr[n, :] -= n * CODEBOOK_SIZE
# _clear_cuda_cache()
return gen_coarse_audio_arr
def generate_fine(
self,
x_coarse_gen,
history_prompt=None,
history_prompt_dir=None,
temp=0.5,
silent=True,
):
"""Generate full audio codes from coarse audio codes."""
assert (
isinstance(x_coarse_gen, np.ndarray)
and len(x_coarse_gen.shape) == 2
and 1 <= x_coarse_gen.shape[0] <= N_FINE_CODEBOOKS - 1
and x_coarse_gen.shape[1] > 0
and x_coarse_gen.min() >= 0
and x_coarse_gen.max() <= CODEBOOK_SIZE - 1
)
if history_prompt is not None:
history_prompt = _load_history_prompt(history_prompt_dir=history_prompt_dir,history_prompt_input=history_prompt)
x_fine_history = history_prompt["fine_prompt"]
assert (
isinstance(x_fine_history, np.ndarray)
and len(x_fine_history.shape) == 2
and x_fine_history.shape[0] == N_FINE_CODEBOOKS
and x_fine_history.shape[1] >= 0
and x_fine_history.min() >= 0
and x_fine_history.max() <= CODEBOOK_SIZE - 1
)
else:
x_fine_history = None
n_coarse = x_coarse_gen.shape[0]
model = self._fine_model.model
# if OFFLOAD_CPU:
# model.to(models_devices["fine"])
device = next(model.parameters()).device
# make input arr
in_arr = np.vstack(
[
x_coarse_gen,
np.zeros((N_FINE_CODEBOOKS - n_coarse, x_coarse_gen.shape[1]))
+ CODEBOOK_SIZE, # padding
]
).astype(np.int32)
# prepend history if available (max 512)
if x_fine_history is not None:
x_fine_history = x_fine_history.astype(np.int32)
in_arr = np.hstack(
[
x_fine_history[:, -512:].astype(np.int32),
in_arr,
]
)
n_history = x_fine_history[:, -512:].shape[1]
else:
n_history = 0
n_remove_from_end = 0
# need to pad if too short (since non-causal model)
if in_arr.shape[1] < 1024:
n_remove_from_end = 1024 - in_arr.shape[1]
in_arr = np.hstack(
[
in_arr,
np.zeros((N_FINE_CODEBOOKS, n_remove_from_end), dtype=np.int32) + CODEBOOK_SIZE,
]
)
# we can be lazy about fractional loop and just keep overwriting codebooks
n_loops = np.max([0, int(np.ceil((x_coarse_gen.shape[1] - (1024 - n_history)) / 512))]) + 1
with _inference_mode():
in_arr = torch.tensor(in_arr.T).to(device)
for n in tqdm.tqdm(range(n_loops), disable=silent):
start_idx = np.min([n * 512, in_arr.shape[0] - 1024])
start_fill_idx = np.min([n_history + n * 512, in_arr.shape[0] - 512])
rel_start_fill_idx = start_fill_idx - start_idx
in_buffer = in_arr[start_idx: start_idx + 1024, :][None]
for nn in range(n_coarse, N_FINE_CODEBOOKS):
logits = model(nn, in_buffer)
if temp is None:
relevant_logits = logits[0, rel_start_fill_idx:, :CODEBOOK_SIZE]
codebook_preds = torch.argmax(relevant_logits, -1)
else:
relevant_logits = logits[0, :, :CODEBOOK_SIZE] / temp
probs = F.softmax(relevant_logits, dim=-1)
codebook_preds = torch.multinomial(
probs[rel_start_fill_idx:1024], num_samples=1
).reshape(-1)
codebook_preds = codebook_preds.to(torch.int32)
in_buffer[0, rel_start_fill_idx:, nn] = codebook_preds
del logits, codebook_preds
# transfer over info into model_in and convert to numpy
for nn in range(n_coarse, N_FINE_CODEBOOKS):
in_arr[
start_fill_idx: start_fill_idx + (1024 - rel_start_fill_idx), nn
] = in_buffer[0, rel_start_fill_idx:, nn]
del in_buffer
gen_fine_arr = in_arr.detach().cpu().numpy().squeeze().T
del in_arr
# if OFFLOAD_CPU:
# model.to("cpu")
gen_fine_arr = gen_fine_arr[:, n_history:]
if n_remove_from_end > 0:
gen_fine_arr = gen_fine_arr[:, :-n_remove_from_end]
assert gen_fine_arr.shape[-1] == x_coarse_gen.shape[-1]
# _clear_cuda_cache()
return gen_fine_arr
def codec_decode(self, fine_tokens):
"""Turn quantized audio codes into audio array using encodec."""
model = self._encodec
device = next(model.parameters()).device
arr = torch.from_numpy(fine_tokens)[None]
arr = arr.to(device)
arr = arr.transpose(0, 1)
emb = model.quantizer.decode(arr)
out = model.decoder(emb)
audio_arr = out.detach().cpu().numpy().squeeze()
del arr, emb, out
return audio_arr
if __name__ == '__main__':
bark_load = BarkModelLoader(tokenizer_path='/media/checkpoint/bark/bert-base-multilingual-cased/',
text_path='/media/checkpoint/bark/suno/bark_v0/text_2.pt',
coarse_path='/media/checkpoint/bark/suno/bark_v0/coarse_2.pt',
fine_path='/media/checkpoint/bark/suno/bark_v0/fine_2.pt')
print(bark_load)
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