import contextlib
import gc
import os
import re
import requests
import gc
import sys
from encodec import EncodecModel
import funcy
import logging
import numpy as np
from scipy.special import softmax
import torch
import torch.nn.functional as F
import tqdm
from transformers import BertTokenizer
from huggingface_hub import hf_hub_download, hf_hub_url
from .model import GPTConfig, GPT
from .model_fine import FineGPT, FineGPTConfig
from .settings import initenv
initenv(sys.argv)
global_force_cpu = os.environ.get("BARK_FORCE_CPU", False)
if (
global_force_cpu != True and
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
# hold models in global scope to lazy load
global models
models = {}
global models_devices
models_devices = {}
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
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"),
]
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}")
logger = logging.getLogger(__name__)
CUR_PATH = os.path.dirname(os.path.abspath(__file__))
#default_cache_dir = os.path.join(os.path.expanduser("~"), ".cache")
#CACHE_DIR = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
#CACHE_DIR = os.path.join(os.getcwd(), "models"
CACHE_DIR = "./models"
def _cast_bool_env_var(s):
return s.lower() in ('true', '1', 't')
USE_SMALL_MODELS = _cast_bool_env_var(os.environ.get("SUNO_USE_SMALL_MODELS", "False"))
GLOBAL_ENABLE_MPS = _cast_bool_env_var(os.environ.get("SUNO_ENABLE_MPS", "False"))
OFFLOAD_CPU = _cast_bool_env_var(os.environ.get("SUNO_OFFLOAD_CPU", "False"))
REMOTE_MODEL_PATHS = {
"text_small": {
"repo_id": "suno/bark",
"file_name": "text.pt",
},
"coarse_small": {
"repo_id": "suno/bark",
"file_name": "coarse.pt",
},
"fine_small": {
"repo_id": "suno/bark",
"file_name": "fine.pt",
},
"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",
},
}
if not hasattr(torch.nn.functional, 'scaled_dot_product_attention') and torch.cuda.is_available():
logger.warning(
"torch version does not support flash attention. You will get faster" +
" inference speed by upgrade torch to newest nightly version."
)
def grab_best_device(use_gpu=True):
if torch.cuda.device_count() > 0 and use_gpu:
device = "cuda"
elif torch.backends.mps.is_available() and use_gpu and GLOBAL_ENABLE_MPS:
device = "mps"
else:
device = "cpu"
return device
def _get_ckpt_path(model_type, use_small=False):
key = model_type
if use_small or USE_SMALL_MODELS:
key += "_small"
return os.path.join(CACHE_DIR, REMOTE_MODEL_PATHS[key]["file_name"])
"""
def _download(from_hf_path, file_name, destfilename):
os.makedirs(CACHE_DIR, exist_ok=True)
hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=CACHE_DIR, local_dir_use_symlinks=False)
# Bug in original repo? Downloaded name differs from expected...
if not os.path.exists(destfilename):
localname = os.path.join(CACHE_DIR, file_name)
os.rename(localname, destfilename)
"""
def _download(from_hf_path, file_name):
os.makedirs(CACHE_DIR, exist_ok=True)
hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=CACHE_DIR)
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
if torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
@contextlib.contextmanager
def _inference_mode():
with InferenceContext(), torch.inference_mode(), torch.no_grad(), autocast():
yield
def _clear_cuda_cache():
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.synchronize()
def clean_models(model_key=None):
global models
model_keys = [model_key] if model_key is not None else models.keys()
for k in model_keys:
if k in models:
del models[k]
_clear_cuda_cache()
gc.collect()
def _load_model(ckpt_path, device, use_small=False, model_type="text"):
if model_type == "text":
ConfigClass = GPTConfig
ModelClass = GPT
elif model_type == "coarse":
ConfigClass = GPTConfig
ModelClass = GPT
elif model_type == "fine":
ConfigClass = FineGPTConfig
ModelClass = FineGPT
else:
raise NotImplementedError()
# Force-remove Models to allow running on >12Gb GPU
# CF: Probably not needed anymore
#global models
#models.clear()
#gc.collect()
#torch.cuda.empty_cache()
# to here...
model_key = f"{model_type}_small" if use_small or USE_SMALL_MODELS else model_type
model_info = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(ckpt_path):
logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`.")
## added next two lines to make it super clear which model is being downloaded
remote_filename = hf_hub_url(model_info["repo_id"], model_info["file_name"])
print(f"Downloading {model_key} {model_info['repo_id']} remote model file {remote_filename} {model_info['file_name']} to {CACHE_DIR}")
_download(model_info["repo_id"], model_info["file_name"])
# add next line to make it super clear which model is being loaded
print(f"Loading {model_key} model from {ckpt_path} to {device}") # added
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 = ConfigClass(**checkpoint["model_args"])
model = ModelClass(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 loaded: {round(n_params/1e6,1)}M params, {round(val_loss,3)} loss")
model.eval()
model.to(device)
del checkpoint, state_dict
_clear_cuda_cache()
if model_type == "text":
tokenizer = BertTokenizer.from_pretrained("bert-base-multilingual-cased")
return {
"model": model,
"tokenizer": tokenizer,
}
return model
def _load_codec_model(device):
model = EncodecModel.encodec_model_24khz()
model.set_target_bandwidth(6.0)
model.eval()
model.to(device)
_clear_cuda_cache()
return model
def load_model(use_gpu=True, use_small=False, force_reload=False, model_type="text"):
_load_model_f = funcy.partial(_load_model, model_type=model_type, use_small=use_small)
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
global models
global models_devices
device = grab_best_device(use_gpu=use_gpu)
model_key = f"{model_type}"
if OFFLOAD_CPU:
models_devices[model_key] = device
device = "cpu"
if model_key not in models or force_reload:
ckpt_path = _get_ckpt_path(model_type, use_small=use_small)
clean_models(model_key=model_key)
model = _load_model_f(ckpt_path, device)
models[model_key] = model
if model_type == "text":
models[model_key]["model"].to(device)
else:
models[model_key].to(device)
return models[model_key]
def load_codec_model(use_gpu=True, force_reload=False):
global models
global models_devices
device = grab_best_device(use_gpu=use_gpu)
if device == "mps":
# encodec doesn't support mps
device = "cpu"
model_key = "codec"
if OFFLOAD_CPU:
models_devices[model_key] = device
device = "cpu"
if model_key not in models or force_reload:
clean_models(model_key=model_key)
model = _load_codec_model(device)
models[model_key] = model
models[model_key].to(device)
return models[model_key]
def preload_models(
text_use_gpu=True,
text_use_small=False,
coarse_use_gpu=True,
coarse_use_small=False,
fine_use_gpu=True,
fine_use_small=False,
codec_use_gpu=True,
force_reload=False
):
"""Load all the necessary models for the pipeline."""
if grab_best_device() == "cpu" and (
text_use_gpu or coarse_use_gpu or fine_use_gpu or codec_use_gpu
):
logger.warning("No GPU being used. Careful, inference might be very slow!")
_ = load_model(
model_type="text", use_gpu=text_use_gpu, use_small=text_use_small, force_reload=force_reload
)
_ = load_model(
model_type="coarse",
use_gpu=coarse_use_gpu,
use_small=coarse_use_small,
force_reload=force_reload,
)
_ = load_model(
model_type="fine", use_gpu=fine_use_gpu, use_small=fine_use_small, force_reload=force_reload
)
_ = load_codec_model(use_gpu=codec_use_gpu, force_reload=force_reload)
####
# Generation Functionality
####
def _tokenize(tokenizer, text):
return tokenizer.encode(text, add_special_tokens=False)
def _detokenize(tokenizer, enc_text):
return tokenizer.decode(enc_text)
def _normalize_whitespace(text):
return re.sub(r"\s+", " ", text).strip()
TEXT_ENCODING_OFFSET = 10_048
SEMANTIC_PAD_TOKEN = 10_000
TEXT_PAD_TOKEN = 129_595
SEMANTIC_INFER_TOKEN = 129_599
def _load_history_prompt(history_prompt_input):
if isinstance(history_prompt_input, str) and history_prompt_input.endswith(".npz"):
history_prompt = np.load(history_prompt_input)
elif isinstance(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(CUR_PATH, "assets", "prompts", f"{history_prompt_input}.npz")
)
elif isinstance(history_prompt_input, dict):
assert("semantic_prompt" in history_prompt_input)
assert("coarse_prompt" in history_prompt_input)
assert("fine_prompt" in history_prompt_input)
history_prompt = history_prompt_input
else:
raise ValueError("history prompt format unrecognized")
return history_prompt
def generate_text_semantic(
text,
history_prompt=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)
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
# load models if not yet exist
global models
global models_devices
if "text" not in models:
preload_models()
model_container = models["text"]
model = model_container["model"]
tokenizer = model_container["tokenizer"]
encoded_text = np.array(_tokenize(tokenizer, text)) + 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=100)
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)
# multinomial bugged on mps: shuttle to cpu if necessary
inf_device = probs.device
if probs.device.type == "mps":
probs = probs.to("cpu")
item_next = torch.multinomial(probs, num_samples=1)
probs = probs.to(inf_device)
item_next = item_next.to(inf_device)
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(100 - 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(100 - pbar_state)
break
if n == n_tot_steps - 1:
pbar.update(100 - pbar_state)
break
del logits, relevant_logits, probs, item_next
req_pbar_state = np.min([100, int(round(100 * n / n_tot_steps))])
if req_pbar_state > pbar_state:
pbar.update(req_pbar_state - pbar_state)
pbar_state = req_pbar_state
pbar.close()
out = x.detach().cpu().numpy().squeeze()[256 + 256 + 1 :]
if OFFLOAD_CPU:
model.to("cpu")
assert all(0 <= out) and all(out < SEMANTIC_VOCAB_SIZE)
_clear_cuda_cache()
return out
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
COARSE_SEMANTIC_PAD_TOKEN = 12_048
COARSE_INFER_TOKEN = 12_050
def generate_coarse(
x_semantic,
history_prompt=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."""
# CF: Uncommented because it breaks swap voice more than once
# 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)
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)
# load models if not yet exist
global models
global models_devices
if "coarse" not in models:
preload_models()
model = models["coarse"]
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)
# multinomial bugged on mps: shuttle to cpu if necessary
inf_device = probs.device
if probs.device.type == "mps":
probs = probs.to("cpu")
item_next = torch.multinomial(probs, num_samples=1)
probs = probs.to(inf_device)
item_next = item_next.to(inf_device)
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(
x_coarse_gen,
history_prompt=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)
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]
# load models if not yet exist
global models
global models_devices
if "fine" not in models:
preload_models()
model = models["fine"]
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)
# multinomial bugged on mps: shuttle to cpu if necessary
inf_device = probs.device
if probs.device.type == "mps":
probs = probs.to("cpu")
codebook_preds = torch.hstack(
[
torch.multinomial(probs[nnn], num_samples=1).to(inf_device)
for nnn in range(rel_start_fill_idx, 1024)
]
)
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(fine_tokens):
"""Turn quantized audio codes into audio array using encodec."""
# load models if not yet exist
global models
global models_devices
if "codec" not in models:
preload_models()
model = models["codec"]
if OFFLOAD_CPU:
model.to(models_devices["codec"])
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
if OFFLOAD_CPU:
model.to("cpu")
return audio_arr