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