first commit

bark/__init__.py

@@ -0,0 +1,2 @@
+from .api import generate_audio, text_to_semantic, semantic_to_waveform
+from .generation import SAMPLE_RATE, preload_models

bark/api.py

@@ -0,0 +1,79 @@
+from typing import Optional
+
+import numpy as np
+
+from .generation import codec_decode, generate_coarse, generate_fine, generate_text_semantic
+
+
+def text_to_semantic(
+    text: str,
+    history_prompt: Optional[str] = None,
+    temp: float = 0.7,
+):
+    """Generate semantic array from text.
+
+    Args:
+        text: text to be turned into audio
+        history_prompt: history choice for audio cloning
+        temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+
+    Returns:
+        numpy semantic array to be fed into `semantic_to_waveform`
+    """
+    x_semantic = generate_text_semantic(
+        text,
+        history_prompt=history_prompt,
+        temp=temp,
+    )
+    return x_semantic
+
+
+def semantic_to_waveform(
+    semantic_tokens: np.ndarray,
+    history_prompt: Optional[str] = None,
+    temp: float = 0.7,
+):
+    """Generate audio array from semantic input.
+
+    Args:
+        semantic_tokens: semantic token output from `text_to_semantic`
+        history_prompt: history choice for audio cloning
+        temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+
+    Returns:
+        numpy audio array at sample frequency 24khz
+    """
+    x_coarse_gen = generate_coarse(
+        semantic_tokens,
+        history_prompt=history_prompt,
+        temp=temp,
+    )
+    x_fine_gen = generate_fine(
+        x_coarse_gen,
+        history_prompt=history_prompt,
+        temp=0.5,
+    )
+    audio_arr = codec_decode(x_fine_gen)
+    return audio_arr
+
+
+def generate_audio(
+    text: str,
+    history_prompt: Optional[str] = None,
+    text_temp: float = 0.7,
+    waveform_temp: float = 0.7,
+):
+    """Generate audio array from input text.
+
+    Args:
+        text: text to be turned into audio
+        history_prompt: history choice for audio cloning
+        text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+        waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+
+    Returns:
+        numpy audio array at sample frequency 24khz
+    """
+    x_semantic = text_to_semantic(text, history_prompt=history_prompt, temp=text_temp)
+    audio_arr = semantic_to_waveform(x_semantic, history_prompt=history_prompt, temp=waveform_temp)
+    return audio_arr

bark/generation.py

@@ -0,0 +1,693 @@
+import contextlib
+import hashlib
+import os
+import re
+import requests
+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 .model import GPTConfig, GPT
+from .model_fine import FineGPT, FineGPTConfig
+
+if (
+    torch.cuda.is_available() and
+    hasattr(torch.cuda, "amp") and
+    hasattr(torch.cuda.amp, "autocast") 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 = {}
+
+
+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
+
+
+ALLOWED_PROMPTS = (
+    "brylcream",
+    "es-woman",
+    "man-narrator",
+)
+
+
+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")
+os.makedirs(CACHE_DIR, exist_ok=True)
+
+
+REMOTE_BASE_URL = "http://s3.amazonaws.com/suno-public/bark/models/v0/"
+REMOTE_MODEL_PATHS = {
+    "text": os.environ.get("SUNO_TEXT_MODEL_PATH", os.path.join(REMOTE_BASE_URL, "text.pt")),
+    "coarse": os.environ.get("SUNO_COARSE_MODEL_PATH", os.path.join(REMOTE_BASE_URL, "coarse.pt")),
+    "fine": os.environ.get("SUNO_FINE_MODEL_PATH", os.path.join(REMOTE_BASE_URL, "fine.pt")),
+}
+
+
+def _compute_md5(s):
+    m = hashlib.md5()
+    m.update(s.encode("utf-8"))
+    return m.hexdigest()
+
+
+def _get_ckpt_path(model_type):
+    model_name = _compute_md5(REMOTE_MODEL_PATHS[model_type])
+    return os.path.join(CACHE_DIR, f"{model_name}.pt")
+
+
+S3_BUCKET_PATH_RE = r"s3\:\/\/(.+?)\/"
+
+
+def _parse_s3_filepath(s3_filepath):
+    bucket_name = re.search(S3_BUCKET_PATH_RE, s3_filepath).group(1)
+    rel_s3_filepath = re.sub(S3_BUCKET_PATH_RE, "", s3_filepath)
+    return bucket_name, rel_s3_filepath
+
+
+def _download(from_s3_path, to_local_path):
+    response = requests.get(from_s3_path, stream=True)
+    total_size_in_bytes = int(response.headers.get('content-length', 0))
+    block_size = 1024  # 1 Kibibyte
+    progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
+    with open(to_local_path, "wb") as file:
+        for data in response.iter_content(block_size):
+            progress_bar.update(len(data))
+            file.write(data)
+    progress_bar.close()
+    if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
+        raise ValueError("ERROR, something went wrong")
+
+
+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()
+
+
+def _load_model(ckpt_path, device, model_type="text"):
+    if "cuda" not in device:
+        logger.warning("No GPU being used. Careful, Inference might be extremely slow!")
+    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()
+    if not os.path.exists(ckpt_path):
+        print(f"{model_type} model not found, downloading...")
+        _download(REMOTE_MODEL_PATHS[model_type], ckpt_path)
+    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()
+    print(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(ckpt_path=None, use_gpu=True, force_reload=False, model_type="text"):
+    _load_model_f = funcy.partial(_load_model, model_type=model_type)
+    if model_type not in ("text", "coarse", "fine"):
+        raise NotImplementedError()
+    global models
+    if torch.cuda.device_count() == 0 or not use_gpu:
+        device = "cpu"
+    else:
+        device = "cuda"
+    model_key = str(device) + f"__{model_type}"
+    if model_key not in models or force_reload:
+        if ckpt_path is None:
+            ckpt_path = _get_ckpt_path(model_type)
+        clean_models(model_key=model_key)
+        model = _load_model_f(ckpt_path, device)
+        models[model_key] = model
+    return models[model_key]
+
+
+def load_codec_model(use_gpu=True, force_reload=False):
+    global models
+    if torch.cuda.device_count() == 0 or not use_gpu:
+        device = "cpu"
+    else:
+        device = "cuda"
+    model_key = str(device) + f"__codec"
+    if model_key not in models or force_reload:
+        clean_models(model_key=model_key)
+        model = _load_codec_model(device)
+        models[model_key] = model
+    return models[model_key]
+
+
+def preload_models(text_ckpt_path=None, coarse_ckpt_path=None, fine_ckpt_path=None, use_gpu=True):
+    _ = load_model(
+        ckpt_path=text_ckpt_path, model_type="text", use_gpu=use_gpu, force_reload=True
+    )
+    _ = load_model(
+        ckpt_path=coarse_ckpt_path, model_type="coarse", use_gpu=use_gpu, force_reload=True
+    )
+    _ = load_model(
+        ckpt_path=fine_ckpt_path, model_type="fine", use_gpu=use_gpu, force_reload=True
+    )
+    _ = load_codec_model(use_gpu=use_gpu, force_reload=True)
+
+
+####
+# 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 generate_text_semantic(
+    text,
+    history_prompt=None,
+    temp=0.7,
+    top_k=None,
+    top_p=None,
+    use_gpu=True,
+    silent=False,
+    min_eos_p=0.2,
+    max_gen_duration_s=None,
+    allow_early_stop=True,
+    model=None,
+):
+    """Generate semantic tokens from text."""
+    assert isinstance(text, str)
+    text = _normalize_whitespace(text)
+    assert len(text.strip()) > 0
+    if history_prompt is not None:
+        assert (history_prompt in ALLOWED_PROMPTS)
+        semantic_history = np.load(
+            os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt}.npz")
+        )["text"]
+        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_container = load_model(use_gpu=use_gpu, model_type="text")
+    if model is None:
+        model = model_container["model"]
+    tokenizer = model_container["tokenizer"]
+    encoded_text = np.array(_tokenize(tokenizer, text)) + TEXT_ENCODING_OFFSET
+    device = "cuda" if use_gpu and torch.cuda.device_count() > 0 else "cpu"
+    if len(encoded_text) > 256:
+        p = round((len(encoded_text) - 256) / len(encoded_text) * 100, 1)
+        print(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
+        for n in range(n_tot_steps):
+            logits = model(x, merge_context=True)
+            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
+                logits_device = relevant_logits.device
+                logits_dtype = relevant_logits.type()
+                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(logits_device).type(logits_dtype)
+            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)
+            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 :]
+    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,
+    use_gpu=True,
+    silent=False,
+    max_coarse_history=630,  # min 60 (faster), max 630 (more context)
+    sliding_window_len=60,
+    model=None,
+):
+    """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:
+        assert (history_prompt in ALLOWED_PROMPTS)
+        x_history = np.load(
+            os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt}.npz")
+        )
+        x_semantic_history = x_history["coarse_1"]
+        x_coarse_history = x_history["coarse_2"]
+        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)
+    if model is None:
+        model = load_model(use_gpu=use_gpu, model_type="coarse")
+    device = "cuda" if use_gpu and torch.cuda.device_count() > 0 else "cpu"
+    # 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:],
+                ]
+            )
+            for _ in range(sliding_window_len):
+                if n_step >= n_steps:
+                    continue
+                is_major_step = n_step % N_COARSE_CODEBOOKS == 0
+                logits = model(x_in)
+                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
+                    logits_device = relevant_logits.device
+                    logits_dtype = relevant_logits.type()
+                    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(logits_device).type(logits_dtype)
+                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)
+                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
+    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,
+    use_gpu=True,
+    silent=True,
+    model=None,
+):
+    """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:
+        assert (history_prompt in ALLOWED_PROMPTS)
+        x_fine_history = np.load(
+            os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt}.npz")
+        )["fine"]
+        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]
+    if model is None:
+        model = load_model(use_gpu=use_gpu, model_type="fine")
+    device = "cuda" if use_gpu and torch.cuda.device_count() > 0 else "cpu"
+    # 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.hstack(
+                        [
+                            torch.multinomial(probs[n], num_samples=1)
+                            for n 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
+    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, model=None, use_gpu=True):
+    """Turn quantized audio codes into audio array using encodec."""
+    if model is None:
+        model = load_codec_model(use_gpu=use_gpu)
+    device = "cuda" if use_gpu and torch.cuda.device_count() > 0 else "cpu"
+    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

bark/model.py

@@ -0,0 +1,174 @@
+"""
+Much of this code is adapted from Andrej Karpathy's NanoGPT
+(https://github.com/karpathy/nanoGPT)
+"""
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+class LayerNorm(nn.Module):
+    """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
+
+    def __init__(self, ndim, bias):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+
+    def forward(self, input):
+        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        # regularization
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+        # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
+        if not self.flash:
+            # print("WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0")
+            # causal mask to ensure that attention is only applied to the left in the input sequence
+            self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
+                                        .view(1, 1, config.block_size, config.block_size))
+
+    def forward(self, x):
+        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        q, k ,v  = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+
+        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
+        if self.flash:
+            # efficient attention using Flash Attention CUDA kernels
+            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=True)
+        else:
+            # manual implementation of attention
+            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+            att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
+            att = F.softmax(att, dim=-1)
+            att = self.attn_dropout(att)
+            y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.resid_dropout(self.c_proj(y))
+        return y
+
+class MLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+        self.gelu = nn.GELU()
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+class Block(nn.Module):
+
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
+        self.mlp = MLP(config)
+        self.layer_idx = layer_idx
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+@dataclass
+class GPTConfig:
+    block_size: int = 1024
+    input_vocab_size: int = 10_048
+    output_vocab_size: int = 10_048
+    n_layer: int = 12
+    n_head: int = 12
+    n_embd: int = 768
+    dropout: float = 0.0
+    bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
+
+class GPT(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.input_vocab_size is not None
+        assert config.output_vocab_size is not None
+        assert config.block_size is not None
+        self.config = config
+
+        self.transformer = nn.ModuleDict(dict(
+            wte = nn.Embedding(config.input_vocab_size, config.n_embd),
+            wpe = nn.Embedding(config.block_size, config.n_embd),
+            drop = nn.Dropout(config.dropout),
+            h = nn.ModuleList([Block(config, idx) for idx in range(config.n_layer)]),
+            ln_f = LayerNorm(config.n_embd, bias=config.bias),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
+
+    def get_num_params(self, non_embedding=True):
+        """
+        Return the number of parameters in the model.
+        For non-embedding count (default), the position embeddings get subtracted.
+        The token embeddings would too, except due to the parameter sharing these
+        params are actually used as weights in the final layer, so we include them.
+        """
+        n_params = sum(p.numel() for p in self.parameters())
+        if non_embedding:
+            n_params -= self.transformer.wte.weight.numel()
+            n_params -= self.transformer.wpe.weight.numel()
+        return n_params
+
+    def forward(self, idx, merge_context=False):
+        device = idx.device
+        b, t = idx.size()
+        if merge_context:
+            assert(idx.shape[1] >= 256+256+1)
+            t = idx.shape[1] - 256
+        else:
+            assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+
+        # forward the GPT model itself
+        if merge_context:
+            tok_emb = torch.cat([
+                self.transformer.wte(idx[:,:256]) + self.transformer.wte(idx[:,256:256+256]),
+                self.transformer.wte(idx[:,256+256:])
+            ], dim=1)
+        else:
+            tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
+
+        pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0) # shape (1, t)
+        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (1, t, n_embd)
+
+        x = self.transformer.drop(tok_emb + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+
+        # inference-time mini-optimization: only forward the lm_head on the very last position
+        logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
+
+        return logits

bark/model_fine.py

@@ -0,0 +1,149 @@
+"""
+Much of this code is adapted from Andrej Karpathy's NanoGPT
+(https://github.com/karpathy/nanoGPT)
+"""
+from dataclasses import dataclass
+import math
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from .model import GPT, GPTConfig, MLP
+
+
+class NonCausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        # regularization
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+        # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
+        self.flash = (
+            hasattr(torch.nn.functional, "scaled_dot_product_attention") and self.dropout == 0.0
+        )
+
+    def forward(self, x):
+        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
+
+        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
+        if self.flash:
+            # efficient attention using Flash Attention CUDA kernels
+            y = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=False
+            )
+        else:
+            # manual implementation of attention
+            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+            att = F.softmax(att, dim=-1)
+            att = self.attn_dropout(att)
+            y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        y = (
+            y.transpose(1, 2).contiguous().view(B, T, C)
+        )  # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.resid_dropout(self.c_proj(y))
+        return y
+
+
+class FineBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = NonCausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class FineGPT(GPT):
+    def __init__(self, config):
+        super().__init__(config)
+        del self.lm_head
+        self.config = config
+        self.n_codes_total = config.n_codes_total
+        self.transformer = nn.ModuleDict(
+            dict(
+                wtes=nn.ModuleList(
+                    [
+                        nn.Embedding(config.input_vocab_size, config.n_embd)
+                        for _ in range(config.n_codes_total)
+                    ]
+                ),
+                wpe=nn.Embedding(config.block_size, config.n_embd),
+                drop=nn.Dropout(config.dropout),
+                h=nn.ModuleList([FineBlock(config) for _ in range(config.n_layer)]),
+                ln_f=nn.LayerNorm(config.n_embd),
+            )
+        )
+        self.lm_heads = nn.ModuleList(
+            [
+                nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
+                for _ in range(config.n_codes_given, self.n_codes_total)
+            ]
+        )
+        for i in range(self.n_codes_total - config.n_codes_given):
+            self.transformer.wtes[i + 1].weight = self.lm_heads[i].weight
+
+    def forward(self, pred_idx, idx):
+        device = idx.device
+        b, t, codes = idx.size()
+        assert (
+            t <= self.config.block_size
+        ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+        assert pred_idx > 0, "cannot predict 0th codebook"
+        assert codes == self.n_codes_total, (b, t, codes)
+        pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0)  # shape (1, t)
+
+        # forward the GPT model itself
+        tok_embs = [
+            wte(idx[:, :, i]).unsqueeze(-1) for i, wte in enumerate(self.transformer.wtes)
+        ]  # token embeddings of shape (b, t, n_embd)
+        tok_emb = torch.cat(tok_embs, dim=-1)
+        pos_emb = self.transformer.wpe(pos)  # position embeddings of shape (1, t, n_embd)
+        x = tok_emb[:, :, :, : pred_idx + 1].sum(dim=-1)
+        x = self.transformer.drop(x + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        logits = self.lm_heads[pred_idx - self.config.n_codes_given](x)
+        return logits
+
+    def get_num_params(self, non_embedding=True):
+        """
+        Return the number of parameters in the model.
+        For non-embedding count (default), the position embeddings get subtracted.
+        The token embeddings would too, except due to the parameter sharing these
+        params are actually used as weights in the final layer, so we include them.
+        """
+        n_params = sum(p.numel() for p in self.parameters())
+        if non_embedding:
+            for wte in self.transformer.wtes:
+                n_params -= wte.weight.numel()
+            n_params -= self.transformer.wpe.weight.numel()
+        return n_params
+
+
+@dataclass
+class FineGPTConfig(GPTConfig):
+    n_codes_total: int = 8
+    n_codes_given: int = 1