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import base64
import json
import logging
from pathlib import Path
import tiktoken
logger = logging.getLogger(__name__)
# This is a modified version of the default pattern from GPT-4o, that better handles punctuations.
FISH_TIKTOKEN_PATTERN = "|".join(
[
r"(?i:'s|'t|'re|'ve|'m|'ll|'d)",
r"\p{P}",
r"[^\r\n\p{L}\p{N}]?\p{L}+",
r"\p{N}",
r" ?[^\s\p{L}\p{N}]+[\r\n]*",
r"\s*[\r\n]+",
r"\s+(\?!\S)",
r"\s+",
]
)
TIKTOKEN_MAX_ENCODE_CHARS = 400_000
BOS_TOKEN = "<|begin_of_text|>"
EOS_TOKEN = "<|end_of_text|>"
PAD_TOKEN = "<|pad|>"
IM_START_TOKEN = "<|im_start|>"
IM_END_TOKEN = "<|im_end|>"
MODALITY_TEXT_TOKEN = "<|text|>"
MODALITY_VOICE_TOKEN = "<|voice|>"
MODALITY_INTERLEAVE_TOKEN = "<|interleave|>"
MODALITY_TOKENS = {
"text": MODALITY_TEXT_TOKEN,
"voice": MODALITY_VOICE_TOKEN,
"interleave": MODALITY_INTERLEAVE_TOKEN,
}
PLACEHOLDER_TOKEN = [""] * 4
for i in range(4):
PLACEHOLDER_TOKEN[i] = f"<|placeholder:{i}|>"
SEMANTIC_TOKEN_TEMPLATE = "<|semantic:{i}|>"
SEMANTIC_TOKENS = [SEMANTIC_TOKEN_TEMPLATE.format(i=i) for i in range(1024)]
# Warning: when you add a new special token, you should only add it to the end of the list.
ALL_SPECIAL_TOKENS = [
BOS_TOKEN,
EOS_TOKEN,
PAD_TOKEN,
IM_START_TOKEN,
IM_END_TOKEN,
PLACEHOLDER_TOKEN[0],
PLACEHOLDER_TOKEN[1],
PLACEHOLDER_TOKEN[2],
PLACEHOLDER_TOKEN[3],
MODALITY_TEXT_TOKEN,
MODALITY_VOICE_TOKEN,
MODALITY_INTERLEAVE_TOKEN,
*SEMANTIC_TOKENS,
]
class FishTokenizer:
def __init__(self, model_path: str) -> None:
mergeable_ranks = self.load_tiktoken_bpe(model_path)
special_token_begin = len(mergeable_ranks)
self.all_special_tokens_with_ids = {
token: special_token_begin + i for i, token in enumerate(ALL_SPECIAL_TOKENS)
}
self.semantic_id_to_token_id = {
i: self.all_special_tokens_with_ids[token]
for i, token in enumerate(SEMANTIC_TOKENS)
}
self.semantic_begin_id = self.all_special_tokens_with_ids[SEMANTIC_TOKENS[0]]
self.semantic_end_id = self.all_special_tokens_with_ids[SEMANTIC_TOKENS[-1]]
self.tkt_model = tiktoken.core.Encoding(
name=Path(model_path).stem,
pat_str=FISH_TIKTOKEN_PATTERN,
mergeable_ranks=mergeable_ranks,
special_tokens=self.all_special_tokens_with_ids,
)
@staticmethod
def load_tiktoken_bpe(tiktoken_bpe_file: str) -> dict[bytes, int]:
data = {}
for line in open(tiktoken_bpe_file).read().splitlines():
if not line:
continue
token, rank = line.split()
data[base64.b64decode(token)] = int(rank)
return data
def get_token_id(self, token: str) -> int:
return self.all_special_tokens_with_ids[token]
def encode(self, s: str, allowed_special: bool | set[str] = True) -> list[int]:
assert isinstance(s, str)
subs = []
for i in range(0, len(s), TIKTOKEN_MAX_ENCODE_CHARS):
subs.append(s[i : i + TIKTOKEN_MAX_ENCODE_CHARS])
if allowed_special is True:
allowed_special = self.tkt_model.special_tokens_set
elif allowed_special is False:
allowed_special = set()
return sum(
self.tkt_model.encode_batch(
subs, allowed_special=allowed_special, disallowed_special=set()
),
start=[],
)
def decode(self, tokens: list[int]) -> str:
return self.tkt_model.decode(tokens)
def save_pretrained(self, path: str):
path = Path(path)
path.mkdir(parents=True, exist_ok=True)
with open(path / "tokenizer.tiktoken", "w") as f:
for token, rank in self.tkt_model._mergeable_ranks.items():
f.write(f"{base64.b64encode(token).decode()} {rank}\n")
with open(path / "special_tokens.json", "w") as f:
json.dump(
self.all_special_tokens_with_ids,
f,
indent=2,
ensure_ascii=False,
)
@staticmethod
def from_pretrained(path: str):
return FishTokenizer(Path(path) / "tokenizer.tiktoken")
if __name__ == "__main__":
tokenizer = FishTokenizer("data/mpacks/v1.4-pretrain/tokenizer.all.tiktoken")
tokenizer.save_pretrained("checkpoints/fish-speech-0.5B")
tokenizer = FishTokenizer.from_pretrained("checkpoints/fish-speech-0.5B")
print(
[
tokenizer.decode([i])
for i in tokenizer.encode(f"{BOS_TOKEN}你好，世界！{EOS_TOKEN}")
]
)
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