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https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
add vc code
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lyuxiang.lx
@@ -4,6 +4,7 @@ import string
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from dataclasses import dataclass, field
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from functools import cached_property, lru_cache
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from typing import Dict, List, Optional, Tuple
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from whisper.tokenizer import Tokenizer
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import tiktoken
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@@ -165,208 +166,6 @@ TTS_Vocal_Token = {
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}
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@dataclass
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class Tokenizer:
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"""A thin wrapper around `tiktoken` providing quick access to special tokens"""
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encoding: tiktoken.Encoding
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num_languages: int
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language: Optional[str] = None
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task: Optional[str] = None
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sot_sequence: Tuple[int] = ()
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special_tokens: Dict[str, int] = field(default_factory=dict)
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def __post_init__(self):
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for special in self.encoding.special_tokens_set:
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special_token = self.encoding.encode_single_token(special)
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self.special_tokens[special] = special_token
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sot: int = self.special_tokens["<|startoftranscript|>"]
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translate: int = self.special_tokens["<|translate|>"]
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transcribe: int = self.special_tokens["<|transcribe|>"]
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langs = tuple(LANGUAGES.keys())[: self.num_languages]
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sot_sequence = [sot]
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if self.language is not None:
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sot_sequence.append(sot + 1 + langs.index(self.language))
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if self.task is not None:
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task_token: int = transcribe if self.task == "transcribe" else translate
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sot_sequence.append(task_token)
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self.sot_sequence = tuple(sot_sequence)
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def encode(self, text, **kwargs):
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return self.encoding.encode(text, **kwargs)
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def decode(self, token_ids: List[int], **kwargs) -> str:
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token_ids = [t for t in token_ids if t < self.timestamp_begin]
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return self.encoding.decode(token_ids, **kwargs)
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def decode_with_timestamps(self, token_ids: List[int], **kwargs) -> str:
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"""
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Timestamp tokens are above other special tokens' id range and are ignored by `decode()`.
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This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
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"""
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return self.encoding.decode(token_ids, **kwargs)
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def get_vocab_size(self) -> int:
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return self.encoding.n_vocab
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@cached_property
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def eot(self) -> int:
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return self.encoding.eot_token
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@cached_property
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def transcribe(self) -> int:
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return self.special_tokens["<|transcribe|>"]
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@cached_property
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def translate(self) -> int:
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return self.special_tokens["<|translate|>"]
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@cached_property
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def sot(self) -> int:
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return self.special_tokens["<|startoftranscript|>"]
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@cached_property
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def sot_lm(self) -> int:
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return self.special_tokens["<|startoflm|>"]
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@cached_property
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def sot_prev(self) -> int:
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return self.special_tokens["<|startofprev|>"]
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@cached_property
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def no_speech(self) -> int:
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return self.special_tokens["<|nospeech|>"]
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@cached_property
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def no_timestamps(self) -> int:
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return self.special_tokens["<|notimestamps|>"]
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@cached_property
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def timestamp_begin(self) -> int:
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return self.special_tokens["<|0.00|>"]
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@cached_property
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def language_token(self) -> int:
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"""Returns the token id corresponding to the value of the `language` field"""
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if self.language is None:
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raise ValueError("This tokenizer does not have language token configured")
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return self.to_language_token(self.language)
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def to_language_token(self, language):
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if token := self.special_tokens.get(f"<|{language}|>", None):
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return token
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raise KeyError(f"Language {language} not found in tokenizer.")
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@cached_property
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def all_language_tokens(self) -> Tuple[int]:
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result = []
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for token, token_id in self.special_tokens.items():
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if token.strip("<|>") in LANGUAGES:
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result.append(token_id)
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return tuple(result)[: self.num_languages]
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@cached_property
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def all_language_codes(self) -> Tuple[str]:
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return tuple(self.decode([_l]).strip("<|>") for _l in self.all_language_tokens)
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@cached_property
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def sot_sequence_including_notimestamps(self) -> Tuple[int]:
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return tuple(list(self.sot_sequence) + [self.no_timestamps])
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@cached_property
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def non_speech_tokens(self) -> Tuple[int]:
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"""
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Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
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annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
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- ♪♪♪
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- ( SPEAKING FOREIGN LANGUAGE )
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- [DAVID] Hey there,
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keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
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"""
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symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』')
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symbols += (
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"<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
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)
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# symbols that may be a single token or multiple tokens depending on the tokenizer.
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# In case they're multiple tokens, suppress the first token, which is safe because:
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# These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
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# in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
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miscellaneous = set("♩♪♫♬♭♮♯")
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assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
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# allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
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result = {self.encoding.encode(" -")[0], self.encoding.encode(" '")[0]}
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for symbol in symbols + list(miscellaneous):
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for tokens in [
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self.encoding.encode(symbol),
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self.encoding.encode(" " + symbol),
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]:
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if len(tokens) == 1 or symbol in miscellaneous:
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result.add(tokens[0])
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return tuple(sorted(result))
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def split_to_word_tokens(self, tokens: List[int]):
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if self.language in {"zh", "ja", "th", "lo", "my", "yue"}:
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# These languages don't typically use spaces, so it is difficult to split words
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# without morpheme analysis. Here, we instead split words at any
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# position where the tokens are decoded as valid unicode points
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return self.split_tokens_on_unicode(tokens)
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return self.split_tokens_on_spaces(tokens)
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def split_tokens_on_unicode(self, tokens: List[int]):
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decoded_full = self.decode_with_timestamps(tokens)
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replacement_char = "\ufffd"
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words = []
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word_tokens = []
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current_tokens = []
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unicode_offset = 0
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for token in tokens:
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current_tokens.append(token)
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decoded = self.decode_with_timestamps(current_tokens)
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if (
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replacement_char not in decoded
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or decoded_full[unicode_offset + decoded.index(replacement_char)]
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== replacement_char
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):
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words.append(decoded)
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word_tokens.append(current_tokens)
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current_tokens = []
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unicode_offset += len(decoded)
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return words, word_tokens
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def split_tokens_on_spaces(self, tokens: List[int]):
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subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens)
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words = []
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word_tokens = []
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for subword, subword_tokens in zip(subwords, subword_tokens_list):
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special = subword_tokens[0] >= self.eot
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with_space = subword.startswith(" ")
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punctuation = subword.strip() in string.punctuation
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if special or with_space or punctuation or len(words) == 0:
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words.append(subword)
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word_tokens.append(subword_tokens)
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else:
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words[-1] = words[-1] + subword
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word_tokens[-1].extend(subword_tokens)
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return words, word_tokens
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@lru_cache(maxsize=None)
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def get_encoding(name: str = "gpt2", num_languages: int = 99):
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vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken")
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