SenseVoice对富文本解码的参数 (#1608)

gaochangfeng

2024-04-11 fce4e1d1b48f23cd8332e60afce3df8d6209a6a7

 funasr/models/sense_voice/whisper_lib/tokenizer.py

@@ -7,6 +7,7 @@

import tiktoken

# FIX(funasr): sense vocie
LANGUAGES = {
    "en": "english",
    "zh": "chinese",
@@ -108,6 +109,11 @@
    "jw": "javanese",
    "su": "sundanese",
    "yue": "cantonese",
    "minnan": "minnan",
    "wuyu": "wuyu",
    "dialect": "dialect",
    "zh/en": "zh/en",
    "en/zh": "en/zh",
}

# language code lookup by name, with a few language aliases
@@ -125,6 +131,28 @@
    "sinhalese": "si",
    "castilian": "es",
    "mandarin": "zh",
}

# FIX(funasr): sense vocie
AUDIO_EVENT = {
    "ASR": "ASR",
    "AED": "AED",
    "SER": "SER",
    "Speech": "Speech",
    "/Speech": "/Speech",
    "BGM": "BGM",
    "/BGM": "/BGM",
    "Laughter": "Laughter",
    "/Laughter": "/Laughter",
    "Applause": "Applause",
    "/Applause": "/Applause",
}

EMOTION = {
    "HAPPY": "HAPPY",
    "SAD": "SAD",
    "ANGRY": "ANGRY",
    "NEUTRAL": "NEUTRAL",
}


@@ -151,7 +179,12 @@
        langs = tuple(LANGUAGES.keys())[: self.num_languages]
        sot_sequence = [sot]
        if self.language is not None:
            sot_sequence.append(sot + 1 + langs.index(self.language))
            if self.language == 'nospeech':
                sot_sequence.append(self.no_speech)
            else:
                sot_sequence.append(sot + 1 + langs.index(self.language))
        # if self.language is not None:
        #     sot_sequence.append(sot + 1 + langs.index(self.language))
        if self.task is not None:
            task_token: int = transcribe if self.task == "transcribe" else translate
            sot_sequence.append(task_token)
@@ -171,6 +204,9 @@
        This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
        """
        return self.encoding.decode(token_ids, **kwargs)
    
    def get_vocab_size(self) -> int:
        return self.encoding.n_vocab

    @cached_property
    def eot(self) -> int:
@@ -186,6 +222,10 @@

    @cached_property
    def sot(self) -> int:
        return self.special_tokens["<|startoftranscript|>"]

    @cached_property
    def sot_sense(self) -> int:
        return self.special_tokens["<|startoftranscript|>"]

    @cached_property
@@ -328,8 +368,10 @@


@lru_cache(maxsize=None)
def get_encoding(name: str = "gpt2", num_languages: int = 99):
    vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken")
def get_encoding(name: str = "gpt2", num_languages: int = 99, vocab_path:str=None):
    if vocab_path is None:
        vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken")

    ranks = {
        base64.b64decode(token): int(rank)
        for token, rank in (line.split() for line in open(vocab_path) if line)
@@ -337,18 +379,35 @@
    n_vocab = len(ranks)
    special_tokens = {}

    specials = [
        "<|endoftext|>",
        "<|startoftranscript|>",
        *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]],
        "<|translate|>",
        "<|transcribe|>",
        "<|startoflm|>",
        "<|startofprev|>",
        "<|nospeech|>",
        "<|notimestamps|>",
        *[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
    ]
    if False: #name == "gpt2" or name == "multilingual":
        specials = [
            "<|endoftext|>",
            "<|startoftranscript|>",
            *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]],
            "<|translate|>",
            "<|transcribe|>",
            "<|startoflm|>",
            "<|startofprev|>",
            "<|nospeech|>",
            "<|notimestamps|>",
            *[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
        ]
    else:
        specials = [
            "<|endoftext|>",
            "<|startoftranscript|>",
            *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]],
            *[f"<|{audio_event}|>" for audio_event in list(AUDIO_EVENT.keys())],
            *[f"<|{emotion}|>" for emotion in list(EMOTION.keys())],
            "<|translate|>",
            "<|transcribe|>",
            "<|startoflm|>",
            "<|startofprev|>",
            "<|nospeech|>",
            "<|notimestamps|>",
            *[f"<|SPECIAL_TOKEN_{i}|>" for i in range(1, 51)],
            *[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
        ]

    for token in specials:
        special_tokens[token] = n_vocab
@@ -370,12 +429,16 @@
    num_languages: int = 99,
    language: Optional[str] = None,
    task: Optional[str] = None,  # Literal["transcribe", "translate", None]
    encoding_path: Optional[str] = None,
    vocab_path: Optional[str] = None,
) -> Tokenizer:
    if language is not None:
        language = language.lower()
        if language not in LANGUAGES:
            if language in TO_LANGUAGE_CODE:
                language = TO_LANGUAGE_CODE[language]
            elif language == 'nospeech':
                pass
            else:
                raise ValueError(f"Unsupported language: {language}")

@@ -387,8 +450,12 @@
        encoding_name = "gpt2"
        language = None
        task = None
    if encoding_path is not None:
        encoding_name = encoding_path

    encoding = get_encoding(name=encoding_name, num_languages=num_languages)

    encoding = get_encoding(name=encoding_name, num_languages=num_languages, vocab_path=vocab_path)


    return Tokenizer(
        encoding=encoding, num_languages=num_languages, language=language, task=task