def get_tokenizer()

in tensorrtllm/whisper_utils.py [0:0]

• 37 lines of code
• 8 McCabe index (conditional complexity)

def get_tokenizer(name: str = "multilingual",
                  num_languages: int = 99,
                  tokenizer_dir: str = None):
    if tokenizer_dir is None:
        vocab_path = os.path.join(os.path.dirname(__file__),
                                  f"assets/{name}.tiktoken")
    else:
        vocab_path = os.path.join(tokenizer_dir, f"{name}.tiktoken")
    ranks = {
        base64.b64decode(token): int(rank)
        for token, rank in (line.split() for line in open(vocab_path) if line)
    }
    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)],
    ]

    for token in specials:
        special_tokens[token] = n_vocab
        n_vocab += 1

    return tiktoken.Encoding(
        name=os.path.basename(vocab_path),
        explicit_n_vocab=n_vocab,
        pat_str=
        r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
        mergeable_ranks=ranks,
        special_tokens=special_tokens,
    )