Delete model/utils.py

model/utils.py

@@ -1,185 +0,0 @@
-from __future__ import annotations
-
-import os
-import random
-from collections import defaultdict
-from importlib.resources import files
-
-import torch
-from torch.nn.utils.rnn import pad_sequence
-
-import jieba
-from pypinyin import lazy_pinyin, Style
-
-
-# seed everything
-
-
-def seed_everything(seed=0):
-    random.seed(seed)
-    os.environ["PYTHONHASHSEED"] = str(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-
-
-# helpers
-
-
-def exists(v):
-    return v is not None
-
-
-def default(v, d):
-    return v if exists(v) else d
-
-
-# tensor helpers
-
-
-def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
-    if not exists(length):
-        length = t.amax()
-
-    seq = torch.arange(length, device=t.device)
-    return seq[None, :] < t[:, None]
-
-
-def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
-    max_seq_len = seq_len.max().item()
-    seq = torch.arange(max_seq_len, device=start.device).long()
-    start_mask = seq[None, :] >= start[:, None]
-    end_mask = seq[None, :] < end[:, None]
-    return start_mask & end_mask
-
-
-def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
-    lengths = (frac_lengths * seq_len).long()
-    max_start = seq_len - lengths
-
-    rand = torch.rand_like(frac_lengths)
-    start = (max_start * rand).long().clamp(min=0)
-    end = start + lengths
-
-    return mask_from_start_end_indices(seq_len, start, end)
-
-
-def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
-    if not exists(mask):
-        return t.mean(dim=1)
-
-    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
-    num = t.sum(dim=1)
-    den = mask.float().sum(dim=1)
-
-    return num / den.clamp(min=1.0)
-
-
-# simple utf-8 tokenizer, since paper went character based
-def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
-    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
-    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
-    return text
-
-
-# char tokenizer, based on custom dataset's extracted .txt file
-def list_str_to_idx(
-    text: list[str] | list[list[str]],
-    vocab_char_map: dict[str, int],  # {char: idx}
-    padding_value=-1,
-) -> int["b nt"]:  # noqa: F722
-    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
-    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
-    return text
-
-
-# Get tokenizer
-
-
-def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
-    """
-    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
-                - "char" for char-wise tokenizer, need .txt vocab_file
-                - "byte" for utf-8 tokenizer
-                - "custom" if you're directly passing in a path to the vocab.txt you want to use
-    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
-                - if use "char", derived from unfiltered character & symbol counts of custom dataset
-                - if use "byte", set to 256 (unicode byte range)
-    """
-    if tokenizer in ["pinyin", "char"]:
-        tokenizer_path = os.path.join(files("f5_tts").joinpath("../../data"), f"{dataset_name}_{tokenizer}/vocab.txt")
-        with open(tokenizer_path, "r", encoding="utf-8") as f:
-            vocab_char_map = {}
-            for i, char in enumerate(f):
-                vocab_char_map[char[:-1]] = i
-        vocab_size = len(vocab_char_map)
-        assert vocab_char_map[" "] == 0, "make sure space is of idx 0 in vocab.txt, cuz 0 is used for unknown char"
-
-    elif tokenizer == "byte":
-        vocab_char_map = None
-        vocab_size = 256
-
-    elif tokenizer == "custom":
-        with open(dataset_name, "r", encoding="utf-8") as f:
-            vocab_char_map = {}
-            for i, char in enumerate(f):
-                vocab_char_map[char[:-1]] = i
-        vocab_size = len(vocab_char_map)
-
-    return vocab_char_map, vocab_size
-
-
-# convert char to pinyin
-
-
-def convert_char_to_pinyin(text_list, polyphone=True):
-    final_text_list = []
-    god_knows_why_en_testset_contains_zh_quote = str.maketrans(
-        {"“": '"', "”": '"', "‘": "'", "’": "'"}
-    )  # in case librispeech (orig no-pc) test-clean
-    custom_trans = str.maketrans({";": ","})  # add custom trans here, to address oov
-    for text in text_list:
-        char_list = []
-        text = text.translate(god_knows_why_en_testset_contains_zh_quote)
-        text = text.translate(custom_trans)
-        for seg in jieba.cut(text):
-            seg_byte_len = len(bytes(seg, "UTF-8"))
-            if seg_byte_len == len(seg):  # if pure alphabets and symbols
-                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
-                    char_list.append(" ")
-                char_list.extend(seg)
-            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure chinese characters
-                seg = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
-                for c in seg:
-                    if c not in "。，、；：？！《》【】—…":
-                        char_list.append(" ")
-                    char_list.append(c)
-            else:  # if mixed chinese characters, alphabets and symbols
-                for c in seg:
-                    if ord(c) < 256:
-                        char_list.extend(c)
-                    else:
-                        if c not in "。，、；：？！《》【】—…":
-                            char_list.append(" ")
-                            char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
-                        else:  # if is zh punc
-                            char_list.append(c)
-        final_text_list.append(char_list)
-
-    return final_text_list
-
-
-# filter func for dirty data with many repetitions
-
-
-def repetition_found(text, length=2, tolerance=10):
-    pattern_count = defaultdict(int)
-    for i in range(len(text) - length + 1):
-        pattern = text[i : i + length]
-        pattern_count[pattern] += 1
-    for pattern, count in pattern_count.items():
-        if count > tolerance:
-            return True
-    return False