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libritrans.py
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from pathlib import Path
import numpy as np
import torch
import whisper
import torchaudio
import torchaudio.transforms as at
import os
from itertools import chain
import re
import jieba
from sacrebleu import BLEU
#####
# Common Functions
#####
LANGUAGES = {
"en": "english",
"zh": "chinese",
"de": "german",
"es": "spanish",
"ru": "russian",
"ko": "korean",
"fr": "french",
"ja": "japanese",
"pt": "portuguese",
"tr": "turkish",
"pl": "polish",
"ca": "catalan",
"nl": "dutch",
"ar": "arabic",
"sv": "swedish",
"it": "italian",
"id": "indonesian",
"hi": "hindi",
"fi": "finnish",
"vi": "vietnamese",
"iw": "hebrew",
"uk": "ukrainian",
"el": "greek",
"ms": "malay",
"cs": "czech",
"ro": "romanian",
"da": "danish",
"hu": "hungarian",
"ta": "tamil",
"no": "norwegian",
"th": "thai",
"ur": "urdu",
"hr": "croatian",
"bg": "bulgarian",
"lt": "lithuanian",
"la": "latin",
"mi": "maori",
"ml": "malayalam",
"cy": "welsh",
"sk": "slovak",
"te": "telugu",
"fa": "persian",
"lv": "latvian",
"bn": "bengali",
"sr": "serbian",
"az": "azerbaijani",
"sl": "slovenian",
"kn": "kannada",
"et": "estonian",
"mk": "macedonian",
"br": "breton",
"eu": "basque",
"is": "icelandic",
"hy": "armenian",
"ne": "nepali",
"mn": "mongolian",
"bs": "bosnian",
"kk": "kazakh",
"sq": "albanian",
"sw": "swahili",
"gl": "galician",
"mr": "marathi",
"pa": "punjabi",
"si": "sinhala",
"km": "khmer",
"sn": "shona",
"yo": "yoruba",
"so": "somali",
"af": "afrikaans",
"oc": "occitan",
"ka": "georgian",
"be": "belarusian",
"tg": "tajik",
"sd": "sindhi",
"gu": "gujarati",
"am": "amharic",
"yi": "yiddish",
"lo": "lao",
"uz": "uzbek",
"fo": "faroese",
"ht": "haitian creole",
"ps": "pashto",
"tk": "turkmen",
"nn": "nynorsk",
"mt": "maltese",
"sa": "sanskrit",
"lb": "luxembourgish",
"my": "myanmar",
"bo": "tibetan",
"tl": "tagalog",
"mg": "malagasy",
"as": "assamese",
"tt": "tatar",
"haw": "hawaiian",
"ln": "lingala",
"ha": "hausa",
"ba": "bashkir",
"jw": "javanese",
"su": "sundanese",
}
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞","؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")","{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。","、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
zh2en = {"，": ",", "。": ".", "？":"?", "！":"!", "；": ";", "‘": "'", "：": ":", "’":"'", "（":"(", "）":")", "【": "[", "】": "]", "～":"~"}
en2zh = {}
for key in zh2en:
en2zh[zh2en[key]] = key
#####
# Metric Helper Functions
#####
def tokenize_for_mer(text):
tokens = list(filter(lambda tok: len(tok.strip()) > 0, jieba.lcut(text)))
tokens = [[tok] if tok.isascii() else list(tok) for tok in tokens]
return list(chain(*tokens))
def tokenize_for_cer(text):
tokens = list(filter(lambda tok: len(tok.strip()) > 0, list(text)))
return tokens
chars_to_ignore_re = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
def remove_special_characters(text):
if chars_to_ignore_re is not None:
return re.sub(chars_to_ignore_re, "", text).lower()
else:
return text.lower()
def replace(item):
return item if item not in en2zh else en2zh[item]
class calc_metrics:
def __init__(self):
# self.converter = opencc.OpenCC('t2s.json')
pass
def __call__(self, refs, preds):
"""
refs are output from dataloader, so uses the collate fn, that already contains the normalization
preds are the output of whisper tokenizer, which doesn't have dataset specific normalization
they should both in list (list of list)
"""
ref4bleu = [[]]
pred4bleu = []
bleu_fn = BLEU()
sentence_blue = []
sentence_blue_fn = BLEU(effective_order=True)
for ref, pred in zip(refs, preds):
if len(ref) > 0:
ref4bleu[0].append(ref)
pred4bleu.append(pred)
sentence_blue.append(sentence_blue_fn.sentence_score(pred, [ref]).score)
bleu = bleu_fn.corpus_score(pred4bleu, ref4bleu)
return {"bleu": bleu}, (sentence_blue, pred4bleu, ref4bleu[0])
def load_wave(wave_path, sample_rate:int=16000) -> torch.Tensor:
waveform, sr = torchaudio.load(wave_path, normalize=True)
if sample_rate != sr:
waveform = at.Resample(sr, sample_rate)(waveform)
return waveform
class LibriTransDataset(torch.utils.data.Dataset):
def __init__(self, args, split, sample_rate):
super().__init__()
self.args = args
self.sample_rate = sample_rate
self.tokenizer = whisper.tokenizer.get_tokenizer(True, language=args.language, task="transcribe")
self.data = []
assert args.language in LANGUAGES, f"language {args.language} is not supported by whisper"
print("running on libri-trans language:", LANGUAGES[args.language])
assert split in ["train", "dev", "test"], f"split {split} not in {['train', 'dev', 'test']}"
lang = "zh-CN" if "zh" in args.language else args.language
assert args.language == "fr", f"language needs to be fr, but it's {args.language}"
for real_split in ['test', 'dev']:
path = os.path.join(args.dataset_dir,real_split)
with open(os.path.join(path, "alignments.meta"), "r") as f, open(os.path.join(path, f"{real_split}.fr"), "r") as g:
all_flines = [l.strip().split("\t") for l in f.readlines()]
all_flines = all_flines[1:]
all_glines = [l.strip() for l in g.readlines()]
assert len(all_flines) == len(all_glines), f"wav files length should equal to translation file length, but they are of length: {len(all_flines)}, and {len(all_glines)}"
for fline, gline in zip(all_flines, all_glines):
wav_fn = os.path.join(path, "audiofiles", fline[4] + ".wav")
trans = gline
self.data.append([wav_fn, None, trans])
print(f"pad audio to {self.args.audio_max_length/16000} seconds")
def __len__(self):
return len(self.data)
def __getitem__(self, id):
cur_path, raw_en, raw_text = self.data[id]
audio_path = cur_path
# audio
audio = load_wave(audio_path, sample_rate=self.sample_rate)
audio = whisper.pad_or_trim(audio.flatten(), length=self.args.audio_max_length)
mel = whisper.log_mel_spectrogram(audio)
return {
"audio_path": audio_path,
"input_mel": mel,
"raw_text": raw_text,
"raw_en": raw_en
}
def collate(self, batch):
audio_paths, input_mels, raw_text, raw_en = [], [], [], []
for f in batch:
raw_text.append(f['raw_text'])
audio_paths.append(f['audio_path'])
input_mels.append(f["input_mel"])
raw_en.append(f['raw_en'])
input_mels = torch.stack(input_mels, dim=0)
collated_batch = {}
collated_batch["input_mels"] = input_mels
collated_batch["audio_paths"] = audio_paths
collated_batch["raw_text"] = raw_text
collated_batch["raw_en"] = raw_en
return collated_batch
def get_dataloader(args):
tokenizer = whisper.tokenizer.get_tokenizer(multilingual=True, language=args.language, task=args.task)
dataset = LibriTransDataset(args, "dev" if args.data_split in ['dev', 'val'] else "test", args.sample_rate) # split doesn't make a difference, will use deev+test, as we are not tuning any hyperparams on this dataset
print("dataset size: ", len(dataset))
loader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
drop_last=False, shuffle=False, num_workers=args.num_workers,
collate_fn=dataset.collate, persistent_workers=True
)
return tokenizer, loader