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ZDisket
100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
import argparse
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import text
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from utils import load_filepaths_and_text
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from tqdm import tqdm
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import os
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from moji.moji import TorchMoji
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import torch
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from bertfe import BERTFrontEnd
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import commons
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--out_extension", default="cleaned")
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parser.add_argument("--text_index", default=1, type=int)
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parser.add_argument("--filelists", nargs="+", default=["filelists/ljs_audio_text_val_filelist.txt", "filelists/ljs_audio_text_test_filelist.txt"])
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parser.add_argument("--text_cleaners", nargs="+", default=["english_cleaners2"])
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parser.add_argument("--bert", default="huawei-noah/TinyBERT_General_4L_312D")
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parser.add_argument("--test", default="")
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args = parser.parse_args()
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moji = TorchMoji(verbose=True)
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bert_f = BERTFrontEnd(model_name=args.bert)
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test_sentences = ["The quick brown fox jumps over the lazy dog",
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"In a galaxy far, far away, a young hero embarks on an epic adventure",
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"Ladies and gentlemen, welcome to the annual science fair!",
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"Peter Piper picked a peck of pickled peppers. How many pickled peppers did Peter Piper pick?",
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"The crowd erupted in cheers as the team scored the winning goal in the final seconds of the match"]
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if len(args.test) > 1:
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with open(args.test) as f:
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test_sentences = f.readlines()
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if "arpa_cleaners" in args.text_cleaners:
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print("ARPA cleaners detected, will run in dual mode")
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for filelist in args.filelists:
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print("START:", filelist)
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filepaths_and_text = load_filepaths_and_text(filelist)
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new_fp_text = []
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for i in tqdm(range(len(filepaths_and_text))):
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original_text = filepaths_and_text[i][args.text_index]
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original_fname = filepaths_and_text[i][0]
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moji_filename = original_fname.replace(".wav",".torchmoji")
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if not os.path.isfile(moji_filename):
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moji_result = moji(original_text)
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torch.save(moji_result, moji_filename)
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bert_filename = original_fname.replace(".wav",".bert")
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if not os.path.isfile(bert_filename):
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bert_res, _ = bert_f.infer(original_text)
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torch.save(bert_res,bert_filename)
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cleaned_text = text._clean_text(original_text, args.text_cleaners)
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text_arrs = filepaths_and_text[i]
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text_arrs[args.text_index] = cleaned_text
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new_fp_text.append(text_arrs)
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if "arpa_cleaners" in args.text_cleaners:
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# ZDisket: Python's default behavior when equalling a list is a reference, so unless we use .copy() this ends up
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# overriding the previous entry and leading into 2 text entries (wtf, why like this?)
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text_arrs2 = filepaths_and_text[i].copy()
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text_arrs2[args.text_index] = text._clean_text(original_text, ["arpa_precleaners"])
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new_fp_text.append(text_arrs2)
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new_filelist = filelist + "." + args.out_extension
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with open(new_filelist, "w", encoding="utf-8") as f:
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for x in new_fp_text:
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f.write("|".join(x) + "\n")
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print("Writing test data...")
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test_path = "test_preproc"
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for i, test_sent in tqdm(enumerate(test_sentences)):
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moji_t = moji(test_sent)
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bert_t, _ = bert_f.infer(test_sent)
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test_cleaned_text = text._clean_text(test_sent, args.text_cleaners)
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cleaned_text_indices = text.cleaned_text_to_sequence(test_cleaned_text)
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text_norm = commons.intersperse(cleaned_text_indices, 0)
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text_norm = torch.LongTensor(text_norm)
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if not os.path.exists(test_path):
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os.makedirs(test_path)
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test_rawfn = f"test{i}.pt"
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test_fullfn = os.path.join(test_path, test_rawfn)
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torch.save([text_norm, moji_t, bert_t], test_fullfn)
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