# Copyright (c) 2021 Mobvoi Inc. (authors: Di Wu) # 2022 Tinnove Inc (authors: Wei Ren) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import argparse import copy import logging import os import sys import torch import yaml from torch.utils.data import DataLoader from textgrid import TextGrid, IntervalTier from wenet.dataset.dataset import Dataset from wenet.utils.checkpoint import load_checkpoint from wenet.utils.file_utils import read_symbol_table, read_non_lang_symbols from wenet.utils.ctc_util import forced_align from wenet.utils.common import get_subsample from wenet.utils.init_model import init_model def generator_textgrid(maxtime, lines, output): # Download Praat: https://www.fon.hum.uva.nl/praat/ interval = maxtime / (len(lines) + 1) margin = 0.0001 tg = TextGrid(maxTime=maxtime) linetier = IntervalTier(name="line", maxTime=maxtime) i = 0 for l in lines: s, e, w = l.split() linetier.add(minTime=float(s) + margin, maxTime=float(e), mark=w) tg.append(linetier) print("successfully generator {}".format(output)) tg.write(output) def get_frames_timestamp(alignment): # convert alignment to a praat format, which is a doing phonetics # by computer and helps analyzing alignment timestamp = [] # get frames level duration for each token start = 0 end = 0 while end < len(alignment): while end < len(alignment) and alignment[end] == 0: end += 1 if end == len(alignment): timestamp[-1] += alignment[start:] break end += 1 while end < len(alignment) and alignment[end - 1] == alignment[end]: end += 1 timestamp.append(alignment[start:end]) start = end return timestamp def get_labformat(timestamp, subsample): begin = 0 duration = 0 labformat = [] for idx, t in enumerate(timestamp): # 25ms frame_length,10ms hop_length, 1/subsample subsample = get_subsample(configs) # time duration duration = len(t) * 0.01 * subsample if idx < len(timestamp) - 1: print("{:.2f} {:.2f} {}".format(begin, begin + duration, char_dict[t[-1]])) labformat.append("{:.2f} {:.2f} {}\n".format( begin, begin + duration, char_dict[t[-1]])) else: non_blank = 0 for i in t: if i != 0: token = i break print("{:.2f} {:.2f} {}".format(begin, begin + duration, char_dict[token])) labformat.append("{:.2f} {:.2f} {}\n".format( begin, begin + duration, char_dict[token])) begin = begin + duration return labformat if __name__ == '__main__': parser = argparse.ArgumentParser( description='use ctc to generate alignment') parser.add_argument('--config', required=True, help='config file') parser.add_argument('--input_file', required=True, help='format data file') parser.add_argument('--data_type', default='raw', choices=['raw', 'shard'], help='train and cv data type') parser.add_argument('--gpu', type=int, default=-1, help='gpu id for this rank, -1 for cpu') parser.add_argument('--checkpoint', required=True, help='checkpoint model') parser.add_argument('--dict', required=True, help='dict file') parser.add_argument('--non_lang_syms', help="non-linguistic symbol file. One symbol per line.") parser.add_argument('--result_file', required=True, help='alignment result file') parser.add_argument('--batch_size', type=int, default=1, help='batch size') parser.add_argument('--gen_praat', action='store_true', help='convert alignment to a praat format') parser.add_argument('--bpe_model', default=None, type=str, help='bpe model for english part') args = parser.parse_args() print(args) logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(message)s') os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu) if args.batch_size > 1: logging.fatal('alignment mode must be running with batch_size == 1') sys.exit(1) with open(args.config, 'r') as fin: configs = yaml.load(fin, Loader=yaml.FullLoader) # Load dict char_dict = {} with open(args.dict, 'r') as fin: for line in fin: arr = line.strip().split() assert len(arr) == 2 char_dict[int(arr[1])] = arr[0] eos = len(char_dict) - 1 symbol_table = read_symbol_table(args.dict) # Init dataset and data loader ali_conf = copy.deepcopy(configs['dataset_conf']) ali_conf['filter_conf']['max_length'] = 102400 ali_conf['filter_conf']['min_length'] = 0 ali_conf['filter_conf']['token_max_length'] = 102400 ali_conf['filter_conf']['token_min_length'] = 0 ali_conf['filter_conf']['max_output_input_ratio'] = 102400 ali_conf['filter_conf']['min_output_input_ratio'] = 0 ali_conf['speed_perturb'] = False ali_conf['spec_aug'] = False ali_conf['shuffle'] = False ali_conf['sort'] = False ali_conf['fbank_conf']['dither'] = 0.0 ali_conf['batch_conf']['batch_type'] = "static" ali_conf['batch_conf']['batch_size'] = args.batch_size non_lang_syms = read_non_lang_symbols(args.non_lang_syms) ali_dataset = Dataset(args.data_type, args.input_file, symbol_table, ali_conf, args.bpe_model, non_lang_syms, partition=False) ali_data_loader = DataLoader(ali_dataset, batch_size=None, num_workers=0) # Init asr model from configs model = init_model(configs) load_checkpoint(model, args.checkpoint) use_cuda = args.gpu >= 0 and torch.cuda.is_available() device = torch.device('cuda' if use_cuda else 'cpu') model = model.to(device) model.eval() with torch.no_grad(), open(args.result_file, 'w', encoding='utf-8') as fout: for batch_idx, batch in enumerate(ali_data_loader): print("#" * 80) key, feat, target, feats_length, target_length = batch print(key) feat = feat.to(device) target = target.to(device) feats_length = feats_length.to(device) target_length = target_length.to(device) # Let's assume B = batch_size and N = beam_size # 1. Encoder encoder_out, encoder_mask = model._forward_encoder( feat, feats_length) # (B, maxlen, encoder_dim) maxlen = encoder_out.size(1) ctc_probs = model.ctc.log_softmax( encoder_out) # (1, maxlen, vocab_size) # print(ctc_probs.size(1)) ctc_probs = ctc_probs.squeeze(0) target = target.squeeze(0) alignment = forced_align(ctc_probs, target) print(alignment) fout.write('{} {}\n'.format(key[0], alignment)) if args.gen_praat: timestamp = get_frames_timestamp(alignment) print(timestamp) subsample = get_subsample(configs) labformat = get_labformat(timestamp, subsample) lab_path = os.path.join(os.path.dirname(args.result_file), key[0] + ".lab") with open(lab_path, 'w', encoding='utf-8') as f: f.writelines(labformat) textgrid_path = os.path.join(os.path.dirname(args.result_file), key[0] + ".TextGrid") generator_textgrid(maxtime=(len(alignment) + 1) * 0.01 * subsample, lines=labformat, output=textgrid_path)