# Copyright (c) 2021 Mobvoi Inc. (authors: Di Wu) # # 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 AudioDataset, CollateFunc from wenet.transformer.asr_model import init_asr_model from wenet.utils.checkpoint import load_checkpoint from wenet.utils.ctc_util import forced_align from wenet.utils.common import get_subsample 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('--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('--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') 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 raw_wav = configs['raw_wav'] # Init dataset and data loader ali_collate_conf = copy.deepcopy(configs['collate_conf']) ali_collate_conf['spec_aug'] = False ali_collate_conf['spec_sub'] = False ali_collate_conf['feature_dither'] = False ali_collate_conf['speed_perturb'] = False if raw_wav: ali_collate_conf['wav_distortion_conf']['wav_distortion_rate'] = 0 ali_collate_func = CollateFunc(**ali_collate_conf, raw_wav=raw_wav) dataset_conf = configs.get('dataset_conf', {}) dataset_conf['batch_size'] = args.batch_size dataset_conf['batch_type'] = 'static' dataset_conf['sort'] = False ali_dataset = AudioDataset(args.input_file, **dataset_conf, raw_wav=raw_wav) ali_data_loader = DataLoader(ali_dataset, collate_fn=ali_collate_func, shuffle=False, batch_size=1, num_workers=0) # Init asr model from configs model = init_asr_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)