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# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
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,
)
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