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aishell example

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游雁 2024-02-19 22:11:49 +08:00
parent 25fbf7110b
commit ce4235b1c8
5 changed files with 108 additions and 98 deletions
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5
examples/aishell/paraformer/conf/train_asr_paraformer_conformer_12e_6d_2048_256.yaml
View File

@ -99,7 +99,10 @@ dataset_conf:
max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
buffer_size: 1024
shuffle: True
num_workers: 0
num_workers: 4
preprocessor_speech: SpeechPreprocessSpeedPerturb
preprocessor_speech_conf:
speed_perturb: [0.9, 1.0, 1.1]
tokenizer: CharTokenizer
tokenizer_conf:

21
examples/aishell/paraformer/run.sh
View File

@ -1,13 +1,8 @@
#!/usr/bin/env bash
workspace=`pwd`
# machines configuration
CUDA_VISIBLE_DEVICES="0,1"
gpu_num=2
gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
njob=1
# general configuration
feats_dir="../DATA" #feature output dictionary
@ -18,7 +13,11 @@ stage=0
stop_stage=5
# feature configuration
nj=64
nj=32
inference_device="cuda" #"cpu"
inference_checkpoint="model.pt"
inference_scp="wav.scp"
# data
raw_data=../raw_data
@ -26,6 +25,7 @@ data_url=www.openslr.org/resources/33
# exp tag
tag="exp1"
workspace=`pwd`
. utils/parse_options.sh || exit 1;
@ -42,11 +42,6 @@ test_sets="dev test"
config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml
model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"
inference_device="cuda" #"cpu"
inference_checkpoint="model.pt"
inference_scp="wav.scp"
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "stage -1: Data Download"
@ -112,6 +107,8 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
mkdir -p ${exp_dir}/exp/${model_dir}
log_file="${exp_dir}/exp/${model_dir}/train.log.txt"
echo "log_file: ${log_file}"
gpu_num=$(echo CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun \
--nnodes 1 \
--nproc_per_node ${gpu_num} \

38
funasr/datasets/audio_datasets/preprocessor.py
View File

@ -41,43 +41,9 @@ class TextPreprocessSegDict(nn.Module):
**kwargs):
super().__init__()
self.seg_dict = None
if seg_dict is not None:
self.seg_dict = {}
with open(seg_dict, "r", encoding="utf8") as f:
lines = f.readlines()
for line in lines:
s = line.strip().split()
key = s[0]
value = s[1:]
self.seg_dict[key] = " ".join(value)
self.text_cleaner = TextCleaner(text_cleaner)
self.split_with_space = split_with_space
def forward(self, text, **kwargs):
if self.seg_dict is not None:
text = self.text_cleaner(text)
if self.split_with_space:
tokens = text.strip().split(" ")
if self.seg_dict is not None:
text = seg_tokenize(tokens, self.seg_dict)
text = self.text_cleaner(text)
return text
def seg_tokenize(txt, seg_dict):
pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
out_txt = ""
for word in txt:
word = word.lower()
if word in seg_dict:
out_txt += seg_dict[word] + " "
else:
if pattern.match(word):
for char in word:
if char in seg_dict:
out_txt += seg_dict[char] + " "
else:
out_txt += "<unk>" + " "
else:
out_txt += "<unk>" + " "
return out_txt.strip().split()

1
funasr/frontends/wav_frontend.py
View File

@ -32,7 +32,6 @@ def load_cmvn(cmvn_file):
rescale_line = line_item[3:(len(line_item) - 1)]
vars_list = list(rescale_line)
continue
import pdb;pdb.set_trace()
means = np.array(means_list).astype(np.float32)
vars = np.array(vars_list).astype(np.float32)
cmvn = np.array([means, vars])

141
funasr/tokenizer/char_tokenizer.py
View File

@ -3,60 +3,105 @@ from typing import Iterable
from typing import List
from typing import Union
import warnings
import re
from funasr.tokenizer.abs_tokenizer import BaseTokenizer
from funasr.register import tables
@tables.register("tokenizer_classes", "CharTokenizer")
class CharTokenizer(BaseTokenizer):
def __init__(
self,
non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
space_symbol: str = "<space>",
remove_non_linguistic_symbols: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.space_symbol = space_symbol
if non_linguistic_symbols is None:
self.non_linguistic_symbols = set()
elif isinstance(non_linguistic_symbols, (Path, str)):
non_linguistic_symbols = Path(non_linguistic_symbols)
try:
with non_linguistic_symbols.open("r", encoding="utf-8") as f:
self.non_linguistic_symbols = set(line.rstrip() for line in f)
except FileNotFoundError:
warnings.warn(f"{non_linguistic_symbols} doesn't exist.")
self.non_linguistic_symbols = set()
else:
self.non_linguistic_symbols = set(non_linguistic_symbols)
self.remove_non_linguistic_symbols = remove_non_linguistic_symbols
def __init__(
self,
non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
space_symbol: str = "<space>",
remove_non_linguistic_symbols: bool = False,
split_with_space: bool = False,
seg_dict: str = None,
**kwargs,
):
super().__init__(**kwargs)
self.space_symbol = space_symbol
if non_linguistic_symbols is None:
self.non_linguistic_symbols = set()
elif isinstance(non_linguistic_symbols, (Path, str)):
non_linguistic_symbols = Path(non_linguistic_symbols)
try:
with non_linguistic_symbols.open("r", encoding="utf-8") as f:
self.non_linguistic_symbols = set(line.rstrip() for line in f)
except FileNotFoundError:
warnings.warn(f"{non_linguistic_symbols} doesn't exist.")
self.non_linguistic_symbols = set()
else:
self.non_linguistic_symbols = set(non_linguistic_symbols)
self.remove_non_linguistic_symbols = remove_non_linguistic_symbols
self.split_with_space = split_with_space
self.seg_dict = None
if seg_dict is not None:
self.seg_dict = load_seg_dict(seg_dict)
def __repr__(self):
return (
f"{self.__class__.__name__}("
f'space_symbol="{self.space_symbol}"'
f'non_linguistic_symbols="{self.non_linguistic_symbols}"'
f")"
)
def text2tokens(self, line: Union[str, list]) -> List[str]:
if self.split_with_space:
tokens = line.strip().split(" ")
if self.seg_dict is not None:
tokens = seg_tokenize(tokens, self.seg_dict)
else:
tokens = []
while len(line) != 0:
for w in self.non_linguistic_symbols:
if line.startswith(w):
if not self.remove_non_linguistic_symbols:
tokens.append(line[: len(w)])
line = line[len(w) :]
break
else:
t = line[0]
if t == " ":
t = "<space>"
tokens.append(t)
line = line[1:]
return tokens
def tokens2text(self, tokens: Iterable[str]) -> str:
tokens = [t if t != self.space_symbol else " " for t in tokens]
return "".join(tokens)
def __repr__(self):
return (
f"{self.__class__.__name__}("
f'space_symbol="{self.space_symbol}"'
f'non_linguistic_symbols="{self.non_linguistic_symbols}"'
f")"
)
def text2tokens(self, line: Union[str, list]) -> List[str]:
tokens = []
while len(line) != 0:
for w in self.non_linguistic_symbols:
if line.startswith(w):
if not self.remove_non_linguistic_symbols:
tokens.append(line[: len(w)])
line = line[len(w) :]
break
else:
t = line[0]
if t == " ":
t = "<space>"
tokens.append(t)
line = line[1:]
return tokens
def load_seg_dict(seg_dict_file):
seg_dict = {}
assert isinstance(seg_dict_file, str)
with open(seg_dict_file, "r", encoding="utf8") as f:
lines = f.readlines()
for line in lines:
s = line.strip().split()
key = s[0]
value = s[1:]
seg_dict[key] = " ".join(value)
return seg_dict
def tokens2text(self, tokens: Iterable[str]) -> str:
tokens = [t if t != self.space_symbol else " " for t in tokens]
return "".join(tokens)
def seg_tokenize(txt, seg_dict):
pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
out_txt = ""
for word in txt:
word = word.lower()
if word in seg_dict:
out_txt += seg_dict[word] + " "
else:
if pattern.match(word):
for char in word:
if char in seg_dict:
out_txt += seg_dict[char] + " "
else:
out_txt += "<unk>" + " "
else:
out_txt += "<unk>" + " "
return out_txt.strip().split()