aishell example

examples/aishell/paraformer/conf/train_asr_paraformer_conformer_12e_6d_2048_256.yaml

@@ -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:

examples/aishell/paraformer/run.sh

@@ -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} \

funasr/datasets/audio_datasets/preprocessor.py

@@ -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()

funasr/frontends/wav_frontend.py

@@ -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])

funasr/tokenizer/char_tokenizer.py

@@ -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()