restructure frontend example

examples/fastspeech2/baker/frontend.py

@@ -61,14 +61,18 @@ class Frontend():
                     phone = match.group(1)
                     tone = match.group(2)
                     # if the merged erhua not in the vocab
+                    # assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, we split 'iaor' into ['iao','er']
+                    # and the tones accordingly change from ['3'] to ['3','2'], while '2' is the tone of 'er2'
                     if len(phone) >= 2 and phone != "er" and phone[
                             -1] == 'r' and phone not in self.vocab_phones and phone[:
                                                                                     -1] in self.vocab_phones:
                         phones.append(phone[:-1])
                         phones.append("er")
-                    else:
                         tones.append(tone)
                         tones.append("2")
+                    else:
+                        phones.append(phone)
+                        tones.append(tone)
                 else:
                     phones.append(full_phone)
                     tones.append('0')
@@ -76,9 +80,11 @@ class Frontend():
             tone_ids = paddle.to_tensor(tone_ids)
             result["tone_ids"] = tone_ids
         else:
-            # if the merged erhua not in the vocab
+
             phones = []
             for phone in phonemes:
+                # if the merged erhua not in the vocab
+                # assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, change ['iaor3'] to ['iao3','er2']
                 if len(phone) >= 3 and phone[:-1] != "er" and phone[
                         -2] == 'r' and phone not in self.vocab_phones and (
                             phone[:-2] + phone[-1]) in self.vocab_phones:

examples/text_frontend/README.md

@@ -15,6 +15,6 @@ Run the command below to get the results of test.
 ```bash
 ./run.sh
 ```
-The `avg WER` of g2p is: 0.02785753389811866
+The `avg WER` of g2p is: 0.027124048652822204
 
-The `avg CER` of text normalization is: 0.014229233983486172
+The `avg CER` of text normalization is: 0.0061629764893859846

examples/text_frontend/data/textnorm_test_cases.txt

@@ -121,3 +121,5 @@ iPad Pro的秒控键盘这次也推出白色版本。|iPad Pro的秒控键盘这
 就连一向看多的任志强|就连一向看多的任志强
 近期也一反常态地发表看空言论|近期也一反常态地发表看空言论
 985|九八五
+12~23|十二到二十三
+12-23|十二到二十三

examples/text_frontend/get_g2p_data.py

@@ -0,0 +1,90 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import argparse
+from collections import defaultdict
+from pathlib import Path
+
+from praatio import tgio
+
+
+def get_baker_data(root_dir):
+    alignment_files = sorted(
+        list((root_dir / "PhoneLabeling").rglob("*.interval")))
+    text_file = root_dir / "ProsodyLabeling/000001-010000.txt"
+    text_file = Path(text_file).expanduser()
+    # filter out several files that have errors in annotation
+    exclude = {'000611', '000662', '002365', '005107'}
+    alignment_files = [f for f in alignment_files if f.stem not in exclude]
+    data_dict = defaultdict(dict)
+    for alignment_fp in alignment_files:
+        alignment = tgio.openTextgrid(alignment_fp)
+        # only with baker's annotation
+        utt_id = alignment.tierNameList[0].split(".")[0]
+        intervals = alignment.tierDict[alignment.tierNameList[0]].entryList
+        phones = []
+        for interval in intervals:
+            label = interval.label
+            phones.append(label)
+        data_dict[utt_id]["phones"] = phones
+    for line in open(text_file, "r"):
+        if line.startswith("0"):
+            utt_id, raw_text = line.strip().split()
+            if utt_id in data_dict:
+                data_dict[utt_id]['text'] = raw_text
+        else:
+            pinyin = line.strip().split()
+            if utt_id in data_dict:
+                data_dict[utt_id]['pinyin'] = pinyin
+    return data_dict
+
+
+def get_g2p_phones(data_dict, frontend):
+    for utt_id in data_dict:
+        g2p_phones = frontend.get_phonemes(data_dict[utt_id]['text'])
+        data_dict[utt_id]["g2p_phones"] = g2p_phones
+    return data_dict
+
+
+def main():
+    parser = argparse.ArgumentParser(description="g2p example.")
+    parser.add_argument(
+        "--root-dir",
+        default=None,
+        type=str,
+        help="directory to baker dataset.")
+    parser.add_argument(
+        "--output-dir",
+        default="data/g2p",
+        type=str,
+        help="directory to output.")
+
+    args = parser.parse_args()
+    root_dir = Path(args.root_dir).expanduser()
+    output_dir = Path(args.output_dir).expanduser()
+    output_dir.mkdir(parents=True, exist_ok=True)
+    assert root_dir.is_dir()
+    data_dict = get_baker_data(root_dir)
+    raw_path = output_dir / "text"
+    ref_path = output_dir / "text.ref"
+    wf_raw = open(raw_path, "w")
+    wf_ref = open(ref_path, "w")
+    for utt_id in data_dict:
+        wf_raw.write(utt_id + " " + data_dict[utt_id]['text'] + "\n")
+        wf_ref.write(utt_id + " " + " ".join(data_dict[utt_id]['phones']) +
+                     "\n")
+
+
+if __name__ == "__main__":
+    main()

examples/text_frontend/get_textnorm_data.py

@@ -0,0 +1,51 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import argparse
+import re
+from pathlib import Path
+
+
+def main():
+    parser = argparse.ArgumentParser(description="text normalization example.")
+    parser.add_argument(
+        "--test-file",
+        default="data/textnorm_test_cases.txt",
+        type=str,
+        help="path of text normalization test file.")
+    parser.add_argument(
+        "--output-dir",
+        default="data/textnorm",
+        type=str,
+        help="directory to output.")
+
+    args = parser.parse_args()
+    test_file = Path(args.test_file).expanduser()
+    output_dir = Path(args.output_dir).expanduser()
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    raw_path = output_dir / "text"
+    ref_path = output_dir / "text.ref"
+    wf_raw = open(raw_path, "w")
+    wf_ref = open(ref_path, "w")
+
+    with open(test_file, "r") as rf:
+        for i, line in enumerate(rf):
+            raw_text, normed_text = line.strip().split("|")
+            wf_raw.write("utt_" + str(i) + " " + raw_text + "\n")
+            wf_ref.write("utt_" + str(i) + " " + normed_text + "\n")
+
+
+if __name__ == "__main__":
+    main()

examples/text_frontend/run.sh

@@ -1,8 +1,14 @@
 #!/bin/bash
 
 # test g2p
+echo "Start get g2p test data."
+python3 get_g2p_data.py --root-dir=~/datasets/BZNSYP --output-dir=data/g2p
 echo "Start test g2p."
-python3 test_g2p.py --root-dir=~/datasets/BZNSYP
+python3 test_g2p.py --input-dir=data/g2p --output-dir=exp/g2p
+
 # test text normalization
+echo "Start get text normalization test data."
+python3 get_textnorm_data.py --test-file=data/textnorm_test_cases.txt --output-dir=data/textnorm
 echo "Start test text normalization."
-python3 test_textnorm.py --test-file=data/textnorm_test_cases.txt
+python3 test_textnorm.py --input-dir=data/textnorm --output-dir=exp/textnorm
+

examples/text_frontend/test_g2p.py

@@ -14,12 +14,12 @@
 
 import argparse
 import re
-from collections import defaultdict
 from pathlib import Path
 
 from parakeet.frontend.cn_frontend import Frontend as cnFrontend
-from parakeet.utils.error_rate import wer
+from parakeet.utils.error_rate import word_errors
-from praatio import tgio
+
+SILENCE_TOKENS = {"sp", "sil", "sp1", "spl"}
 
 
 def text_cleaner(raw_text):
@@ -29,80 +29,68 @@ def text_cleaner(raw_text):
     return text
 
 
-def get_baker_data(root_dir):
+def get_avg_wer(raw_dict, ref_dict, frontend, output_dir):
-    alignment_files = sorted(
+    edit_distances = []
-        list((root_dir / "PhoneLabeling").rglob("*.interval")))
+    ref_lens = []
-    text_file = root_dir / "ProsodyLabeling/000001-010000.txt"
+    wf_g2p = open(output_dir / "text.g2p", "w")
-    text_file = Path(text_file).expanduser()
+    wf_ref = open(output_dir / "text.ref.clean", "w")
-    data_dict = defaultdict(dict)
+    for utt_id in raw_dict:
-    # filter out several files that have errors in annotation
+        if utt_id not in ref_dict:
-    exclude = {'000611', '000662', '002365', '005107'}
+            continue
-    alignment_files = [f for f in alignment_files if f.stem not in exclude]
+        raw_text = raw_dict[utt_id]
-    # biaobei 前后有 sil ，中间没有 sp
+        text = text_cleaner(raw_text)
-    data_dict = defaultdict(dict)
+        g2p_phones = frontend.get_phonemes(text)
-    for alignment_fp in alignment_files:
+        gt_phones = ref_dict[utt_id].split(" ")
-        alignment = tgio.openTextgrid(alignment_fp)
+        # delete silence tokens in predicted phones and ground truth phones
-        # only with baker's annotation
+        g2p_phones = [phn for phn in g2p_phones if phn not in SILENCE_TOKENS]
-        utt_id = alignment.tierNameList[0].split(".")[0]
+        gt_phones = [phn for phn in gt_phones if phn not in SILENCE_TOKENS]
-        intervals = alignment.tierDict[alignment.tierNameList[0]].entryList
-        phones = []
-        for interval in intervals:
-            label = interval.label
-            # Baker has sp1 rather than sp
-            label = label.replace("sp1", "sp")
-            phones.append(label)
-        data_dict[utt_id]["phones"] = phones
-    for line in open(text_file, "r"):
-        if line.startswith("0"):
-            utt_id, raw_text = line.strip().split()
-            text = text_cleaner(raw_text)
-            if utt_id in data_dict:
-                data_dict[utt_id]['text'] = text
-        else:
-            pinyin = line.strip().split()
-            if utt_id in data_dict:
-                data_dict[utt_id]['pinyin'] = pinyin
-    return data_dict
-
-
-def get_g2p_phones(data_dict, frontend):
-    for utt_id in data_dict:
-        g2p_phones = frontend.get_phonemes(data_dict[utt_id]['text'])
-        data_dict[utt_id]["g2p_phones"] = g2p_phones
-    return data_dict
-
-
-def get_avg_wer(data_dict):
-    wer_list = []
-    for utt_id in data_dict:
-        g2p_phones = data_dict[utt_id]['g2p_phones']
-        # delete silence tokens in predicted phones
-        g2p_phones = [phn for phn in g2p_phones if phn not in {"sp", "sil"}]
-        gt_phones = data_dict[utt_id]['phones']
-        # delete silence tokens in baker phones
-        gt_phones = [phn for phn in gt_phones if phn not in {"sp", "sil"}]
         gt_phones = " ".join(gt_phones)
         g2p_phones = " ".join(g2p_phones)
-        single_wer = wer(gt_phones, g2p_phones)
+        wf_ref.write(utt_id + " " + gt_phones + "\n")
-        wer_list.append(single_wer)
+        wf_g2p.write(utt_id + " " + g2p_phones + "\n")
-    return sum(wer_list) / len(wer_list)
+        edit_distance, ref_len = word_errors(gt_phones, g2p_phones)
+        edit_distances.append(edit_distance)
+        ref_lens.append(ref_len)
+
+    return sum(edit_distances) / sum(ref_lens)
 
 
 def main():
     parser = argparse.ArgumentParser(description="g2p example.")
     parser.add_argument(
-        "--root-dir",
+        "--input-dir",
-        default=None,
+        default="data/g2p",
         type=str,
-        help="directory to baker dataset.")
+        help="directory to preprocessed test data.")
+    parser.add_argument(
+        "--output-dir",
+        default="exp/g2p",
+        type=str,
+        help="directory to save g2p results.")
 
     args = parser.parse_args()
-    root_dir = Path(args.root_dir).expanduser()
+    input_dir = Path(args.input_dir).expanduser()
-    assert root_dir.is_dir()
+    output_dir = Path(args.output_dir).expanduser()
+    output_dir.mkdir(parents=True, exist_ok=True)
+    assert input_dir.is_dir()
+    raw_dict, ref_dict = dict(), dict()
+    raw_path = input_dir / "text"
+    ref_path = input_dir / "text.ref"
+
+    with open(raw_path, "r") as rf:
+        for line in rf:
+            line = line.strip()
+            line_list = line.split(" ")
+            utt_id, raw_text = line_list[0], " ".join(line_list[1:])
+            raw_dict[utt_id] = raw_text
+    with open(ref_path, "r") as rf:
+        for line in rf:
+            line = line.strip()
+            line_list = line.split(" ")
+            utt_id, phones = line_list[0], " ".join(line_list[1:])
+            ref_dict[utt_id] = phones
     frontend = cnFrontend()
-    data_dict = get_baker_data(root_dir)
+    avg_wer = get_avg_wer(raw_dict, ref_dict, frontend, output_dir)
-    data_dict = get_g2p_phones(data_dict, frontend)
-    avg_wer = get_avg_wer(data_dict)
     print("The avg WER of g2p is:", avg_wer)
 
 

examples/text_frontend/test_textnorm.py

@@ -17,7 +17,7 @@ import re
 from pathlib import Path
 
 from parakeet.frontend.cn_normalization.text_normlization import TextNormalizer
-from parakeet.utils.error_rate import cer
+from parakeet.utils.error_rate import char_errors
 
 
 # delete english characters
@@ -29,31 +29,67 @@ def del_en_add_space(input: str):
     return output
 
 
-def get_avg_cer(test_file, text_normalizer):
+def get_avg_cer(raw_dict, ref_dict, text_normalizer, output_dir):
-    cer_list = []
+    edit_distances = []
-    for line in open(test_file, "r"):
+    ref_lens = []
-        line = line.strip()
+    wf_ref = open(output_dir / "text.ref.clean", "w")
-        raw_text, gt_text = line.split("|")
+    wf_tn = open(output_dir / "text.tn", "w")
+    for text_id in raw_dict:
+        if text_id not in ref_dict:
+            continue
+        raw_text = raw_dict[text_id]
+        gt_text = ref_dict[text_id]
         textnorm_text = text_normalizer.normalize_sentence(raw_text)
+
         gt_text = del_en_add_space(gt_text)
         textnorm_text = del_en_add_space(textnorm_text)
-        single_cer = cer(gt_text, textnorm_text)
+        wf_ref.write(text_id + " " + gt_text + "\n")
-        cer_list.append(single_cer)
+        wf_tn.write(text_id + " " + textnorm_text + "\n")
-    return sum(cer_list) / len(cer_list)
+        edit_distance, ref_len = char_errors(gt_text, textnorm_text)
+        edit_distances.append(edit_distance)
+        ref_lens.append(ref_len)
+
+    return sum(edit_distances) / sum(ref_lens)
 
 
 def main():
     parser = argparse.ArgumentParser(description="text normalization example.")
     parser.add_argument(
-        "--test-file",
+        "--input-dir",
-        default=None,
+        default="data/textnorm",
         type=str,
-        help="path of text normalization test file.")
+        help="directory to preprocessed test data.")
+    parser.add_argument(
+        "--output-dir",
+        default="exp/textnorm",
+        type=str,
+        help="directory to save textnorm results.")
 
     args = parser.parse_args()
-    test_file = Path(args.test_file).expanduser()
+    input_dir = Path(args.input_dir).expanduser()
+    output_dir = Path(args.output_dir).expanduser()
+    output_dir.mkdir(parents=True, exist_ok=True)
+    assert input_dir.is_dir()
+    raw_dict, ref_dict = dict(), dict()
+    raw_path = input_dir / "text"
+    ref_path = input_dir / "text.ref"
+
+    with open(raw_path, "r") as rf:
+        for line in rf:
+            line = line.strip()
+            line_list = line.split(" ")
+            text_id, raw_text = line_list[0], " ".join(line_list[1:])
+            raw_dict[text_id] = raw_text
+    with open(ref_path, "r") as rf:
+        for line in rf:
+            line = line.strip()
+            line_list = line.split(" ")
+            text_id, normed_text = line_list[0], " ".join(line_list[1:])
+            ref_dict[text_id] = normed_text
+
     text_normalizer = TextNormalizer()
-    avg_cer = get_avg_cer(test_file, text_normalizer)
+
+    avg_cer = get_avg_cer(raw_dict, ref_dict, text_normalizer, output_dir)
     print("The avg CER of text normalization is:", avg_cer)
 
 

parakeet/frontend/cn_normalization/num.py

@@ -60,7 +60,7 @@ def replace_percentage(match: re.Match) -> str:
 
 
 # 整数表达式
-# 带负号或者不带负号的整数 12, -10
+# 带负号的整数 -10
 RE_INTEGER = re.compile(r'(-)' r'(\d+)')
 
 
@@ -116,7 +116,7 @@ def replace_number(match: re.Match) -> str:
 
 # 范围表达式
 # 12-23, 12~23
-RE_RANGE = re.compile(r'(\d+)[~](\d+)')
+RE_RANGE = re.compile(r'(\d+)[-~](\d+)')
 
 
 def replace_range(match: re.Match) -> str:

parakeet/frontend/cn_normalization/text_normlization.py

@@ -55,11 +55,11 @@ class TextNormalizer():
         sentence = RE_DATE2.sub(replace_date2, sentence)
         sentence = RE_TIME.sub(replace_time, sentence)
         sentence = RE_TEMPERATURE.sub(replace_temperature, sentence)
-        sentence = RE_RANGE.sub(replace_range, sentence)
         sentence = RE_FRAC.sub(replace_frac, sentence)
         sentence = RE_PERCENTAGE.sub(replace_percentage, sentence)
         sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence)
         sentence = RE_TELEPHONE.sub(replace_phone, sentence)
+        sentence = RE_RANGE.sub(replace_range, sentence)
         sentence = RE_INTEGER.sub(replace_negative_num, sentence)
         sentence = RE_DECIMAL_NUM.sub(replace_number, sentence)
         sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier,