Merge branch 'develop' of https://github.com/PaddlePaddle/Parakeet into fix_pwg
This commit is contained in:
TianYuan
commit
30f344a6d0
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@ -1,5 +1,3 @@
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# FastSpeech2 with BZNSYP
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## Dataset
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@ -12,10 +12,14 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pathlib import Path
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from yacs.config import CfgNode as Configuration
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import yaml
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with open("conf/default.yaml", 'rt') as f:
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config_path = (Path(__file__).parent / "conf" / "default.yaml").resolve()
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with open(config_path, 'rt') as f:
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_C = yaml.safe_load(f)
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_C = Configuration(_C)
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@ -48,9 +48,7 @@ class Frontend():
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tone_ids = [self.vocab_tones[item] for item in tones]
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return np.array(tone_ids, np.int64)
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def get_input_ids(self, sentence, get_tone_ids=False):
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phonemes = self.frontend.get_phonemes(sentence)
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result = {}
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def _get_phone_tone(self, phonemes, get_tone_ids=False):
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phones = []
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tones = []
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if get_tone_ids and self.vocab_tones:
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@ -58,17 +56,59 @@ class Frontend():
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# split tone from finals
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match = re.match(r'^(\w+)([012345])$', full_phone)
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if match:
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phones.append(match.group(1))
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tones.append(match.group(2))
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phone = match.group(1)
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tone = match.group(2)
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# if the merged erhua not in the vocab
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# assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, we split 'iaor' into ['iao','er']
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# and the tones accordingly change from ['3'] to ['3','2'], while '2' is the tone of 'er2'
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if len(phone) >= 2 and phone != "er" and phone[
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-1] == 'r' and phone not in self.vocab_phones and phone[:
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-1] in self.vocab_phones:
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phones.append(phone[:-1])
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phones.append("er")
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tones.append(tone)
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tones.append("2")
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else:
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phones.append(phone)
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tones.append(tone)
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else:
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phones.append(full_phone)
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tones.append('0')
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tone_ids = self._t2id(tones)
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tone_ids = paddle.to_tensor(tone_ids)
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result["tone_ids"] = tone_ids
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else:
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phones = phonemes
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phone_ids = self._p2id(phones)
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phone_ids = paddle.to_tensor(phone_ids)
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result["phone_ids"] = phone_ids
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for phone in phonemes:
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# if the merged erhua not in the vocab
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# assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, change ['iaor3'] to ['iao3','er2']
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if len(phone) >= 3 and phone[:-1] != "er" and phone[
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-2] == 'r' and phone not in self.vocab_phones and (
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phone[:-2] + phone[-1]) in self.vocab_phones:
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phones.append((phone[:-2] + phone[-1]))
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phones.append("er2")
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else:
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phones.append(phone)
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return phones, tones
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def get_input_ids(self, sentence, merge_sentences=True,
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get_tone_ids=False):
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phonemes = self.frontend.get_phonemes(
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sentence, merge_sentences=merge_sentences)
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result = {}
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phones = []
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tones = []
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temp_phone_ids = []
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temp_tone_ids = []
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for part_phonemes in phonemes:
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phones, tones = self._get_phone_tone(
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part_phonemes, get_tone_ids=get_tone_ids)
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if tones:
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tone_ids = self._t2id(tones)
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tone_ids = paddle.to_tensor(tone_ids)
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temp_tone_ids.append(tone_ids)
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if phones:
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phone_ids = self._p2id(phones)
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phone_ids = paddle.to_tensor(phone_ids)
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temp_phone_ids.append(phone_ids)
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if temp_tone_ids:
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result["tone_ids"] = temp_tone_ids
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if temp_phone_ids:
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result["phone_ids"] = temp_phone_ids
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return result
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@ -32,12 +32,12 @@ def get_phn_dur(file_name):
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read MFA duration.txt
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Parameters
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----------
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file_name : str or Path
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path of gen_duration_from_textgrid.py's result
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file_name : str or Path
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path of gen_duration_from_textgrid.py's result
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Returns
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----------
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Dict
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sentence: {'utt': ([char], [int])}
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Dict
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sentence: {'utt': ([char], [int])}
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'''
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f = open(file_name, 'r')
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sentence = {}
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@ -58,8 +58,8 @@ def deal_silence(sentence):
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merge silences, set <eos>
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Parameters
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----------
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sentence : Dict
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sentence: {'utt': ([char], [int])}
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sentence : Dict
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sentence: {'utt': ([char], [int])}
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'''
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for utt in sentence:
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cur_phn, cur_dur = sentence[utt]
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@ -91,10 +91,10 @@ def get_input_token(sentence, output_path):
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get phone set from training data and save it
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Parameters
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----------
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sentence : Dict
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sentence: {'utt': ([char], [int])}
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output_path : str or path
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path to save phone_id_map
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sentence : Dict
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sentence: {'utt': ([char], [int])}
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output_path : str or path
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path to save phone_id_map
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'''
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phn_token = set()
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for utt in sentence:
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@ -117,12 +117,12 @@ def compare_duration_and_mel_length(sentences, utt, mel):
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check duration error, correct sentences[utt] if possible, else pop sentences[utt]
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Parameters
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----------
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sentences : Dict
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sentences[utt] = [phones_list ,durations_list]
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utt : str
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utt_id
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mel : np.ndarry
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features (num_frames, n_mels)
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sentences : Dict
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sentences[utt] = [phones_list ,durations_list]
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utt : str
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utt_id
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mel : np.ndarry
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features (num_frames, n_mels)
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'''
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if utt in sentences:
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@ -267,7 +267,7 @@ def main():
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type=str,
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help="directory to baker dataset.")
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parser.add_argument(
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"--dur-path",
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"--dur-file",
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default=None,
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type=str,
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help="path to baker durations.txt.")
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@ -308,8 +308,13 @@ def main():
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root_dir = Path(args.rootdir).expanduser()
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dumpdir = Path(args.dumpdir).expanduser()
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dumpdir.mkdir(parents=True, exist_ok=True)
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dur_file = Path(args.dur_file).expanduser()
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assert root_dir.is_dir()
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assert dur_file.is_file()
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sentences = get_phn_dur(dur_file)
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sentences = get_phn_dur(args.dur_path)
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deal_silence(sentences)
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phone_id_map_path = dumpdir / "phone_id_map.txt"
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get_input_token(sentences, phone_id_map_path)
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@ -4,7 +4,7 @@
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python3 gen_duration_from_textgrid.py --inputdir ./baker_alignment_tone --output durations.txt
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# extract features
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python3 preprocess.py --rootdir=~/datasets/BZNSYP/ --dumpdir=dump --dur-path durations.txt --num-cpu 4 --cut-sil True
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python3 preprocess.py --rootdir=~/datasets/BZNSYP/ --dumpdir=dump --dur-file durations.txt --num-cpu 4 --cut-sil True
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# # get features' stats(mean and std)
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python3 compute_statistics.py --metadata=dump/train/raw/metadata.jsonl --field-name="speech"
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@ -72,19 +72,25 @@ def evaluate(args, fastspeech2_config, pwg_config):
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std = paddle.to_tensor(std)
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pwg_normalizer = ZScore(mu, std)
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fastspeech2_inferencce = FastSpeech2Inference(fastspeech2_normalizer,
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model)
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fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model)
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pwg_inference = PWGInference(pwg_normalizer, vocoder)
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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for utt_id, sentence in sentences:
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input_ids = frontend.get_input_ids(sentence)
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input_ids = frontend.get_input_ids(sentence, merge_sentences=True)
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phone_ids = input_ids["phone_ids"]
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with paddle.no_grad():
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mel = fastspeech2_inferencce(phone_ids)
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wav = pwg_inference(mel)
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flags = 0
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for part_phone_ids in phone_ids:
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with paddle.no_grad():
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mel = fastspeech2_inference(part_phone_ids)
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temp_wav = pwg_inference(mel)
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if flags == 0:
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wav = temp_wav
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flags = 1
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else:
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wav = paddle.concat([wav, temp_wav])
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sf.write(
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str(output_dir / (utt_id + ".wav")),
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wav.numpy(),
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@ -0,0 +1,118 @@
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# Parallel WaveGAN with the Baker dataset
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This example contains code used to train a [parallel wavegan](http://arxiv.org/abs/1910.11480) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html).
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## Preprocess the dataset
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Download the dataset from the [official website of data-baker](https://www.data-baker.com/data/index/source) and extract it to `~/datasets`. Then the dataset is in directory `~/datasets/BZNSYP`.
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Run the script for preprocessing.
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```bash
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bash preprocess.sh
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── norm
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├── raw
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└── stats.npy
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```
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The dataset is split into 3 parts, namely train, dev and test, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains log magnitude of mel spectrogram of each utterances, while the norm folder contains normalized spectrogram. The statistics used to normalize the spectrogram is computed from the training set, which is located in `dump/train/stats.npy`.
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Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains id and paths to spectrogam of each utterance.
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## Train the model
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To train the model use the `run.sh`. It is an example script to run `train.py`.
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```bash
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bash run.sh
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```
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Or you can use the `train.py` directly. Here's the complete help message to run it.
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|
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```text
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usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
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[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
|
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[--device DEVICE] [--nprocs NPROCS] [--verbose VERBOSE]
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|
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Train a Parallel WaveGAN model with Baker Mandrin TTS dataset.
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|
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optional arguments:
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-h, --help show this help message and exit
|
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--config CONFIG config file to overwrite default config
|
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--train-metadata TRAIN_METADATA
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training data
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--dev-metadata DEV_METADATA
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dev data
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--output-dir OUTPUT_DIR
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output dir
|
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--device DEVICE device type to use
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--nprocs NPROCS number of processes
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--verbose VERBOSE verbose
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```
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|
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1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
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2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
|
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3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
|
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4. `--device` is the type of the device to run the experiment, 'cpu' or 'gpu' are supported.
|
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5. `--nprocs` is the number of processes to run in parallel, note that nprocs > 1 is only supported when `--device` is 'gpu'.
|
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|
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## Pretrained Models
|
||||
|
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Pretrained models can be downloaded here:
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1. Parallel WaveGAN checkpoint. [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip), which is used as a vocoder in the end-to-end inference script.
|
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|
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Parallel WaveGAN checkpoint contains files listed below.
|
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|
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```text
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pwg_baker_ckpt_0.4
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├── pwg_default.yaml # default config used to train parallel wavegan
|
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├── pwg_snapshot_iter_400000.pdz # generator parameters of parallel wavegan
|
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└── pwg_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
|
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```
|
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|
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## Synthesize
|
||||
|
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When training is done or pretrained models are downloaded. You can run `synthesize.py` to synthsize.
|
||||
|
||||
```text
|
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usage: synthesize.py [-h] [--config CONFIG] [--checkpoint CHECKPOINT]
|
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[--test-metadata TEST_METADATA] [--output-dir OUTPUT_DIR]
|
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[--device DEVICE] [--verbose VERBOSE]
|
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|
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synthesize with parallel wavegan.
|
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|
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optional arguments:
|
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-h, --help show this help message and exit
|
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--config CONFIG config file to overwrite default config
|
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--checkpoint CHECKPOINT
|
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snapshot to load
|
||||
--test-metadata TEST_METADATA
|
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dev data
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir
|
||||
--device DEVICE device to run
|
||||
--verbose VERBOSE verbose
|
||||
```
|
||||
|
||||
1. `--config` is the extra configuration file to overwrite the default config. You should use the same config with which the model is trained.
|
||||
2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `/checkpoints` inside the training output directory. If you use the pretrained model, use the `pwg_snapshot_iter_400000.pdz`.
|
||||
3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
|
||||
4. `--output-dir` is the directory to save the synthesized audio files.
|
||||
5. `--device` is the type of device to run synthesis, 'cpu' and 'gpu' are supported.
|
||||
|
||||
## Acknowledgement
|
||||
|
||||
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
|
||||
|
|
@ -0,0 +1,141 @@
|
|||
# Speedyspeech with the Baker dataset
|
||||
|
||||
This example contains code used to train a [Speedyspeech](http://arxiv.org/abs/2008.03802) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html). NOTE that we only implement the student part of the Speedyspeech model. The ground truth alignment used to train the model is extracted from the dataset.
|
||||
|
||||
## Preprocess the dataset
|
||||
|
||||
Download the dataset from the [official website of data-baker](https://www.data-baker.com/data/index/source) and extract it to `~/datasets`. Then the dataset is in directory `~/datasets/BZNSYP`.
|
||||
|
||||
Run the script for preprocessing.
|
||||
|
||||
```bash
|
||||
bash preprocess.sh
|
||||
```
|
||||
|
||||
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
|
||||
|
||||
```text
|
||||
dump
|
||||
├── dev
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
├── test
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
└── train
|
||||
├── norm
|
||||
├── raw
|
||||
└── stats.npy
|
||||
```
|
||||
|
||||
The dataset is split into 3 parts, namely train, dev and test, each of which contains a `norm` and `raw` sub folder. The raw folder contains log magnitude of mel spectrogram of each utterances, while the norm folder contains normalized spectrogram. The statistics used to normalize the spectrogram is computed from the training set, which is located in `dump/train/stats.npy`.
|
||||
|
||||
Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, tones, durations, path of spectrogram, and id of each utterance.
|
||||
|
||||
## Train the model
|
||||
|
||||
To train the model use the `run.sh`. It is an example script to run `train.py`.
|
||||
|
||||
```bash
|
||||
bash run.sh
|
||||
```
|
||||
|
||||
Or you can use `train.py` directly. Here's the complete help message.
|
||||
|
||||
```text
|
||||
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
|
||||
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
|
||||
[--device DEVICE] [--nprocs NPROCS] [--verbose VERBOSE]
|
||||
|
||||
Train a Speedyspeech model with Baker Mandrin TTS dataset.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--config CONFIG config file to overwrite default config
|
||||
--train-metadata TRAIN_METADATA
|
||||
training data
|
||||
--dev-metadata DEV_METADATA
|
||||
dev data
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir
|
||||
--device DEVICE device type to use
|
||||
--nprocs NPROCS number of processes
|
||||
--verbose VERBOSE verbose
|
||||
```
|
||||
|
||||
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
|
||||
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
|
||||
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
|
||||
4. `--device` is the type of the device to run the experiment, 'cpu' or 'gpu' are supported.
|
||||
5. `--nprocs` is the number of processes to run in parallel, note that nprocs > 1 is only supported when `--device` is 'gpu'.
|
||||
|
||||
## Pretrained Models
|
||||
|
||||
Pretrained models can be downloaded here:
|
||||
1. Speedyspeech checkpoint. [speedyspeech_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_baker_ckpt_0.4.zip)
|
||||
2. Parallel WaveGAN checkpoint. [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip), which is used as a vocoder in the end-to-end inference script.
|
||||
|
||||
Speedyspeech checkpoint contains files listed below.
|
||||
|
||||
```text
|
||||
speedyspeech_baker_ckpt_0.4
|
||||
├── speedyspeech_default.yaml # default config used to train speedyseech
|
||||
├── speedy_speech_stats.npy # statistics used to normalize spectrogram when training speedyspeech
|
||||
└── speedyspeech_snapshot_iter_91800.pdz # model parameters and optimizer states
|
||||
```
|
||||
|
||||
Parallel WaveGAN checkpoint contains files listed below.
|
||||
|
||||
```text
|
||||
pwg_baker_ckpt_0.4
|
||||
├── pwg_default.yaml # default config used to train parallel wavegan
|
||||
├── pwg_snapshot_iter_400000.pdz # model parameters and optimizer states of parallel wavegan
|
||||
└── pwg_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
|
||||
```
|
||||
|
||||
## Synthesize End to End
|
||||
|
||||
When training is done or pretrained models are downloaded. You can run `synthesize_e2e.py` to synthsize.
|
||||
|
||||
```text
|
||||
usage: synthesize_e2e.py [-h] [--speedyspeech-config SPEEDYSPEECH_CONFIG]
|
||||
[--speedyspeech-checkpoint SPEEDYSPEECH_CHECKPOINT]
|
||||
[--speedyspeech-stat SPEEDYSPEECH_STAT]
|
||||
[--pwg-config PWG_CONFIG] [--pwg-params PWG_PARAMS]
|
||||
[--pwg-stat PWG_STAT] [--text TEXT]
|
||||
[--output-dir OUTPUT_DIR]
|
||||
[--inference-dir INFERENCE_DIR] [--device DEVICE]
|
||||
[--verbose VERBOSE]
|
||||
|
||||
Synthesize with speedyspeech & parallel wavegan.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--speedyspeech-config SPEEDYSPEECH_CONFIG
|
||||
config file for speedyspeech.
|
||||
--speedyspeech-checkpoint SPEEDYSPEECH_CHECKPOINT
|
||||
speedyspeech checkpoint to load.
|
||||
--speedyspeech-stat SPEEDYSPEECH_STAT
|
||||
mean and standard deviation used to normalize
|
||||
spectrogram when training speedyspeech.
|
||||
--pwg-config PWG_CONFIG
|
||||
config file for parallelwavegan.
|
||||
--pwg-checkpoint PWG_PARAMS
|
||||
parallel wavegan checkpoint to load.
|
||||
--pwg-stat PWG_STAT mean and standard deviation used to normalize
|
||||
spectrogram when training speedyspeech.
|
||||
--text TEXT text to synthesize, a 'utt_id sentence' pair per line
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir
|
||||
--inference-dir INFERENCE_DIR
|
||||
dir to save inference models
|
||||
--device DEVICE device type to use
|
||||
--verbose VERBOSE verbose
|
||||
```
|
||||
|
||||
1. `--speedyspeech-config`, `--speedyspeech-checkpoint`, `--speedyspeech-stat` are arguments for speedyspeech, which correspond to the 3 files in the speedyspeech pretrained model.
|
||||
2. `--pwg-config`, `--pwg-checkpoint`, `--pwg-stat` are arguments for speedyspeech, which correspond to the 3 files in the parallel wavegan pretrained model.
|
||||
3. `--text` is the text file, which contains sentences to synthesize.
|
||||
4. `--output-dir` is the directory to save synthesized audio files.
|
||||
5. `--inference-dir` is the directory to save exported model, which can be used with paddle infernece.
|
||||
6. `--device` is the type of device to run synthesis, 'cpu' and 'gpu' are supported. 'gpu' is recommended for faster synthesis.
|
||||
|
|
@ -89,6 +89,11 @@ def main():
|
|||
|
||||
with jsonlines.open(args.metadata, 'r') as reader:
|
||||
metadata = list(reader)
|
||||
|
||||
metadata_dir = Path(args.metadata).parent
|
||||
for item in metadata:
|
||||
item["feats"] = str(metadata_dir / item["feats"])
|
||||
|
||||
dataset = DataTable(
|
||||
metadata,
|
||||
fields=[args.field_name],
|
||||
|
|
|
|||
|
|
@ -14,6 +14,9 @@
|
|||
|
||||
import yaml
|
||||
from yacs.config import CfgNode as Configuration
|
||||
from pathlib import Path
|
||||
|
||||
config_path = (Path(__file__).parent / "conf" / "default.yaml").resolve()
|
||||
|
||||
with open("conf/default.yaml", 'rt') as f:
|
||||
_C = yaml.safe_load(f)
|
||||
|
|
|
|||
|
|
@ -13,6 +13,8 @@
|
|||
# limitations under the License.
|
||||
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
import pypinyin
|
||||
|
|
@ -22,10 +24,11 @@ import phkit
|
|||
phkit.initialize()
|
||||
from parakeet.frontend.vocab import Vocab
|
||||
|
||||
with open("phones.txt", 'rt') as f:
|
||||
file_dir = Path(__file__).parent.resolve()
|
||||
with open(file_dir / "phones.txt", 'rt') as f:
|
||||
phones = [line.strip() for line in f.readlines()]
|
||||
|
||||
with open("tones.txt", 'rt') as f:
|
||||
with open(file_dir / "tones.txt", 'rt') as f:
|
||||
tones = [line.strip() for line in f.readlines()]
|
||||
voc_phones = Vocab(phones, start_symbol=None, end_symbol=None)
|
||||
voc_tones = Vocab(tones, start_symbol=None, end_symbol=None)
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ def main():
|
|||
help="text to synthesize, a 'utt_id sentence' pair per line")
|
||||
parser.add_argument("--output-dir", type=str, help="output dir")
|
||||
|
||||
args = parser.parse_args()
|
||||
args, _ = parser.parse_known_args()
|
||||
|
||||
speedyspeech_config = inference.Config(
|
||||
str(Path(args.inference_dir) / "speedyspeech.pdmodel"),
|
||||
|
|
|
|||
|
|
@ -96,6 +96,10 @@ def main():
|
|||
# get dataset
|
||||
with jsonlines.open(args.metadata, 'r') as reader:
|
||||
metadata = list(reader)
|
||||
metadata_dir = Path(args.metadata).parent
|
||||
for item in metadata:
|
||||
item["feats"] = str(metadata_dir / item["feats"])
|
||||
|
||||
dataset = DataTable(metadata, converters={'feats': np.load, })
|
||||
logging.info(f"The number of files = {len(dataset)}.")
|
||||
|
||||
|
|
@ -136,7 +140,7 @@ def main():
|
|||
'num_phones': item['num_phones'],
|
||||
'num_frames': item['num_frames'],
|
||||
'durations': item['durations'],
|
||||
'feats': str(mel_path),
|
||||
'feats': str(mel_path.relative_to(dumpdir)),
|
||||
})
|
||||
output_metadata.sort(key=itemgetter('utt_id'))
|
||||
output_metadata_path = Path(args.dumpdir) / "metadata.jsonl"
|
||||
|
|
|
|||
|
|
@ -113,7 +113,7 @@ def process_sentence(config: Dict[str, Any],
|
|||
"num_phones": len(phones),
|
||||
"num_frames": num_frames,
|
||||
"durations": durations_frame,
|
||||
"feats": str(mel_path.resolve()), # use absolute path
|
||||
"feats": mel_path, # Path object
|
||||
}
|
||||
return record
|
||||
|
||||
|
|
@ -147,8 +147,12 @@ def process_sentences(config,
|
|||
results.append(ft.result())
|
||||
|
||||
results.sort(key=itemgetter("utt_id"))
|
||||
with jsonlines.open(output_dir / "metadata.jsonl", 'w') as writer:
|
||||
output_dir = Path(output_dir)
|
||||
metadata_path = output_dir / "metadata.jsonl"
|
||||
# NOTE: use relative path to the meta jsonlines file
|
||||
with jsonlines.open(metadata_path, 'w') as writer:
|
||||
for item in results:
|
||||
item["feats"] = str(item["feats"].relative_to(output_dir))
|
||||
writer.write(item)
|
||||
print("Done")
|
||||
|
||||
|
|
|
|||
|
|
@ -70,7 +70,6 @@ class SpeedySpeechUpdater(StandardUpdater):
|
|||
|
||||
class SpeedySpeechEvaluator(StandardEvaluator):
|
||||
def evaluate_core(self, batch):
|
||||
print("fire")
|
||||
decoded, predicted_durations = self.model(
|
||||
text=batch["phones"],
|
||||
tones=batch["tones"],
|
||||
|
|
|
|||
|
|
@ -150,7 +150,7 @@ def main():
|
|||
"--device", type=str, default="gpu", help="device type to use")
|
||||
parser.add_argument("--verbose", type=int, default=1, help="verbose")
|
||||
|
||||
args = parser.parse_args()
|
||||
args, _ = parser.parse_known_args()
|
||||
with open(args.speedyspeech_config) as f:
|
||||
speedyspeech_config = CfgNode(yaml.safe_load(f))
|
||||
with open(args.pwg_config) as f:
|
||||
|
|
|
|||
|
|
@ -57,7 +57,8 @@ def evaluate(args, speedyspeech_config, pwg_config):
|
|||
model.eval()
|
||||
|
||||
vocoder = PWGGenerator(**pwg_config["generator_params"])
|
||||
vocoder.set_state_dict(paddle.load(args.pwg_params))
|
||||
vocoder.set_state_dict(
|
||||
paddle.load(args.pwg_checkpoint)["generator_params"])
|
||||
vocoder.remove_weight_norm()
|
||||
vocoder.eval()
|
||||
print("model done!")
|
||||
|
|
@ -133,9 +134,9 @@ def main():
|
|||
parser.add_argument(
|
||||
"--pwg-config", type=str, help="config file for parallelwavegan.")
|
||||
parser.add_argument(
|
||||
"--pwg-params",
|
||||
"--pwg-checkpoint",
|
||||
type=str,
|
||||
help="parallel wavegan generator parameters to load.")
|
||||
help="parallel wavegan checkpoint to load.")
|
||||
parser.add_argument(
|
||||
"--pwg-stat",
|
||||
type=str,
|
||||
|
|
@ -152,7 +153,7 @@ def main():
|
|||
"--device", type=str, default="gpu", help="device type to use")
|
||||
parser.add_argument("--verbose", type=int, default=1, help="verbose")
|
||||
|
||||
args = parser.parse_args()
|
||||
args, _ = parser.parse_known_args()
|
||||
with open(args.speedyspeech_config) as f:
|
||||
speedyspeech_config = CfgNode(yaml.safe_load(f))
|
||||
with open(args.pwg_config) as f:
|
||||
|
|
|
|||
|
|
@ -63,6 +63,10 @@ def train_sp(args, config):
|
|||
# construct dataset for training and validation
|
||||
with jsonlines.open(args.train_metadata, 'r') as reader:
|
||||
train_metadata = list(reader)
|
||||
metadata_dir = Path(args.train_metadata).parent
|
||||
for item in train_metadata:
|
||||
item["feats"] = str(metadata_dir / item["feats"])
|
||||
|
||||
train_dataset = DataTable(
|
||||
data=train_metadata,
|
||||
fields=[
|
||||
|
|
@ -71,6 +75,9 @@ def train_sp(args, config):
|
|||
converters={"feats": np.load, }, )
|
||||
with jsonlines.open(args.dev_metadata, 'r') as reader:
|
||||
dev_metadata = list(reader)
|
||||
metadata_dir = Path(args.dev_metadata).parent
|
||||
for item in dev_metadata:
|
||||
item["feats"] = str(metadata_dir / item["feats"])
|
||||
dev_dataset = DataTable(
|
||||
data=dev_metadata,
|
||||
fields=[
|
||||
|
|
@ -124,13 +131,13 @@ def train_sp(args, config):
|
|||
trainer.extend(VisualDL(writer), trigger=(1, "iteration"))
|
||||
trainer.extend(
|
||||
Snapshot(max_size=config.num_snapshots), trigger=(1, 'epoch'))
|
||||
print(trainer.extensions)
|
||||
# print(trainer.extensions)
|
||||
trainer.run()
|
||||
|
||||
|
||||
def main():
|
||||
# parse args and config and redirect to train_sp
|
||||
parser = argparse.ArgumentParser(description="Train a SpeedySpeech "
|
||||
parser = argparse.ArgumentParser(description="Train a Speedyspeech "
|
||||
"model with Baker Mandrin TTS dataset.")
|
||||
parser.add_argument(
|
||||
"--config", type=str, help="config file to overwrite default config.")
|
||||
|
|
@ -143,12 +150,18 @@ def main():
|
|||
"--nprocs", type=int, default=1, help="number of processes.")
|
||||
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
|
||||
|
||||
args = parser.parse_args()
|
||||
args, rest = parser.parse_known_args()
|
||||
if args.device == "cpu" and args.nprocs > 1:
|
||||
raise RuntimeError("Multiprocess training on CPU is not supported.")
|
||||
config = get_cfg_default()
|
||||
if args.config:
|
||||
config.merge_from_file(args.config)
|
||||
if rest:
|
||||
extra = []
|
||||
# to support key=value format
|
||||
for item in rest:
|
||||
extra.extend(item.split("=", maxsplit=1))
|
||||
config.merge_from_list(extra)
|
||||
|
||||
print("========Args========")
|
||||
print(yaml.safe_dump(vars(args)))
|
||||
|
|
|
|||
|
|
@ -0,0 +1,20 @@
|
|||
# Chinese Text Frontend Example
|
||||
Here's an example for Chinese text frontend, including g2p and text normalization.
|
||||
## G2P
|
||||
For g2p, we use BZNSYP's phone label as the ground truth and we delete silence tokens in labels and predicted phones.
|
||||
|
||||
You should Download BZNSYP from it's [Official Website](https://test.data-baker.com/data/index/source) and extract it. Assume the path to the dataset is `~/datasets/BZNSYP`.
|
||||
|
||||
We use `WER` as evaluation criterion.
|
||||
## Text Normalization
|
||||
For text normalization, the test data is `data/textnorm_test_cases.txt`, we use `|` as the separator of raw_data and normed_data.
|
||||
|
||||
We use `CER` as evaluation criterion.
|
||||
## Start
|
||||
Run the command below to get the results of test.
|
||||
```bash
|
||||
./run.sh
|
||||
```
|
||||
The `avg WER` of g2p is: 0.027124048652822204
|
||||
|
||||
The `avg CER` of text normalization is: 0.0061629764893859846
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
今天的最低气温达到-10°C.|今天的最低气温达到零下十度.
|
||||
只要有33/4的人同意，就可以通过决议。|只要有四分之三十三的人同意，就可以通过决议。
|
||||
1945年5月2日，苏联士兵在德国国会大厦上升起了胜利旗，象征着攻占柏林并战胜了纳粹德国。|一九四五年五月二日，苏联士兵在德国国会大厦上升起了胜利旗，象征着攻占柏林并战胜了纳粹德国。
|
||||
4月16日，清晨的战斗以炮击揭幕，数以千计的大炮和喀秋莎火箭炮开始炮轰德军阵地，炮击持续了数天之久。|四月十六日，清晨的战斗以炮击揭幕，数以千计的大炮和喀秋莎火箭炮开始炮轰德军阵地，炮击持续了数天之久。
|
||||
如果剩下的30.6%是过去，那么还有69.4%.|如果剩下的百分之三十点六是过去，那么还有百分之六十九点四.
|
||||
事情发生在2020/03/31的上午8:00.|事情发生在二零二零年三月三十一日的上午八点.
|
||||
警方正在找一支.22口径的手枪。|警方正在找一支点二二口径的手枪。
|
||||
欢迎致电中国联通，北京2022年冬奥会官方合作伙伴为您服务|欢迎致电中国联通，北京二零二二年冬奥会官方合作伙伴为您服务
|
||||
充值缴费请按1，查询话费及余量请按2，跳过本次提醒请按井号键。|充值缴费请按一，查询话费及余量请按二，跳过本次提醒请按井号键。
|
||||
快速解除流量封顶请按星号键，腾讯王卡产品介绍、使用说明、特权及活动请按9，查询话费、套餐余量、积分及活动返款请按1，手机上网流量开通及取消请按2，查询本机号码及本号所使用套餐请按4，密码修改及重置请按5，紧急开机请按6，挂失请按7，查询充值记录请按8，其它自助服务及人工服务请按0|快速解除流量封顶请按星号键，腾讯王卡产品介绍、使用说明、特权及活动请按九，查询话费、套餐余量、积分及活动返款请按一，手机上网流量开通及取消请按二，查询本机号码及本号所使用套餐请按四，密码修改及重置请按五，紧急开机请按六，挂失请按七，查询充值记录请按八，其它自助服务及人工服务请按零
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||||
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|
||||
您的帐户当前可用余额为63.89元，本月消费为2.17元。您的消费、套餐余量和其它信息将以短信形式下发，请您注意查收。谢谢使用，再见！。|您的帐户当前可用余额为六十三点八九元，本月消费为二点一七元。您的消费、套餐余量和其它信息将以短信形式下发，请您注意查收。谢谢使用，再见！。
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||||
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||||
尊敬的客户，您目前的话费余额为负14.60元，已低于10元，为保证您的通信畅通，请及时缴纳费用。|尊敬的客户，您目前的话费余额为负十四点六元，已低于十元，为保证您的通信畅通，请及时缴纳费用。
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||||
您的流量已用完，为避免您产生额外费用，建议您根据需求开通一个流量包以作补充。|您的流量已用完，为避免您产生额外费用，建议您根据需求开通一个流量包以作补充。
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||||
您可以直接说，查询话费及余量、开通流量包、缴费，您也可以说出其它需求，请问有什么可以帮您？|您可以直接说，查询话费及余量、开通流量包、缴费，您也可以说出其它需求，请问有什么可以帮您？
|
||||
您的账户当前可用余额为负36.00元，本月消费36.00元。|您的账户当前可用余额为负三十六元，本月消费三十六元。
|
||||
请问你是电话13985608526的机主吗？|请问你是电话一三九八五六零八五二六的机主吗？
|
||||
如您对处理结果不满意，可拨打中国联通集团投诉电话10015进行投诉，按本地通话费收费，返回自助服务请按井号键|如您对处理结果不满意，可拨打中国联通集团投诉电话一零零一五进行投诉，按本地通话费收费，返回自助服务请按井号键
|
||||
“26314”号VIP客服代表为您服务。|“二六三一四”号VIP客服代表为您服务。
|
||||
尊敬的5G用户，欢迎您致电中国联通|尊敬的五G用户，欢迎您致电中国联通
|
||||
首先是应用了M1芯片的iPad Pro，新款的iPad Pro支持5G，这也是苹果的第二款5G产品线。|首先是应用了M一芯片的iPad Pro，新款的iPad Pro支持五G，这也是苹果的第二款五G产品线。
|
||||
除此之外，摄像头方面再次升级，增加了前摄全新超广角摄像头，支持人物居中功能，搭配超广角可实现视频中始终让人物居中效果。|除此之外，摄像头方面再次升级，增加了前摄全新超广角摄像头，支持人物居中功能，搭配超广角可实现视频中始终让人物居中效果。
|
||||
屏幕方面，iPad Pro 12.9版本支持XDR体验的Mini-LEDS显示屏，支持HDR10、杜比视界，还支持杜比全景声。|屏幕方面，iPad Pro 十二点九版本支持XDR体验的Mini-LEDS显示屏，支持HDR十、杜比视界，还支持杜比全景声。
|
||||
iPad Pro的秒控键盘这次也推出白色版本。|iPad Pro的秒控键盘这次也推出白色版本。
|
||||
售价方面，11英寸版本售价799美元起，12.9英寸售价1099美元起。|售价方面，十一英寸版本售价七百九十九美元起，十二点九英寸售价一千零九十九美元起。
|
||||
这块黄金重达324.75克|这块黄金重达三百二十四点七五克
|
||||
她出生于86年8月18日，她弟弟出生于1995年3月1日|她出生于八六年八月十八日，她弟弟出生于一九九五年三月一日
|
||||
电影中梁朝伟扮演的陈永仁的编号27149|电影中梁朝伟扮演的陈永仁的编号二七一四九
|
||||
现场有7/12的观众投出了赞成票|现场有十二分之七的观众投出了赞成票
|
||||
随便来几个价格12块5，34.5元，20.1万|随便来几个价格十二块五，三十四点五元，二十点一万
|
||||
明天有62%的概率降雨|明天有百分之六十二的概率降雨
|
||||
这是固话0421-33441122|这是固话零四二一三三四四一一二二
|
||||
这是手机+86 18544139121|这是手机八六一八五四四一三九一二一
|
||||
小王的身高是153.5cm,梦想是打篮球!我觉得有0.1%的可能性。|小王的身高是一百五十三点五cm,梦想是打篮球!我觉得有百分之零点一的可能性。
|
||||
不管三七二十一|不管三七二十一
|
||||
九九八十一难|九九八十一难
|
||||
2018年5月23号上午10点10分|二零一八年五月二十三号上午十点十分
|
||||
10076|一零零七六
|
||||
32.68%|百分之三十二点六八
|
||||
比分测试17:16|比分测试十七比十六
|
||||
比分测试37:16|比分测试三十七比十六
|
||||
1.1|一点一
|
||||
一点一滴|一点一滴
|
||||
八九十|八九十
|
||||
1个人一定要|一个人一定要
|
||||
10000棵树|一万棵树
|
||||
1234个人|一千二百三十四个人
|
||||
35553座楼|三万五千五百五十三座楼
|
||||
15873690|一五八七三六九零
|
||||
27930122|二七九三零一二二
|
||||
85307499|八五三零七四九九
|
||||
26149787|二六一四九七八七
|
||||
15964862|一五九六四八六二
|
||||
45698723|四五六九八七二三
|
||||
48615964|四八六一五九六四
|
||||
17864589|一七八六四五八九
|
||||
123加456|一百二十三加四百五十六
|
||||
9786加3384|九千七百八十六加三千三百八十四
|
||||
发电站每天发电30029度电|发电站每天发电三万零二十九度电
|
||||
银行月交易总额七千九百零三亿元|银行月交易总额七千九百零三亿元
|
||||
深圳每月平均工资在13000元|深圳每月平均工资在一万三千元
|
||||
每月房租要交1500元|每月房租要交一千五百元
|
||||
我每月交通费用在400元左右|我每月交通费用在四百元左右
|
||||
本月开销费用是51328元|本月开销费用是五万一千三百二十八元
|
||||
如果你中了五千万元奖金会分我一半吗|如果你中了五千万元奖金会分我一半吗
|
||||
这个月工资我发了3529元|这个月工资我发了三千五百二十九元
|
||||
学会了这个技能你至少可以涨薪5000元|学会了这个技能你至少可以涨薪五千元
|
||||
我们的会议时间定在9点25分开始|我们的会议时间定在九点二十五分开始
|
||||
上课时间是8点15分请不要迟到|上课时间是八点十五分请不要迟到
|
||||
昨天你9点21分才到教室|昨天你九点二十一分才到教室
|
||||
今天是2019年1月31号|今天是二零一九年一月三十一号
|
||||
今年的除夕夜是2019年2月4号|今年的除夕夜是二零一九年二月四号
|
||||
这根水管的长度不超过35米|这根水管的长度不超过三十五米
|
||||
400米是最短的长跑距离|四百米是最短的长跑距离
|
||||
最高的撑杆跳为11米|最高的撑杆跳为十一米
|
||||
等会请在12:05请通知我|等会请在十二点零五分请通知我
|
||||
23点15分开始|二十三点十五分开始
|
||||
你生日那天我会送你999朵玫瑰|你生日那天我会送你九百九十九朵玫瑰
|
||||
给我1双鞋我可以跳96米远|给我一双鞋我可以跳九十六米远
|
||||
虽然我们的身高相差356毫米也不影响我们交往|虽然我们的身高相差三百五十六毫米也不影响我们交往
|
||||
我们班的最高总分为583分|我们班的最高总分为五百八十三分
|
||||
今天考试老师多扣了我21分|今天考试老师多扣了我二十一分
|
||||
我量过这张桌子总长为1.37米|我量过这张桌子总长为一点三七米
|
||||
乘务员身高必须超过185公分|乘务员身高必须超过一百八十五公分
|
||||
这台电脑分辨率为1024|这台电脑分辨率为一零二四
|
||||
手机价格不超过1500元|手机价格不超过一千五百元
|
||||
101.23|一百零一点二三
|
||||
123.116|一百二十三点一一六
|
||||
456.147|四百五十六点一四七
|
||||
0.1594|零点一五九四
|
||||
3.1415|三点一四一五
|
||||
0.112233|零点一一二二三三
|
||||
0.1|零点一
|
||||
40001.987|四万零一点九八七
|
||||
56.878|五十六点八七八
|
||||
0.00123|零点零零一二三
|
||||
0.0001|零点零零零一
|
||||
0.92015|零点九二零一五
|
||||
999.0001|九百九十九点零零零一
|
||||
10000.123|一万点一二三
|
||||
666.555|六百六十六点五五五
|
||||
444.789|四百四十四点七八九
|
||||
789.666|七百八十九点六六六
|
||||
0.12345|零点一二三四五
|
||||
1.05649|一点零五六四九
|
||||
环比上调1.86%|环比上调百分之一点八六
|
||||
环比分别下跌3.46%及微涨0.70%|环比分别下跌百分之三点四六及微涨百分之零点七
|
||||
单价在30000元的二手房购房个案当中|单价在三万元的二手房购房个案当中
|
||||
6月仍有7%单价在30000元的房源|六月仍有百分之七单价在三万元的房源
|
||||
最终也只是以总积分1分之差屈居第2|最终也只是以总积分一分之差屈居第二
|
||||
中新网8月29日电今日|中新网八月二十九日电今日
|
||||
自6月底呼和浩特市率先宣布取消限购后|自六月底呼和浩特市率先宣布取消限购后
|
||||
仅1个多月的时间里|仅一个多月的时间里
|
||||
除了北京上海广州深圳4个一线城市和三亚之外|除了北京上海广州深圳四个一线城市和三亚之外
|
||||
46个限购城市当中|四十六个限购城市当中
|
||||
41个已正式取消或变相放松了限购|四十一个已正式取消或变相放松了限购
|
||||
其中包括对拥有一套住房并已结清相应购房贷款的家庭|其中包括对拥有一套住房并已结清相应购房贷款的家庭
|
||||
这个后来被称为930新政策的措施|这个后来被称为九三零新政策的措施
|
||||
今年有望超三百亿美元|今年有望超三百亿美元
|
||||
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
|
||||
|
|
@ -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()
|
||||
|
|
@ -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()
|
||||
|
|
@ -0,0 +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 --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 --input-dir=data/textnorm --output-dir=exp/textnorm
|
||||
|
||||
|
|
@ -0,0 +1,98 @@
|
|||
# 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
|
||||
|
||||
from parakeet.frontend.cn_frontend import Frontend as cnFrontend
|
||||
from parakeet.utils.error_rate import word_errors
|
||||
|
||||
SILENCE_TOKENS = {"sp", "sil", "sp1", "spl"}
|
||||
|
||||
|
||||
def text_cleaner(raw_text):
|
||||
text = re.sub('#[1-4]|“|”|（|）', '', raw_text)
|
||||
text = text.replace("…。", "。")
|
||||
text = re.sub('：|；|——|……|、|…|—', '，', text)
|
||||
return text
|
||||
|
||||
|
||||
def get_avg_wer(raw_dict, ref_dict, frontend, output_dir):
|
||||
edit_distances = []
|
||||
ref_lens = []
|
||||
wf_g2p = open(output_dir / "text.g2p", "w")
|
||||
wf_ref = open(output_dir / "text.ref.clean", "w")
|
||||
for utt_id in raw_dict:
|
||||
if utt_id not in ref_dict:
|
||||
continue
|
||||
raw_text = raw_dict[utt_id]
|
||||
text = text_cleaner(raw_text)
|
||||
g2p_phones = frontend.get_phonemes(text)
|
||||
gt_phones = ref_dict[utt_id].split(" ")
|
||||
# delete silence tokens in predicted phones and ground truth phones
|
||||
g2p_phones = [phn for phn in g2p_phones if phn not in SILENCE_TOKENS]
|
||||
gt_phones = [phn for phn in gt_phones if phn not in SILENCE_TOKENS]
|
||||
gt_phones = " ".join(gt_phones)
|
||||
g2p_phones = " ".join(g2p_phones)
|
||||
wf_ref.write(utt_id + " " + gt_phones + "\n")
|
||||
wf_g2p.write(utt_id + " " + g2p_phones + "\n")
|
||||
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(
|
||||
"--input-dir",
|
||||
default="data/g2p",
|
||||
type=str,
|
||||
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()
|
||||
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(" ")
|
||||
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()
|
||||
avg_wer = get_avg_wer(raw_dict, ref_dict, frontend, output_dir)
|
||||
print("The avg WER of g2p is:", avg_wer)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,97 @@
|
|||
# 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
|
||||
|
||||
from parakeet.frontend.cn_normalization.text_normlization import TextNormalizer
|
||||
from parakeet.utils.error_rate import char_errors
|
||||
|
||||
|
||||
# delete english characters
|
||||
# e.g. "你好aBC" -> "你 好"
|
||||
def del_en_add_space(input: str):
|
||||
output = re.sub('[a-zA-Z]', '', input)
|
||||
output = [char + " " for char in output]
|
||||
output = "".join(output).strip()
|
||||
return output
|
||||
|
||||
|
||||
def get_avg_cer(raw_dict, ref_dict, text_normalizer, output_dir):
|
||||
edit_distances = []
|
||||
ref_lens = []
|
||||
wf_ref = open(output_dir / "text.ref.clean", "w")
|
||||
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)
|
||||
wf_ref.write(text_id + " " + gt_text + "\n")
|
||||
wf_tn.write(text_id + " " + textnorm_text + "\n")
|
||||
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(
|
||||
"--input-dir",
|
||||
default="data/textnorm",
|
||||
type=str,
|
||||
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()
|
||||
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(raw_dict, ref_dict, text_normalizer, output_dir)
|
||||
print("The avg CER of text normalization is:", avg_cer)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -35,6 +35,14 @@ class Frontend():
|
|||
self.g2pM_model = G2pM()
|
||||
self.pinyin2phone = generate_lexicon(
|
||||
with_tone=True, with_erhua=False)
|
||||
self.must_erhua = {"小院儿", "胡同儿", "范儿", "老汉儿", "撒欢儿", "寻老礼儿", "妥妥儿"}
|
||||
self.not_erhua = {
|
||||
"虐儿", "为儿", "护儿", "瞒儿", "救儿", "替儿", "有儿", "一儿", "我儿", "俺儿", "妻儿",
|
||||
"拐儿", "聋儿", "乞儿", "患儿", "幼儿", "孤儿", "婴儿", "婴幼儿", "连体儿", "脑瘫儿",
|
||||
"流浪儿", "体弱儿", "混血儿", "蜜雪儿", "舫儿", "祖儿", "美儿", "应采儿", "可儿", "侄儿",
|
||||
"孙儿", "侄孙儿", "女儿", "男儿", "红孩儿", "花儿", "虫儿", "马儿", "鸟儿", "猪儿", "猫儿",
|
||||
"狗儿"
|
||||
}
|
||||
|
||||
def _get_initials_finals(self, word):
|
||||
initials = []
|
||||
|
|
@ -71,26 +79,31 @@ class Frontend():
|
|||
return initials, finals
|
||||
|
||||
# if merge_sentences, merge all sentences into one phone sequence
|
||||
def _g2p(self, sentences, merge_sentences=True):
|
||||
def _g2p(self, sentences, merge_sentences=True, with_erhua=True):
|
||||
segments = sentences
|
||||
phones_list = []
|
||||
for seg in segments:
|
||||
phones = []
|
||||
seg = psg.lcut(seg)
|
||||
seg_cut = psg.lcut(seg)
|
||||
initials = []
|
||||
finals = []
|
||||
seg = self.tone_modifier.pre_merge_for_modify(seg)
|
||||
for word, pos in seg:
|
||||
seg_cut = self.tone_modifier.pre_merge_for_modify(seg_cut)
|
||||
for word, pos in seg_cut:
|
||||
if pos == 'eng':
|
||||
continue
|
||||
sub_initials, sub_finals = self._get_initials_finals(word)
|
||||
|
||||
sub_finals = self.tone_modifier.modified_tone(word, pos,
|
||||
sub_finals)
|
||||
if with_erhua:
|
||||
sub_initials, sub_finals = self._merge_erhua(
|
||||
sub_initials, sub_finals, word, pos)
|
||||
initials.append(sub_initials)
|
||||
finals.append(sub_finals)
|
||||
# assert len(sub_initials) == len(sub_finals) == len(word)
|
||||
initials = sum(initials, [])
|
||||
finals = sum(finals, [])
|
||||
|
||||
for c, v in zip(initials, finals):
|
||||
# NOTE: post process for pypinyin outputs
|
||||
# we discriminate i, ii and iii
|
||||
|
|
@ -103,11 +116,30 @@ class Frontend():
|
|||
phones.append('sp')
|
||||
phones_list.append(phones)
|
||||
if merge_sentences:
|
||||
phones_list = sum(phones_list, [])
|
||||
merge_list = sum(phones_list, [])
|
||||
phones_list = []
|
||||
phones_list.append(merge_list)
|
||||
return phones_list
|
||||
|
||||
def get_phonemes(self, sentence):
|
||||
def _merge_erhua(self, initials, finals, word, pos):
|
||||
if word not in self.must_erhua and (word in self.not_erhua or
|
||||
pos in {"a", "j", "nr"}):
|
||||
return initials, finals
|
||||
new_initials = []
|
||||
new_finals = []
|
||||
assert len(finals) == len(word)
|
||||
for i, phn in enumerate(finals):
|
||||
if i == len(finals) - 1 and word[i] == "儿" and phn in {
|
||||
"er2", "er5"
|
||||
} and word[-2:] not in self.not_erhua and new_finals:
|
||||
new_finals[-1] = new_finals[-1][:-1] + "r" + new_finals[-1][-1]
|
||||
else:
|
||||
new_finals.append(phn)
|
||||
new_initials.append(initials[i])
|
||||
return new_initials, new_finals
|
||||
|
||||
def get_phonemes(self, sentence, merge_sentences=True, with_erhua=True):
|
||||
sentences = self.text_normalizer.normalize(sentence)
|
||||
phonemes = self._g2p(sentences)
|
||||
print("phonemes:", phonemes)
|
||||
phonemes = self._g2p(
|
||||
sentences, merge_sentences=merge_sentences, with_erhua=with_erhua)
|
||||
return phonemes
|
||||
|
|
|
|||
|
|
@ -29,6 +29,8 @@ UNITS = OrderedDict({
|
|||
8: '亿',
|
||||
})
|
||||
|
||||
COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块|元|(亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|美|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)'
|
||||
|
||||
# 分数表达式
|
||||
RE_FRAC = re.compile(r'(-?)(\d+)/(\d+)')
|
||||
|
||||
|
|
@ -58,8 +60,18 @@ 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
|
||||
RE_INTEGER = re.compile(r'(-?)' r'(\d+)')
|
||||
# 带负号的整数 -10
|
||||
RE_INTEGER = re.compile(r'(-)' r'(\d+)')
|
||||
|
||||
|
||||
def replace_negative_num(match: re.Match) -> str:
|
||||
sign = match.group(1)
|
||||
number = match.group(2)
|
||||
sign: str = "负" if sign else ""
|
||||
number: str = num2str(number)
|
||||
result = f"{sign}{number}"
|
||||
return result
|
||||
|
||||
|
||||
# 编号-无符号整形
|
||||
# 00078
|
||||
|
|
@ -72,12 +84,23 @@ def replace_default_num(match: re.Match):
|
|||
|
||||
|
||||
# 数字表达式
|
||||
# 1. 整数: -10, 10;
|
||||
# 2. 浮点数: 10.2, -0.3
|
||||
# 3. 不带符号和整数部分的纯浮点数: .22, .38
|
||||
# 纯小数
|
||||
RE_DECIMAL_NUM = re.compile(r'(-?)((\d+)(\.\d+))' r'|(\.(\d+))')
|
||||
# 正整数 + 量词
|
||||
RE_POSITIVE_QUANTIFIERS = re.compile(r"(\d+)([多余几])?" + COM_QUANTIFIERS)
|
||||
RE_NUMBER = re.compile(r'(-?)((\d+)(\.\d+)?)' r'|(\.(\d+))')
|
||||
|
||||
|
||||
def replace_positive_quantifier(match: re.Match) -> str:
|
||||
number = match.group(1)
|
||||
match_2 = match.group(2)
|
||||
match_2: str = match_2 if match_2 else ""
|
||||
quantifiers: str = match.group(3)
|
||||
number: str = num2str(number)
|
||||
result = f"{number}{match_2}{quantifiers}"
|
||||
return result
|
||||
|
||||
|
||||
def replace_number(match: re.Match) -> str:
|
||||
sign = match.group(1)
|
||||
number = match.group(2)
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ from .num import verbalize_digit
|
|||
RE_MOBILE_PHONE = re.compile(
|
||||
r"(?<!\d)((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})(?!\d)")
|
||||
RE_TELEPHONE = re.compile(
|
||||
r"(?<!\d)((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})(?!\d)")
|
||||
r"(?<!\d)((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{7,8})(?!\d)")
|
||||
|
||||
|
||||
def phone2str(phone_string: str, mobile=True) -> str:
|
||||
|
|
@ -44,4 +44,8 @@ def phone2str(phone_string: str, mobile=True) -> str:
|
|||
|
||||
|
||||
def replace_phone(match: re.Match) -> str:
|
||||
return phone2str(match.group(0), mobile=False)
|
||||
|
||||
|
||||
def replace_mobile(match: re.Match) -> str:
|
||||
return phone2str(match.group(0))
|
||||
|
|
|
|||
|
|
@ -12,16 +12,15 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import opencc
|
||||
import re
|
||||
from typing import List
|
||||
|
||||
from .chronology import RE_TIME, RE_DATE, RE_DATE2
|
||||
from .chronology import replace_time, replace_date, replace_date2
|
||||
from .constants import F2H_ASCII_LETTERS, F2H_DIGITS, F2H_SPACE
|
||||
from .num import RE_NUMBER, RE_FRAC, RE_PERCENTAGE, RE_RANGE, RE_INTEGER, RE_DEFAULT_NUM
|
||||
from .num import replace_number, replace_frac, replace_percentage, replace_range, replace_default_num
|
||||
from .phonecode import RE_MOBILE_PHONE, RE_TELEPHONE, replace_phone
|
||||
from .num import RE_NUMBER, RE_FRAC, RE_PERCENTAGE, RE_RANGE, RE_INTEGER, RE_DEFAULT_NUM, RE_DECIMAL_NUM, RE_POSITIVE_QUANTIFIERS
|
||||
from .num import replace_number, replace_frac, replace_percentage, replace_range, replace_default_num, replace_negative_num, replace_positive_quantifier
|
||||
from .phonecode import RE_MOBILE_PHONE, RE_TELEPHONE, replace_phone, replace_mobile
|
||||
from .quantifier import RE_TEMPERATURE
|
||||
from .quantifier import replace_temperature
|
||||
|
||||
|
|
@ -29,8 +28,6 @@ from .quantifier import replace_temperature
|
|||
class TextNormalizer():
|
||||
def __init__(self):
|
||||
self.SENTENCE_SPLITOR = re.compile(r'([：，；。？！,;?!][”’]?)')
|
||||
self._t2s_converter = opencc.OpenCC("t2s.json")
|
||||
self._s2t_converter = opencc.OpenCC('s2t.json')
|
||||
|
||||
def _split(self, text: str) -> List[str]:
|
||||
"""Split long text into sentences with sentence-splitting punctuations.
|
||||
|
|
@ -48,15 +45,8 @@ class TextNormalizer():
|
|||
sentences = [sentence.strip() for sentence in re.split(r'\n+', text)]
|
||||
return sentences
|
||||
|
||||
def _tranditional_to_simplified(self, text: str) -> str:
|
||||
return self._t2s_converter.convert(text)
|
||||
|
||||
def _simplified_to_traditional(self, text: str) -> str:
|
||||
return self._s2t_converter.convert(text)
|
||||
|
||||
def normalize_sentence(self, sentence):
|
||||
# basic character conversions
|
||||
sentence = self._tranditional_to_simplified(sentence)
|
||||
sentence = sentence.translate(F2H_ASCII_LETTERS).translate(
|
||||
F2H_DIGITS).translate(F2H_SPACE)
|
||||
|
||||
|
|
@ -65,11 +55,15 @@ 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_phone, 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,
|
||||
sentence)
|
||||
sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence)
|
||||
sentence = RE_NUMBER.sub(replace_number, sentence)
|
||||
|
||||
|
|
|
|||
|
|
@ -56,102 +56,133 @@ class ToneSandhi():
|
|||
'凑合', '凉快', '冷战', '冤枉', '冒失', '养活', '关系', '先生', '兄弟', '便宜', '使唤',
|
||||
'佩服', '作坊', '体面', '位置', '似的', '伙计', '休息', '什么', '人家', '亲戚', '亲家',
|
||||
'交情', '云彩', '事情', '买卖', '主意', '丫头', '丧气', '两口', '东西', '东家', '世故',
|
||||
'不由', '不在', '下水', '下巴', '上头', '上司', '丈夫', '丈人', '一辈', '那个'
|
||||
'不由', '不在', '下水', '下巴', '上头', '上司', '丈夫', '丈人', '一辈', '那个', '菩萨',
|
||||
'父亲', '母亲', '咕噜', '邋遢', '费用', '冤家', '甜头', '介绍', '荒唐', '大人', '泥鳅',
|
||||
'幸福', '熟悉', '计划', '扑腾', '蜡烛', '姥爷', '照顾', '喉咙', '吉他', '弄堂', '蚂蚱',
|
||||
'凤凰', '拖沓', '寒碜', '糟蹋', '倒腾', '报复', '逻辑', '盘缠', '喽啰', '牢骚', '咖喱',
|
||||
'扫把', '惦记'
|
||||
}
|
||||
self.must_not_neural_tone_words = {
|
||||
"男子", "女子", "分子", "原子", "量子", "莲子", "石子", "瓜子", "电子"
|
||||
}
|
||||
|
||||
# the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
|
||||
# e.g.
|
||||
# word: "家里"
|
||||
# pos: "s"
|
||||
# pos: "s"
|
||||
# finals: ['ia1', 'i3']
|
||||
def _neural_sandhi(self, word: str, pos: str,
|
||||
finals: List[str]) -> List[str]:
|
||||
|
||||
# reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
|
||||
for j, item in enumerate(word):
|
||||
if j - 1 >= 0 and item == word[j - 1] and pos[
|
||||
0] in {"n", "v", "a"}:
|
||||
finals[j] = finals[j][:-1] + "5"
|
||||
ge_idx = word.find("个")
|
||||
if len(word) == 1 and word in "吧呢啊嘛" and pos == 'y':
|
||||
if len(word) >= 1 and word[-1] in "吧呢哈啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
elif len(word) == 1 and word in "的地得" and pos in {"ud", "uj", "uv"}:
|
||||
elif len(word) >= 1 and word[-1] in "的地得":
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
# e.g. 走了, 看着, 去过
|
||||
elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
elif len(word) > 1 and word[-1] in "们子" and pos in {"r", "n"}:
|
||||
elif len(word) > 1 and word[-1] in "们子" and pos in {
|
||||
"r", "n"
|
||||
} and word not in self.must_not_neural_tone_words:
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
# e.g. 桌上, 地下, 家里
|
||||
elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
# e.g. 上来, 下去
|
||||
elif len(word) > 1 and word[-1] in "来去" and pos[0] in {"v"}:
|
||||
elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
# 个做量词
|
||||
elif ge_idx >= 1 and word[ge_idx - 1].isnumeric():
|
||||
elif (ge_idx >= 1 and
|
||||
(word[ge_idx - 1].isnumeric() or
|
||||
word[ge_idx - 1] in "几有两半多各整每做是")) or word == '个':
|
||||
finals[ge_idx] = finals[ge_idx][:-1] + "5"
|
||||
# reduplication words for n. and v. e.g. 奶奶, 试试
|
||||
elif len(word) >= 2 and word[-1] == word[-2] and pos[0] in {"n", "v"}:
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
# conventional tone5 in Chinese
|
||||
elif word in self.must_neural_tone_words or word[
|
||||
-2:] in self.must_neural_tone_words:
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
else:
|
||||
if word in self.must_neural_tone_words or word[
|
||||
-2:] in self.must_neural_tone_words:
|
||||
finals[-1] = finals[-1][:-1] + "5"
|
||||
|
||||
word_list = self._split_word(word)
|
||||
finals_list = [finals[:len(word_list[0])], finals[len(word_list[0]):]]
|
||||
for i, word in enumerate(word_list):
|
||||
# conventional neural in Chinese
|
||||
if word in self.must_neural_tone_words or word[
|
||||
-2:] in self.must_neural_tone_words:
|
||||
finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
|
||||
finals = sum(finals_list, [])
|
||||
|
||||
return finals
|
||||
|
||||
def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
||||
# "不" before tone4 should be bu2, e.g. 不怕
|
||||
if len(word) > 1 and word[0] == "不" and finals[1][-1] == "4":
|
||||
finals[0] = finals[0][:-1] + "2"
|
||||
# e.g. 看不懂
|
||||
elif len(word) == 3 and word[1] == "不":
|
||||
if len(word) == 3 and word[1] == "不":
|
||||
finals[1] = finals[1][:-1] + "5"
|
||||
|
||||
else:
|
||||
for i, char in enumerate(word):
|
||||
# "不" before tone4 should be bu2, e.g. 不怕
|
||||
if char == "不" and i + 1 < len(word) and finals[i + 1][
|
||||
-1] == "4":
|
||||
finals[i] = finals[i][:-1] + "2"
|
||||
return finals
|
||||
|
||||
def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
||||
# "一" in number sequences, e.g. 一零零
|
||||
if len(word) > 1 and word[0] == "一" and all(
|
||||
[item.isnumeric() for item in word]):
|
||||
# "一" in number sequences, e.g. 一零零, 二一零
|
||||
if word.find("一") != -1 and all(
|
||||
[item.isnumeric() for item in word if item != "一"]):
|
||||
return finals
|
||||
# "一" before tone4 should be yi2, e.g. 一段
|
||||
elif len(word) > 1 and word[0] == "一" and finals[1][-1] == "4":
|
||||
finals[0] = finals[0][:-1] + "2"
|
||||
# "一" before non-tone4 should be yi4, e.g. 一天
|
||||
elif len(word) > 1 and word[0] == "一" and finals[1][-1] != "4":
|
||||
finals[0] = finals[0][:-1] + "4"
|
||||
# "一" between reduplication words shold be yi5, e.g. 看一看
|
||||
elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
|
||||
finals[1] = finals[1][:-1] + "5"
|
||||
# when "一" is ordinal word, it should be yi1
|
||||
elif word.startswith("第一"):
|
||||
finals[1] = finals[1][:-1] + "1"
|
||||
else:
|
||||
for i, char in enumerate(word):
|
||||
if char == "一" and i + 1 < len(word):
|
||||
# "一" before tone4 should be yi2, e.g. 一段
|
||||
if finals[i + 1][-1] == "4":
|
||||
finals[i] = finals[i][:-1] + "2"
|
||||
# "一" before non-tone4 should be yi4, e.g. 一天
|
||||
else:
|
||||
finals[i] = finals[i][:-1] + "4"
|
||||
return finals
|
||||
|
||||
def _split_word(self, word):
|
||||
word_list = jieba.cut_for_search(word)
|
||||
word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
|
||||
new_word_list = []
|
||||
|
||||
first_subword = word_list[0]
|
||||
first_begin_idx = word.find(first_subword)
|
||||
if first_begin_idx == 0:
|
||||
second_subword = word[len(first_subword):]
|
||||
new_word_list = [first_subword, second_subword]
|
||||
else:
|
||||
second_subword = word[:-len(first_subword)]
|
||||
new_word_list = [second_subword, first_subword]
|
||||
return new_word_list
|
||||
|
||||
def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
||||
if len(word) == 2 and self._all_tone_three(finals):
|
||||
finals[0] = finals[0][:-1] + "2"
|
||||
elif len(word) == 3:
|
||||
word_list = jieba.cut_for_search(word)
|
||||
word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
|
||||
new_word_list = []
|
||||
first_subword = word_list[0]
|
||||
first_begin_idx = word.find(first_subword)
|
||||
if first_begin_idx == 0:
|
||||
second_subword = word[len(first_subword):]
|
||||
new_word_list = [first_subword, second_subword]
|
||||
else:
|
||||
second_subword = word[:-len(first_subword)]
|
||||
|
||||
new_word_list = [second_subword, first_subword]
|
||||
word_list = self._split_word(word)
|
||||
if self._all_tone_three(finals):
|
||||
# disyllabic + monosyllabic, e.g. 蒙古/包
|
||||
if len(new_word_list[0]) == 2:
|
||||
if len(word_list[0]) == 2:
|
||||
finals[0] = finals[0][:-1] + "2"
|
||||
finals[1] = finals[1][:-1] + "2"
|
||||
# monosyllabic + disyllabic, e.g. 纸/老虎
|
||||
elif len(new_word_list[0]) == 1:
|
||||
elif len(word_list[0]) == 1:
|
||||
finals[1] = finals[1][:-1] + "2"
|
||||
else:
|
||||
finals_list = [
|
||||
finals[:len(new_word_list[0])],
|
||||
finals[len(new_word_list[0]):]
|
||||
finals[:len(word_list[0])], finals[len(word_list[0]):]
|
||||
]
|
||||
if len(finals_list) == 2:
|
||||
for i, sub in enumerate(finals_list):
|
||||
|
|
@ -192,11 +223,10 @@ class ToneSandhi():
|
|||
if last_word == "不":
|
||||
new_seg.append((last_word, 'd'))
|
||||
last_word = ""
|
||||
seg = new_seg
|
||||
return seg
|
||||
return new_seg
|
||||
|
||||
# function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
|
||||
# function 2: merge single "一" and the word behind it
|
||||
# function 2: merge single "一" and the word behind it
|
||||
# if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
|
||||
# e.g.
|
||||
# input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
|
||||
|
|
@ -222,9 +252,9 @@ class ToneSandhi():
|
|||
new_seg[-1][0] = new_seg[-1][0] + word
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
seg = new_seg
|
||||
return seg
|
||||
return new_seg
|
||||
|
||||
# the first and the second words are all_tone_three
|
||||
def _merge_continuous_three_tones(
|
||||
self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
|
|
@ -239,21 +269,73 @@ class ToneSandhi():
|
|||
if i - 1 >= 0 and self._all_tone_three(sub_finals_list[
|
||||
i - 1]) and self._all_tone_three(sub_finals_list[
|
||||
i]) and not merge_last[i - 1]:
|
||||
if len(seg[i - 1][0]) + len(seg[i][0]) <= 3:
|
||||
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
||||
if not self._is_reduplication(seg[i - 1][0]) and len(seg[
|
||||
i - 1][0]) + len(seg[i][0]) <= 3:
|
||||
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
||||
merge_last[i] = True
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
seg = new_seg
|
||||
return seg
|
||||
|
||||
return new_seg
|
||||
|
||||
def _is_reduplication(self, word):
|
||||
return len(word) == 2 and word[0] == word[1]
|
||||
|
||||
# the last char of first word and the first char of second word is tone_three
|
||||
def _merge_continuous_three_tones_2(
|
||||
self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
sub_finals_list = [
|
||||
lazy_pinyin(
|
||||
word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
||||
for (word, pos) in seg
|
||||
]
|
||||
assert len(sub_finals_list) == len(seg)
|
||||
merge_last = [False] * len(seg)
|
||||
for i, (word, pos) in enumerate(seg):
|
||||
if i - 1 >= 0 and sub_finals_list[i - 1][-1][-1] == "3" and sub_finals_list[i][0][-1] == "3" and not \
|
||||
merge_last[i - 1]:
|
||||
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
||||
if not self._is_reduplication(seg[i - 1][0]) and len(seg[
|
||||
i - 1][0]) + len(seg[i][0]) <= 3:
|
||||
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
||||
merge_last[i] = True
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
return new_seg
|
||||
|
||||
def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
for i, (word, pos) in enumerate(seg):
|
||||
if i - 1 >= 0 and word == "儿":
|
||||
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
return new_seg
|
||||
|
||||
def _merge_reduplication(
|
||||
self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
new_seg = []
|
||||
for i, (word, pos) in enumerate(seg):
|
||||
if new_seg and word == new_seg[-1][0]:
|
||||
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
||||
else:
|
||||
new_seg.append([word, pos])
|
||||
return new_seg
|
||||
|
||||
def pre_merge_for_modify(
|
||||
self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||||
seg = self._merge_bu(seg)
|
||||
seg = self._merge_yi(seg)
|
||||
seg = self._merge_reduplication(seg)
|
||||
seg = self._merge_continuous_three_tones(seg)
|
||||
seg = self._merge_continuous_three_tones_2(seg)
|
||||
seg = self._merge_er(seg)
|
||||
return seg
|
||||
|
||||
def modified_tone(self, word: str, pos: str,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,239 @@
|
|||
# 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.
|
||||
"""This module provides functions to calculate error rate in different level.
|
||||
e.g. wer for word-level, cer for char-level.
|
||||
"""
|
||||
import numpy as np
|
||||
|
||||
__all__ = ['word_errors', 'char_errors', 'wer', 'cer']
|
||||
|
||||
|
||||
def _levenshtein_distance(ref, hyp):
|
||||
"""Levenshtein distance is a string metric for measuring the difference
|
||||
between two sequences. Informally, the levenshtein disctance is defined as
|
||||
the minimum number of single-character edits (substitutions, insertions or
|
||||
deletions) required to change one word into the other. We can naturally
|
||||
extend the edits to word level when calculate levenshtein disctance for
|
||||
two sentences.
|
||||
"""
|
||||
m = len(ref)
|
||||
n = len(hyp)
|
||||
|
||||
# special case
|
||||
if ref == hyp:
|
||||
return 0
|
||||
if m == 0:
|
||||
return n
|
||||
if n == 0:
|
||||
return m
|
||||
|
||||
if m < n:
|
||||
ref, hyp = hyp, ref
|
||||
m, n = n, m
|
||||
|
||||
# use O(min(m, n)) space
|
||||
distance = np.zeros((2, n + 1), dtype=np.int32)
|
||||
|
||||
# initialize distance matrix
|
||||
for j in range(n + 1):
|
||||
distance[0][j] = j
|
||||
|
||||
# calculate levenshtein distance
|
||||
for i in range(1, m + 1):
|
||||
prev_row_idx = (i - 1) % 2
|
||||
cur_row_idx = i % 2
|
||||
distance[cur_row_idx][0] = i
|
||||
for j in range(1, n + 1):
|
||||
if ref[i - 1] == hyp[j - 1]:
|
||||
distance[cur_row_idx][j] = distance[prev_row_idx][j - 1]
|
||||
else:
|
||||
s_num = distance[prev_row_idx][j - 1] + 1
|
||||
i_num = distance[cur_row_idx][j - 1] + 1
|
||||
d_num = distance[prev_row_idx][j] + 1
|
||||
distance[cur_row_idx][j] = min(s_num, i_num, d_num)
|
||||
|
||||
return distance[m % 2][n]
|
||||
|
||||
|
||||
def word_errors(reference, hypothesis, ignore_case=False, delimiter=' '):
|
||||
"""Compute the levenshtein distance between reference sequence and
|
||||
hypothesis sequence in word-level.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
reference : str
|
||||
The reference sentence.
|
||||
hypothesis : str
|
||||
The hypothesis sentence.
|
||||
ignore_case : bool
|
||||
Whether case-sensitive or not.
|
||||
delimiter : char(str)
|
||||
Delimiter of input sentences.
|
||||
|
||||
Returns
|
||||
----------
|
||||
list
|
||||
Levenshtein distance and word number of reference sentence.
|
||||
"""
|
||||
if ignore_case:
|
||||
reference = reference.lower()
|
||||
hypothesis = hypothesis.lower()
|
||||
|
||||
ref_words = list(filter(None, reference.split(delimiter)))
|
||||
hyp_words = list(filter(None, hypothesis.split(delimiter)))
|
||||
|
||||
edit_distance = _levenshtein_distance(ref_words, hyp_words)
|
||||
return float(edit_distance), len(ref_words)
|
||||
|
||||
|
||||
def char_errors(reference, hypothesis, ignore_case=False, remove_space=False):
|
||||
"""Compute the levenshtein distance between reference sequence and
|
||||
hypothesis sequence in char-level.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
reference: str
|
||||
The reference sentence.
|
||||
hypothesis: str
|
||||
The hypothesis sentence.
|
||||
ignore_case: bool
|
||||
Whether case-sensitive or not.
|
||||
remove_space: bool
|
||||
Whether remove internal space characters
|
||||
|
||||
Returns
|
||||
----------
|
||||
list
|
||||
Levenshtein distance and length of reference sentence.
|
||||
"""
|
||||
if ignore_case:
|
||||
reference = reference.lower()
|
||||
hypothesis = hypothesis.lower()
|
||||
|
||||
join_char = ' '
|
||||
if remove_space:
|
||||
join_char = ''
|
||||
|
||||
reference = join_char.join(list(filter(None, reference.split(' '))))
|
||||
hypothesis = join_char.join(list(filter(None, hypothesis.split(' '))))
|
||||
|
||||
edit_distance = _levenshtein_distance(reference, hypothesis)
|
||||
return float(edit_distance), len(reference)
|
||||
|
||||
|
||||
def wer(reference, hypothesis, ignore_case=False, delimiter=' '):
|
||||
"""Calculate word error rate (WER). WER compares reference text and
|
||||
hypothesis text in word-level. WER is defined as:
|
||||
.. math::
|
||||
WER = (Sw + Dw + Iw) / Nw
|
||||
where
|
||||
.. code-block:: text
|
||||
Sw is the number of words subsituted,
|
||||
Dw is the number of words deleted,
|
||||
Iw is the number of words inserted,
|
||||
Nw is the number of words in the reference
|
||||
We can use levenshtein distance to calculate WER. Please draw an attention
|
||||
that empty items will be removed when splitting sentences by delimiter.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
reference: str
|
||||
The reference sentence.
|
||||
|
||||
hypothesis: str
|
||||
The hypothesis sentence.
|
||||
ignore_case: bool
|
||||
Whether case-sensitive or not.
|
||||
delimiter: char
|
||||
Delimiter of input sentences.
|
||||
|
||||
Returns
|
||||
----------
|
||||
float
|
||||
Word error rate.
|
||||
|
||||
Raises
|
||||
----------
|
||||
ValueError
|
||||
If word number of reference is zero.
|
||||
"""
|
||||
edit_distance, ref_len = word_errors(reference, hypothesis, ignore_case,
|
||||
delimiter)
|
||||
|
||||
if ref_len == 0:
|
||||
raise ValueError("Reference's word number should be greater than 0.")
|
||||
|
||||
wer = float(edit_distance) / ref_len
|
||||
return wer
|
||||
|
||||
|
||||
def cer(reference, hypothesis, ignore_case=False, remove_space=False):
|
||||
"""Calculate charactor error rate (CER). CER compares reference text and
|
||||
hypothesis text in char-level. CER is defined as:
|
||||
.. math::
|
||||
CER = (Sc + Dc + Ic) / Nc
|
||||
where
|
||||
.. code-block:: text
|
||||
Sc is the number of characters substituted,
|
||||
Dc is the number of characters deleted,
|
||||
Ic is the number of characters inserted
|
||||
Nc is the number of characters in the reference
|
||||
We can use levenshtein distance to calculate CER. Chinese input should be
|
||||
encoded to unicode. Please draw an attention that the leading and tailing
|
||||
space characters will be truncated and multiple consecutive space
|
||||
characters in a sentence will be replaced by one space character.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
reference: str
|
||||
The reference sentence.
|
||||
hypothesis: str
|
||||
The hypothesis sentence.
|
||||
ignore_case: bool
|
||||
Whether case-sensitive or not.
|
||||
remove_space: bool
|
||||
Whether remove internal space characters
|
||||
|
||||
Returns
|
||||
----------
|
||||
float
|
||||
Character error rate.
|
||||
|
||||
Raises
|
||||
----------
|
||||
ValueError
|
||||
If the reference length is zero.
|
||||
"""
|
||||
edit_distance, ref_len = char_errors(reference, hypothesis, ignore_case,
|
||||
remove_space)
|
||||
|
||||
if ref_len == 0:
|
||||
raise ValueError("Length of reference should be greater than 0.")
|
||||
|
||||
cer = float(edit_distance) / ref_len
|
||||
return cer
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
reference = [
|
||||
'j', 'iou4', 'zh', 'e4', 'iang5', 'x', 'v2', 'b', 'o1', 'k', 'ai1',
|
||||
'sh', 'iii3', 'l', 'e5', 'b', 'ei3', 'p', 'iao1', 'sh', 'eng1', 'ia2'
|
||||
]
|
||||
hypothesis = [
|
||||
'j', 'iou4', 'zh', 'e4', 'iang4', 'x', 'v2', 'b', 'o1', 'k', 'ai1',
|
||||
'sh', 'iii3', 'l', 'e5', 'b', 'ei3', 'p', 'iao1', 'sh', 'eng1', 'ia2'
|
||||
]
|
||||
reference = " ".join(reference)
|
||||
hypothesis = " ".join(hypothesis)
|
||||
print(wer(reference, hypothesis))
|
||||
8
setup.py
8
setup.py
|
|
@ -64,17 +64,21 @@ setup_info = dict(
|
|||
'scipy',
|
||||
'pandas',
|
||||
'sox',
|
||||
'soundfile',
|
||||
'soundfile~=0.10',
|
||||
'g2p_en',
|
||||
'yacs',
|
||||
'visualdl',
|
||||
'pypinyin',
|
||||
'webrtcvad',
|
||||
'g2pM',
|
||||
'praatio',
|
||||
'praatio~=4.1',
|
||||
"h5py",
|
||||
"timer",
|
||||
'jsonlines',
|
||||
'pyworld',
|
||||
'typeguard',
|
||||
'jieba',
|
||||
"phkit",
|
||||
],
|
||||
extras_require={'doc': ["sphinx", "sphinx-rtd-theme", "numpydoc"], },
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue