From 6f7e27eb7c2d0a7649ec8f14d167c8da8e29f906 Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 16 五月 2023 15:07:20 +0800
Subject: [PATCH] Merge pull request #518 from alibaba-damo-academy/dev_wjm2
---
funasr/datasets/small_datasets/preprocessor.py | 875 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 875 insertions(+), 0 deletions(-)
diff --git a/funasr/datasets/small_datasets/preprocessor.py b/funasr/datasets/small_datasets/preprocessor.py
new file mode 100644
index 0000000..d80f48a
--- /dev/null
+++ b/funasr/datasets/small_datasets/preprocessor.py
@@ -0,0 +1,875 @@
+from abc import ABC
+from abc import abstractmethod
+from pathlib import Path
+from typing import Collection
+from typing import Dict
+from typing import Iterable
+from typing import List
+from typing import Union
+
+import numpy as np
+import scipy.signal
+import soundfile
+from typeguard import check_argument_types
+from typeguard import check_return_type
+
+from funasr.text.build_tokenizer import build_tokenizer
+from funasr.text.cleaner import TextCleaner
+from funasr.text.token_id_converter import TokenIDConverter
+
+
+class AbsPreprocessor(ABC):
+ def __init__(self, train: bool):
+ self.train = train
+
+ @abstractmethod
+ def __call__(
+ self, uid: str, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, np.ndarray]:
+ raise NotImplementedError
+
+
+def forward_segment(text, dic):
+ word_list = []
+ i = 0
+ while i < len(text):
+ longest_word = text[i]
+ for j in range(i + 1, len(text) + 1):
+ word = text[i:j]
+ if word in dic:
+ if len(word) > len(longest_word):
+ longest_word = word
+ word_list.append(longest_word)
+ i += len(longest_word)
+ return word_list
+
+
+def seg_tokenize(txt, seg_dict):
+ out_txt = ""
+ for word in txt:
+ if word in seg_dict:
+ out_txt += seg_dict[word] + " "
+ else:
+ out_txt += "<unk>" + " "
+ return out_txt.strip().split()
+
+
+def seg_tokenize_wo_pattern(txt, seg_dict):
+ out_txt = ""
+ for word in txt:
+ if word in seg_dict:
+ out_txt += seg_dict[word] + " "
+ else:
+ out_txt += "<unk>" + " "
+ return out_txt.strip().split()
+
+
+def framing(
+ x,
+ frame_length: int = 512,
+ frame_shift: int = 256,
+ centered: bool = True,
+ padded: bool = True,
+):
+ if x.size == 0:
+ raise ValueError("Input array size is zero")
+ if frame_length < 1:
+ raise ValueError("frame_length must be a positive integer")
+ if frame_length > x.shape[-1]:
+ raise ValueError("frame_length is greater than input length")
+ if 0 >= frame_shift:
+ raise ValueError("frame_shift must be greater than 0")
+
+ if centered:
+ pad_shape = [(0, 0) for _ in range(x.ndim - 1)] + [
+ (frame_length // 2, frame_length // 2)
+ ]
+ x = np.pad(x, pad_shape, mode="constant", constant_values=0)
+
+ if padded:
+ # Pad to integer number of windowed segments
+ # I.e make x.shape[-1] = frame_length + (nseg-1)*nstep,
+ # with integer nseg
+ nadd = (-(x.shape[-1] - frame_length) % frame_shift) % frame_length
+ pad_shape = [(0, 0) for _ in range(x.ndim - 1)] + [(0, nadd)]
+ x = np.pad(x, pad_shape, mode="constant", constant_values=0)
+
+ # Created strided array of data segments
+ if frame_length == 1 and frame_length == frame_shift:
+ result = x[..., None]
+ else:
+ shape = x.shape[:-1] + (
+ (x.shape[-1] - frame_length) // frame_shift + 1,
+ frame_length,
+ )
+ strides = x.strides[:-1] + (frame_shift * x.strides[-1], x.strides[-1])
+ result = np.lib.stride_tricks.as_strided(x, shape=shape, strides=strides)
+ return result
+
+
+def detect_non_silence(
+ x: np.ndarray,
+ threshold: float = 0.01,
+ frame_length: int = 1024,
+ frame_shift: int = 512,
+ window: str = "boxcar",
+) -> np.ndarray:
+ """Power based voice activity detection.
+
+ Args:
+ x: (Channel, Time)
+ >>> x = np.random.randn(1000)
+ >>> detect = detect_non_silence(x)
+ >>> assert x.shape == detect.shape
+ >>> assert detect.dtype == np.bool
+ """
+ if x.shape[-1] < frame_length:
+ return np.full(x.shape, fill_value=True, dtype=np.bool)
+
+ if x.dtype.kind == "i":
+ x = x.astype(np.float64)
+ # framed_w: (C, T, F)
+ framed_w = framing(
+ x,
+ frame_length=frame_length,
+ frame_shift=frame_shift,
+ centered=False,
+ padded=True,
+ )
+ framed_w *= scipy.signal.get_window(window, frame_length).astype(framed_w.dtype)
+ # power: (C, T)
+ power = (framed_w ** 2).mean(axis=-1)
+ # mean_power: (C, 1)
+ mean_power = np.mean(power, axis=-1, keepdims=True)
+ if np.all(mean_power == 0):
+ return np.full(x.shape, fill_value=True, dtype=np.bool)
+ # detect_frames: (C, T)
+ detect_frames = power / mean_power > threshold
+ # detects: (C, T, F)
+ detects = np.broadcast_to(
+ detect_frames[..., None], detect_frames.shape + (frame_shift,)
+ )
+ # detects: (C, TF)
+ detects = detects.reshape(*detect_frames.shape[:-1], -1)
+ # detects: (C, TF)
+ return np.pad(
+ detects,
+ [(0, 0)] * (x.ndim - 1) + [(0, x.shape[-1] - detects.shape[-1])],
+ mode="edge",
+ )
+
+
+class CommonPreprocessor(AbsPreprocessor):
+ def __init__(
+ self,
+ train: bool,
+ token_type: str = None,
+ token_list: Union[Path, str, Iterable[str]] = None,
+ bpemodel: Union[Path, str, Iterable[str]] = None,
+ text_cleaner: Collection[str] = None,
+ g2p_type: str = None,
+ unk_symbol: str = "<unk>",
+ space_symbol: str = "<space>",
+ non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
+ delimiter: str = None,
+ rir_scp: str = None,
+ rir_apply_prob: float = 1.0,
+ noise_scp: str = None,
+ noise_apply_prob: float = 1.0,
+ noise_db_range: str = "3_10",
+ speech_volume_normalize: float = None,
+ speech_name: str = "speech",
+ text_name: str = "text",
+ split_with_space: bool = False,
+ seg_dict_file: str = None,
+ ):
+ super().__init__(train)
+ self.train = train
+ self.speech_name = speech_name
+ self.text_name = text_name
+ self.speech_volume_normalize = speech_volume_normalize
+ self.rir_apply_prob = rir_apply_prob
+ self.noise_apply_prob = noise_apply_prob
+ self.split_with_space = split_with_space
+ self.seg_dict = None
+ if seg_dict_file is not None:
+ self.seg_dict = {}
+ with open(seg_dict_file) as f:
+ lines = f.readlines()
+ for line in lines:
+ s = line.strip().split()
+ key = s[0]
+ value = s[1:]
+ self.seg_dict[key] = " ".join(value)
+
+ if token_type is not None:
+ if token_list is None:
+ raise ValueError("token_list is required if token_type is not None")
+ self.text_cleaner = TextCleaner(text_cleaner)
+
+ self.tokenizer = build_tokenizer(
+ token_type=token_type,
+ bpemodel=bpemodel,
+ delimiter=delimiter,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ g2p_type=g2p_type,
+ )
+ self.token_id_converter = TokenIDConverter(
+ token_list=token_list,
+ unk_symbol=unk_symbol,
+ )
+ else:
+ self.text_cleaner = None
+ self.tokenizer = None
+ self.token_id_converter = None
+
+ if train and rir_scp is not None:
+ self.rirs = []
+ with open(rir_scp, "r", encoding="utf-8") as f:
+ for line in f:
+ sps = line.strip().split(None, 1)
+ if len(sps) == 1:
+ self.rirs.append(sps[0])
+ else:
+ self.rirs.append(sps[1])
+ else:
+ self.rirs = None
+
+ if train and noise_scp is not None:
+ self.noises = []
+ with open(noise_scp, "r", encoding="utf-8") as f:
+ for line in f:
+ sps = line.strip().split(None, 1)
+ if len(sps) == 1:
+ self.noises.append(sps[0])
+ else:
+ self.noises.append(sps[1])
+ sps = noise_db_range.split("_")
+ if len(sps) == 1:
+ self.noise_db_low, self.noise_db_high = float(sps[0])
+ elif len(sps) == 2:
+ self.noise_db_low, self.noise_db_high = float(sps[0]), float(sps[1])
+ else:
+ raise ValueError(
+ "Format error: '{noise_db_range}' e.g. -3_4 -> [-3db,4db]"
+ )
+ else:
+ self.noises = None
+
+ def _speech_process(
+ self, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, Union[str, np.ndarray]]:
+ assert check_argument_types()
+ if self.speech_name in data:
+ if self.train and (self.rirs is not None or self.noises is not None):
+ speech = data[self.speech_name]
+ nsamples = len(speech)
+
+ # speech: (Nmic, Time)
+ if speech.ndim == 1:
+ speech = speech[None, :]
+ else:
+ speech = speech.T
+ # Calc power on non shlence region
+ power = (speech[detect_non_silence(speech)] ** 2).mean()
+
+ # 1. Convolve RIR
+ if self.rirs is not None and self.rir_apply_prob >= np.random.random():
+ rir_path = np.random.choice(self.rirs)
+ if rir_path is not None:
+ rir, _ = soundfile.read(
+ rir_path, dtype=np.float64, always_2d=True
+ )
+
+ # rir: (Nmic, Time)
+ rir = rir.T
+
+ # speech: (Nmic, Time)
+ # Note that this operation doesn't change the signal length
+ speech = scipy.signal.convolve(speech, rir, mode="full")[
+ :, : speech.shape[1]
+ ]
+ # Reverse mean power to the original power
+ power2 = (speech[detect_non_silence(speech)] ** 2).mean()
+ speech = np.sqrt(power / max(power2, 1e-10)) * speech
+
+ # 2. Add Noise
+ if (
+ self.noises is not None
+ and self.noise_apply_prob >= np.random.random()
+ ):
+ noise_path = np.random.choice(self.noises)
+ if noise_path is not None:
+ noise_db = np.random.uniform(
+ self.noise_db_low, self.noise_db_high
+ )
+ with soundfile.SoundFile(noise_path) as f:
+ if f.frames == nsamples:
+ noise = f.read(dtype=np.float64, always_2d=True)
+ elif f.frames < nsamples:
+ offset = np.random.randint(0, nsamples - f.frames)
+ # noise: (Time, Nmic)
+ noise = f.read(dtype=np.float64, always_2d=True)
+ # Repeat noise
+ noise = np.pad(
+ noise,
+ [(offset, nsamples - f.frames - offset), (0, 0)],
+ mode="wrap",
+ )
+ else:
+ offset = np.random.randint(0, f.frames - nsamples)
+ f.seek(offset)
+ # noise: (Time, Nmic)
+ noise = f.read(
+ nsamples, dtype=np.float64, always_2d=True
+ )
+ if len(noise) != nsamples:
+ raise RuntimeError(f"Something wrong: {noise_path}")
+ # noise: (Nmic, Time)
+ noise = noise.T
+
+ noise_power = (noise ** 2).mean()
+ scale = (
+ 10 ** (-noise_db / 20)
+ * np.sqrt(power)
+ / np.sqrt(max(noise_power, 1e-10))
+ )
+ speech = speech + scale * noise
+
+ speech = speech.T
+ ma = np.max(np.abs(speech))
+ if ma > 1.0:
+ speech /= ma
+ data[self.speech_name] = speech
+
+ if self.speech_volume_normalize is not None:
+ speech = data[self.speech_name]
+ ma = np.max(np.abs(speech))
+ data[self.speech_name] = speech * self.speech_volume_normalize / ma
+ assert check_return_type(data)
+ return data
+
+ def _text_process(
+ self, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, np.ndarray]:
+ if self.text_name in data and self.tokenizer is not None:
+ text = data[self.text_name]
+ text = self.text_cleaner(text)
+ if self.split_with_space:
+ tokens = text.strip().split(" ")
+ if self.seg_dict is not None:
+ tokens = forward_segment("".join(tokens), self.seg_dict)
+ tokens = seg_tokenize(tokens, self.seg_dict)
+ else:
+ tokens = self.tokenizer.text2tokens(text)
+ text_ints = self.token_id_converter.tokens2ids(tokens)
+ data[self.text_name] = np.array(text_ints, dtype=np.int64)
+ assert check_return_type(data)
+ return data
+
+ def __call__(
+ self, uid: str, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, np.ndarray]:
+ assert check_argument_types()
+
+ data = self._speech_process(data)
+ data = self._text_process(data)
+ return data
+
+
+## FIXME
+class LMPreprocessor(CommonPreprocessor):
+ def __init__(
+ self,
+ train: bool,
+ token_type: str = None,
+ token_list: Union[Path, str, Iterable[str]] = None,
+ bpemodel: Union[Path, str, Iterable[str]] = None,
+ text_cleaner: Collection[str] = None,
+ g2p_type: str = None,
+ unk_symbol: str = "<unk>",
+ space_symbol: str = "<space>",
+ non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
+ delimiter: str = None,
+ rir_scp: str = None,
+ rir_apply_prob: float = 1.0,
+ noise_scp: str = None,
+ noise_apply_prob: float = 1.0,
+ noise_db_range: str = "3_10",
+ speech_volume_normalize: float = None,
+ speech_name: str = "speech",
+ text_name: str = "text",
+ split_with_space: bool = False,
+ seg_dict_file: str = None,
+ ):
+ super().__init__(train,
+ token_type,
+ token_list,
+ bpemodel,
+ text_cleaner,
+ g2p_type,
+ unk_symbol,
+ space_symbol,
+ non_linguistic_symbols,
+ delimiter,
+ rir_scp,
+ rir_apply_prob,
+ noise_scp,
+ noise_apply_prob,
+ noise_db_range,
+ speech_volume_normalize,
+ speech_name,
+ text_name,
+ split_with_space,
+ seg_dict_file,
+ )
+
+ def _text_process(
+ self, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, np.ndarray]:
+ if self.text_name in data and self.tokenizer is not None:
+ text = data[self.text_name]
+ text = self.text_cleaner(text)
+ if self.split_with_space:
+ tokens = text.strip().split(" ")
+ if self.seg_dict is not None:
+ tokens = seg_tokenize_wo_pattern(tokens, self.seg_dict)
+ else:
+ tokens = self.tokenizer.text2tokens(text)
+ text_ints = self.token_id_converter.tokens2ids(tokens)
+ data[self.text_name] = np.array(text_ints, dtype=np.int64)
+ assert check_return_type(data)
+ return data
+
+
+class CommonPreprocessor_multi(AbsPreprocessor):
+ def __init__(
+ self,
+ train: bool,
+ token_type: str = None,
+ token_list: Union[Path, str, Iterable[str]] = None,
+ bpemodel: Union[Path, str, Iterable[str]] = None,
+ text_cleaner: Collection[str] = None,
+ g2p_type: str = None,
+ unk_symbol: str = "<unk>",
+ space_symbol: str = "<space>",
+ non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
+ delimiter: str = None,
+ speech_name: str = "speech",
+ text_name: List[str] = ["text"],
+ ):
+ super().__init__(train)
+ self.train = train
+ self.speech_name = speech_name
+ self.text_name = text_name
+
+ if token_type is not None:
+ if token_list is None:
+ raise ValueError("token_list is required if token_type is not None")
+ self.text_cleaner = TextCleaner(text_cleaner)
+
+ self.tokenizer = build_tokenizer(
+ token_type=token_type,
+ bpemodel=bpemodel,
+ delimiter=delimiter,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ g2p_type=g2p_type,
+ )
+ self.token_id_converter = TokenIDConverter(
+ token_list=token_list,
+ unk_symbol=unk_symbol,
+ )
+ else:
+ self.text_cleaner = None
+ self.tokenizer = None
+ self.token_id_converter = None
+
+ def _text_process(
+ self, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, np.ndarray]:
+ for text_n in self.text_name:
+ if text_n in data and self.tokenizer is not None:
+ text = data[text_n]
+ text = self.text_cleaner(text)
+ tokens = self.tokenizer.text2tokens(text)
+ text_ints = self.token_id_converter.tokens2ids(tokens)
+ data[text_n] = np.array(text_ints, dtype=np.int64)
+ assert check_return_type(data)
+ return data
+
+ def __call__(
+ self, uid: str, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, np.ndarray]:
+ assert check_argument_types()
+
+ if self.speech_name in data:
+ # Nothing now: candidates:
+ # - STFT
+ # - Fbank
+ # - CMVN
+ # - Data augmentation
+ pass
+
+ data = self._text_process(data)
+ return data
+
+
+class MutliTokenizerCommonPreprocessor(CommonPreprocessor):
+ def __init__(
+ self,
+ train: bool,
+ token_type: List[str] = [None],
+ token_list: List[Union[Path, str, Iterable[str]]] = [None],
+ bpemodel: List[Union[Path, str, Iterable[str]]] = [None],
+ text_cleaner: Collection[str] = None,
+ g2p_type: str = None,
+ unk_symbol: str = "<unk>",
+ space_symbol: str = "<space>",
+ non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
+ delimiter: str = None,
+ rir_scp: str = None,
+ rir_apply_prob: float = 1.0,
+ noise_scp: str = None,
+ noise_apply_prob: float = 1.0,
+ noise_db_range: str = "3_10",
+ speech_volume_normalize: float = None,
+ speech_name: str = "speech",
+ text_name: List[str] = ["text"],
+ ):
+ # TODO(jiatong): sync with Kamo and Jing on interface for preprocessor
+ super().__init__(
+ train=train,
+ token_type=token_type[0],
+ token_list=token_list[0],
+ bpemodel=bpemodel[0],
+ text_cleaner=text_cleaner,
+ g2p_type=g2p_type,
+ unk_symbol=unk_symbol,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ delimiter=delimiter,
+ speech_name=speech_name,
+ text_name=text_name[0],
+ rir_scp=rir_scp,
+ rir_apply_prob=rir_apply_prob,
+ noise_scp=noise_scp,
+ noise_apply_prob=noise_apply_prob,
+ noise_db_range=noise_db_range,
+ speech_volume_normalize=speech_volume_normalize,
+ )
+
+ assert (
+ len(token_type) == len(token_list) == len(bpemodel) == len(text_name)
+ ), "token_type, token_list, bpemodel, or processing text_name mismatched"
+ self.num_tokenizer = len(token_type)
+ self.tokenizer = []
+ self.token_id_converter = []
+
+ for i in range(self.num_tokenizer):
+ if token_type[i] is not None:
+ if token_list[i] is None:
+ raise ValueError("token_list is required if token_type is not None")
+
+ self.tokenizer.append(
+ build_tokenizer(
+ token_type=token_type[i],
+ bpemodel=bpemodel[i],
+ delimiter=delimiter,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ g2p_type=g2p_type,
+ )
+ )
+ self.token_id_converter.append(
+ TokenIDConverter(
+ token_list=token_list[i],
+ unk_symbol=unk_symbol,
+ )
+ )
+ else:
+ self.tokenizer.append(None)
+ self.token_id_converter.append(None)
+
+ self.text_cleaner = TextCleaner(text_cleaner)
+ self.text_name = text_name # override the text_name from CommonPreprocessor
+
+ def _text_process(
+ self, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, np.ndarray]:
+ for i in range(self.num_tokenizer):
+ text_name = self.text_name[i]
+ if text_name in data and self.tokenizer[i] is not None:
+ text = data[text_name]
+ text = self.text_cleaner(text)
+ tokens = self.tokenizer[i].text2tokens(text)
+ text_ints = self.token_id_converter[i].tokens2ids(tokens)
+ data[text_name] = np.array(text_ints, dtype=np.int64)
+ assert check_return_type(data)
+ return data
+
+
+class CodeMixTokenizerCommonPreprocessor(CommonPreprocessor):
+ def __init__(
+ self,
+ train: bool,
+ token_type: str = None,
+ token_list: Union[Path, str, Iterable[str]] = None,
+ bpemodel: Union[Path, str, Iterable[str]] = None,
+ text_cleaner: Collection[str] = None,
+ g2p_type: str = None,
+ unk_symbol: str = "<unk>",
+ space_symbol: str = "<space>",
+ non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
+ delimiter: str = None,
+ rir_scp: str = None,
+ rir_apply_prob: float = 1.0,
+ noise_scp: str = None,
+ noise_apply_prob: float = 1.0,
+ noise_db_range: str = "3_10",
+ speech_volume_normalize: float = None,
+ speech_name: str = "speech",
+ text_name: str = "text",
+ split_text_name: str = "split_text",
+ split_with_space: bool = False,
+ seg_dict_file: str = None,
+ ):
+ super().__init__(
+ train=train,
+ # Force to use word.
+ token_type="word",
+ token_list=token_list,
+ bpemodel=bpemodel,
+ text_cleaner=text_cleaner,
+ g2p_type=g2p_type,
+ unk_symbol=unk_symbol,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ delimiter=delimiter,
+ speech_name=speech_name,
+ text_name=text_name,
+ rir_scp=rir_scp,
+ rir_apply_prob=rir_apply_prob,
+ noise_scp=noise_scp,
+ noise_apply_prob=noise_apply_prob,
+ noise_db_range=noise_db_range,
+ speech_volume_normalize=speech_volume_normalize,
+ split_with_space=split_with_space,
+ seg_dict_file=seg_dict_file,
+ )
+ # The data field name for split text.
+ self.split_text_name = split_text_name
+
+ @classmethod
+ def split_words(cls, text: str):
+ words = []
+ segs = text.split()
+ for seg in segs:
+ # There is no space in seg.
+ current_word = ""
+ for c in seg:
+ if len(c.encode()) == 1:
+ # This is an ASCII char.
+ current_word += c
+ else:
+ # This is a Chinese char.
+ if len(current_word) > 0:
+ words.append(current_word)
+ current_word = ""
+ words.append(c)
+ if len(current_word) > 0:
+ words.append(current_word)
+ return words
+
+ def __call__(
+ self, uid: str, data: Dict[str, Union[list, str, np.ndarray]]
+ ) -> Dict[str, Union[list, np.ndarray]]:
+ assert check_argument_types()
+ # Split words.
+ if isinstance(data[self.text_name], str):
+ split_text = self.split_words(data[self.text_name])
+ else:
+ split_text = data[self.text_name]
+ data[self.text_name] = " ".join(split_text)
+ data = self._speech_process(data)
+ data = self._text_process(data)
+ data[self.split_text_name] = split_text
+ return data
+
+ def pop_split_text_data(self, data: Dict[str, Union[str, np.ndarray]]):
+ result = data[self.split_text_name]
+ del data[self.split_text_name]
+ return result
+
+
+class PuncTrainTokenizerCommonPreprocessor(CommonPreprocessor):
+ def __init__(
+ self,
+ train: bool,
+ token_type: List[str] = [None],
+ token_list: List[Union[Path, str, Iterable[str]]] = [None],
+ bpemodel: List[Union[Path, str, Iterable[str]]] = [None],
+ text_cleaner: Collection[str] = None,
+ g2p_type: str = None,
+ unk_symbol: str = "<unk>",
+ space_symbol: str = "<space>",
+ non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
+ delimiter: str = None,
+ rir_scp: str = None,
+ rir_apply_prob: float = 1.0,
+ noise_scp: str = None,
+ noise_apply_prob: float = 1.0,
+ noise_db_range: str = "3_10",
+ speech_volume_normalize: float = None,
+ speech_name: str = "speech",
+ text_name: List[str] = ["text"],
+ vad_name: str = "vad_indexes",
+ ):
+ # TODO(jiatong): sync with Kamo and Jing on interface for preprocessor
+ super().__init__(
+ train=train,
+ token_type=token_type[0],
+ token_list=token_list[0],
+ bpemodel=bpemodel[0],
+ text_cleaner=text_cleaner,
+ g2p_type=g2p_type,
+ unk_symbol=unk_symbol,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ delimiter=delimiter,
+ speech_name=speech_name,
+ text_name=text_name[0],
+ rir_scp=rir_scp,
+ rir_apply_prob=rir_apply_prob,
+ noise_scp=noise_scp,
+ noise_apply_prob=noise_apply_prob,
+ noise_db_range=noise_db_range,
+ speech_volume_normalize=speech_volume_normalize,
+ )
+
+ assert (
+ len(token_type) == len(token_list) == len(bpemodel) == len(text_name)
+ ), "token_type, token_list, bpemodel, or processing text_name mismatched"
+ self.num_tokenizer = len(token_type)
+ self.tokenizer = []
+ self.token_id_converter = []
+
+ for i in range(self.num_tokenizer):
+ if token_type[i] is not None:
+ if token_list[i] is None:
+ raise ValueError("token_list is required if token_type is not None")
+
+ self.tokenizer.append(
+ build_tokenizer(
+ token_type=token_type[i],
+ bpemodel=bpemodel[i],
+ delimiter=delimiter,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ g2p_type=g2p_type,
+ )
+ )
+ self.token_id_converter.append(
+ TokenIDConverter(
+ token_list=token_list[i],
+ unk_symbol=unk_symbol,
+ )
+ )
+ else:
+ self.tokenizer.append(None)
+ self.token_id_converter.append(None)
+
+ self.text_cleaner = TextCleaner(text_cleaner)
+ self.text_name = text_name # override the text_name from CommonPreprocessor
+ self.vad_name = vad_name
+
+ def _text_process(
+ self, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, np.ndarray]:
+ for i in range(self.num_tokenizer):
+ text_name = self.text_name[i]
+ if text_name in data and self.tokenizer[i] is not None:
+ text = data[text_name]
+ text = self.text_cleaner(text)
+ tokens = self.tokenizer[i].text2tokens(text)
+ if "vad:" in tokens[-1]:
+ vad = tokens[-1][4:]
+ tokens = tokens[:-1]
+ if len(vad) == 0:
+ vad = -1
+ else:
+ vad = int(vad)
+ data[self.vad_name] = np.array([vad], dtype=np.int64)
+ text_ints = self.token_id_converter[i].tokens2ids(tokens)
+ data[text_name] = np.array(text_ints, dtype=np.int64)
+
+
+def split_to_mini_sentence(words: list, word_limit: int = 20):
+ assert word_limit > 1
+ if len(words) <= word_limit:
+ return [words]
+ sentences = []
+ length = len(words)
+ sentence_len = length // word_limit
+ for i in range(sentence_len):
+ sentences.append(words[i * word_limit:(i + 1) * word_limit])
+ if length % word_limit > 0:
+ sentences.append(words[sentence_len * word_limit:])
+ return sentences
+
+
+def build_preprocess(args, train):
+ if not args.use_preprocessor:
+ return None
+ if args.task_name in ["asr", "data2vec", "diar", "sv"]:
+ retval = CommonPreprocessor(
+ train=train,
+ token_type=args.token_type,
+ token_list=args.token_list,
+ bpemodel=args.bpemodel,
+ non_linguistic_symbols=args.non_linguistic_symbols if hasattr(args, "non_linguistic_symbols") else None,
+ text_cleaner=args.cleaner,
+ g2p_type=args.g2p,
+ split_with_space=args.split_with_space if hasattr(args, "split_with_space") else False,
+ seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
+ rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None,
+ rir_apply_prob=args.rir_apply_prob if hasattr(args, "rir_apply_prob") else 1.0,
+ noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None,
+ noise_apply_prob=args.noise_apply_prob if hasattr(args, "noise_apply_prob") else 1.0,
+ noise_db_range=args.noise_db_range if hasattr(args, "noise_db_range") else "13_15",
+ speech_volume_normalize=args.speech_volume_normalize if hasattr(args, "rir_scp") else None,
+ )
+ elif args.task_name == "punc":
+ token_types = [args.token_type, args.token_type]
+ token_lists = [args.token_list, args.punc_list]
+ bpemodels = [args.bpemodel, args.bpemodel]
+ text_names = ["text", "punc"]
+ retval = PuncTrainTokenizerCommonPreprocessor(
+ train=train,
+ token_type=token_types,
+ token_list=token_lists,
+ bpemodel=bpemodels,
+ text_cleaner=args.cleaner,
+ g2p_type=args.g2p,
+ text_name=text_names,
+ non_linguistic_symbols=args.non_linguistic_symbols,
+ )
+ elif args.task_name == "lm":
+ retval = LMPreprocessor(
+ train=train,
+ token_type=args.token_type,
+ token_list=args.token_list,
+ bpemodel=args.bpemodel,
+ text_cleaner=args.cleaner,
+ g2p_type=args.g2p,
+ text_name="text",
+ non_linguistic_symbols=args.non_linguistic_symbols,
+ split_with_space=args.split_with_space,
+ seg_dict_file=args.seg_dict_file
+ )
+ elif args.task_name == "vad":
+ retval = None
+ else:
+ raise ValueError(f"Not supported task={args.task_name}")
+ return retval
--
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