From b5d3df75cf6462aa3bf42fd3c86fa2aa7f1c8a15 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 24 十一月 2023 00:54:44 +0800
Subject: [PATCH] setup jamo
---
funasr/datasets/small_datasets/preprocessor.py | 84 ++++++++++++++++++++++--------------------
1 files changed, 44 insertions(+), 40 deletions(-)
diff --git a/funasr/datasets/small_datasets/preprocessor.py b/funasr/datasets/small_datasets/preprocessor.py
index 4708cab..01a8c6f 100644
--- a/funasr/datasets/small_datasets/preprocessor.py
+++ b/funasr/datasets/small_datasets/preprocessor.py
@@ -9,13 +9,11 @@
import numpy as np
import scipy.signal
-import soundfile
-from typeguard import check_argument_types
-from typeguard import check_return_type
+import librosa
-from funasr.text.build_tokenizer import build_tokenizer
-from funasr.text.cleaner import TextCleaner
-from funasr.text.token_id_converter import TokenIDConverter
+from funasr.tokenizer.build_tokenizer import build_tokenizer
+from funasr.tokenizer.cleaner import TextCleaner
+from funasr.tokenizer.token_id_converter import TokenIDConverter
class AbsPreprocessor(ABC):
@@ -260,7 +258,6 @@
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]
@@ -278,7 +275,7 @@
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, _ = librosa.load(
rir_path, dtype=np.float64, always_2d=True
)
@@ -304,28 +301,30 @@
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}")
+ audio_data = librosa.load(noise_path, dtype='float32')[0][None, :]
+ frames = len(audio_data[0])
+ if frames == nsamples:
+ noise = audio_data
+ elif frames < nsamples:
+ offset = np.random.randint(0, nsamples - frames)
+ # noise: (Time, Nmic)
+ noise = audio_data
+ # Repeat noise
+ noise = np.pad(
+ noise,
+ [(offset, nsamples - frames - offset), (0, 0)],
+ mode="wrap",
+ )
+ else:
+ noise = audio_data[:, nsamples]
+ # offset = np.random.randint(0, 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
@@ -347,7 +346,6 @@
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(
@@ -365,13 +363,11 @@
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)
@@ -439,7 +435,6 @@
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
@@ -496,13 +491,11 @@
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:
@@ -606,7 +599,6 @@
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
@@ -685,7 +677,6 @@
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])
@@ -820,7 +811,7 @@
def build_preprocess(args, train):
- if args.use_preprocessor:
+ if not args.use_preprocessor:
return None
if args.task_name in ["asr", "data2vec", "diar", "sv"]:
retval = CommonPreprocessor(
@@ -828,7 +819,7 @@
token_type=args.token_type,
token_list=args.token_list,
bpemodel=args.bpemodel,
- non_linguistic_symbols=args.non_linguistic_symbols,
+ 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,
@@ -855,6 +846,19 @@
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:
--
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