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# coding=utf-8
# Copyright 2025 The OpenBMB Team. 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 logging
import re
import librosa
import numpy as np
logger = logging.getLogger(__name__)
def is_silent(data):
if np.abs(data).max() < 3e-3:
return True
else:
return False
def sentence_end(txt):
for c in [".", "。", "!", "?", "！", "？"]:
if c in txt:
if c == ".": # check not number before it like 1.
idx = txt.find(c)
if idx > 0:
if txt[idx - 1].isdigit():
continue
return c
return ""
class NumberToTextConverter:
r"""
A helper class to ensure text-to-speech (TTS) systems read numeric digits
in the desired language (Chinese or English) digit-by-digit. It forcibly
replaces all numeric substrings in text with their language-specific
textual representations, thereby reducing the likelihood of TTS mistakes
on numbers.
Note: MiniCPM-o 2.6 only use this in streaming mode.
Attributes:
num_to_chinese (dict):
Mapping from digit (str) to its Chinese textual form (str).
num_to_english (dict):
Mapping from digit (str) to its English textual form (str).
Example:
>>> converter = NumberToTextConverter()
>>> converter.replace_numbers_with_text("我有2个苹果", language="chinese")
'我有两个苹果'
>>> converter.replace_numbers_with_text("I have 23 books", language="english")
'I have two three books'
"""
def __init__(self):
self.num_to_chinese = {
"0": "零",
"1": "一",
"2": "二",
"3": "三",
"4": "四",
"5": "五",
"6": "六",
"7": "七",
"8": "八",
"9": "九",
}
self.num_to_english = {
"0": "zero",
"1": "one",
"2": "two",
"3": "three",
"4": "four",
"5": "five",
"6": "six",
"7": "seven",
"8": "eight",
"9": "nine",
}
def number_to_chinese_digit_by_digit(self, num_str):
result = ""
for char in num_str:
if char in self.num_to_chinese:
result += self.num_to_chinese[char]
return result
def number_to_english_digit_by_digit(self, num_str):
result = []
for char in num_str:
if char in self.num_to_english:
result.append(self.num_to_english[char])
return " ".join(result)
def detect_language(self, text):
chinese_count = len(re.findall(r"[\u4e00-\u9fff]", text))
english_count = len(re.findall(r"[a-zA-Z]", text))
return "chinese" if chinese_count >= english_count else "english"
def replace_numbers_with_text(self, text, language=None):
if language is None:
language = self.detect_language(text)
numbers = re.findall(r"\d+", text)
for num in numbers:
if language == "chinese":
replacement = self.number_to_chinese_digit_by_digit(num)
else:
replacement = self.number_to_english_digit_by_digit(num)
text = text.replace(num, replacement, 1)
return text
class VoiceChecker:
r"""
A simple utility class to detect silence or low variation in consecutive audio chunks by comparing
the mel-spectrogram distances. It keeps track of consecutive zero-distance and low-distance chunks
to decide if the audio is considered "bad" (e.g., overly silent or not changing enough).
Attributes:
previous_mel (`np.ndarray` or `None`):
Holds the previously observed mel-spectrogram in decibel scale. Used to compute
the next distance; reset via :meth:`reset`.
consecutive_zeros (`int`):
The number of consecutive chunks that were detected as silent (distance = 0).
consecutive_low_distance (`int`):
The number of consecutive chunks whose distance was below the threshold.
Example:
>>> checker = VoiceChecker()
>>> # Suppose we have audio_wav (list or np.ndarray) and mel_spec (np.ndarray)
>>> # We split them into chunks and call checker.is_bad(...)
>>> is_audio_bad = checker.is_bad(audio_wav, mel_spec, chunk_size=2560, thresh=100.0)
>>> if is_audio_bad:
... print("Audio deemed bad!")
>>> # Reset states if needed
>>> checker.reset()
"""
def __init__(self):
self.previous_mel = None
self.consecutive_zeros = 0
self.consecutive_low_distance = 0
def compute_distance(self, audio_chunk, mel_spec):
if is_silent(audio_chunk):
return 0.0 # 检查是否为空白片段
mel_db = librosa.power_to_db(mel_spec)
if self.previous_mel is None:
self.previous_mel = mel_db
return -1.0
distance = np.linalg.norm(np.mean(mel_db, axis=1) - np.mean(self.previous_mel, axis=1))
self.previous_mel = mel_db
return distance
def is_bad(self, audio_wav, mel_spec, chunk_size=2560, thresh=100.0):
num_chunks = len(audio_wav) // chunk_size
mel_chunk_size = mel_spec.shape[-1] // num_chunks
for i in range(num_chunks):
audio_chunk = audio_wav[i * chunk_size : (i + 1) * chunk_size]
mel_spec_chunk = mel_spec[:, i * mel_chunk_size : (i + 1) * mel_chunk_size]
distance = self.compute_distance(audio_chunk, mel_spec_chunk)
logger.warning(
f"mel dist: {distance:.1f}, zero: {self.consecutive_zeros}, low: {self.consecutive_low_distance}"
)
if distance == 0:
self.consecutive_low_distance = 0 # reset
self.consecutive_zeros += 1
if self.consecutive_zeros >= 12:
logger.warning("VoiceChecker detected 1.2 s silent. Marking as failed.")
return True
elif distance < thresh:
self.consecutive_zeros = 0
self.consecutive_low_distance += 1
if self.consecutive_low_distance >= 5:
logger.warning("VoiceChecker detected 5 consecutive low distance chunks. Marking as failed.")
return True
else:
self.consecutive_low_distance = 0
self.consecutive_zeros = 0
return False
def reset(self):
self.previous_mel = None
self.consecutive_zeros = 0
self.consecutive_low_distance = 0
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