初始化项目，由ModelHub XC社区提供模型

Model: OpenBMB/MiniCPM-o-2_6
Source: Original Platform

utils.py

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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