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[紫薇六大资产·代码资产] speech_pipeline.py

By tat6…lkrp September 2, 2026

“”“
Speech-to-Speech AI Pipeline — 2026-07-05

热点信号：HuggingFace speech-to-speech趋势第一 / Gemini 3.5 Live Translate /
Google Gemini Omni全模态 / 实时语音翻译成为AGI标配
技术落地：语音→文本→推理→语音 全链路管道
→ ASR(语音识别) → NLU(语言理解) → NLG(生成) → TTS(语音合成)
→ 流式处理 + 低延迟优化
参考：hf.co/speech-to-speech, Gemini Omni, OpenAI Realtime API

独立运行: python speech_pipeline.py
“”“

import time
import hashlib
import random
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass, field
from collections import deque

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音频数据模型

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@dataclass
class AudioChunk:
“”“音频块 — 流式处理的基本单位”““
chunk_id: int
samples: List[float] # 16kHz PCM采样
sample_rate: int = 16000
duration_ms: float = 0.0
timestamp: float = 0.0

@property
def num_samples(self) -> int:
    return len(self.samples)

@property
def is_silence(self) -> bool:
    """检测静音段"""
    if not self.samples:
        return True
    rms = math.sqrt(sum(s * s for s in self.samples) / len(self.samples))
    return rms < 0.01

@dataclass
class SpeechSegment:
“”“语音段 — ASR后的结构化产物”““
text: str
language: str # zh, en, ja, etc.
confidence: float
start_ms: float
end_ms: float
speaker_id: Optional[int] = None
emotion: Optional[str] = None # neutral, happy, sad, angry

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前端: ASR — 语音识别

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import math

class SpeechRecognizer:
“”“语音识别器 — 模拟Whisper/DeepSpeech范式”““

# 多语言唤醒词/常见词映射
LANGUAGE_PROFILES = {
    "zh": {"confidence": 0.95, "keywords": ["你好", "谢谢", "请", "我", "你", "他", "是"]},
    "en": {"confidence": 0.92, "keywords": ["hello", "thanks", "please", "the", "I", "you"]},
    "ja": {"confidence": 0.88, "keywords": ["こんにちは", "ありがとう", "お願い"]},
}

def __init__(self, model_name: str = "whisper-v3-turbo"):
    self.model_name = model_name
    self.latency_ms = 80  # 模拟ASR延迟

def recognize(self, audio: AudioChunk) -> SpeechSegment:
    """单个音频块识别"""
    # 模拟语音识别: 基于能量分析+语言检测
    energy = sum(abs(s) for s in audio.samples) / max(len(audio.samples), 1)

    # 语言检测 (基于频率特征简化模拟)
    lang_scores = {}
    for lang, profile in self.LANGUAGE_PROFILES.items():
        # 模拟: 不同语言的频谱特征
        lang_scores[lang] = profile["confidence"]  *(0.8 + 0.2*  random.random())

    detected_lang = max(lang_scores, key=lang_scores.get)
    confidence = lang_scores[detected_lang]

    # 文本生成 (模拟)
    text = self._simulate_recognition(audio, detected_lang)

    return SpeechSegment(
        text=text,
        language=detected_lang,
        confidence=confidence,
        start_ms=audio.timestamp,
        end_ms=audio.timestamp + audio.duration_ms,
    )

def _simulate_recognition(self, audio: AudioChunk, lang: str) -> str:
    """模拟ASR输出"""
    energy = sum(abs(s) for s in audio.samples) / max(len(audio.samples), 1)
    templates = {
        "zh": ["你好，请问今天天气怎么样？", "我需要帮助处理这个问题。",
                "请把那个文件发给我。", "会议安排在下午三点。"],
        "en": ["Hello, what's the weather like today?", "I need help with this issue.",
               "Please send me that file.", "The meeting is at 3 PM."],
        "ja": ["こんにちは、今日の天気はどうですか？", "この問題について助けが必要です。",
               "そのファイルを送ってください。", "会議は午後3時です。"],
    }
    idx = audio.chunk_id % len(templates.get(lang, templates["en"]))
    return templates.get(lang, templates["en"])[idx]

def recognize_stream(self, audio_stream: List[AudioChunk]) -> List[SpeechSegment]:
    """流式识别"""
    segments = []
    for chunk in audio_stream:
        if not chunk.is_silence:
            seg = self.recognize(chunk)
            segments.append(seg)
    return segments

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中端: NLU + NLG — 语言理解与生成

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class LanguageProcessor:
“”“语言处理 — 翻译+推理+生成”““

def __init__(self):
    self.translation_cache: Dict[str, str] = {}

def translate(self, text: str, source_lang: str, target_lang: str) -> str:
    """实时翻译 (Gemini 3.5 Live Translate范式)"""
    cache_key = f"{source_lang}→{target_lang}:{text}"
    if cache_key in self.translation_cache:
        return self.translation_cache[cache_key]

    # 模拟翻译映射表
    translations = {
        ("zh", "en"): {
            "你好，请问今天天气怎么样？": "Hello, what's the weather like today?",
            "我需要帮助处理这个问题。": "I need help with this issue.",
            "请把那个文件发给我。": "Please send me that file.",
            "会议安排在下午三点。": "The meeting is scheduled at 3 PM.",
        },
        ("en", "zh"): {
            "Hello, what's the weather like today?": "你好，请问今天天气怎么样？",
            "I need help with this issue.": "我需要帮助处理这个问题。",
            "Please send me that file.": "请把那个文件发给我。",
            "The meeting is at 3 PM.": "会议安排在下午三点。",
        },
    }

    result = translations.get((source_lang, target_lang), {}).get(
        text, f"[TRANSLATED:{source_lang}→{target_lang}] {text}"
    )
    self.translation_cache[cache_key] = result
    return result

def comprehend(self, text: str) -> dict:
    """意图理解 — 提取结构化信息"""
    intents = {
        "greeting": ["hello", "hi", "你好", "こんにちは"],
        "question": ["what", "how", "why", "什么", "怎么", "为什么"],
        "request": ["please", "send", "help", "请", "发", "帮"],
        "schedule": ["meeting", "会议", "schedule", "安排"],
        "weather": ["weather", "天气", "天気"],
    }

    text_lower = text.lower()
    detected = []
    for intent, keywords in intents.items():
        if any(kw in tex

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