最新TTS技术突破：从ElevenLabs到OpenAI的语音合成革命

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

# ElevenLabs API 集成示例
from elevenlabs import generate, set_api_key, Voice, VoiceSettings
import numpy as np

class ElevenLabsTTS:
    def __init__(self, api_key):
        set_api_key(api_key)
        self.voice_settings = VoiceSettings(
            stability=0.75,  # 语音稳定性
            similarity_boost=0.85,  # 相似度增强
            style=0.5,  # 风格强度
            use_speaker_boost=True  # 说话人增强
        )
    
    def generate_speech(self, text, voice_id="21m00Tcm4TlvDq8ikWAM"):
        """生成超逼真语音"""
        audio = generate(
            text=text,
            voice=Voice(
                voice_id=voice_id,
                settings=self.voice_settings
            ),
            model="eleven_turbo_v2_5"  # 最新模型
        )
        return audio
    
    def clone_voice(self, audio_samples):
        """语音克隆 - 仅需1分钟样本"""
        from elevenlabs import clone
        
        voice = clone(
            name="Custom Voice",
            files=audio_samples,
            description="Cloned voice with minimal data"
        )
        return voice.voice_id

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46

class MultilingualTTS:
    def __init__(self):
        self.supported_languages = {
            'en': 'English',
            'zh': 'Chinese',
            'es': 'Spanish',
            'fr': 'French',
            'de': 'German',
            'ja': 'Japanese',
            'ko': 'Korean',
            'ar': 'Arabic'
        }
        
    def generate_multilingual(self, text, source_lang, target_accent):
        """跨语言语音生成与口音控制"""
        from elevenlabs import generate, Voice
        
        audio = generate(
            text=text,
            voice=Voice(
                voice_id="multilingual_v2",
                settings={
                    "source_language": source_lang,
                    "target_accent": target_accent,
                    "accent_strength": 0.7,
                    "preserve_emotion": True
                }
            ),
            model="eleven_multilingual_v2"
        )
        return audio
    
    def code_switching(self, mixed_text):
        """处理混合语言文本"""
        segments = self.detect_language_segments(mixed_text)
        audio_segments = []
        
        for segment in segments:
            audio = self.generate_multilingual(
                segment['text'],
                segment['language'],
                segment.get('accent', 'neutral')
            )
            audio_segments.append(audio)
        
        return self.concatenate_audio(audio_segments)

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65

import websocket
import json
import asyncio
from typing import Optional, Callable

class OpenAIRealtimeVoice:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.ws_url = "wss://api.openai.com/v1/realtime"
        self.session_config = {
            "modalities": ["text", "audio"],
            "voice": "alloy",  # 可选: alloy, echo, fable, onyx, nova, shimmer
            "instructions": "You are a helpful assistant",
            "input_audio_format": "pcm16",
            "output_audio_format": "pcm16",
            "turn_detection": {
                "type": "server_vad",
                "threshold": 0.5,
                "prefix_padding_ms": 300,
                "silence_duration_ms": 500
            }
        }
    
    async def connect(self):
        """建立WebSocket连接"""
        self.ws = await websocket.create_connection(
            self.ws_url,
            header={"Authorization": f"Bearer {self.api_key}"}
        )
        
        # 发送会话配置
        await self.send_event({
            "type": "session.update",
            "session": self.session_config
        })
    
    async def stream_audio(self, audio_callback: Callable):
        """实时音频流处理"""
        while True:
            try:
                message = await self.ws.recv()
                event = json.loads(message)
                
                if event["type"] == "response.audio.delta":
                    # 处理音频增量
                    audio_chunk = event["delta"]
                    await audio_callback(audio_chunk)
                    
                elif event["type"] == "response.audio.done":
                    # 音频生成完成
                    break
                    
                elif event["type"] == "conversation.item.created":
                    # 新对话项创建
                    self.handle_conversation_item(event["item"])
                    
            except Exception as e:
                print(f"Error in audio stream: {e}")
                break
    
    def handle_interruption(self):
        """处理用户打断"""
        self.send_event({
            "type": "response.cancel"
        })

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38

class AdvancedVoiceFeatures:
    def __init__(self):
        self.emotion_map = {
            "happy": {"pitch": 1.1, "speed": 1.05, "energy": 1.2},
            "sad": {"pitch": 0.9, "speed": 0.95, "energy": 0.8},
            "excited": {"pitch": 1.2, "speed": 1.15, "energy": 1.3},
            "calm": {"pitch": 1.0, "speed": 0.9, "energy": 0.9},
            "angry": {"pitch": 1.05, "speed": 1.1, "energy": 1.25}
        }
    
    def apply_emotion(self, text: str, emotion: str):
        """应用情感参数"""
        params = self.emotion_map.get(emotion, {})
        
        return {
            "type": "response.create",
            "response": {
                "modalities": ["audio"],
                "instructions": f"Speak with {emotion} emotion",
                "voice_settings": params,
                "input": [{
                    "type": "input_text",
                    "text": text
                }]
            }
        }
    
    def function_calling_with_voice(self, function_name: str, parameters: dict):
        """语音函数调用"""
        return {
            "type": "conversation.item.create",
            "item": {
                "type": "function_call",
                "name": function_name,
                "arguments": json.dumps(parameters),
                "voice_response": True  # 启用语音响应
            }
        }

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60

import torch
import torch.nn as nn
from transformers import AutoModel

class EmotionalTTS(nn.Module):
    def __init__(self, model_name="microsoft/speecht5_tts"):
        super().__init__()
        self.base_model = AutoModel.from_pretrained(model_name)
        
        # 情感嵌入层
        self.emotion_embedding = nn.Embedding(32, 256)
        
        # 韵律控制网络
        self.prosody_controller = nn.Sequential(
            nn.Linear(256, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.Tanh()
        )
        
        # 风格迁移网络
        self.style_transfer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=256, nhead=8),
            num_layers=4
        )
    
    def forward(self, text_ids, emotion_id, intensity=1.0):
        """生成带情感的语音"""
        # 文本编码
        text_features = self.base_model.encoder(text_ids)
        
        # 情感编码
        emotion_features = self.emotion_embedding(emotion_id)
        emotion_features = self.prosody_controller(emotion_features)
        
        # 强度调节
        emotion_features = emotion_features * intensity
        
        # 特征融合
        combined_features = text_features + emotion_features.unsqueeze(1)
        
        # 风格迁移
        styled_features = self.style_transfer(combined_features)
        
        # 声学解码
        mel_outputs = self.base_model.decoder(styled_features)
        
        return mel_outputs
    
    def control_prosody(self, mel_spec, pitch_shift=0, speed_ratio=1.0):
        """精细韵律控制"""
        # 基频调整
        if pitch_shift != 0:
            mel_spec = self.shift_pitch(mel_spec, pitch_shift)
        
        # 语速调整
        if speed_ratio != 1.0:
            mel_spec = self.adjust_speed(mel_spec, speed_ratio)
        
        return mel_spec

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40

class ContextAwareEmotionalTTS:
    def __init__(self):
        self.context_window = 5  # 考虑前5句话的上下文
        self.emotion_history = []
        
    def analyze_context(self, text_history):
        """分析上下文情感"""
        from transformers import pipeline
        
        emotion_classifier = pipeline(
            "text-classification",
            model="j-hartmann/emotion-english-distilroberta-base"
        )
        
        emotions = []
        for text in text_history[-self.context_window:]:
            result = emotion_classifier(text)[0]
            emotions.append({
                'label': result['label'],
                'score': result['score']
            })
        
        return self.compute_emotion_trajectory(emotions)
    
    def compute_emotion_trajectory(self, emotions):
        """计算情感轨迹"""
        import numpy as np
        
        # 情感状态转移矩阵
        emotion_transitions = {
            'joy': {'joy': 0.7, 'neutral': 0.2, 'surprise': 0.1},
            'sadness': {'sadness': 0.6, 'neutral': 0.3, 'anger': 0.1},
            'anger': {'anger': 0.5, 'neutral': 0.3, 'sadness': 0.2},
            'neutral': {'neutral': 0.5, 'joy': 0.2, 'sadness': 0.2, 'surprise': 0.1}
        }
        
        current_emotion = emotions[-1]['label'] if emotions else 'neutral'
        next_emotion_probs = emotion_transitions.get(current_emotion, {})
        
        return next_emotion_probs

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74

import asyncio
from collections import deque
import numpy as np

class RealtimeTTSPipeline:
    def __init__(self):
        self.audio_buffer = deque(maxlen=100)  # 100ms缓冲
        self.processing_queue = asyncio.Queue()
        self.output_stream = None
        
    async def process_pipeline(self):
        """实时处理管道"""
        tasks = [
            self.text_streaming(),
            self.tts_processing(),
            self.audio_streaming(),
            self.latency_optimization()
        ]
        await asyncio.gather(*tasks)
    
    async def text_streaming(self):
        """文本流处理"""
        async for text_chunk in self.receive_text():
            # 智能分句
            sentences = self.smart_sentence_split(text_chunk)
            
            for sentence in sentences:
                await self.processing_queue.put({
                    'text': sentence,
                    'timestamp': asyncio.get_event_loop().time(),
                    'priority': self.calculate_priority(sentence)
                })
    
    async def tts_processing(self):
        """TTS处理与优化"""
        while True:
            item = await self.processing_queue.get()
            
            # 并行处理多个TTS请求
            audio_task = asyncio.create_task(
                self.generate_audio_chunk(item['text'])
            )
            
            # 预测性缓存
            if self.should_prefetch(item['text']):
                prefetch_task = asyncio.create_task(
                    self.prefetch_common_phrases(item['text'])
                )
            
            audio = await audio_task
            self.audio_buffer.append(audio)
    
    def smart_sentence_split(self, text):
        """智能句子分割"""
        import re
        
        # 基于标点和语义的分割
        sentences = re.split(r'[。！？；.!?;]', text)
        
        # 处理过短句子
        merged_sentences = []
        buffer = ""
        
        for sentence in sentences:
            if len(sentence) < 10:
                buffer += sentence
            else:
                if buffer:
                    merged_sentences.append(buffer + sentence)
                    buffer = ""
                else:
                    merged_sentences.append(sentence)
        
        return merged_sentences

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

class LatencyOptimizer:
    def __init__(self):
        self.cache = {}
        self.prediction_model = self.load_prediction_model()
        
    def optimize_latency(self, text):
        """多维度延迟优化"""
        optimizations = []
        
        # 1. 流式处理优化
        if self.is_streamable(text):
            optimizations.append(self.enable_streaming(text))
        
        # 2. 缓存优化
        cached_audio = self.check_cache(text)
        if cached_audio:
            return cached_audio
        
        # 3. 模型量化
        if self.should_quantize():
            optimizations.append(self.apply_quantization())
        
        # 4. 批处理优化
        if self.can_batch():
            optimizations.append(self.batch_processing())
        
        return optimizations
    
    def enable_streaming(self, text):
        """启用流式生成"""
        return {
            'mode': 'streaming',
            'chunk_size': 256,  # 256个token一个块
            'overlap': 32,      # 32个token重叠
            'first_chunk_priority': 'high'
        }
    
    def apply_quantization(self):
        """应用模型量化"""
        return {
            'quantization': 'int8',
            'dynamic': True,
            'calibration_samples': 1000
        }

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59

class EdgeTTSDeployment:
    def __init__(self):
        self.model_size_limits = {
            'mobile': 50,    # 50MB
            'embedded': 20,  # 20MB
            'web': 10        # 10MB (WASM)
        }
    
    def optimize_for_edge(self, model, target='mobile'):
        """边缘设备优化"""
        import torch.quantization as quant
        
        # 1. 模型剪枝
        pruned_model = self.prune_model(
            model,
            sparsity=0.5,
            structured=True
        )
        
        # 2. 知识蒸馏
        student_model = self.distill_model(
            teacher=model,
            student_size=self.model_size_limits[target]
        )
        
        # 3. 量化
        quantized_model = quant.quantize_dynamic(
            student_model,
            {nn.Linear, nn.Conv1d},
            dtype=torch.qint8
        )
        
        # 4. ONNX转换
        self.export_to_onnx(quantized_model, f"{target}_tts.onnx")
        
        return quantized_model
    
    def prune_model(self, model, sparsity=0.5, structured=True):
        """结构化剪枝"""
        import torch.nn.utils.prune as prune
        
        for name, module in model.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv1d)):
                if structured:
                    prune.ln_structured(
                        module,
                        name='weight',
                        amount=sparsity,
                        n=2,
                        dim=0
                    )
                else:
                    prune.l1_unstructured(
                        module,
                        name='weight',
                        amount=sparsity
                    )
        
        return model

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41

class DistributedTTS:
    def __init__(self, num_workers=4):
        self.num_workers = num_workers
        self.load_balancer = self.setup_load_balancer()
        
    async def distributed_inference(self, texts):
        """分布式推理"""
        import ray
        
        ray.init()
        
        @ray.remote(num_gpus=0.25)
        class TTSWorker:
            def __init__(self):
                self.model = self.load_model()
            
            def process(self, text):
                return self.model.generate(text)
        
        # 创建工作节点
        workers = [TTSWorker.remote() for _ in range(self.num_workers)]
        
        # 分配任务
        futures = []
        for i, text in enumerate(texts):
            worker = workers[i % self.num_workers]
            futures.append(worker.process.remote(text))
        
        # 收集结果
        results = ray.get(futures)
        
        return results
    
    def setup_load_balancer(self):
        """设置负载均衡"""
        return {
            'strategy': 'round_robin',
            'health_check_interval': 30,
            'timeout': 5000,
            'retry_count': 3
        }

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

class CustomerServiceTTS:
    def __init__(self):
        self.emotion_detector = EmotionDetector()
        self.tts_engine = ElevenLabsTTS(api_key="...")
        
    def handle_customer_interaction(self, customer_text, sentiment_score):
        """智能客服语音响应"""
        # 1. 情感分析
        customer_emotion = self.emotion_detector.analyze(customer_text)
        
        # 2. 响应策略
        response_params = self.determine_response_strategy(
            customer_emotion,
            sentiment_score
        )
        
        # 3. 生成个性化语音
        if sentiment_score < 0.3:  # 不满意客户
            voice_settings = {
                'empathy': 'high',
                'pace': 'slower',
                'tone': 'apologetic'
            }
        else:
            voice_settings = {
                'friendliness': 'high',
                'energy': 'medium',
                'tone': 'helpful'
            }
        
        audio = self.tts_engine.generate_with_emotion(
            response_params['text'],
            voice_settings
        )
        
        return audio

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

class ContentCreationTTS:
    def __init__(self):
        self.voice_library = self.load_voice_library()
        
    def create_audiobook(self, book_text, narrator_style='dramatic'):
        """有声书制作"""
        chapters = self.split_chapters(book_text)
        audio_book = []
        
        for chapter in chapters:
            # 识别角色对话
            dialogues = self.extract_dialogues(chapter)
            
            for segment in dialogues:
                if segment['type'] == 'narration':
                    voice = self.voice_library['narrator'][narrator_style]
                else:
                    # 为不同角色分配不同声音
                    character = segment['character']
                    voice = self.assign_character_voice(character)
                
                audio = self.generate_segment(
                    segment['text'],
                    voice,
                    segment.get('emotion', 'neutral')
                )
                
                audio_book.append(audio)
        
        return self.merge_audio_segments(audio_book)

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

class RealtimeVoiceAssistant:
    def __init__(self):
        self.asr = QwenAudioASR()  # 使用Qwen-Audio进行语音识别
        self.llm = OpenAIChat()     # LLM处理
        self.tts = ElevenLabsTTS()  # TTS输出
        
    async def run(self):
        """主循环"""
        print("Voice Assistant Started...")
        
        async with self.create_audio_stream() as stream:
            while True:
                # 1. 语音输入
                audio_chunk = await stream.read()
                
                # 2. ASR识别
                text = await self.asr.transcribe_streaming(audio_chunk)
                
                if text:
                    print(f"User: {text}")
                    
                    # 3. LLM处理
                    response = await self.llm.generate_response(text)
                    print(f"Assistant: {response}")
                    
                    # 4. TTS合成
                    audio_response = await self.tts.synthesize_streaming(
                        response,
                        emotion=self.detect_response_emotion(response)
                    )
                    
                    # 5. 播放音频
                    await stream.write(audio_response)
    
    def detect_response_emotion(self, text):
        """检测响应情感"""
        if "sorry" in text.lower() or "apologize" in text.lower():
            return "apologetic"
        elif "!" in text:
            return "excited"
        elif "?" in text:
            return "curious"
        else:
            return "friendly"