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Spring AI+硅基流动DeepSeek语音识别全栈方案：从FFmpeg预处理到分布式推理

news 来源：原创 2025/5/21 16:38:58

一、语音识别技术选型与硅基流动生态解析

1.1 DeepSeek-ASR核心优势

硅基流动平台提供的DeepSeek语音识别服务，在AISHELL-3测试集上实现**96.2%**的准确率，其技术特性包括：

多方言支持：覆盖普通话、粤语、川渝方言等8种语言变体
噪声抑制：采用Wave-U-Net降噪算法2
时间戳定位：支持词语级精度的音频定位（±50ms）
免费额度：新用户赠送2000万token（约处理1万小时音频）
价格便宜：新用户注册即送14元，而且可以自由充值。注册地址：硅基流动官网

1.2 Spring AI技术栈整合方案

通过Spring AI的统一AI模型接口，开发者可实现：

@Configuration 
public class AiConfig {
    @Bean 
    public DeepSeekAudioTranscriptionClient transcriptionClient() {
        return new DeepSeekAudioTranscriptionClient(
            new SiliconFlowService("sk-xxx"), 
            new AudioTranscriptionOptions());
    }
}

注意：使用spring-AI功能，必须保证springboot版本在3.0以上，且Java版本至少为17+。

二、环境搭建与SDK深度集成

2.1 硅基流动账号配置

访问硅基流动控制台，创建ASR专属应用
获取API密钥并配置Quota策略（建议设置QPS≤20）
下载Java SDK并导入本地Maven仓库：

<dependency>
    <groupId>cn.siliconflow</groupId> 
    <artifactId>deepseek-sdk</artifactId>
    <version>2.3.1</version>
</dependency>

创建秘钥地址如下图
在这里插入图片描述

2.2 Spring Boot工程配置

application.yml

siliconflow:
  api-key: sk-xxx 
  audio:
    endpoint: https://api.siliconflow.cn/v1/audio/transcriptions  
    max-duration: 3600 # 最大音频时长（秒）
    allowed-formats: [wav, mp3, flac]

三、工业级语音处理流水线设计

3.1 音频预处理模块

public AudioFile preprocessAudio(MultipartFile file) throws IOException {
    // FFmpeg格式转换 
    String cmd = String.format("ffmpeg  -i %s -ar 16000 -ac 1 %s", 
        file.getOriginalFilename(),  "output.wav"); 
    Runtime.getRuntime().exec(cmd); 
    
    // 分块处理（每5分钟一个块）
    return AudioSplitter.splitByDuration( 
        Paths.get("output.wav"),  Duration.ofMinutes(5)); 
}

3.2 异步批处理实现

@Async("audioTaskExecutor")
public CompletableFuture<Transcript> processChunk(AudioChunk chunk) {
    TranscriptionRequest request = new TranscriptionRequest(
        chunk.getPath(),  
        new TranscriptionParams(LanguageType.MANDARIN, true));
    
    return CompletableFuture.supplyAsync(()  -> 
        siliconFlowService.transcribe(request)); 
}

四、核心业务逻辑实现

4.1 控制器层实现

@PostMapping("/transcribe")
public ResponseEntity<TranscriptResult> transcribe(
    @RequestParam("file") MultipartFile file,
    @RequestParam(value = "diarization", defaultValue = "false") boolean diarization) {
    
    // 参数校验 
    if (!audioService.validateFormat(file))  {
        throw new InvalidAudioFormatException();
    }
    
    // 预处理与识别 
    AudioFile processed = audioService.preprocess(file); 
    List<CompletableFuture<Transcript>> futures = audioService.splitAndRecognize(processed); 
    
    // 结果合并 
    return ResponseEntity.ok(TranscriptMerger.merge(futures)); 
}

4.2 语音识别核心服务

@Service 
public class AudioTranscriptionService {
    private final SiliconFlowService sfService;
    private final ThreadPoolTaskExecutor executor;
    
    @Autowired 
    public AudioTranscriptionService(SiliconFlowService sfService) {
        this.sfService  = sfService;
        this.executor  = new ThreadPoolTaskExecutor();
        this.executor.setCorePoolSize(10); 
        this.executor.setMaxPoolSize(50); 
    }
    
    public Transcript recognize(Path audioPath) {
        TranscriptionRequest request = new TranscriptionRequest(
            audioPath, 
            new TranscriptionParams(LanguageType.MANDARIN, true));
        
        return sfService.transcribe(request) 
            .retryWhen(Retry.backoff(3,  Duration.ofSeconds(1))); 
    }
}

五、高级特性实现方案

5.1 说话人分离（Diarization）

public DiarizationResult diarize(Transcript transcript) {
    List<SpeakerSegment> segments = transcript.getSegments() 
        .stream()
        .filter(s -> s.getSpeakerTag()  != null)
        .collect(Collectors.groupingBy(Segment::getSpeakerTag)) 
        .entrySet()
        .stream()
        .map(e -> new SpeakerSegment(e.getKey(),  mergeText(e.getValue()))) 
        .collect(Collectors.toList()); 
    
    return new DiarizationResult(segments);
}

5.2 实时流式识别

@GetMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
public Flux<TranscriptChunk> streamTranscription(@RequestParam String audioUrl) {
    return WebClient.create() 
        .get()
        .uri(audioUrl)
        .accept(MediaType.APPLICATION_OCTET_STREAM)
        .retrieve()
        .bodyToFlux(DataBuffer.class) 
        .window(Duration.ofSeconds(5)) 
        .flatMap(window -> 
            sfService.streamTranscribe(window,  new TranscriptionParams()))
        .timeout(Duration.ofMinutes(30)); 
}

六、性能优化与生产部署

6.1 负载均衡策略

siliconflow:
  cluster-nodes:
    - host: node1.siliconflow.cn  
      weight: 30 
    - host: node2.siliconflow.cn   
      weight: 70

6.2 监控指标采集

@Bean 
public MeterRegistryCustomizer<PrometheusMeterRegistry> configureMetrics() {
    return registry -> {
        registry.config().meterFilter( 
            new MeterFilter() {
                @Override 
                public DistributionStatisticConfig configure(
                    Meter.Id id, 
                    DistributionStatisticConfig config) {
                    if (id.getName().contains("audio_transcription"))  {
                        return DistributionStatisticConfig.builder() 
                            .percentiles(0.5, 0.95, 0.99)
                            .build()
                            .merge(config);
                    }
                    return config;
                }
            });
    };
}

七、安全防护方案

7.1 音频文件病毒扫描

public void scanForMalware(Path filePath) throws VirusDetectedException {
    try (ClamAVClient client = new ClamAVClient("192.168.1.100", 3310)) {
        byte[] reply = client.scan(filePath); 
        if (!ClamAVClient.isCleanReply(reply))  {
            throw new VirusDetectedException(ClamAVClient.getResult(reply)); 
        }
    }
}

7.2 敏感词过滤

public Transcript filterSensitiveWords(Transcript transcript) {
    SensitiveWordFilter filter = new AhoCorasickFilter();
    return transcript.getSegments() 
        .stream()
        .map(segment -> 
            new Segment(
                filter.filter(segment.getText()), 
                segment.getStart(), 
                segment.getEnd())) 
        .collect(Transcript.collector()); 
}