到ai智能工牌的录音后上传到minio并保存数据库的逻辑

src/test/java/com/rj/audio/SenseVoiceAsrTest.java

@@ -0,0 +1,96 @@
+package com.rj.audio;
+
+import org.springframework.core.io.ByteArrayResource;
+import org.springframework.http.*;
+import org.springframework.util.LinkedMultiValueMap;
+import org.springframework.util.MultiValueMap;
+import org.springframework.web.client.RestTemplate;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+
+public class SenseVoiceAsrTest {
+    // Xinference集群地址（替换为你的实际地址）
+    private static final String XINFERENCE_ASR_URL = "http://101.35.52.237:19997/v1/audio/transcriptions";
+    // 已部署的SenseVoiceSmall模型名称（必须与部署时一致）
+    private static final String MODEL_NAME = "SenseVoiceSmall";
+    // 本地测试音频文件路径（需是模型支持的格式，如.wav）
+    private static final String AUDIO_FILE_PATH = "D:\\bCard\\code\\Langchain4j-rj\\output_longyingling.mp3"; // 替换为你的音频文件路径
+//    private static final String AUDIO_FILE_PATH = "http://101.35.52.237:19005/car/20251027_141728_de21b339944c4e43867afc903a511905.mp3?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=minioadmin%2F20251102%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20251102T012236Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=9ceb9ea5361da2dbf830e5c0ad68c1a0cb73573cf039148225f53457d83e1b47"; // 替换为你的音频文件路径
+
+    public static void main(String[] args) {
+        // 1. 初始化RestTemplate
+        RestTemplate restTemplate = new RestTemplate();
+
+        try {
+            // 2. 读取音频文件为字节数组
+            byte[] audioData = Files.readAllBytes(Paths.get(AUDIO_FILE_PATH));
+
+            // 3. 构造multipart/form-data请求体
+            MultiValueMap<String, Object> body = new LinkedMultiValueMap<>();
+            // 添加模型参数
+            body.add("model", MODEL_NAME);
+            // 添加音频文件（key固定为"file"，文件名可自定义）
+            body.add("file", new ByteArrayResource(audioData) {
+                @Override
+                public String getFilename() {
+                    return "output_longyingling.mp3"; // 必须指定文件名（含扩展名）
+                }
+            });
+            // 可选参数：指定语言（如中文"zh-CN"，根据模型支持添加）
+            body.add("language", "zh-CN");
+
+            // 4. 设置请求头（multipart/form-data）
+            HttpHeaders headers = new HttpHeaders();
+            headers.setContentType(MediaType.MULTIPART_FORM_DATA);
+            HttpEntity<MultiValueMap<String, Object>> request = new HttpEntity<>(body, headers);
+
+            // 5. 发送POST请求，获取转录结果
+            ResponseEntity<String> response = restTemplate.postForEntity(
+                    XINFERENCE_ASR_URL,
+                    request,
+                    String.class
+            );
+            /**
+             * {
+             *     "text": "转录文本",
+             *     "audio_duration": 13.114,      # 音频时长（秒）
+             *     "transcription_time": 0.337,   # 转录耗时（秒）
+             *     "segment_count": 1,              # 分段数
+             *     "char_count": 50                # 字数（新增）
+             * }
+             */
+            System.out.println("音频识别结果：" + response.toString());
+            // 6. 处理响应（解析JSON获取文本）
+            if (response.getStatusCode() == HttpStatus.OK) {
+                String responseBody = response.getBody();
+                // 解析JSON：Xinference返回格式通常为 {"text": "识别结果文本"}
+                // 实际项目中建议使用Jackson/Gson解析，这里简化处理
+                String transcription = extractTextFromJson(responseBody);
+                System.out.println("音频识别结果：" + transcription);
+            } else {
+                System.out.println("请求失败，状态码：" + response.getStatusCode());
+                System.out.println("错误信息：" + response.getBody());
+            }
+
+        } catch (IOException e) {
+            System.err.println("读取音频文件失败：" + e.getMessage());
+            e.printStackTrace();
+        } catch (Exception e) {
+            System.err.println("API调用失败：" + e.getMessage());
+            e.printStackTrace();
+        }
+    }
+
+    // 简易JSON解析（提取"text"字段）
+    private static String extractTextFromJson(String json) {
+        if (json == null || !json.contains("\"text\":")) {
+            return "解析失败，响应：" + json;
+        }
+        int start = json.indexOf("\"text\":") + 7;
+        int end = json.indexOf("}", start);
+        return json.substring(start, end).replace("\"", "").trim();
+    }
+}

src/test/java/com/rj/audio/SenseVoiceByXinferenceTest.java

@@ -0,0 +1,59 @@
+package com.rj.audio;
+
+import org.springframework.http.*;
+import org.springframework.web.client.RestTemplate;
+import java.io.File;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.util.HashMap;
+import java.util.Map;
+
+public class SenseVoiceByXinferenceTest {
+    // Xinference 集群地址（替换为你的实际地址）
+    private static final String XINFERENCE_URL = "http://192.168.1.39:9997/v1/audio/speech";
+    // 已部署的模型名称
+    private static final String MODEL_NAME = "SenseVoiceSmall";
+
+    public static void main(String[] args) {
+        // 1. 初始化 RestTemplate
+        RestTemplate restTemplate = new RestTemplate();
+
+        // 2. 构造请求体：指定模型、输入文本
+        Map<String, Object> requestBody = new HashMap<>();
+        requestBody.put("model", MODEL_NAME);
+        requestBody.put("input", "这是一段测试SenseVoiceSmall模型的语音合成文本。");
+        // 可选参数：调整语音风格、语速等（根据模型支持的参数添加）
+        requestBody.put("voice", "default"); // 语音风格
+        requestBody.put("speed", 1.0);       // 语速（1.0为正常）
+
+        // 3. 设置请求头为 JSON 格式
+        HttpHeaders headers = new HttpHeaders();
+        headers.setContentType(MediaType.APPLICATION_JSON);
+        HttpEntity<Map<String, Object>> request = new HttpEntity<>(requestBody, headers);
+
+        try {
+            // 4. 发送 POST 请求，获取音频二进制数据
+            ResponseEntity<byte[]> response = restTemplate.postForEntity(
+                    XINFERENCE_URL,
+                    request,
+                    byte[].class
+            );
+
+            // 5. 处理响应：保存音频到本地
+            if (response.getStatusCode() == HttpStatus.OK) {
+                byte[] audioData = response.getBody();
+                if (audioData != null) {
+                    String outputPath = "sense_voice_test.wav";
+                    try (FileOutputStream fos = new FileOutputStream(new File(outputPath))) {
+                        fos.write(audioData);
+                        System.out.println("语音合成成功，已保存到：" + outputPath);
+                    }
+                }
+            } else {
+                System.out.println("请求失败，状态码：" + response.getStatusCode());
+            }
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+    }
+}

src/test/java/com/rj/service/FaceDetectImageCountTest.java

@@ -255,6 +255,8 @@ public class FaceDetectImageCountTest {
 
 
 
+
+
 
 
 

src/test/java/com/rj/service/TtsRequestLogShortUrlTest.java

@@ -196,6 +196,8 @@ public class TtsRequestLogShortUrlTest {
 
 
 
+
+
 
 
 

src/test/java/com/rj/service/VideoSynthesisTempUrlTest.java

@@ -174,6 +174,8 @@ public class VideoSynthesisTempUrlTest {
 
 
 
+
+
 
 
 

src/test/java/com/rj/service/VideoSynthesisVideoNameTest.java

@@ -149,6 +149,8 @@ public class VideoSynthesisVideoNameTest {
 
 
 
+
+