300 lines
10 KiB
C++
300 lines
10 KiB
C++
#include "whisper.h"
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#include <portaudio.h>
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#include <cstdio>
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#include <cstdlib>
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#include <cstring>
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#include <ctime>
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#include <iostream>
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#include <vector>
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#include <string>
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// ====================== 1. 枚举并选择麦克风设备（纯PortAudio原生实现） ======================
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void enumerate_audio_devices() {
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PaError err = Pa_Initialize();
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if (err != paNoError) {
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fprintf(stderr, "❌ PortAudio初始化失败: %s\n", Pa_GetErrorText(err));
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return;
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}
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int numDevices = Pa_GetDeviceCount();
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printf("\n📜 系统可用麦克风设备列表：\n");
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printf("=============================================\n");
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for (int i = 0; i < numDevices; i++) {
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const PaDeviceInfo* pInfo = Pa_GetDeviceInfo(i);
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// 只显示输入设备（麦克风，至少1个输入声道）
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if (pInfo->maxInputChannels > 0) {
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printf("🔧 设备ID: %d | 名称: %s\n", i, pInfo->name);
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printf(" 最大输入声道: %d | 默认采样率: %.1f Hz\n",
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pInfo->maxInputChannels, pInfo->defaultSampleRate);
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printf("---------------------------------------------\n");
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}
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}
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printf("=============================================\n\n");
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Pa_Terminate();
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}
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int select_mic_device() {
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int selected_id = -1;
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printf("👉 请输入你要使用的麦克风设备ID（比如苹果耳机对应的ID）：");
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std::cin >> selected_id;
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// 验证设备ID有效性
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PaError err = Pa_Initialize();
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if (err != paNoError) {
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fprintf(stderr, "❌ PortAudio初始化失败: %s\n", Pa_GetErrorText(err));
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return -1;
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}
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int numDevices = Pa_GetDeviceCount();
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if (selected_id < 0 || selected_id >= numDevices) {
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fprintf(stderr, "❌ 设备ID无效！请输入列表中的有效ID\n");
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Pa_Terminate();
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return -1;
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}
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const PaDeviceInfo* pInfo = Pa_GetDeviceInfo(selected_id);
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if (pInfo->maxInputChannels == 0) {
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fprintf(stderr, "❌ 选择的设备不是麦克风（无输入声道）！\n");
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Pa_Terminate();
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return -1;
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}
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printf("\n✅ 已选择麦克风：\n");
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printf(" ID: %d | 名称: %s\n", selected_id, pInfo->name);
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printf(" 采样率: %.1f Hz | 声道数: %d\n\n",
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pInfo->defaultSampleRate, pInfo->maxInputChannels);
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Pa_Terminate();
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return selected_id;
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}
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// ====================== 2. 音频采集函数（纯PortAudio原生实现） ======================
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int audio_record(short* buffer, int buffer_size, int sample_rate, int channels, int max_seconds, int device_id) {
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PaError err;
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PaStream* stream;
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PaStreamParameters input_params;
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// 初始化PortAudio
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err = Pa_Initialize();
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if (err != paNoError) {
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fprintf(stderr, "❌ PortAudio初始化失败: %s\n", Pa_GetErrorText(err));
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return -1;
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}
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// 配置输入参数（指定麦克风设备ID）
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input_params.device = device_id;
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input_params.channelCount = channels;
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input_params.sampleFormat = paInt16; // 16位深（Whisper要求）
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input_params.suggestedLatency = Pa_GetDeviceInfo(device_id)->defaultLowInputLatency;
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input_params.hostApiSpecificStreamInfo = NULL;
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// 打开音频流
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err = Pa_OpenStream(
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&stream,
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&input_params,
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NULL, // 无输出
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sample_rate,
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1024, // 缓冲区大小
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paClipOff, // 关闭裁剪
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NULL, // 无回调
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NULL
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);
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if (err != paNoError) {
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fprintf(stderr, "❌ 打开音频流失败: %s\n", Pa_GetErrorText(err));
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Pa_Terminate();
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return -1;
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}
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// 开始录制
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err = Pa_StartStream(stream);
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if (err != paNoError) {
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fprintf(stderr, "❌ 开始录制失败: %s\n", Pa_GetErrorText(err));
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Pa_CloseStream(stream);
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Pa_Terminate();
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return -1;
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}
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printf("🎙️ 录制中（按回车键停止，最长%d秒）...\n", max_seconds);
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int total_samples = 0;
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time_t start_time = time(NULL);
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// 录制逻辑：要么按回车停止，要么超时停止
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while (1) {
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// 读取音频数据
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int samples_to_read = buffer_size - total_samples;
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if (samples_to_read <= 0) break;
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err = Pa_ReadStream(stream, buffer + total_samples, 1024);
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if (err != paNoError) {
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fprintf(stderr, "❌ 读取音频失败: %s\n", Pa_GetErrorText(err));
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break;
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}
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total_samples += 1024;
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// 超时检查（max_seconds秒）
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if (difftime(time(NULL), start_time) >= max_seconds) {
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printf("\n⏰ 录制超时（%d秒），自动停止\n", max_seconds);
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break;
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}
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// 检查是否按了回车
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if (std::cin.rdbuf()->in_avail() > 0) {
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getchar();
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printf("\n🛑 用户停止录制\n");
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break;
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}
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}
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// 停止录制
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Pa_StopStream(stream);
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Pa_CloseStream(stream);
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Pa_Terminate();
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return total_samples;
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}
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// ====================== 3. 新增：short转float（Whisper要求） ======================
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void convert_short_to_float(const short* src, float* dst, int count) {
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// 16位short的范围是[-32768, 32767]，归一化到float的[-1.0, 1.0]
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for (int i = 0; i < count; i++) {
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dst[i] = static_cast<float>(src[i]) / 32768.0f;
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}
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}
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// ====================== 4. 主函数（修正数据类型转换） ======================
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int main(int argc, char **argv) {
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// 检查参数
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if (argc < 2) {
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fprintf(stderr, "用法: %s 模型文件路径（如 ./models/ggml-medium.bin）\n", argv[0]);
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return 1;
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}
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const char* model_path = argv[1];
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// 步骤1：枚举并选择麦克风
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enumerate_audio_devices();
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int mic_device_id = select_mic_device();
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if (mic_device_id < 0) {
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fprintf(stderr, "❌ 麦克风选择失败，程序退出\n");
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return 1;
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}
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// 步骤2：GPU加速配置说明
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printf("\n🔍 GPU加速配置说明...\n");
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printf(" 当前已启用GPU加速（use_gpu = true）\n");
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printf(" ✅ 如果编译时链接了CUDA库，模型会自动使用GPU\n");
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printf(" ❌ 如果识别速度很慢，说明实际使用CPU运行\n");
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printf(" 验证方法：观察识别耗时，GPU版本比CPU快5-10倍\n\n");
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// 步骤3：加载Whisper模型（启用GPU）
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printf("🚀 正在加载模型：%s\n", model_path);
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struct whisper_context_params cparams = whisper_context_default_params();
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cparams.use_gpu = true;
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cparams.gpu_device = 0;
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struct whisper_context* ctx = whisper_init_from_file_with_params(model_path, cparams);
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if (!ctx) {
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fprintf(stderr, "❌ 加载模型失败: %s\n", model_path);
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return 1;
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}
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// 打印模型信息
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whisper_print_system_info();
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printf("✅ 模型加载成功！\n");
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printf(" 📌 若识别速度快（几秒内完成）= GPU运行\n");
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printf(" 📌 若识别速度慢（十几秒/分钟）= CPU运行\n");
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printf("=============================================\n");
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printf("🎤 语音识别程序（指定麦克风版）\n");
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printf("操作说明：\n");
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printf(" 1. 按下【回车键】开始录制\n");
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printf(" 2. 说话完成后，再次按下【回车键】停止录制并识别\n");
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printf(" 3. 录制超过30秒会自动停止\n");
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printf(" 4. Ctrl+C 退出程序\n");
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printf("=============================================\n\n");
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// 步骤4：准备音频缓冲区
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const int sample_rate = 16000; // Whisper标准采样率
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const int channels = 1; // 单声道
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const int max_seconds = 30; // 最长录制30秒
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const int buffer_size = sample_rate * channels * max_seconds;
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// 原始音频缓冲区（short类型）
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short* buffer_short = (short*)malloc(buffer_size * sizeof(short));
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// Whisper输入缓冲区（float类型）
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float* buffer_float = (float*)malloc(buffer_size * sizeof(float));
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if (!buffer_short || !buffer_float) {
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fprintf(stderr, "❌ 分配音频缓冲区失败\n");
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free(buffer_short);
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free(buffer_float);
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whisper_free(ctx);
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return 1;
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}
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// 步骤5：等待用户开始录制
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printf("👉 按下回车键开始录制...\n");
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getchar();
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// 步骤6：录制音频（指定选择的麦克风）
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int samples_read = audio_record(buffer_short, buffer_size, sample_rate, channels, max_seconds, mic_device_id);
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if (samples_read <= 0) {
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fprintf(stderr, "❌ 录制音频失败\n");
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free(buffer_short);
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free(buffer_float);
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whisper_free(ctx);
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return 1;
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}
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// 步骤7：关键修正：short转float（Whisper要求）
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convert_short_to_float(buffer_short, buffer_float, samples_read);
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// 步骤8：语音识别（传入float缓冲区）
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printf("\n🔍 正在识别...\n");
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clock_t start = clock();
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struct whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
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wparams.language = "zh"; // 中文识别
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wparams.translate = false;
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wparams.print_special = false;
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wparams.print_progress = false;
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wparams.print_realtime = false;
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wparams.print_timestamps = false;
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// 传入float类型的buffer_float，而非short类型的buffer_short
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if (whisper_full(ctx, wparams, buffer_float, samples_read) != 0) {
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fprintf(stderr, "❌ 识别音频失败\n");
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free(buffer_short);
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free(buffer_float);
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whisper_free(ctx);
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return 1;
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}
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// 步骤9：输出结果
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clock_t end = clock();
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double elapsed = (double)(end - start) / CLOCKS_PER_SEC;
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printf("⏱️ 识别耗时：%.2f 秒\n", elapsed);
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if (elapsed < 5.0) {
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printf(" 🎯 识别速度快，应该是GPU在运行！\n");
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} else {
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printf(" ⚠️ 识别速度慢，当前使用CPU运行（需编译CUDA版本）\n");
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}
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printf("📝 识别结果：\n ");
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const int n_segments = whisper_full_n_segments(ctx);
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for (int i = 0; i < n_segments; i++) {
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const char* text = whisper_full_get_segment_text(ctx, i);
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printf("%s\n ", text);
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}
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printf("\n");
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// 步骤10：清理资源
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free(buffer_short);
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free(buffer_float);
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whisper_free(ctx);
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return 0;
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}
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