fix FireRedVad detect_frame bug

src/models/fire_red_vad/vad.rs

@@ -103,13 +103,14 @@ impl FireRedVad {
         let probs_len = probs.dim(0)?;
         // 输入数据中 is_speech > 0.1, 认为这帧数据可用
         let final_data = if preds_sum as f32 > probs_len as f32 * 0.1 {
-            // 通过最后10个数据，判断说话是否结束
+            // 通过最后10个数据，判断说话是否结束，如果数据长度小于10就取整个长度
+            let select_len = if probs_len > 10 { 10 } else { probs_len };
             let last_10_preds_sum = binary_preds
-                .narrow(0, probs_len - 10, 10)?
+                .narrow(0, probs_len - select_len, select_len)?
                 .sum_all()?
                 .to_scalar::<u32>()?;
-            // 10个数据中，至少8个是 speech, 认为说话没有结束，缓存数据，等待下一帧
+            // 选中数据中，至少0.8个是 speech, 认为说话没有结束，缓存数据，等待下一帧
-            if last_10_preds_sum >= 8 {
+            if last_10_preds_sum >= (select_len as f32 * 0.8).ceil() as u32 {
                 self.speech_cache.push(audio_frame.clone());
                 None
             } else {

src/utils/audio_utils.rs

@@ -388,7 +388,7 @@ pub fn load_audio_use_hound(audio_path: PathBuf, device: &Device) -> Result<(Ten
                     .collect::<Result<Vec<_>, _>>()?,
                 24 => reader
                     .samples::<i32>()
-                    .map(|s| s.map(|sample| sample as f32 / 8388607.0))
+                    .map(|s| s.map(|sample| sample as f32 / i32::MAX as f32))
                     .collect::<Result<Vec<_>, _>>()?,
                 _ => {
                     return Err(anyhow::anyhow!(

tests/test_fire_red_vad.rs

@@ -23,7 +23,7 @@ fn stream_vad() -> Result<()> {
     let device = aha::Device::Cpu;
     let save_dir =
         aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
-    let model_path = format!("{}/xukaituo/FireRedVAD/Stream-VAD/", save_dir);
+    let model_path = format!("{}/jiangjiangaha/FireRedVAD-Stream-VAD/", save_dir);
 
     let vad = FireRedVad::init(&model_path, Some(&device), None)?;
     let res = vad.detect_file(audio_path)?;

tests/test_voxcpm.rs

@@ -60,7 +60,7 @@ fn voxcpm_use_message_generate() -> Result<()> {
 
 #[test]
 fn voxcpm_generate() -> Result<()> {
-    // RUST_BACKTRACE=1 cargo test -F cuda voxcpm_generate -r -- --nocapture
+    // RUST_BACKTRACE=1 cargo test -F cuda --test test_voxcpm voxcpm_generate -r -- --nocapture
     let save_dir =
         aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
     let model_path = format!("{}/OpenBMB/VoxCPM-0.5B/", save_dir);
@@ -70,38 +70,39 @@ fn voxcpm_generate() -> Result<()> {
     let i_duration = i_start.elapsed();
     println!("Time elapsed in load model is: {:?}", i_duration);
 
-    let i_start = Instant::now();
+    // let i_start = Instant::now();
     // let generate = voxcpm_generate.generate_simple("太阳当空照，花儿对我笑，小鸟说早早早".to_string())?;
-    let generate = voxcpm_generate.inference(
+    // let generate = voxcpm_generate.inference(
-        "VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly realistic speech.".to_string(),
+    //     "VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly realistic speech.".to_string(),
-        Some("啥子小师叔，打狗还要看主人，你再要继续，我，就是你的对手".to_string()),
+    //     Some("啥子小师叔，打狗还要看主人，你再要继续，我，就是你的对手".to_string()),
-        Some("file://./assets/audio/voice_01.wav".to_string()),
+    //     Some("file://./assets/audio/voice_01.wav".to_string()),
-        // Some("一定被灰太狼给吃了，我已经为他准备好了花圈了".to_string()),
+    //     // Some("一定被灰太狼给吃了，我已经为他准备好了花圈了".to_string()),
-        // Some("file://./assets/audio/voice_05.wav".to_string()),
+    //     // Some("file://./assets/audio/voice_05.wav".to_string()),
-        2,
-        100,
-        10,
-        2.0,
-        // false,
-        6.0,
-    )?;
-
-    // 创建prompt_cache
-    // let _ = voxcpm_generate.build_prompt_cache(
-    //     "啥子小师叔，打狗还要看主人，你再要继续，我，就是你的对手".to_string(),
-    //     "file://./assets/audio/voice_01.wav".to_string(),
-    // )?;
-    // // 使用prompt_cache生成语音
-    // let generate = voxcpm_generate.generate_use_prompt_cache(
-    //     "太阳当空照，花儿对我笑，小鸟说早早早".to_string(),
     //     2,
     //     100,
     //     10,
     //     2.0,
-    //     false,
+    //     // false,
     //     6.0,
     // )?;
 
+    // 创建prompt_cache
+    let _ = voxcpm_generate.build_prompt_cache(
+        "啥子小师叔，打狗还要看主人，你再要继续，我，就是你的对手".to_string(),
+        "file://./assets/audio/voice_01.wav".to_string(),
+    )?;
+    // 使用prompt_cache生成语音
+    let i_start = Instant::now();
+    let generate = voxcpm_generate.generate_use_prompt_cache(
+        "太阳当空照，花儿对我笑，小鸟说早早早".to_string(),
+        2,
+        100,
+        10,
+        2.0,
+        false,
+        6.0,
+    )?;
+
     let i_duration = i_start.elapsed();
     println!("Time elapsed in generate is: {:?}", i_duration);
     save_wav(&generate, "voxcpm.wav", 16000)?;