refactor: reorganize imports and improve code formatting across multiple modules

src/exec/qwen3_asr.rs

@@ -5,14 +5,13 @@ use std::time::Instant;
 use anyhow::{Ok, Result};
 
 use crate::exec::ExecModel;
+use crate::models::GenerateModel;
 use crate::models::qwen3_asr::generate::Qwen3AsrGenerateModel;
-use crate::models::{GenerateModel};
 
 pub struct Qwen3ASRExec;
 
 impl ExecModel for Qwen3ASRExec {
     fn run(input: &[String], output: Option<&str>, weight_path: &str) -> Result<()> {
-        
         let i_start = Instant::now();
         let mut model = Qwen3AsrGenerateModel::init(weight_path, None, None)?;
         let i_duration = i_start.elapsed();

src/models/campplus/mod.rs

@@ -25,7 +25,11 @@ impl Shortcut {
     ) -> Result<Self> {
         let conv_0 = get_conv2d(vb.pp("0"), in_c, out_c, ks, padding, 1, 1, 1, bias)?;
         let bn_1 = get_batch_norm(vb.pp("1"), 1e-5, out_c, true)?;
-        Ok(Self { conv_0, bn_1, stride })
+        Ok(Self {
+            conv_0,
+            bn_1,
+            stride,
+        })
     }
 
     pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
@@ -36,7 +40,7 @@ impl Shortcut {
             let indices = Tensor::arange(0u32, half_h as u32, x.device())?.affine(2.0, 0.0)?;
             x = x.index_select(&indices, 2)?;
         }
-        x = self.bn_1.forward_t(&x, false)?;        
+        x = self.bn_1.forward_t(&x, false)?;
         Ok(x)
     }
 }
@@ -106,7 +110,7 @@ impl BasicResBlock {
         } else {
             xs = xs.add(&residual)?;
         }
-        xs = xs.relu()?;       
+        xs = xs.relu()?;
         Ok(xs)
     }
 }

src/models/feature_extractor/config.rs

@@ -18,4 +18,4 @@ pub struct FeatureExtractor {
 
 fn default_sampling_rate() -> usize {
     16000
-}
+}

src/models/feature_extractor/mod.rs

@@ -1,3 +1,3 @@
-pub mod seamless_m4t_feature_extractor;
+pub mod config;
 pub mod feature_extraction_whisper;
-pub mod config;
+pub mod seamless_m4t_feature_extractor;

src/models/glm_asr_nano/config.rs

@@ -11,8 +11,6 @@ pub struct GlmAsrNanoProcessorConfig {
     pub max_audio_len: usize,
 }
 
-
-
 #[derive(Debug, Clone, PartialEq, Deserialize)]
 pub struct GlmAsrNanoConfig {
     pub audio_config: GlmAsrAudioConfig,

src/models/glm_asr_nano/processor.rs

@@ -1,4 +1,3 @@
-
 use aha_openai_dive::v1::resources::chat::ChatCompletionParameters;
 use anyhow::Result;
 use candle_core::{D, DType, Device, IndexOp, Tensor};

src/models/index_tts2/generate.rs

@@ -19,7 +19,7 @@ impl IndexTTS2Generate {
         let device = get_device(device);
         let dtype = get_dtype(dtype, "bf16");
         let processor = IndexTTS2Processor::new(path, &save_dir, &config, &device, dtype)?;
-        
+
         Ok(Self { config, processor })
     }
     pub fn generate(&mut self, mes: ChatCompletionParameters) -> Result<()> {

src/models/index_tts2/mod.rs

@@ -2,4 +2,4 @@ pub mod config;
 pub mod generate;
 pub mod model;
 pub mod processor;
-pub mod utils;
+pub mod utils;

src/models/index_tts2/processor.rs

@@ -5,13 +5,16 @@ use candle_nn::VarBuilder;
 
 use crate::{
     models::{
-        campplus::CAMPPlus, feature_extractor::seamless_m4t_feature_extractor::SeamlessM4TFeatureExtractor, index_tts2::config::{IndexTTS2Config, PreprocessParams}, mask_gct::model::RepCodec, w2v_bert_2_0::model::W2VBert2_0Model
+        campplus::CAMPPlus,
+        feature_extractor::seamless_m4t_feature_extractor::SeamlessM4TFeatureExtractor,
+        index_tts2::config::{IndexTTS2Config, PreprocessParams},
+        mask_gct::model::RepCodec,
+        w2v_bert_2_0::model::W2VBert2_0Model,
     },
     utils::{
         audio_utils::{
             create_hann_window, extract_audio_url, get_waveform_and_window_properties, kaldi_fbank,
-            kaldi_get_mel_banks, load_audio, mel_filter_bank, resample_simple, 
-            torch_stft,
+            kaldi_get_mel_banks, load_audio, mel_filter_bank, resample_simple, torch_stft,
         },
         get_vb_model_path,
         tensor_utils::pad_reflect_last_dim,

src/models/index_tts2/utils.rs

@@ -7,12 +7,12 @@ pub async fn download_index_tts2_need_model(save_dir: Option<&str>) -> anyhow::R
     };
 
     let w2v_bert2_0 = "facebook/w2v-bert-2.0";
-    let mask_gct= "amphion/MaskGCT";
+    let mask_gct = "amphion/MaskGCT";
     // let campplus= "funasr/campplus"; // huggingface
-    let campplus = "iic/speech_campplus_sv_zh-cn_16k-common";  // modelscope
+    let campplus = "iic/speech_campplus_sv_zh-cn_16k-common"; // modelscope
     download_model(w2v_bert2_0, &save_dir, 3).await?;
     download_model(mask_gct, &save_dir, 3).await?;
     download_model(campplus, &save_dir, 3).await?;
-    
+
     Ok(())
-}
+}

src/models/mask_gct/config.rs

@@ -18,4 +18,4 @@ fn default_num_quantizers() -> usize {
 
 fn default_downsample_scale() -> usize {
     1
-}
+}

src/models/mask_gct/mod.rs

@@ -1,2 +1,2 @@
+pub mod config;
 pub mod model;
-pub mod config;

src/models/mask_gct/model.rs

@@ -116,10 +116,28 @@ impl FactorizedVectorQuantize {
         use_l2_normlize: bool,
     ) -> Result<Self> {
         let (in_project, out_project) = if input_dim != codebook_dim {
-            let in_project =
-                WNConv1d::new(vb.pp("in_project"), input_dim, codebook_dim, 1, 1, 0, 1, 1, true)?;
-            let out_project =
-                WNConv1d::new(vb.pp("out_project"), codebook_dim, input_dim, 1, 1, 0, 1, 1, true)?;
+            let in_project = WNConv1d::new(
+                vb.pp("in_project"),
+                input_dim,
+                codebook_dim,
+                1,
+                1,
+                0,
+                1,
+                1,
+                true,
+            )?;
+            let out_project = WNConv1d::new(
+                vb.pp("out_project"),
+                codebook_dim,
+                input_dim,
+                1,
+                1,
+                0,
+                1,
+                1,
+                true,
+            )?;
             (Some(in_project), Some(out_project))
         } else {
             (None, None)

src/models/qwen3_asr/generate.rs

@@ -1,4 +1,3 @@
-
 use aha_openai_dive::v1::resources::chat::{
     ChatCompletionChunkResponse, ChatCompletionParameters, ChatCompletionResponse,
 };

src/models/w2v_bert_2_0/config.rs

@@ -58,4 +58,4 @@ pub struct W2VBert2_0Config {
     pub use_weighted_layer_sum: bool,
     pub vocab_size: Option<usize>,
     pub xvector_output_dim: usize,
-}
+}

src/models/w2v_bert_2_0/mod.rs

@@ -1,2 +1,2 @@
 pub mod config;
-pub mod model;
+pub mod model;

src/models/w2v_bert_2_0/model.rs

@@ -7,9 +7,7 @@ use candle_nn::{
 
 use crate::{
     models::{
-        common::{
-            GLU, TwoLinearMLP, eager_attention_forward, get_conv1d, get_layer_norm,
-        },
+        common::{GLU, TwoLinearMLP, eager_attention_forward, get_conv1d, get_layer_norm},
         w2v_bert_2_0::config::W2VBert2_0Config,
     },
     position_embed::rope::{RoPE, apply_rotary_pos_emb},
@@ -488,7 +486,7 @@ impl Wav2Vec2BertEncoder {
         for (i, layer) in (&self.layers).iter().enumerate() {
             if output_hidden_states {
                 hidden_states.push(xs.clone());
-            } 
+            }
             if let Some(id) = layer_id
                 && id == i
             {
@@ -500,7 +498,7 @@ impl Wav2Vec2BertEncoder {
                 sin.as_ref(),
                 attention_mask.as_ref(),
                 conv_attention_mask,
-            )?;                      
+            )?;
         }
         let hidden_states = if hidden_states.len() > 0 {
             Some(hidden_states)

src/position_embed/rope.rs

@@ -350,7 +350,7 @@ impl Qwen3VLTextRotaryEmbedding {
 
         // for dim in 1..3 {
         for (dim, offset) in (1..3).enumerate() {
-            let dim = dim +1;
+            let dim = dim + 1;
             let length = mrope_section[dim];
             let idx = Tensor::arange_step(offset as u32, length as u32, 3, freqs.device())?;
             let src = freqs.i(dim)?.contiguous()?; // (bs, seq_len, head_dim //2)

src/utils/audio_utils.rs

@@ -32,7 +32,9 @@ use symphonia::core::meta::MetadataOptions;
 use symphonia::core::probe::Hint;
 
 use crate::utils::get_default_save_dir;
-use crate::utils::tensor_utils::{linspace, log10, pad_reflect_last_dim, pad_replicate_last_dim, split_tensor};
+use crate::utils::tensor_utils::{
+    linspace, log10, pad_reflect_last_dim, pad_replicate_last_dim, split_tensor,
+};
 
 // 重采样方法枚举
 #[derive(Debug, Clone, Copy)]

src/utils/tensor_utils.rs

@@ -111,7 +111,7 @@ pub fn split_tensor_with_size<D: Dim>(
     //     "input tensor dim size % splits_size must be equal to 0"
     // );
     for (i, split) in (0..dim_size).step_by(splits_size).enumerate() {
-        let size = splits_size.min(dim_size - i*splits_size);
+        let size = splits_size.min(dim_size - i * splits_size);
         split_res.push(t.narrow(dim, split, size)?);
     }
     Ok(split_res)