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This commit is contained in:
jhqxxx
2026-04-22 16:53:51 +08:00
parent dc15a48d14
commit 9e1adff2a1
8 changed files with 90 additions and 120 deletions
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src/models/common/modules.rs
+1 -2
View File
@@ -16,8 +16,7 @@ use crate::{
pub struct VadFrameResult {
pub is_speech: bool,
// pub is_speech_start: bool,
pub is_i16: bool,
pub orig_audio: Option<Tensor>,
pub orig_audio: Option<Tensor>, // nedd f32 data type
// pub kaldi_audio: Option<Tensor>,
pub model_name: String,
pub mode: String,
src/models/fire_red_vad/processor.rs
+3 -2
View File
@@ -133,8 +133,9 @@ impl AudioFeat {
}
pub fn extract_file(&self, audio_path: &str, device: &Device) -> Result<(Tensor, f32)> {
let wave_tensor =
load_audio_with_resample(audio_path, device, Some(16000), true)?.squeeze(0)?;
let wave_tensor = load_audio_with_resample(audio_path, device, Some(16000))?.squeeze(0)?;
// fire_red_vad need i16 type data
let wave_tensor = wave_tensor.affine(32768.0, 0.0)?;
let dur = wave_tensor.dim(0)? as f32 / 16000.0;
let fbank = self.extract(&wave_tensor)?;
Ok((fbank, dur))
src/models/fire_red_vad/vad.rs
+46 -34
View File
@@ -36,6 +36,11 @@ pub struct FireRedVad {
caches: Option<Vec<Tensor>>,
frame_length_sample: usize,
speech_cache: Vec<Tensor>,
pred_cache: Vec<u32>,
min_speach_frames: usize,
look_back_frames: usize,
min_speach_ratio: f32,
end_silence_ratio: f32,
}
impl FireRedVad {
@@ -78,6 +83,11 @@ impl FireRedVad {
caches: None,
frame_length_sample: 400,
speech_cache: vec![],
pred_cache: vec![],
min_speach_frames: 30, // 约 250ms
look_back_frames: 15, // 约 80ms
min_speach_ratio: 0.1,
end_silence_ratio: 0.8,
})
}
@@ -89,7 +99,8 @@ impl FireRedVad {
audio_frame.dim(0)?
));
}
let feats = self.audio_feat.extract(audio_frame)?;
let wave_tensor = audio_frame.affine(32768.0, 0.0)?;
let feats = self.audio_feat.extract(&wave_tensor)?;
let (probs, caches) = self
.vad_model
.forward(&feats.unsqueeze(0)?, self.caches.as_ref())?;
@@ -101,42 +112,45 @@ impl FireRedVad {
.to_dtype(DType::U32)?;
let preds_sum = binary_preds.sum_all()?.to_scalar::<u32>()?;
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，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 - select_len, select_len)?
.sum_all()?
.to_scalar::<u32>()?;
// 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 >= (select_len as f32 * 0.8).ceil() as u32 {
self.speech_cache.push(audio_frame.clone());
// 输入数据中 is_speech > 0.1, 认为这帧数据有人声
let final_data = if preds_sum as f32 > probs_len as f32 * self.min_speach_ratio {
self.speech_cache.push(audio_frame.clone());
let preds = binary_preds.to_vec1::<u32>()?;
self.pred_cache.extend_from_slice(&preds);
// 人声缓存数据过少，等待下一帧
if self.pred_cache.len() < self.min_speach_frames {
None
} else {
// 否则认为此次说话结束，结合缓存数据，一起返回
let data = if self.speech_cache.is_empty() {
// 缓存数据为空，直接返回
audio_frame.clone()
// 判断是否停止说话
let start = self.pred_cache.len() - self.look_back_frames;
let look_back = self.pred_cache[start..].iter().sum::<u32>();
// 判断结尾是否静音
let silence_ratio = 1.0 - (look_back as f32 / self.look_back_frames as f32);
if silence_ratio >= self.end_silence_ratio {
// 静音返回缓存数据并清空缓存
let speech = Tensor::cat(&self.speech_cache, 0)?;
self.speech_cache.clear();
self.pred_cache.clear();
Some(speech)
} else {
// 缓存数据不为空，cat数据
self.speech_cache.push(audio_frame.clone());
let audio_frame = Tensor::cat(&self.speech_cache, 0)?;
self.speech_cache = vec![]; // 清空缓存
audio_frame
};
Some(data)
// 不是静音此次返回None
None
}
}
} else {
// 认为这帧数据不可用，是否有缓存数据
if self.speech_cache.is_empty() {
// 没有返回None
None
// 认为这帧数据没有人声
// 有缓存数据, 且数据够长
if self.pred_cache.len() >= self.min_speach_frames {
let data = Tensor::cat(&self.speech_cache, 0)?;
self.speech_cache.clear();
self.pred_cache.clear();
Some(data)
} else {
// 有缓存，返回缓存数据
let data = Some(Tensor::cat(&self.speech_cache, 0)?);
self.speech_cache.clear(); // 清空缓存
data
// 否则直接清空缓存
self.speech_cache.clear();
self.pred_cache.clear();
None
}
};
@@ -145,7 +159,6 @@ impl FireRedVad {
} else {
Ok(Some(VadFrameResult {
is_speech: true,
is_i16: true,
orig_audio: final_data,
model_name: self.model_name.clone(),
mode: "speech".to_string(),
@@ -168,7 +181,6 @@ impl FireRedVad {
channels,
orig_sr,
Some(16000),
true,
)?
.squeeze(0)?;
self.detect_frame(&audio_frame)
@@ -179,7 +191,7 @@ impl FireRedVad {
return Err(anyhow!("only stream model support detect_frame"));
}
let audio_frame =
resample_audio_from_bytes(audio_bytes, &self.device, Some(16000), true)?.squeeze(0)?;
resample_audio_from_bytes(audio_bytes, &self.device, Some(16000))?.squeeze(0)?;
self.detect_frame(&audio_frame)
}
src/models/qwen3_asr/generate.rs
+5 -8
View File
@@ -90,19 +90,16 @@ impl<'a> Qwen3AsrGenerateModel<'a> {
return Ok(AsrResult::init_empty());
}
if let Some(audio) = vad_res.orig_audio {
self.asr_audio(&audio, vad_res.is_i16)
self.asr_audio(&audio)
} else {
Ok(AsrResult::init_empty())
}
}
pub fn asr_audio(&mut self, audio: &Tensor, is_i16: bool) -> Result<AsrResult> {
let audio_data = self.processor.process_audio_tensor(
&self.default_template,
audio,
is_i16,
&self.tokenizer,
)?;
pub fn asr_audio(&mut self, audio: &Tensor) -> Result<AsrResult> {
let audio_data =
self.processor
.process_audio_tensor(&self.default_template, audio, &self.tokenizer)?;
let input_ids = audio_data.input_ids.clone();
let input_features = Some(audio_data.input_features.clone().to_dtype(self.dtype)?);
let mut ctx = GenerationContext::new(
src/models/qwen3_asr/processor.rs
+1 -19
View File
@@ -1,6 +1,5 @@
use crate::{
models::common::modules::{VadFrameResult, float_range_normalize},
params::chat::ChatCompletionParameters,
models::common::modules::float_range_normalize, params::chat::ChatCompletionParameters,
};
use anyhow::{Result, anyhow};
use candle_core::{Device, Tensor};
@@ -101,7 +100,6 @@ impl Qwen3AsrProcessor {
&self,
render: &str,
audio: &Tensor,
is_i16: bool,
tokenizer: &TokenizerModel,
) -> Result<AudioData> {
let audio_len = audio.dim(0)? as f32;
@@ -109,9 +107,6 @@ impl Qwen3AsrProcessor {
return Err(anyhow!("vad_res orig_audio is too long!"));
}
let mut audio = audio.unsqueeze(0)?;
if is_i16 {
audio = audio.affine(1.0 / 32768.0, 0.0)?;
}
audio = float_range_normalize(&audio)?;
let (input_features, _) =
self.whisper_feature_extracor
@@ -128,19 +123,6 @@ impl Qwen3AsrProcessor {
Ok(audio_data)
}
pub fn process_vad_res(
&self,
render: &str,
vad_res: VadFrameResult,
tokenizer: &TokenizerModel,
) -> Result<AudioData> {
if let Some(audio) = &vad_res.orig_audio {
self.process_audio_tensor(render, audio, vad_res.is_i16, tokenizer)
} else {
Err(anyhow!("vad_res orig_audio is none!"))
}
}
pub fn process_info(
&self,
mes: &ChatCompletionParameters,
src/models/voxcpm/model.rs
+4 -10
View File
@@ -534,8 +534,7 @@ impl VoxCPMModel {
let audio_start = Tensor::new(vec![self.audio_start_token], &self.device)?;
let text_token = Tensor::cat(&[text_token, audio_start], D::Minus1)?;
let text_length = text_token.dim(0)?;
let mut audio =
load_audio_with_resample(&path, &self.device, Some(self.sample_rate), false)?;
let mut audio = load_audio_with_resample(&path, &self.device, Some(self.sample_rate))?;
let patch_len = self.patch_size * self.chunk_size;
if audio.dim(1)? % patch_len != 0 {
audio =
@@ -575,8 +574,7 @@ impl VoxCPMModel {
let audio_start = Tensor::new(vec![self.audio_start_token], &self.device)?;
let text_token = Tensor::cat(&[text_token, audio_start], D::Minus1)?;
let text_length = text_token.dim(0)?;
let mut audio =
load_audio_with_resample(&path, &self.device, Some(self.sample_rate), false)?;
let mut audio = load_audio_with_resample(&path, &self.device, Some(self.sample_rate))?;
let patch_len = self.patch_size * self.chunk_size;
if audio.dim(1)? % patch_len != 0 {
audio =
@@ -843,12 +841,8 @@ impl VoxCPMModel {
) -> Result<HashMap<String, Tensor>> {
let text_token = self.tokenizer.encode(prompt_text)?;
let text_token = Tensor::from_slice(&text_token, text_token.len(), &self.device)?;
let mut audio = load_audio_with_resample(
&prompt_wav_path,
&self.device,
Some(self.sample_rate),
false,
)?;
let mut audio =
load_audio_with_resample(&prompt_wav_path, &self.device, Some(self.sample_rate))?;
let patch_len = self.patch_size * self.chunk_size;
if audio.dim(1)? % patch_len != 0 {
audio = audio.pad_with_zeros(D::Minus1, 0, patch_len - audio.dim(1)? % patch_len)?;
src/utils/audio_utils.rs
+15 -45
View File
@@ -462,11 +462,7 @@ pub fn get_audio_format_from_bytes(bytes: &[u8]) -> Result<String> {
}
}
pub fn load_audio_use_symphonia(
audio_vec: Vec<u8>,
is_i16: bool,
device: &Device,
) -> Result<(Tensor, usize)> {
pub fn load_audio_use_symphonia(audio_vec: Vec<u8>, device: &Device) -> Result<(Tensor, usize)> {
let extension = get_audio_format_from_bytes(&audio_vec)?;
let content = Cursor::new(audio_vec);
let mss = MediaSourceStream::new(Box::new(content), Default::default());
@@ -509,14 +505,7 @@ pub fn load_audio_use_symphonia(
all_samples.push(Vec::new());
}
let channel_data = buf.chan(channel);
if is_i16 {
// 将[-1.0, 1.0] => [-i16_max, i16_max]
let i16_data: Vec<f32> =
channel_data.iter().map(|&s| s * 32768.0).collect();
all_samples[channel].extend_from_slice(&i16_data);
} else {
all_samples[channel].extend_from_slice(channel_data);
}
all_samples[channel].extend_from_slice(channel_data);
}
}
AudioBufferRef::S16(buf) => {
@@ -527,17 +516,10 @@ pub fn load_audio_use_symphonia(
all_samples.push(Vec::new());
}
let channel_data = buf.chan(channel);
let float_samples: Vec<f32> = if is_i16 {
channel_data
.iter()
.map(|&s| s as f32) // 转换为f32类型
.collect()
} else {
channel_data
.iter()
.map(|&s| s as f32 / 32768.0) // 转换为[-1, 1]
.collect()
};
let float_samples: Vec<f32> = channel_data
.iter()
.map(|&s| s as f32 / 32768.0) // 转换为[-1, 1]
.collect();
all_samples[channel].extend(float_samples);
}
}
@@ -549,17 +531,10 @@ pub fn load_audio_use_symphonia(
all_samples.push(Vec::new());
}
let channel_data = buf.chan(channel);
let float_samples: Vec<f32> = if is_i16 {
channel_data
.iter()
.map(|&s| s.inner() as f32 / 8388608.0 * 32768.0) // 转换为[-i16_max, i16_max]
.collect()
} else {
channel_data
.iter()
.map(|&s| s.inner() as f32 / 8388608.0) // 转换为[-1, 1]
.collect()
};
let float_samples: Vec<f32> = channel_data
.iter()
.map(|&s| s.inner() as f32 / 8388608.0) // 转换为[-1, 1]
.collect();
all_samples[channel].extend(float_samples);
}
}
@@ -584,7 +559,7 @@ pub fn load_audio_use_symphonia(
pub fn load_audio(path: &str, device: &Device) -> Result<(Tensor, usize)> {
let audio_vec = get_audio_bytes_vec(path)?;
load_audio_use_symphonia(audio_vec, false, device)
load_audio_use_symphonia(audio_vec, device)
}
pub fn resample_audio_from_vec_f32(
@@ -593,11 +568,11 @@ pub fn resample_audio_from_vec_f32(
channels: usize,
orig_sr: Option<usize>,
target_sample_rate: Option<usize>,
is_i16: bool,
) -> Result<Tensor> {
let frame_len = audio_vec.len() / channels;
let audio = Tensor::new(&audio_vec[0..frame_len * channels], device)?;
let mut audio = if channels > 1 {
// 交错格式（立体声时 LRLR...）
audio
.reshape((frame_len, channels))?
.mean_keepdim(1)?
@@ -613,9 +588,6 @@ pub fn resample_audio_from_vec_f32(
{
audio = resample_simple(&audio, sr as i64, target_sample_rate as i64)?;
}
if is_i16 {
audio = audio.affine(32768.0, 0.0)?;
}
Ok(audio)
}
@@ -623,9 +595,8 @@ pub fn resample_audio_from_bytes(
audio_vec: Vec<u8>,
device: &Device,
target_sample_rate: Option<usize>,
is_i16: bool,
) -> Result<Tensor> {
let (mut audio, sr) = load_audio_use_symphonia(audio_vec, is_i16, device)?;
let (mut audio, sr) = load_audio_use_symphonia(audio_vec, device)?;
if let Some(target_sample_rate) = target_sample_rate
&& target_sample_rate != sr
{
@@ -638,13 +609,12 @@ pub fn load_audio_with_resample(
path: &str,
device: &Device,
target_sample_rate: Option<usize>,
is_i16: bool,
) -> Result<Tensor> {
// hound 只支持wav文件
// let audio_path = get_audio_path(path)?;
// let (mut audio, sr) = load_audio_use_hound(audio_path, device)?;
let audio_vec = get_audio_bytes_vec(path)?;
resample_audio_from_bytes(audio_vec, device, target_sample_rate, is_i16)
resample_audio_from_bytes(audio_vec, device, target_sample_rate)
}
pub fn save_wav(audio: &Tensor, save_path: &str, sample_rate: u32) -> Result<()> {
@@ -719,7 +689,7 @@ pub fn extract_audios(
// 并行加载音频
audio_url_vec
.par_iter()
.map(|url| load_audio_with_resample(url, device, target_sample_rate, false))
.map(|url| load_audio_with_resample(url, device, target_sample_rate))
.collect()
// #[cfg(not(feature = "ffmpeg"))]
// {
tests/weight_test.rs
+15
View File
@@ -410,3 +410,18 @@ fn fire_red_vad_weight() -> Result<()> {
Ok(())
}
#[test]
fn silero_vad_weight() -> Result<()> {
// cargo test -F cuda --test weight_test silero_vad_weight -r -- --nocapture
let save_dir =
aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
let model_path = format!("{}/silero_vad/silero_vad_16k.safetensors", save_dir);
let device = get_device(None);
let weights = safetensors::load(model_path, &device)?;
for (key, tensor) in weights.iter() {
println!("=== {} === {:?}", key, tensor);
}
Ok(())
}