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//! Model inference module for IndexTTS
//!
//! Provides ONNX Runtime-based model inference for TTS components
mod gpt;
mod embedding;
mod session;
pub use gpt::{GptModel, GptConfig};
pub use embedding::{SpeakerEncoder, EmotionEncoder, SemanticEncoder};
pub use session::{OnnxSession, ModelCache};
/// Sampling strategy for generation
#[derive(Debug, Clone)]
pub enum SamplingStrategy {
/// Greedy decoding (always pick most likely token)
Greedy,
/// Top-k sampling
TopK { k: usize },
/// Top-p (nucleus) sampling
TopP { p: f32 },
/// Combined top-k and top-p
TopKP { k: usize, p: f32 },
/// Temperature-scaled sampling
Temperature { temp: f32 },
}
impl Default for SamplingStrategy {
fn default() -> Self {
SamplingStrategy::TopKP { k: 50, p: 0.95 }
}
}
/// Sample from logits using specified strategy
pub fn sample_from_logits(logits: &[f32], strategy: &SamplingStrategy) -> usize {
match strategy {
SamplingStrategy::Greedy => {
logits
.iter()
.enumerate()
.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
.map(|(i, _)| i)
.unwrap_or(0)
}
SamplingStrategy::TopK { k } => {
let mut indexed: Vec<(usize, f32)> = logits.iter().cloned().enumerate().collect();
indexed.sort_by(|(_, a), (_, b)| b.partial_cmp(a).unwrap());
indexed.truncate(*k);
// Apply softmax to top-k
let max_logit = indexed[0].1;
let exp_sum: f32 = indexed.iter().map(|(_, l)| (l - max_logit).exp()).sum();
let probs: Vec<f32> = indexed
.iter()
.map(|(_, l)| (l - max_logit).exp() / exp_sum)
.collect();
sample_categorical(&indexed.iter().map(|(i, _)| *i).collect::<Vec<_>>(), &probs)
}
SamplingStrategy::TopP { p } => {
let mut indexed: Vec<(usize, f32)> = logits.iter().cloned().enumerate().collect();
indexed.sort_by(|(_, a), (_, b)| b.partial_cmp(a).unwrap());
// Apply softmax
let max_logit = indexed[0].1;
let exp_sum: f32 = indexed.iter().map(|(_, l)| (l - max_logit).exp()).sum();
let probs: Vec<f32> = indexed
.iter()
.map(|(_, l)| (l - max_logit).exp() / exp_sum)
.collect();
// Find nucleus
let mut cumsum = 0.0;
let mut nucleus_size = probs.len();
for (i, prob) in probs.iter().enumerate() {
cumsum += prob;
if cumsum >= *p {
nucleus_size = i + 1;
break;
}
}
// Renormalize nucleus
let nucleus_sum: f32 = probs[..nucleus_size].iter().sum();
let nucleus_probs: Vec<f32> = probs[..nucleus_size]
.iter()
.map(|p| p / nucleus_sum)
.collect();
sample_categorical(
&indexed[..nucleus_size]
.iter()
.map(|(i, _)| *i)
.collect::<Vec<_>>(),
&nucleus_probs,
)
}
SamplingStrategy::TopKP { k, p } => {
let mut indexed: Vec<(usize, f32)> = logits.iter().cloned().enumerate().collect();
indexed.sort_by(|(_, a), (_, b)| b.partial_cmp(a).unwrap());
indexed.truncate(*k);
// Apply softmax
let max_logit = indexed[0].1;
let exp_sum: f32 = indexed.iter().map(|(_, l)| (l - max_logit).exp()).sum();
let probs: Vec<f32> = indexed
.iter()
.map(|(_, l)| (l - max_logit).exp() / exp_sum)
.collect();
// Find nucleus within top-k
let mut cumsum = 0.0;
let mut nucleus_size = probs.len();
for (i, prob) in probs.iter().enumerate() {
cumsum += prob;
if cumsum >= *p {
nucleus_size = i + 1;
break;
}
}
let nucleus_sum: f32 = probs[..nucleus_size].iter().sum();
let nucleus_probs: Vec<f32> = probs[..nucleus_size]
.iter()
.map(|p| p / nucleus_sum)
.collect();
sample_categorical(
&indexed[..nucleus_size]
.iter()
.map(|(i, _)| *i)
.collect::<Vec<_>>(),
&nucleus_probs,
)
}
SamplingStrategy::Temperature { temp } => {
let scaled: Vec<f32> = logits.iter().map(|l| l / temp).collect();
let max_logit = scaled.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
let exp_sum: f32 = scaled.iter().map(|l| (l - max_logit).exp()).sum();
let probs: Vec<f32> = scaled
.iter()
.map(|l| (l - max_logit).exp() / exp_sum)
.collect();
sample_categorical(&(0..probs.len()).collect::<Vec<_>>(), &probs)
}
}
}
/// Sample from categorical distribution
fn sample_categorical(indices: &[usize], probs: &[f32]) -> usize {
use rand::Rng;
let mut rng = rand::thread_rng();
let r: f32 = rng.gen();
let mut cumsum = 0.0;
for (i, &p) in probs.iter().enumerate() {
cumsum += p;
if r <= cumsum {
return indices[i];
}
}
indices[indices.len() - 1]
}
/// Apply repetition penalty to logits
pub fn apply_repetition_penalty(logits: &mut [f32], previous_tokens: &[usize], penalty: f32) {
for &token in previous_tokens {
if token < logits.len() {
if logits[token] > 0.0 {
logits[token] /= penalty;
} else {
logits[token] *= penalty;
}
}
}
}
/// Softmax function
pub fn softmax(logits: &[f32]) -> Vec<f32> {
let max_logit = logits.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
let exp_sum: f32 = logits.iter().map(|l| (l - max_logit).exp()).sum();
logits
.iter()
.map(|l| (l - max_logit).exp() / exp_sum)
.collect()
}
/// Log softmax function
pub fn log_softmax(logits: &[f32]) -> Vec<f32> {
let max_logit = logits.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
let exp_sum: f32 = logits.iter().map(|l| (l - max_logit).exp()).sum();
let log_sum = exp_sum.ln();
logits.iter().map(|l| l - max_logit - log_sum).collect()
}
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