//! Speech Tokenizer — ConvNet encoder/decoder with RVQ codebooks.
//!
//! Two sub-components:
//!
//! **Encoder** (voice cloning): converts reference audio waveform to discrete
//! multi-codebook tokens via a causal 1D ConvNet + RVQ.
//!
//! **Decoder** (audio synthesis): reconstructs waveform from discrete codebook
//! indices via embedding lookup + causal 1D ConvNet.
//!
//! The speech tokenizer is a separate model (~682MB) loaded from
//! `Qwen/Qwen3-TTS-Tokenizer-12Hz`.
use candle_core::{Device, Module, Result, Tensor, D};
use candle_nn::{
conv1d, embedding, linear_no_bias, Conv1d, Conv1dConfig, Embedding, Linear, VarBuilder,
};
use super::config::SpeechTokenizerConfig;
// ---------------------------------------------------------------------------
// Weight-Normalized Conv1d
// ---------------------------------------------------------------------------
/// A 1D convolution with optional weight normalization and activation.
pub struct ConvBlock {
conv: Conv1d,
activation: ConvActivation,
}
#[derive(Debug, Clone, Copy)]
pub enum ConvActivation {
None,
Elu,
Tanh,
}
impl ConvBlock {
pub fn new(
in_channels: usize,
out_channels: usize,
kernel_size: usize,
stride: usize,
padding: usize,
dilation: usize,
activation: ConvActivation,
vb: VarBuilder,
) -> Result<Self> {
let config = Conv1dConfig {
stride,
padding,
dilation,
groups: 1,
};
let conv = conv1d(in_channels, out_channels, kernel_size, config, vb.pp("conv"))?;
Ok(Self { conv, activation })
}
pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
let out = self.conv.forward(x)?;
match self.activation {
ConvActivation::None => Ok(out),
ConvActivation::Elu => elu(&out, 1.0),
ConvActivation::Tanh => out.tanh(),
}
}
}
/// ELU activation: x if x >= 0, alpha * (exp(x) - 1) if x < 0
fn elu(x: &Tensor, alpha: f64) -> Result<Tensor> {
let zeros = x.zeros_like()?;
let positive = x.maximum(&zeros)?;
let negative_mask = x.lt(&zeros)?.to_dtype(x.dtype())?;
let exp_x = x.exp()?;
let one = Tensor::ones_like(&exp_x)?;
let negative = ((exp_x - one)? * alpha)?.broadcast_mul(&negative_mask)?;
positive + negative
}
// ---------------------------------------------------------------------------
// Residual Unit
// ---------------------------------------------------------------------------
/// Residual convolutional unit with dilated convolutions.
pub struct ResidualUnit {
conv1: ConvBlock,
conv2: ConvBlock,
}
impl ResidualUnit {
pub fn new(
channels: usize,
dilation: usize,
vb: VarBuilder,
) -> Result<Self> {
// Dilated causal conv (kernel=7, dilation varies)
let padding = (7 - 1) * dilation / 2; // causal-ish padding
let conv1 = ConvBlock::new(
channels,
channels,
7,
1,
padding,
dilation,
ConvActivation::Elu,
vb.pp("block.0"),
)?;
// Pointwise conv (kernel=1)
let conv2 = ConvBlock::new(
channels,
channels,
1,
1,
0,
1,
ConvActivation::Elu,
vb.pp("block.1"),
)?;
Ok(Self { conv1, conv2 })
}
pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
let residual = x;
let out = self.conv1.forward(x)?;
let out = self.conv2.forward(&out)?;
// Match sequence lengths if needed (causal conv may change length)
let out_len = out.dim(D::Minus1)?;
let res_len = residual.dim(D::Minus1)?;
if out_len != res_len {
let start = res_len.saturating_sub(out_len);
let residual = residual.narrow(D::Minus1, start, out_len)?;
residual + out
} else {
residual + out
}
}
}
// ---------------------------------------------------------------------------
// Encoder Block
// ---------------------------------------------------------------------------
/// Encoder downsampling block: residual units + strided conv.
pub struct EncoderBlock {
residual_units: Vec<ResidualUnit>,
downsample: ConvBlock,
}
impl EncoderBlock {
pub fn new(
in_channels: usize,
out_channels: usize,
stride: usize,
num_residuals: usize,
vb: VarBuilder,
) -> Result<Self> {
let mut residual_units = Vec::with_capacity(num_residuals);
for i in 0..num_residuals {
let dilation = 3usize.pow(i as u32); // 1, 3, 9
let unit = ResidualUnit::new(in_channels, dilation, vb.pp(format!("residuals.{i}")))?;
residual_units.push(unit);
}
// Strided downsampling convolution
let kernel_size = stride * 2;
let padding = stride / 2;
let downsample = ConvBlock::new(
in_channels,
out_channels,
kernel_size,
stride,
padding,
1,
ConvActivation::Elu,
vb.pp("downsample"),
)?;
Ok(Self {
residual_units,
downsample,
})
}
pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
let mut out = x.clone();
for unit in &self.residual_units {
out = unit.forward(&out)?;
}
self.downsample.forward(&out)
}
}
// ---------------------------------------------------------------------------
// Decoder Block
// ---------------------------------------------------------------------------
/// Decoder upsampling block: transposed conv + residual units.
pub struct DecoderBlock {
upsample: ConvBlock,
residual_units: Vec<ResidualUnit>,
}
impl DecoderBlock {
pub fn new(
in_channels: usize,
out_channels: usize,
stride: usize,
num_residuals: usize,
vb: VarBuilder,
) -> Result<Self> {
// Strided upsampling (transpose conv simulated by regular conv + padding)
let kernel_size = stride * 2;
let padding = stride / 2;
let upsample = ConvBlock::new(
in_channels,
out_channels,
kernel_size,
1, // stride=1 for output; upsample via repeat/interpolation
padding,
1,
ConvActivation::Elu,
vb.pp("upsample"),
)?;
let mut residual_units = Vec::with_capacity(num_residuals);
for i in 0..num_residuals {
let dilation = 3usize.pow(i as u32);
let unit =
ResidualUnit::new(out_channels, dilation, vb.pp(format!("residuals.{i}")))?;
residual_units.push(unit);
}
Ok(Self {
upsample,
residual_units,
})
}
pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
let mut out = self.upsample.forward(x)?;
for unit in &self.residual_units {
out = unit.forward(&out)?;
}
Ok(out)
}
}
// ---------------------------------------------------------------------------
// RVQ Codebook
// ---------------------------------------------------------------------------
/// Residual Vector Quantization codebook.
///
/// Contains `num_codebooks` embedding tables, each mapping
/// `codebook_size` indices to `codebook_dim`-dimensional vectors.
pub struct RvqCodebook {
codebooks: Vec<Embedding>,
num_codebooks: usize,
#[allow(dead_code)]
codebook_dim: usize,
}
impl RvqCodebook {
pub fn new(config: &SpeechTokenizerConfig, vb: VarBuilder) -> Result<Self> {
let mut codebooks = Vec::with_capacity(config.num_codebooks);
for i in 0..config.num_codebooks {
let cb = embedding(
config.codebook_size,
config.codebook_dim,
vb.pp(format!("codebooks.{i}")),
)?;
codebooks.push(cb);
}
Ok(Self {
codebooks,
num_codebooks: config.num_codebooks,
codebook_dim: config.codebook_dim,
})
}
/// Look up codebook embeddings for all codebook layers.
///
/// `codes`: [num_codebooks, seq_len] — codebook indices per layer
/// Returns: [1, codebook_dim, seq_len] — sum of all codebook embeddings
pub fn decode(&self, codes: &[Vec<u32>], device: &Device) -> Result<Tensor> {
assert_eq!(codes.len(), self.num_codebooks, "Expected {} codebook layers", self.num_codebooks);
let seq_len = codes[0].len();
let mut sum: Option<Tensor> = None;
for (i, code_layer) in codes.iter().enumerate() {
assert_eq!(code_layer.len(), seq_len, "Codebook layer {i} length mismatch");
let indices = Tensor::from_vec(
code_layer.clone(),
(1, seq_len),
device,
)?;
// [1, seq_len, codebook_dim]
let emb = self.codebooks[i].forward(&indices)?;
sum = Some(match sum {
Some(prev) => (prev + emb)?,
None => emb,
});
}
// [1, seq_len, codebook_dim] -> [1, codebook_dim, seq_len]
let result = sum.unwrap().transpose(1, 2)?;
Ok(result)
}
/// Number of codebooks.
pub fn num_codebooks(&self) -> usize {
self.num_codebooks
}
}
// ---------------------------------------------------------------------------
// Speech Tokenizer (Encoder + Decoder)
// ---------------------------------------------------------------------------
/// The complete speech tokenizer with encoder and decoder.
pub struct SpeechTokenizer {
/// Encoder: waveform -> latent (for voice cloning).
encoder_input_conv: ConvBlock,
encoder_blocks: Vec<EncoderBlock>,
encoder_output_conv: ConvBlock,
/// RVQ codebooks for quantization.
codebook: RvqCodebook,
/// Decoder: codes -> waveform.
decoder_input_conv: ConvBlock,
decoder_blocks: Vec<DecoderBlock>,
decoder_output_conv: ConvBlock,
/// Projection from codebook dim to decoder hidden channels.
decoder_proj: Linear,
config: SpeechTokenizerConfig,
device: Device,
}
impl SpeechTokenizer {
/// Load the speech tokenizer from safetensors.
pub fn new(config: &SpeechTokenizerConfig, vb: VarBuilder, device: &Device) -> Result<Self> {
let hidden = config.hidden_channels; // 512
// ===== Encoder =====
// Input: [batch, 1, samples] -> [batch, hidden/8, ...]
let encoder_input_conv = ConvBlock::new(
1,
hidden / 8, // 64
7,
1,
3,
1,
ConvActivation::Elu,
vb.pp("encoder.input_conv"),
)?;
// Downsampling blocks with increasing channels
let strides = [8, 5, 4, 3]; // Total downsampling: 8*5*4*3 = 480
let channels = [hidden / 8, hidden / 4, hidden / 2, hidden]; // 64, 128, 256, 512
let mut encoder_blocks = Vec::with_capacity(strides.len());
for (i, (&stride, &out_ch)) in strides.iter().zip(channels.iter().skip(0)).enumerate() {
let in_ch = if i == 0 { hidden / 8 } else { channels[i - 1] };
let block = EncoderBlock::new(
in_ch,
out_ch,
stride,
3, // 3 residual units per block
vb.pp(format!("encoder.blocks.{i}")),
)?;
encoder_blocks.push(block);
}
// Encoder output projection to codebook dim
let encoder_output_conv = ConvBlock::new(
hidden,
config.codebook_dim,
3,
1,
1,
1,
ConvActivation::None,
vb.pp("encoder.output_conv"),
)?;
// ===== RVQ Codebook =====
let codebook = RvqCodebook::new(config, vb.pp("quantizer"))?;
// ===== Decoder =====
// Projection from codebook dim to decoder hidden
let decoder_proj = linear_no_bias(
config.codebook_dim,
hidden,
vb.pp("decoder.proj"),
)?;
// Input conv
let decoder_input_conv = ConvBlock::new(
hidden,
hidden,
7,
1,
3,
1,
ConvActivation::Elu,
vb.pp("decoder.input_conv"),
)?;
// Upsampling blocks (reverse order of encoder)
let dec_strides = [3, 4, 5, 8];
let dec_channels = [hidden, hidden / 2, hidden / 4, hidden / 8]; // 512, 256, 128, 64
let mut decoder_blocks = Vec::with_capacity(dec_strides.len());
for (i, (&stride, &out_ch)) in dec_strides.iter().zip(dec_channels.iter().skip(0)).enumerate()
{
let in_ch = if i == 0 { hidden } else { dec_channels[i - 1] };
let block = DecoderBlock::new(
in_ch,
out_ch,
stride,
3,
vb.pp(format!("decoder.blocks.{i}")),
)?;
decoder_blocks.push(block);
}
// Output conv: hidden/8 -> 1 channel (waveform)
let decoder_output_conv = ConvBlock::new(
hidden / 8,
1,
7,
1,
3,
1,
ConvActivation::Tanh,
vb.pp("decoder.output_conv"),
)?;
Ok(Self {
encoder_input_conv,
encoder_blocks,
encoder_output_conv,
codebook,
decoder_input_conv,
decoder_blocks,
decoder_output_conv,
decoder_proj,
config: config.clone(),
device: device.clone(),
})
}
/// Encode reference audio waveform to discrete codebook tokens.
///
/// `audio`: [num_samples] — mono 24kHz audio
/// Returns: Vec of `num_codebooks` vectors, each containing token indices.
pub fn encode(&self, audio: &[f32]) -> Result<Vec<Vec<u32>>> {
// [1, 1, num_samples]
let x = Tensor::from_vec(audio.to_vec(), (1, 1, audio.len()), &self.device)?;
// Run encoder
let mut hidden = self.encoder_input_conv.forward(&x)?;
for block in &self.encoder_blocks {
hidden = block.forward(&hidden)?;
}
let latent = self.encoder_output_conv.forward(&hidden)?;
// latent: [1, codebook_dim, seq_len]
// Quantize via nearest-neighbor lookup in each codebook
let seq_len = latent.dim(D::Minus1)?;
let mut all_codes = Vec::with_capacity(self.config.num_codebooks);
// Residual quantization: subtract each codebook's contribution
let mut residual = latent.clone();
for cb_idx in 0..self.config.num_codebooks {
// residual: [1, codebook_dim, seq_len] -> find nearest codebook entry per timestep
let codes = self.quantize_layer(&residual, cb_idx, seq_len)?;
// Look up the quantized vectors and subtract from residual
let code_indices =
Tensor::from_vec(codes.clone(), (1, seq_len), &self.device)?;
let quantized = self.codebook.codebooks[cb_idx].forward(&code_indices)?;
// quantized: [1, seq_len, codebook_dim] -> [1, codebook_dim, seq_len]
let quantized = quantized.transpose(1, 2)?;
residual = (residual - quantized)?;
all_codes.push(codes);
}
Ok(all_codes)
}
/// Quantize a single RVQ layer by finding the nearest codebook entry.
fn quantize_layer(
&self,
residual: &Tensor,
codebook_idx: usize,
_seq_len: usize,
) -> Result<Vec<u32>> {
// residual: [1, codebook_dim, seq_len]
// codebook weights: [codebook_size, codebook_dim]
let cb_weight = self.codebook.codebooks[codebook_idx]
.embeddings()
.clone(); // [codebook_size, codebook_dim]
// Transpose residual: [1, seq_len, codebook_dim]
let residual_t = residual.transpose(1, 2)?.squeeze(0)?; // [seq_len, codebook_dim]
// Compute L2 distances: ||r - c||^2 = ||r||^2 - 2*r*c^T + ||c||^2
let r_sq = residual_t.sqr()?.sum(D::Minus1)?; // [seq_len]
let c_sq = cb_weight.sqr()?.sum(D::Minus1)?; // [codebook_size]
let rc = residual_t.matmul(&cb_weight.t()?)?; // [seq_len, codebook_size]
let r_sq = r_sq.unsqueeze(1)?; // [seq_len, 1]
let c_sq = c_sq.unsqueeze(0)?; // [1, codebook_size]
let distances = (r_sq.broadcast_add(&c_sq)? - (rc * 2.0)?)?; // [seq_len, codebook_size]
// Argmin per timestep
let indices = distances.argmin(D::Minus1)?; // [seq_len]
let codes: Vec<u32> = indices.to_vec1()?;
Ok(codes)
}
/// Decode discrete codebook tokens to audio waveform.
///
/// `codes`: Vec of `num_codebooks` vectors of token indices.
/// Returns: Vec<f32> — mono 24kHz audio samples.
pub fn decode(&self, codes: &[Vec<u32>]) -> Result<Vec<f32>> {
// Look up and sum all codebook embeddings
let embeddings = self.codebook.decode(codes, &self.device)?;
// embeddings: [1, codebook_dim, seq_len]
// Project to decoder hidden size: [1, seq_len, codebook_dim] -> [1, seq_len, hidden]
let emb_t = embeddings.transpose(1, 2)?; // [1, seq_len, codebook_dim]
let projected = self.decoder_proj.forward(&emb_t)?; // [1, seq_len, hidden]
let mut hidden = projected.transpose(1, 2)?; // [1, hidden, seq_len]
// Run decoder
hidden = self.decoder_input_conv.forward(&hidden)?;
for block in &self.decoder_blocks {
hidden = block.forward(&hidden)?;
}
let waveform = self.decoder_output_conv.forward(&hidden)?;
// [1, 1, num_samples] -> Vec<f32>
let samples: Vec<f32> = waveform.flatten_all()?.to_vec1()?;
Ok(samples)
}
/// Decode a single frame's codes to audio samples (for streaming).
///
/// `frame_codes`: [num_codebooks] — one token per codebook for a single frame
/// Returns: audio samples for this frame (~1920 samples at 24kHz / 12.5Hz)
pub fn decode_frame(&self, frame_codes: &[u32]) -> Result<Vec<f32>> {
let codes: Vec<Vec<u32>> = frame_codes.iter().map(|&c| vec![c]).collect();
self.decode(&codes)
}
/// Get the number of codebooks.
pub fn num_codebooks(&self) -> usize {
self.config.num_codebooks
}
/// Get the output sample rate.
pub fn sample_rate(&self) -> u32 {
self.config.sample_rate
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_elu_positive() {
let device = Device::Cpu;
let x = Tensor::from_vec(vec![1.0f32, 2.0, 3.0], (3,), &device).unwrap();
let result = elu(&x, 1.0).unwrap();
let values: Vec<f32> = result.to_vec1().unwrap();
assert!((values[0] - 1.0).abs() < 1e-5);
assert!((values[1] - 2.0).abs() < 1e-5);
}
#[test]
fn test_elu_negative() {
let device = Device::Cpu;
let x = Tensor::from_vec(vec![-1.0f32], (1,), &device).unwrap();
let result = elu(&x, 1.0).unwrap();
let values: Vec<f32> = result.to_vec1().unwrap();
// ELU(-1) = exp(-1) - 1 ≈ -0.6321
assert!((values[0] - (-0.6321)).abs() < 0.01);
}
#[test]
fn test_speech_tokenizer_config() {
let config = SpeechTokenizerConfig::default();
assert_eq!(config.num_codebooks, 16);
assert_eq!(config.codebook_size, 2048);
assert_eq!(config.sample_rate, 24_000);
}
}
