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Diffstat (limited to 'makima/src/tts/qwen3/speech_tokenizer.rs')
| -rw-r--r-- | makima/src/tts/qwen3/speech_tokenizer.rs | 612 |
1 files changed, 612 insertions, 0 deletions
diff --git a/makima/src/tts/qwen3/speech_tokenizer.rs b/makima/src/tts/qwen3/speech_tokenizer.rs new file mode 100644 index 0000000..752050a --- /dev/null +++ b/makima/src/tts/qwen3/speech_tokenizer.rs @@ -0,0 +1,612 @@ +//! 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::{DType, 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, + 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); + } +} |