Add the DAC model. (#2433)

* Add the DAC model.

* More quantization support.

* Handle DAC decoding.

* Plug the DAC decoding in parler-tts.
This commit is contained in:
Laurent Mazare
2024-08-19 07:59:51 +01:00
committed by GitHub
parent 58197e1896
commit 236b29ff15
7 changed files with 404 additions and 8 deletions

1
.gitignore vendored
View File

@ -42,3 +42,4 @@ candle-wasm-examples/**/config*.json
.idea/*
__pycache__
out.safetensors
out.wav

View File

@ -5,6 +5,7 @@ from parler_tts import DACModel
tensors = load_file("out.safetensors")
dac_model = DACModel.from_pretrained("parler-tts/dac_44khZ_8kbps")
print(dac_model.model)
output_ids = tensors["codes"][None, None]
print(output_ids, "\n", output_ids.shape)
batch_size = 1

View File

@ -7,7 +7,7 @@ extern crate accelerate_src;
use anyhow::Error as E;
use clap::Parser;
use candle::{DType, Tensor};
use candle::{DType, IndexOp, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::models::parler_tts::{Config, Model};
use tokenizers::Tokenizer;
@ -36,7 +36,7 @@ struct Args {
description: String,
/// The temperature used to generate samples.
#[arg(long, default_value_t = 1.0)]
#[arg(long, default_value_t = 0.0)]
temperature: f64,
/// Nucleus sampling probability cutoff.
@ -82,6 +82,10 @@ struct Args {
#[arg(long, default_value_t = 512)]
max_steps: usize,
/// The output wav file.
#[arg(long, default_value = "out.wav")]
out_file: String,
}
fn main() -> anyhow::Result<()> {
@ -152,24 +156,32 @@ fn main() -> anyhow::Result<()> {
.get_ids()
.to_vec();
let description_tokens = Tensor::new(description_tokens, &device)?.unsqueeze(0)?;
println!("{description_tokens}");
let prompt_tokens = tokenizer
.encode(args.prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let prompt_tokens = Tensor::new(prompt_tokens, &device)?.unsqueeze(0)?;
println!("{prompt_tokens}");
let lp = candle_transformers::generation::LogitsProcessor::new(
args.seed,
Some(args.temperature),
args.top_p,
);
println!("starting generation...");
let codes = model.generate(&prompt_tokens, &description_tokens, lp, args.max_steps)?;
println!("{codes}");
println!("generated codes\n{codes}");
let codes = codes.to_dtype(DType::I64)?;
codes.save_safetensors("codes", "out.safetensors")?;
let codes = codes.unsqueeze(0)?;
let pcm = model
.audio_encoder
.decode_codes(&codes.to_device(&device)?)?;
println!("{pcm}");
let pcm = pcm.i((0, 0))?;
let pcm = candle_examples::audio::normalize_loudness(&pcm, 24_000, true)?;
let pcm = pcm.to_vec1::<f32>()?;
let mut output = std::fs::File::create(&args.out_file)?;
candle_examples::wav::write_pcm_as_wav(&mut output, &pcm, config.audio_encoder.sampling_rate)?;
Ok(())
}

View File

@ -0,0 +1,376 @@
/// Adapted from https://github.com/descriptinc/descript-audio-codec
use crate::models::encodec;
use candle::{IndexOp, Result, Tensor, D};
use candle_nn::{Conv1d, Conv1dConfig, ConvTranspose1d, ConvTranspose1dConfig, VarBuilder};
#[derive(serde::Deserialize, Debug, Clone)]
pub struct Config {
pub num_codebooks: usize,
pub model_bitrate: u32,
pub codebook_size: usize,
pub latent_dim: usize,
pub frame_rate: u32,
pub sampling_rate: u32,
}
#[derive(Debug, Clone)]
pub struct Snake1d {
alpha: Tensor,
}
impl Snake1d {
pub fn new(channels: usize, vb: VarBuilder) -> Result<Self> {
let alpha = vb.get((1, channels, 1), "alpha")?;
Ok(Self { alpha })
}
}
impl candle::Module for Snake1d {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let xs_shape = xs.shape();
let xs = xs.flatten_from(2)?;
let sin = self.alpha.broadcast_mul(&xs)?.sin()?;
let sin = (&sin * &sin)?;
(xs + (&self.alpha + 1e-9)?.recip()?.broadcast_mul(&sin)?)?.reshape(xs_shape)
}
}
#[derive(Debug, Clone)]
pub struct ResidualUnit {
snake1: Snake1d,
conv1: Conv1d,
snake2: Snake1d,
conv2: Conv1d,
}
impl ResidualUnit {
pub fn new(dim: usize, dilation: usize, vb: VarBuilder) -> Result<Self> {
let pad = ((7 - 1) * dilation) / 2;
let vb = vb.pp("block");
let snake1 = Snake1d::new(dim, vb.pp(0))?;
let cfg1 = Conv1dConfig {
dilation,
padding: pad,
..Default::default()
};
let conv1 = encodec::conv1d_weight_norm(dim, dim, 7, cfg1, vb.pp(1))?;
let snake2 = Snake1d::new(dim, vb.pp(2))?;
let conv2 = encodec::conv1d_weight_norm(dim, dim, 1, Default::default(), vb.pp(3))?;
Ok(Self {
snake1,
conv1,
snake2,
conv2,
})
}
}
impl candle::Module for ResidualUnit {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let ys = xs
.apply(&self.snake1)?
.apply(&self.conv1)?
.apply(&self.snake2)?
.apply(&self.conv2)?;
let pad = (xs.dim(D::Minus1)? - ys.dim(D::Minus1)?) / 2;
if pad > 0 {
&ys + xs.narrow(D::Minus1, pad, ys.dim(D::Minus1)?)
} else {
ys + xs
}
}
}
#[derive(Debug, Clone)]
pub struct EncoderBlock {
res1: ResidualUnit,
res2: ResidualUnit,
res3: ResidualUnit,
snake1: Snake1d,
conv1: Conv1d,
}
impl EncoderBlock {
pub fn new(dim: usize, stride: usize, vb: VarBuilder) -> Result<Self> {
let vb = vb.pp("block");
let res1 = ResidualUnit::new(dim / 2, 1, vb.pp(0))?;
let res2 = ResidualUnit::new(dim / 2, 3, vb.pp(1))?;
let res3 = ResidualUnit::new(dim / 2, 9, vb.pp(2))?;
let snake1 = Snake1d::new(dim / 2, vb.pp(3))?;
let cfg1 = Conv1dConfig {
stride,
padding: (stride + 1) / 2,
..Default::default()
};
let conv1 = encodec::conv1d_weight_norm(dim / 2, dim, 2 * stride, cfg1, vb.pp(4))?;
Ok(Self {
res1,
res2,
res3,
snake1,
conv1,
})
}
}
impl candle::Module for EncoderBlock {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
xs.apply(&self.res1)?
.apply(&self.res2)?
.apply(&self.res3)?
.apply(&self.snake1)?
.apply(&self.conv1)
}
}
#[derive(Debug, Clone)]
pub struct Encoder {
conv1: Conv1d,
blocks: Vec<EncoderBlock>,
snake1: Snake1d,
conv2: Conv1d,
}
impl candle::Module for Encoder {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let mut xs = xs.apply(&self.conv1)?;
for block in self.blocks.iter() {
xs = xs.apply(block)?
}
xs.apply(&self.snake1)?.apply(&self.conv2)
}
}
impl Encoder {
pub fn new(
mut d_model: usize,
strides: &[usize],
d_latent: usize,
vb: VarBuilder,
) -> Result<Self> {
let vb = vb.pp("block");
let cfg1 = Conv1dConfig {
padding: 3,
..Default::default()
};
let conv1 = encodec::conv1d_weight_norm(1, d_model, 7, cfg1, vb.pp(0))?;
let mut blocks = Vec::with_capacity(strides.len());
for (block_idx, stride) in strides.iter().enumerate() {
d_model *= 2;
let block = EncoderBlock::new(d_model, *stride, vb.pp(block_idx + 1))?;
blocks.push(block)
}
let snake1 = Snake1d::new(d_model, vb.pp(strides.len() + 1))?;
let cfg2 = Conv1dConfig {
padding: 1,
..Default::default()
};
let conv2 =
encodec::conv1d_weight_norm(d_model, d_latent, 3, cfg2, vb.pp(strides.len() + 2))?;
Ok(Self {
conv1,
blocks,
snake1,
conv2,
})
}
}
#[derive(Debug, Clone)]
pub struct DecoderBlock {
snake1: Snake1d,
conv_tr1: ConvTranspose1d,
res1: ResidualUnit,
res2: ResidualUnit,
res3: ResidualUnit,
}
impl DecoderBlock {
pub fn new(in_dim: usize, out_dim: usize, stride: usize, vb: VarBuilder) -> Result<Self> {
let vb = vb.pp("block");
let snake1 = Snake1d::new(in_dim, vb.pp(0))?;
let cfg = ConvTranspose1dConfig {
stride,
padding: (stride + 1) / 2,
..Default::default()
};
let conv_tr1 = encodec::conv_transpose1d_weight_norm(
in_dim,
out_dim,
2 * stride,
true,
cfg,
vb.pp(1),
)?;
let res1 = ResidualUnit::new(out_dim, 1, vb.pp(2))?;
let res2 = ResidualUnit::new(out_dim, 3, vb.pp(3))?;
let res3 = ResidualUnit::new(out_dim, 9, vb.pp(4))?;
Ok(Self {
snake1,
conv_tr1,
res1,
res2,
res3,
})
}
}
impl candle_nn::Module for DecoderBlock {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
xs.apply(&self.snake1)?
.apply(&self.conv_tr1)?
.apply(&self.res1)?
.apply(&self.res2)?
.apply(&self.res3)
}
}
#[derive(Debug, Clone)]
pub struct Decoder {
conv1: Conv1d,
blocks: Vec<DecoderBlock>,
snake1: Snake1d,
conv2: Conv1d,
}
impl Decoder {
pub fn new(
in_c: usize,
mut channels: usize,
rates: &[usize],
d_out: usize,
vb: VarBuilder,
) -> Result<Self> {
let vb = vb.pp("model");
let cfg1 = Conv1dConfig {
padding: 3,
..Default::default()
};
let conv1 = encodec::conv1d_weight_norm(in_c, channels, 7, cfg1, vb.pp(0))?;
let mut blocks = Vec::with_capacity(rates.len());
for (idx, stride) in rates.iter().enumerate() {
let block = DecoderBlock::new(channels, channels / 2, *stride, vb.pp(idx + 1))?;
channels /= 2;
blocks.push(block)
}
let snake1 = Snake1d::new(channels, vb.pp(rates.len() + 1))?;
let conv2 = encodec::conv1d_weight_norm(channels, d_out, 7, cfg1, vb.pp(rates.len() + 2))?;
Ok(Self {
conv1,
blocks,
snake1,
conv2,
})
}
}
impl candle::Module for Decoder {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let mut xs = xs.apply(&self.conv1)?;
for block in self.blocks.iter() {
xs = xs.apply(block)?
}
xs.apply(&self.snake1)?.apply(&self.conv2)
}
}
#[allow(unused)]
#[derive(Clone, Debug)]
pub struct VectorQuantizer {
in_proj: Conv1d,
out_proj: Conv1d,
codebook: candle_nn::Embedding,
}
impl VectorQuantizer {
pub fn new(in_dim: usize, cb_size: usize, cb_dim: usize, vb: VarBuilder) -> Result<Self> {
let in_proj =
encodec::conv1d_weight_norm(in_dim, cb_dim, 1, Default::default(), vb.pp("in_proj"))?;
let out_proj =
encodec::conv1d_weight_norm(cb_dim, in_dim, 1, Default::default(), vb.pp("out_proj"))?;
let codebook = candle_nn::embedding(cb_size, cb_dim, vb.pp("codebook"))?;
Ok(Self {
in_proj,
out_proj,
codebook,
})
}
pub fn embed_code(&self, embed_id: &Tensor) -> Result<Tensor> {
embed_id.apply(&self.codebook)
}
pub fn decode_code(&self, embed_id: &Tensor) -> Result<Tensor> {
self.embed_code(embed_id)?.transpose(1, 2)
}
}
#[derive(Clone, Debug)]
pub struct ResidualVectorQuantizer {
quantizers: Vec<VectorQuantizer>,
}
impl ResidualVectorQuantizer {
pub fn new(
input_dim: usize,
n_codebooks: usize,
cb_size: usize,
cb_dim: usize,
vb: VarBuilder,
) -> Result<Self> {
let vb = &vb.pp("quantizers");
let quantizers = (0..n_codebooks)
.map(|i| VectorQuantizer::new(input_dim, cb_size, cb_dim, vb.pp(i)))
.collect::<Result<Vec<_>>>()?;
Ok(Self { quantizers })
}
pub fn from_codes(&self, codes: &Tensor) -> Result<Tensor> {
let mut sum = None;
for (idx, quantizer) in self.quantizers.iter().enumerate() {
let z_p_i = quantizer.decode_code(&codes.i((.., idx))?)?;
let z_q_i = z_p_i.apply(&quantizer.out_proj)?;
let s = match sum {
None => z_q_i,
Some(s) => (s + z_q_i)?,
};
sum = Some(s)
}
match sum {
Some(s) => Ok(s),
None => candle::bail!("empty codebooks"),
}
}
}
#[derive(Debug, Clone)]
pub struct Model {
pub encoder: Encoder,
pub quantizer: ResidualVectorQuantizer,
pub decoder: Decoder,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let vb = vb.pp("model");
let encoder = Encoder::new(64, &[2, 4, 8, 8], cfg.latent_dim, vb.pp("encoder"))?;
let quantizer = ResidualVectorQuantizer::new(
cfg.latent_dim,
cfg.num_codebooks,
cfg.codebook_size,
8,
vb.pp("quantizer"),
)?;
let decoder = Decoder::new(cfg.latent_dim, 1536, &[8, 8, 4, 2], 1, vb.pp("decoder"))?;
Ok(Self {
encoder,
decoder,
quantizer,
})
}
pub fn decode_codes(&self, audio_codes: &Tensor) -> Result<Tensor> {
let audio_values = self.quantizer.from_codes(audio_codes)?;
audio_values.apply(&self.decoder)
}
}

View File

@ -136,7 +136,7 @@ pub fn conv1d_weight_norm(
Ok(Conv1d::new(weight, Some(bias), config))
}
fn conv_transpose1d_weight_norm(
pub fn conv_transpose1d_weight_norm(
in_c: usize,
out_c: usize,
kernel_size: usize,

View File

@ -9,6 +9,7 @@ pub mod clip;
pub mod codegeex4_9b;
pub mod convmixer;
pub mod convnext;
pub mod dac;
pub mod depth_anything_v2;
pub mod dinov2;
pub mod dinov2reg4;

View File

@ -31,6 +31,7 @@ pub struct Config {
pub decoder: DecoderConfig,
pub text_encoder: t5::Config,
pub vocab_size: usize,
pub audio_encoder: crate::models::dac::Config,
}
#[derive(Debug, Clone)]
@ -325,6 +326,7 @@ pub struct Model {
pub text_encoder: t5::T5EncoderModel,
pub decoder_start_token_id: u32,
pub pad_token_id: u32,
pub audio_encoder: crate::models::dac::Model,
}
impl Model {
@ -347,6 +349,8 @@ impl Model {
} else {
None
};
let audio_encoder =
crate::models::dac::Model::new(&cfg.audio_encoder, vb.pp("audio_encoder"))?;
Ok(Self {
decoder,
text_encoder,
@ -354,6 +358,7 @@ impl Model {
enc_to_dec_proj,
decoder_start_token_id: cfg.decoder_start_token_id,
pad_token_id: cfg.pad_token_id,
audio_encoder,
})
}