Add the DAC model. (huggingface#2433)

* Add the DAC model.

* More quantization support.

* Handle DAC decoding.

* Plug the DAC decoding in parler-tts.

.gitignore

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

candle-examples/examples/parler-tts/decode.py

@@ -5,6 +5,7 @@
 
 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

candle-examples/examples/parler-tts/main.rs

@@ -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(())
 }