use crate::error::{Error, Result};
use crate::execution::ModelConfig as ExecutionConfig;
use ndarray::{Array1, Array2, Array3};
use ort::session::Session;
use std::path::{Path, PathBuf};
/// TDT model configs
#[derive(Debug, Clone)]
pub struct TDTModelConfig {
pub vocab_size: usize,
}
impl Default for TDTModelConfig {
fn default() -> Self {
Self {
vocab_size: 8193,
}
}
}
pub struct ParakeetTDTModel {
encoder: Session,
decoder_joint: Session,
config: TDTModelConfig,
}
impl ParakeetTDTModel {
/// Load TDT model from directory containing encoder and decoder_joint ONNX files
pub fn from_pretrained<P: AsRef<Path>>(
model_dir: P,
exec_config: ExecutionConfig,
) -> Result<Self> {
let model_dir = model_dir.as_ref();
// Find encoder and decoder_joint files
let encoder_path = Self::find_encoder(model_dir)?;
let decoder_joint_path = Self::find_decoder_joint(model_dir)?;
let config = TDTModelConfig::default();
// Load encoder
let builder = Session::builder()?;
let builder = exec_config.apply_to_session_builder(builder)?;
let encoder = builder.commit_from_file(&encoder_path)?;
// Load decoder_joint
let builder = Session::builder()?;
let builder = exec_config.apply_to_session_builder(builder)?;
let decoder_joint = builder.commit_from_file(&decoder_joint_path)?;
Ok(Self {
encoder,
decoder_joint,
config,
})
}
fn find_encoder(dir: &Path) -> Result<PathBuf> {
let candidates = ["encoder-model.onnx", "encoder.onnx"];
for candidate in &candidates {
let path = dir.join(candidate);
if path.exists() {
return Ok(path);
}
}
Err(Error::Config(format!(
"No encoder model found in {}",
dir.display()
)))
}
fn find_decoder_joint(dir: &Path) -> Result<PathBuf> {
let candidates = [
"decoder_joint-model.onnx",
"decoder_joint.onnx",
"decoder-model.onnx",
];
for candidate in &candidates {
let path = dir.join(candidate);
if path.exists() {
return Ok(path);
}
}
Err(Error::Config(format!(
"No decoder_joint model found in {}",
dir.display()
)))
}
/// Run greedy decoding - returns (token_ids, frame_indices, durations)
pub fn forward(&mut self, features: Array2<f32>) -> Result<(Vec<usize>, Vec<usize>, Vec<usize>)> {
// Run encoder
let (encoder_out, encoder_len) = self.run_encoder(&features)?;
// Run greedy decoding with decoder_joint
let (tokens, frame_indices, durations) = self.greedy_decode(&encoder_out, encoder_len)?;
Ok((tokens, frame_indices, durations))
}
fn run_encoder(&mut self, features: &Array2<f32>) -> Result<(Array3<f32>, i64)> {
let batch_size = 1;
let time_steps = features.shape()[0];
let feature_size = features.shape()[1];
// TDT encoder expects (batch, features, time) not (batch, time, features)
let input = features
.t()
.to_shape((batch_size, feature_size, time_steps))
.map_err(|e| Error::Model(format!("Failed to reshape encoder input: {e}")))?
.to_owned();
let input_length = Array1::from_vec(vec![time_steps as i64]);
let input_value = ort::value::Value::from_array(input)?;
let length_value = ort::value::Value::from_array(input_length)?;
let outputs = self.encoder.run(ort::inputs!(
"audio_signal" => input_value,
"length" => length_value
))?;
let encoder_out = &outputs["outputs"];
let encoder_lens = &outputs["encoded_lengths"];
let (shape, data) = encoder_out
.try_extract_tensor::<f32>()
.map_err(|e| Error::Model(format!("Failed to extract encoder output: {e}")))?;
let (_, lens_data) = encoder_lens
.try_extract_tensor::<i64>()
.map_err(|e| Error::Model(format!("Failed to extract encoder lengths: {e}")))?;
let shape_dims = shape.as_ref();
if shape_dims.len() != 3 {
return Err(Error::Model(format!(
"Expected 3D encoder output, got shape: {shape_dims:?}"
)));
}
let b = shape_dims[0] as usize;
let t = shape_dims[1] as usize;
let d = shape_dims[2] as usize;
let encoder_array = Array3::from_shape_vec((b, t, d), data.to_vec())
.map_err(|e| Error::Model(format!("Failed to create encoder array: {e}")))?;
// TDT encoder outputs [batch, encoder_dim, time] directly
Ok((encoder_array, lens_data[0]))
}
fn greedy_decode(&mut self, encoder_out: &Array3<f32>, _encoder_len: i64) -> Result<(Vec<usize>, Vec<usize>, Vec<usize>)> {
// encoder_out shape: [batch, encoder_dim, time]
let encoder_dim = encoder_out.shape()[1];
let time_steps = encoder_out.shape()[2];
let vocab_size = self.config.vocab_size;
let max_tokens_per_step = 10;
let blank_id = vocab_size - 1;
// States: (num_layers=2, batch=1, hidden_dim=640)
let mut state_h = Array3::<f32>::zeros((2, 1, 640));
let mut state_c = Array3::<f32>::zeros((2, 1, 640));
let mut tokens = Vec::new();
let mut frame_indices = Vec::new();
let mut durations = Vec::new();
let mut t = 0;
let mut emitted_tokens = 0;
let mut last_emitted_token = blank_id as i32;
// Frame-by-frame RNN-T/TDT greedy decoding
while t < time_steps {
// Get single encoder frame: slice [0, :, t] and reshape to [1, encoder_dim, 1]
let frame = encoder_out.slice(ndarray::s![0, .., t]).to_owned();
let frame_reshaped = frame
.to_shape((1, encoder_dim, 1))
.map_err(|e| Error::Model(format!("Failed to reshape frame: {e}")))?
.to_owned();
// Current token for prediction network
let targets = Array2::from_shape_vec((1, 1), vec![last_emitted_token])
.map_err(|e| Error::Model(format!("Failed to create targets: {e}")))?;
// Run decoder_joint
let outputs = self.decoder_joint.run(ort::inputs!(
"encoder_outputs" => ort::value::Value::from_array(frame_reshaped)?,
"targets" => ort::value::Value::from_array(targets)?,
"target_length" => ort::value::Value::from_array(Array1::from_vec(vec![1i32]))?,
"input_states_1" => ort::value::Value::from_array(state_h.clone())?,
"input_states_2" => ort::value::Value::from_array(state_c.clone())?
))?;
// Extract logits
let (_, logits_data) = outputs["outputs"]
.try_extract_tensor::<f32>()
.map_err(|e| Error::Model(format!("Failed to extract logits: {e}")))?;
// TDT outputs vocab_size + 5 durations (8193 + 5 = 8198)
let vocab_logits: Vec<f32> = logits_data.iter().take(vocab_size).copied().collect();
let duration_logits: Vec<f32> = logits_data.iter().skip(vocab_size).copied().collect();
let token_id = vocab_logits
.iter()
.enumerate()
.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
.map(|(idx, _)| idx)
.unwrap_or(blank_id);
let duration_step = if !duration_logits.is_empty() {
duration_logits
.iter()
.enumerate()
.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
.map(|(idx, _)| idx)
.unwrap_or(0)
} else {
0
};
// Check if blank token
if token_id != blank_id {
// Update states when we emit a token
if let Ok((h_shape, h_data)) = outputs["output_states_1"].try_extract_tensor::<f32>() {
let dims = h_shape.as_ref();
state_h = Array3::from_shape_vec((dims[0] as usize, dims[1] as usize, dims[2] as usize), h_data.to_vec())
.map_err(|e| Error::Model(format!("Failed to update state_h: {e}")))?;
}
if let Ok((c_shape, c_data)) = outputs["output_states_2"].try_extract_tensor::<f32>() {
let dims = c_shape.as_ref();
state_c = Array3::from_shape_vec((dims[0] as usize, dims[1] as usize, dims[2] as usize), c_data.to_vec())
.map_err(|e| Error::Model(format!("Failed to update state_c: {e}")))?;
}
tokens.push(token_id);
frame_indices.push(t);
durations.push(duration_step);
last_emitted_token = token_id as i32;
emitted_tokens += 1;
// Don't advance yet - try to emit more tokens from the same frame
} else {
// Blank token - advance frame pointer
// Duration prediction applies when we finally move to next frame after emitting tokens
if duration_step > 0 && emitted_tokens > 0 {
t += duration_step;
} else {
t += 1;
}
emitted_tokens = 0;
}
// Safety check: if we've emitted too many tokens from the same frame, advance
if emitted_tokens >= max_tokens_per_step {
t += 1;
emitted_tokens = 0;
}
}
Ok((tokens, frame_indices, durations))
}
}
