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-rw-r--r--parakeet-rs/src/model_tdt.rs263
1 files changed, 0 insertions, 263 deletions
diff --git a/parakeet-rs/src/model_tdt.rs b/parakeet-rs/src/model_tdt.rs
deleted file mode 100644
index e00ebdc..0000000
--- a/parakeet-rs/src/model_tdt.rs
+++ /dev/null
@@ -1,263 +0,0 @@
-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))
- }
-}
generated by cgit v1.2.3 (git 2.39.1) at 2026-05-21 19:34:06 +0000