1 files changed, 0 insertions, 93 deletions
diff --git a/parakeet-rs/src/model.rs b/parakeet-rs/src/model.rs
deleted file mode 100644
index b3cd131..0000000
--- a/parakeet-rs/src/model.rs
+++ /dev/null
@@ -1,93 +0,0 @@
-use crate::config::ModelConfig;
-use crate::error::{Error, Result};
-use crate::execution::ModelConfig as ExecutionConfig;
-use ndarray::Array2;
-use ort::session::Session;
-use std::path::Path;
-
-pub struct ParakeetModel {
-    session: Session,
-    config: ModelConfig,
-}
-
-impl ParakeetModel {
-    pub fn from_pretrained<P: AsRef<Path>>(model_path: P) -> Result<Self> {
-        Self::from_pretrained_with_config(model_path, ExecutionConfig::default())
-    }
-
-    pub fn from_pretrained_with_config<P: AsRef<Path>>(
-        model_path: P,
-        exec_config: ExecutionConfig,
-    ) -> Result<Self> {
-        let model_path = model_path.as_ref();
-
-        // Use default config (hardcoded constants for Parakeet-CTC-0.6b: please see: json files https://huggingface.co/onnx-community/parakeet-ctc-0.6b-ONNX/tree/main)
-        let config = ModelConfig::default();
-
-        let builder = Session::builder()?;
-        let builder = exec_config.apply_to_session_builder(builder)?;
-        let session = builder.commit_from_file(model_path)?;
-
-        Ok(Self { session, config })
-    }
-    pub fn forward(&mut self, features: Array2<f32>) -> Result<Array2<f32>> {
-        let batch_size = 1;
-        let time_steps = features.shape()[0];
-        let feature_size = features.shape()[1];
-
-        let input = features
-            .to_shape((batch_size, time_steps, feature_size))
-            .map_err(|e| Error::Model(format!("Failed to reshape input: {e}")))?
-            .to_owned();
-
-        use ndarray::Array2;
-        let attention_mask = Array2::<i64>::ones((batch_size, time_steps));
-
-        let input_value = ort::value::Value::from_array(input)?;
-        let attention_mask_value = ort::value::Value::from_array(attention_mask)?;
-
-        let outputs = self.session.run(ort::inputs!(
-            "input_features" => input_value,
-            "attention_mask" => attention_mask_value
-        ))?;
-
-        let logits_value = &outputs["logits"];
-        let (shape, data) = logits_value
-            .try_extract_tensor::<f32>()
-            .map_err(|e| Error::Model(format!("Failed to extract logits: {e}")))?;
-
-        let shape_dims = shape.as_ref();
-        if shape_dims.len() != 3 {
-            return Err(Error::Model(format!(
-                "Expected 3D logits, got shape: {shape_dims:?}"
-            )));
-        }
-
-        let batch_size = shape_dims[0] as usize;
-        let time_steps_out = shape_dims[1] as usize;
-        let vocab_size = shape_dims[2] as usize;
-
-        if batch_size != 1 {
-            return Err(Error::Model(format!(
-                "Expected batch size 1, got {batch_size}"
-            )));
-        }
-
-        let logits_2d = Array2::from_shape_vec((time_steps_out, vocab_size), data.to_vec())
-            .map_err(|e| Error::Model(format!("Failed to create array: {e}")))?;
-
-        Ok(logits_2d)
-    }
-
-    pub fn config(&self) -> &ModelConfig {
-        &self.config
-    }
-
-    pub fn vocab_size(&self) -> usize {
-        self.config.vocab_size
-    }
-
-    pub fn pad_token_id(&self) -> usize {
-        self.config.pad_token_id
-    }
-}