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use crate::audio;
use crate::config::PreprocessorConfig;
use crate::decoder::TranscriptionResult;
use crate::decoder_tdt::ParakeetTDTDecoder;
use crate::error::{Error, Result};
use crate::execution::ModelConfig as ExecutionConfig;
use crate::model_tdt::ParakeetTDTModel;
use crate::timestamps::{process_timestamps, TimestampMode};
use crate::vocab::Vocabulary;
use std::path::{Path, PathBuf};
/// Parakeet TDT model for multilingual ASR
pub struct ParakeetTDT {
model: ParakeetTDTModel,
decoder: ParakeetTDTDecoder,
preprocessor_config: PreprocessorConfig,
model_dir: PathBuf,
}
impl ParakeetTDT {
/// Load Parakeet TDT model from path with optional configuration.
///
/// # Arguments
/// * `path` - Directory containing encoder-model.onnx, decoder_joint-model.onnx, and vocab.txt
/// * `config` - Optional execution configuration (defaults to CPU if None)
pub fn from_pretrained<P: AsRef<Path>>(
path: P,
config: Option<ExecutionConfig>,
) -> Result<Self> {
let path = path.as_ref();
if !path.is_dir() {
return Err(Error::Config(format!(
"TDT model path must be a directory: {}",
path.display()
)));
}
let vocab_path = path.join("vocab.txt");
if !vocab_path.exists() {
return Err(Error::Config(format!(
"vocab.txt not found in {}",
path.display()
)));
}
// TDT-specific preprocessor config (128 features instead of 80)
let preprocessor_config = PreprocessorConfig {
feature_extractor_type: "ParakeetFeatureExtractor".to_string(),
feature_size: 128,
hop_length: 160,
n_fft: 512,
padding_side: "right".to_string(),
padding_value: 0.0,
preemphasis: 0.97,
processor_class: "ParakeetProcessor".to_string(),
return_attention_mask: true,
sampling_rate: 16000,
win_length: 400,
};
let exec_config = config.unwrap_or_default();
let model = ParakeetTDTModel::from_pretrained(path, exec_config)?;
let vocab = Vocabulary::from_file(&vocab_path)?;
let decoder = ParakeetTDTDecoder::from_vocab(vocab);
Ok(Self {
model,
decoder,
preprocessor_config,
model_dir: path.to_path_buf(),
})
}
/// Transcribe audio samples.
///
/// # Arguments
///
/// * `audio` - Audio samples as f32 values
/// * `sample_rate` - Sample rate in Hz
/// * `channels` - Number of audio channels
/// * `mode` - Optional timestamp mode (Token, Word, or Segment)
///
/// # Returns
///
/// A `TranscriptionResult` containing the transcribed text and timestamps at the requested mode.
pub fn transcribe_samples(
&mut self,
audio: Vec<f32>,
sample_rate: u32,
channels: u16,
mode: Option<TimestampMode>,
) -> Result<TranscriptionResult> {
let features = audio::extract_features_raw(audio, sample_rate, channels, &self.preprocessor_config)?;
let (tokens, frame_indices, durations) = self.model.forward(features)?;
let mut result = self.decoder.decode_with_timestamps(
&tokens,
&frame_indices,
&durations,
self.preprocessor_config.hop_length,
self.preprocessor_config.sampling_rate,
)?;
// Apply timestamp mode conversion
let mode = mode.unwrap_or(TimestampMode::Tokens);
result.tokens = process_timestamps(&result.tokens, mode);
// Rebuild full text from processed tokens
result.text = result.tokens.iter()
.map(|t| t.text.as_str())
.collect::<Vec<_>>()
.join(" ");
Ok(result)
}
/// Transcribe an audio file with timestamps
///
/// # Arguments
///
/// * `audio_path` - A path to the audio file that needs to be transcribed.
/// * `mode` - Optional timestamp mode (Token, Word, or Segment)
///
/// # Returns
///
/// This function returns a `TranscriptionResult` which includes the transcribed text along with timestamps at the requested mode.
pub fn transcribe_file<P: AsRef<Path>>(
&mut self,
audio_path: P,
mode: Option<TimestampMode>,
) -> Result<TranscriptionResult> {
let audio_path = audio_path.as_ref();
let (audio, spec) = audio::load_audio(audio_path)?;
self.transcribe_samples(audio, spec.sample_rate, spec.channels, mode)
}
/// Transcribes multiple audio files in batch.
///
/// # Arguments
///
/// * `audio_paths`: A slice of paths to the audio files that need to be transcribed.
/// * `mode` - Optional timestamp mode (Token, Word, or Segment)
///
/// # Returns
///
/// This function returns a `TranscriptionResult` which includes the transcribed text along with timestamps at the requested mode.
pub fn transcribe_file_batch<P: AsRef<Path>>(
&mut self,
audio_paths: &[P],
mode: Option<TimestampMode>,
) -> Result<Vec<TranscriptionResult>> {
let mut results = Vec::with_capacity(audio_paths.len());
for path in audio_paths {
let result = self.transcribe_file(path, mode)?;
results.push(result);
}
Ok(results)
}
/// Get model directory path
pub fn model_dir(&self) -> &Path {
&self.model_dir
}
}
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