4 files changed, 0 insertions, 458 deletions
diff --git a/parakeet-rs/examples/diarization.rs b/parakeet-rs/examples/diarization.rs
deleted file mode 100644
index 5982ecb..0000000
--- a/parakeet-rs/examples/diarization.rs
+++ /dev/null
@@ -1,137 +0,0 @@
-/*
-Speaker Diarization with NVIDIA Sortformer v2 (Streaming)
-
-Download the Sortformer v2 model:
-https://huggingface.co/altunenes/parakeet-rs/blob/main/diar_streaming_sortformer_4spk-v2.onnx
-Download test audio:
-wget https://github.com/thewh1teagle/pyannote-rs/releases/download/v0.1.0/6_speakers.wav
-
-Usage:
-cargo run --example diarization --features sortformer 6_speakers.wav
-
-NOTE: This example combines two NVIDIA models:
-- Parakeet-TDT: Provides transcription with sentence-level timestamps
-- Sortformer v2: Provides streaming speaker identification (4 speakers max)
-- We use TDT's sentence timestamps + Sortformer's speaker IDs
-- Even if Sortformer can't detect a segment, we still get the transcription (marked UNKNOWN)
-- For more information:
-https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2
-
-WARNING: Sortformer handles long audio natively (streaming), but TDT has sequence
-length limitations (~8-10 minutes max). For production use with long audio files,
-run Sortformer on the full audio for diarization, then chunk the audio into
-~5-minute segments for TDT transcription, and map the results back together.
-*/
-
-#[cfg(feature = "sortformer")]
-use parakeet_rs::sortformer::{DiarizationConfig, Sortformer};
-#[cfg(feature = "sortformer")]
-use parakeet_rs::TimestampMode;
-#[cfg(feature = "sortformer")]
-use hound;
-#[cfg(feature = "sortformer")]
-use std::env;
-#[cfg(feature = "sortformer")]
-use std::time::Instant;
-
-#[allow(unreachable_code)]
-fn main() -> Result<(), Box<dyn std::error::Error>> {
-    #[cfg(not(feature = "sortformer"))]
-    {
-        eprintln!("Error: This example requires the 'sortformer' feature.");
-        eprintln!("Please run with: cargo run --example diarization --features sortformer <audio.wav>");
-        return Err("sortformer feature not enabled".into());
-    }
-
-    #[cfg(feature = "sortformer")]
-    {
-        let start_time = Instant::now();
-        let args: Vec<String> = env::args().collect();
-        let audio_path = args.get(1)
-            .expect("Please specify audio file: cargo run --example diarization --features sortformer <audio.wav>");
-
-        println!("{}", "=".repeat(80));
-        println!("Step 1/3: Loading audio...");
-
-        let mut reader = hound::WavReader::open(audio_path)?;
-        let spec = reader.spec();
-
-        let audio: Vec<f32> = match spec.sample_format {
-            hound::SampleFormat::Float => reader
-                .samples::<f32>()
-                .collect::<Result<Vec<_>, _>>()?,
-            hound::SampleFormat::Int => reader
-                .samples::<i16>()
-                .map(|s| s.map(|s| s as f32 / 32768.0))
-                .collect::<Result<Vec<_>, _>>()?,
-        };
-
-        let duration = audio.len() as f32 / spec.sample_rate as f32 / spec.channels as f32;
-        println!("Loaded {} samples ({} Hz, {} channels, {:.1}s)",
-            audio.len(), spec.sample_rate, spec.channels, duration);
-
-        println!("{}", "=".repeat(80));
-        println!("Step 2/3: Performing speaker diarization with Sortformer v2 (streaming)...");
-
-        // Create Sortformer with default config (callhome)
-        let mut sortformer = Sortformer::with_config(
-            "diar_streaming_sortformer_4spk-v2.onnx",
-            None,  // default exec config
-            DiarizationConfig::callhome(),
-        )?;
-
-        let speaker_segments = sortformer.diarize(audio.clone(), spec.sample_rate, spec.channels)?;
-
-        println!("Found {} speaker segments from Sortformer", speaker_segments.len());
-
-        // Print raw diarization segments
-        println!("\nRaw diarization segments:");
-        for seg in &speaker_segments {
-            println!("  [{:06.2}s - {:06.2}s] Speaker {}", seg.start, seg.end, seg.speaker_id);
-        }
-
-        println!("\n{}", "=".repeat(80));
-        println!("Step 3/3: Transcribing with Parakeet-TDT and attributing speakers...\n");
-
-        // Use TDT for transcription with sentence-level timestamps
-        let mut parakeet = parakeet_rs::ParakeetTDT::from_pretrained("./tdt", None)?;
-
-        // Transcribe with Sentences mode (TDT provides punctuation for proper segmentation)
-        if let Ok(result) = parakeet.transcribe_samples(audio, spec.sample_rate, spec.channels, Some(TimestampMode::Sentences)) {
-            // For each sentence from TDT, find the corresponding speaker from Sortformer
-            for segment in &result.tokens {
-                // Find speaker with maximum overlap 
-                let speaker = speaker_segments
-                    .iter()
-                    .filter_map(|s| {
-                        // Calculate overlap between transcription and diarization segment
-                        let overlap_start = segment.start.max(s.start);
-                        let overlap_end = segment.end.min(s.end);
-                        let overlap = (overlap_end - overlap_start).max(0.0);
-                        if overlap > 0.0 {
-                            Some((s.speaker_id, overlap))
-                        } else {
-                            None
-                        }
-                    })
-                    .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
-                    .map(|(id, _)| format!("Speaker {}", id))
-                    .unwrap_or_else(|| "UNKNOWN".to_string());
-
-                println!("[{:.2}s - {:.2}s] {}: {}",
-                    segment.start, segment.end, speaker, segment.text);
-            }
-        }
-
-        println!("\n{}", "=".repeat(80));
-        let elapsed = start_time.elapsed();
-        println!("\n✓ Diarization and transcription completed in {:.2}s", elapsed.as_secs_f32());
-        println!("• UNKNOWN: Segments where no speaker was detected by Sortformer");
-        println!("• Config: callhome v2 (onset=0.641, offset=0.561, min_on=0.511, min_off=0.296)");
-
-        Ok(())
-    }
-
-    #[cfg(not(feature = "sortformer"))]
-    unreachable!()
-}
diff --git a/parakeet-rs/examples/raw.rs b/parakeet-rs/examples/raw.rs
deleted file mode 100644
index a1a2adc..0000000
--- a/parakeet-rs/examples/raw.rs
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
-Demonstrates using transcribe_samples()
-
-This example shows manual audio loading and calling transcribe_samples() directly
-with sample_rate and channels instead of using transcribe_file()
-
-Usage:
-cargo run --example raw 6_speakers.wav
-cargo run --example raw 6_speakers.wav tdt
-
-WARNING: TDT model has sequence length limitations (~8-10 minutes max).
-For longer audio files, you must split into chunks (e.g., 5-minute segments)
-and transcribe each chunk separately. Attempting to transcribe 25+ minute
-audio files in one call will cause ONNX runtime errors.
-Otherwise you will likely get a error like:
-"Error: Ort(Error { code: RuntimeException, msg: "Non-zero status code returned while running Add node. Name:'/layers.0/self_attn/Add_2' Status Message: /Users/runner/work/ort-artifacts/ort-artifacts/onnxruntime/onnxruntime/core/providers/cpu/math/element_wise_ops.h:540 void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t) axis == 1 || axis == largest was false. })"
-*/
-
-use parakeet_rs::{Parakeet, ParakeetTDT, TimestampMode};
-use std::env;
-use std::time::Instant;
-
-fn main() -> Result<(), Box<dyn std::error::Error>> {
-    let start_time = Instant::now();
-    let args: Vec<String> = env::args().collect();
-    let audio_path = if args.len() > 1 {
-        &args[1]
-    } else {
-        "6_speakers.wav"
-    };
-
-    let use_tdt = args.len() > 2 && args[2] == "tdt";
-
-    // Load audio manually using hound (or any other audio library)
-    // remember if you use raw audio API, you need to handle audio preprocessing yourself!
-    let mut reader = hound::WavReader::open(audio_path)?;
-    let spec = reader.spec();
-
-    println!("Audio info: {}Hz, {} channel(s)", spec.sample_rate, spec.channels);
-
-    let audio: Vec<f32> = match spec.sample_format {
-        hound::SampleFormat::Float => reader
-            .samples::<f32>()
-            .collect::<Result<Vec<_>, _>>()?,
-        hound::SampleFormat::Int => reader
-            .samples::<i16>()
-            .map(|s| s.map(|s| s as f32 / 32768.0))
-            .collect::<Result<Vec<_>, _>>()?,
-    };
-
-    if use_tdt {
-        println!("Loading TDT model...");
-        let mut parakeet = ParakeetTDT::from_pretrained("./tdt", None)?;
-
-        // Use transcribe_samples() with raw parameters and timestamp mode
-        let result = parakeet.transcribe_samples(audio, spec.sample_rate, spec.channels, Some(TimestampMode::Sentences))?;
-
-        println!("{}", result.text);
-        println!("\nSentencess:");
-        for segment in result.tokens.iter() {
-            println!("[{:.2}s - {:.2}s]: {}", segment.start, segment.end, segment.text);
-        }
-    } else {
-        println!("Loading CTC model...");
-        let mut parakeet = Parakeet::from_pretrained(".", None)?;
-
-        // CTC model doesn't predict punctuation (lowercase alphabet only)
-        // This means no sentence boundaries. we use Words mode instead of Sentences
-        let result = parakeet.transcribe_samples(audio, spec.sample_rate, spec.channels, Some(TimestampMode::Words))?;
-
-        println!("{}", result.text);
-
-        // Access word-level timestamps (showing first 10 for brevity)
-        // Note: CTC generates word-level timestamps but cannot segment into sentences
-        // due to lack of punctuation prediction - this is a model limitation if I not mistake
-        println!("\nWords (first 10):");
-        for word in result.tokens.iter().take(10) {
-            println!("[{:.2}s - {:.2}s]: {}", word.start, word.end, word.text);
-        }
-    }
-
-    let elapsed = start_time.elapsed();
-    println!("\n✓ Transcription completed in {:.2}s", elapsed.as_secs_f32());
-
-    Ok(())
-}
diff --git a/parakeet-rs/examples/streaming.rs b/parakeet-rs/examples/streaming.rs
deleted file mode 100644
index f5d36c9..0000000
--- a/parakeet-rs/examples/streaming.rs
+++ /dev/null
@@ -1,129 +0,0 @@
-/*
-Demonstrates streaming ASR with Parakeet RealTime EOU
-
-Download models files from:
-https://huggingface.co/altunenes/parakeet-rs/tree/main/realtime_eou_120m-v1-onnx
-
-This example 
-- Maintains 4-second ring buffer for feature extraction context
-- Processes 160ms chunks (2560 samples at 16kHz)
-- Extracts features from full buffer, then slices last 25 frames
-- Encoder receives: 9 frames (pre-encode cache) + 16 frames (new) = 25 total
-- Cache states (cache_last_channel/time) maintain temporal context
-
-Model files required in ./fullstr/:
-  - encoder.onnx (cache_aware_stream_step export)
-  - decoder_joint.onnx
-  - tokenizer.json
-
-Additional notes:
-let reset_on_eou: bool = false;
-I must admit that this is not work very well on my real world tests :/
-
-
-Usage:
-cargo run --release --example streaming <audio.wav>
-*/
-
-use hound;
-use parakeet_rs::ParakeetEOU;
-use std::env;
-use std::time::Instant;
-
-fn main() -> Result<(), Box<dyn std::error::Error>> {
-    let start_time = Instant::now();
-
-    let args: Vec<String> = env::args().collect();
-    let audio_path = args
-        .get(1)
-        .expect("Usage: cargo run --release --example streaming <audio.wav>");
-
-    println!("Loading model from ./fullstr...");
-    let mut parakeet = ParakeetEOU::from_pretrained("./fullstr", None)?;
-
-    println!("Loading audio: {}", audio_path);
-    let mut reader = hound::WavReader::open(audio_path)?;
-    let spec = reader.spec();
-
-    let mut audio: Vec<f32> = match spec.sample_format {
-        hound::SampleFormat::Float => reader
-            .samples::<f32>()
-            .collect::<Result<Vec<_>, _>>()?,
-        hound::SampleFormat::Int => reader
-            .samples::<i16>()
-            .map(|s| s.map(|s| s as f32 / 32768.0))
-            .collect::<Result<Vec<_>, _>>()?,
-    };
-
-    if spec.sample_rate != 16000 {
-        return Err(format!(
-            "Expected 16kHz audio, got {}Hz. Please resample first.",
-            spec.sample_rate
-        )
-        .into());
-    }
-
-    if spec.channels > 1 {
-        audio = audio
-            .chunks(spec.channels as usize)
-            .map(|chunk| chunk.iter().sum::<f32>() / spec.channels as f32)
-            .collect();
-    }
-
-    let max_val = audio.iter().fold(0.0f32, |a, &b| a.max(b.abs()));
-    if max_val > 1e-6 {
-        let norm_factor = max_val + 1e-5;
-        for sample in &mut audio {
-            *sample /= norm_factor;
-        }
-    }
-
-    let duration = audio.len() as f32 / 16000.0;
-    // 160ms at 16kHz
-    const CHUNK_SIZE: usize = 2560;
-    let reset_on_eou: bool = false;
-
-    println!("Streaming transcription (160ms chunks with 4s buffer)...\n");
-
-    let mut full_text = String::new();
-
-    for chunk in audio.chunks(CHUNK_SIZE) {
-        let chunk_vec = if chunk.len() < CHUNK_SIZE {
-            let mut padded = chunk.to_vec();
-            padded.resize(CHUNK_SIZE, 0.0);
-            padded
-        } else {
-            chunk.to_vec()
-        };
-
-        let text = parakeet.transcribe(&chunk_vec, reset_on_eou)?;
-        if !text.is_empty() {
-            print!("{}", text);
-            std::io::Write::flush(&mut std::io::stdout())?;
-            full_text.push_str(&text);
-        }
-    }
-
-    println!("\n\nFlushing decoder...");
-    let silence = vec![0.0f32; CHUNK_SIZE];
-    for _ in 0..3 {
-        let text = parakeet.transcribe(&silence, reset_on_eou)?;
-        if !text.is_empty() {
-            print!("{}", text);
-            std::io::Write::flush(&mut std::io::stdout())?;
-            full_text.push_str(&text);
-        }
-    }
-
-    println!("\n\nFinal Transcription:\n{}", full_text.trim());
-
-    let elapsed = start_time.elapsed();
-    println!(
-        "\nTranscription completed in {:.2}s (audio: {:.2}s, RTF: {:.2}x)",
-        elapsed.as_secs_f32(),
-        duration,
-        duration / elapsed.as_secs_f32()
-    );
-
-    Ok(())
-}
diff --git a/parakeet-rs/examples/transcribe.rs b/parakeet-rs/examples/transcribe.rs
deleted file mode 100644
index 685e8de..0000000
--- a/parakeet-rs/examples/transcribe.rs
+++ /dev/null
@@ -1,106 +0,0 @@
-/*
-transcribes entire audio, no diarization
-wget https://github.com/thewh1teagle/pyannote-rs/releases/download/v0.1.0/6_speakers.wav
-
-CTC (English-only):
-cargo run --example transcribe 6_speakers.wav
-
-TDT (Multilingual):
-cargo run --example transcribe 6_speakers.wav tdt
-
-NOTE: For manual audio loading without using transcribe_file(), see examples/raw.rs
-- Shows transcribe_samples(audio, sample_rate, channels, timestamps) usage
-
-WARNING: This may fail on very long audio files (>8 min).
-For longer audio, use the pyannote example which processes segments, or split your audio into chunks.
-
-Note: The coreml feature flag is only for reproducing a known ONNX Runtime bug.
-Just ignore it :). See: https://github.com/microsoft/onnxruntime/issues/26355
-*/
-use parakeet_rs::{Parakeet, TimestampMode};
-use std::env;
-use std::time::Instant;
-
-#[cfg(feature = "coreml")]
-use parakeet_rs::{ExecutionConfig, ExecutionProvider};
-
-fn main() -> Result<(), Box<dyn std::error::Error>> {
-    let start_time = Instant::now();
-    let args: Vec<String> = env::args().collect();
-    let audio_path = if args.len() > 1 {
-        &args[1]
-    } else {
-        "6_speakers.wav"
-    };
-
-    let use_tdt = args.len() > 2 && args[2] == "tdt";
-
-    // TDT model (multilingual, 25 languages)
-    if use_tdt {
-        #[cfg(feature = "coreml")]
-        {
-            let config = ExecutionConfig::new().with_execution_provider(ExecutionProvider::CoreML);
-            let mut parakeet = parakeet_rs::ParakeetTDT::from_pretrained("./tdt", Some(config))?;
-            let result = parakeet.transcribe_file(audio_path, Some(TimestampMode::Sentences))?;
-            println!("{}", result.text);
-
-            println!("\nSentencess:");
-            for segment in result.tokens.iter() {
-                println!("[{:.2}s - {:.2}s]: {}", segment.start, segment.end, segment.text);
-            }
-
-            let elapsed = start_time.elapsed();
-            println!("\n✓ Transcription completed in {:.2}s", elapsed.as_secs_f32());
-            return Ok(());
-        }
-
-        #[cfg(not(feature = "coreml"))]
-        {
-            let mut parakeet = parakeet_rs::ParakeetTDT::from_pretrained("./tdt", None)?;
-            let result = parakeet.transcribe_file(audio_path, Some(TimestampMode::Sentences))?;
-            println!("{}", result.text);
-
-            println!("\nSentencess:");
-            for segment in result.tokens.iter() {
-                println!("[{:.2}s - {:.2}s]: {}", segment.start, segment.end, segment.text);
-            }
-
-            let elapsed = start_time.elapsed();
-            println!("\n✓ Transcription completed in {:.2}s", elapsed.as_secs_f32());
-            return Ok(());
-        }
-    }
-
-    // CTC model (English-only)
-    #[cfg(feature = "coreml")]
-    let mut parakeet = {
-        let config = ExecutionConfig::new().with_execution_provider(ExecutionProvider::CoreML);
-        Parakeet::from_pretrained(".", Some(config))?
-    };
-
-    // Default: CPU execution provider (works correctly)
-    // Auto-detects model with priority: model.onnx > model_fp16.onnx > model_int8.onnx > model_q4.onnx
-    // Or specify exact model: Parakeet::from_pretrained("model_q4.onnx", None)?
-    #[cfg(not(feature = "coreml"))]
-    let mut parakeet = Parakeet::from_pretrained(".", None)?;
-
-    // CTC model doesn't predict punctuation (lowercase alphabet only)
-    // This means no sentence boundaries - use Words mode instead of Sentences
-    let result = parakeet.transcribe_file(audio_path, Some(TimestampMode::Words))?;
-
-    // Print transcription
-    println!("{}", result.text);
-
-    // Access word-level timestamps (showing first 10 for brevity)
-    // Note: CTC generates word-level timestamps but cannot segment into sentences
-    // due to lack of punctuation prediction - this is a model limitation
-    println!("\nWords (first 10):");
-    for word in result.tokens.iter().take(10) {
-        println!("[{:.2}s - {:.2}s]: {}", word.start, word.end, word.text);
-    }
-
-    let elapsed = start_time.elapsed();
-    println!("\n✓ Transcription completed in {:.2}s", elapsed.as_secs_f32());
-
-    Ok(())
-}