From 365a5160bea9816e57cc82cba7f0b37793800a2f Mon Sep 17 00:00:00 2001 From: miro Date: Mon, 16 Mar 2026 05:45:44 +0000 Subject: [PATCH 1/4] feat: add TDT CoreML export for parakeet-tdt-ctc-110m Add convert-tdt-coreml.py which exports the TDT decoder components (fused mel+encoder, RNNT decoder LSTM, joint decision with duration) instead of the CTC head. The CTC export only produces blank-dominant log-probabilities unsuitable for greedy transcription in hybrid models. Components: - convert-tdt-coreml.py: Full TDT export pipeline (iOS 18 target) - individual_components.py: Shared torch.nn.Module wrappers for tracing - Updated README.md: Documents both TDT and CTC export paths - Updated pyproject.toml: Adds script entry point and includes --- .../parakeet-tdt-ctc-110m/coreml/README.md | 100 +++- .../coreml/convert-tdt-coreml.py | 565 ++++++++++++++++++ .../coreml/individual_components.py | 227 +++++++ .../coreml/pyproject.toml | 5 +- 4 files changed, 864 insertions(+), 33 deletions(-) create mode 100644 models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py create mode 100644 models/stt/parakeet-tdt-ctc-110m/coreml/individual_components.py diff --git a/models/stt/parakeet-tdt-ctc-110m/coreml/README.md b/models/stt/parakeet-tdt-ctc-110m/coreml/README.md index 4bf8566..2fe5d10 100644 --- a/models/stt/parakeet-tdt-ctc-110m/coreml/README.md +++ b/models/stt/parakeet-tdt-ctc-110m/coreml/README.md @@ -1,17 +1,21 @@ -# Parakeet‑TDT CTC 110M — CoreML Export (Mel+Encoder+CTC) +# Parakeet-TDT-CTC 110M — CoreML Export -This target contains tools to export NVIDIA's `nvidia/parakeet-tdt_ctc-110m` hybrid RNNT/CTC model to CoreML, with an emphasis on the **CTC branch** used for keyword spotting and custom vocabulary support. +This directory contains tools to export NVIDIA's `nvidia/parakeet-tdt_ctc-110m` hybrid RNNT/CTC model to CoreML. -The goal is to produce a CoreML bundle that mirrors NeMo's preprocessing and CTC head, so that Swift-side CTC keyword spotting can rely on **model-correct** acoustics rather than the Argmax MelSpectrogram export. +The hybrid model has two decoder heads sharing one encoder: +- **TDT (Token Duration Transducer)**: Primary transcription head with duration prediction — used by FluidAudio for ASR +- **CTC**: Auxiliary head for keyword spotting and custom vocabulary support ## Layout ```text mobius/models/stt/parakeet-tdt-ctc-110m/coreml -├── README.md # This file -├── convert-coreml.py # CLI for exporting fused Mel+Encoder+CTC to CoreML -├── pyproject.toml # Per-target Python environment (NeMo + coremltools) -└── audio/ # Optional trace audio (15s 16kHz) for export +├── README.md # This file +├── convert-tdt-coreml.py # TDT export: fused mel+encoder, RNNT decoder, joint decision +├── convert-coreml.py # CTC export: fused mel+encoder+CTC head (for keyword spotting) +├── individual_components.py # Shared torch.nn.Module wrappers for CoreML tracing +├── pyproject.toml # Per-target Python environment (NeMo + coremltools) +└── audio/ # Optional trace audio (15s 16kHz) for export ``` ## Environment @@ -24,43 +28,75 @@ uv sync This will create/update a local environment pinned by `pyproject.toml` and `uv.lock` (Python 3.10.12, NeMo, coremltools, etc.). -## Export CoreML (Mel+Encoder+CTC) +## Export TDT (for FluidAudio ASR) -The `convert-coreml.py` script exports a fused module: +The `convert-tdt-coreml.py` script exports the TDT components used by FluidAudio: ```text -audio_signal [1, S] (16kHz mono, 15s window) - ↓ preprocessor (NeMo) -mel spectrogram - ↓ encoder (NeMo) -encoder features - ↓ ctc_decoder (NeMo aux_ctc branch) -log_probs [1, T, V+1] (CTC log-probabilities) +Preprocessor.mlpackage — fused waveform → mel → encoder features +Decoder.mlpackage — RNNT prediction network (LSTM) +JointDecision.mlpackage — joint network (full T×U grid, with TDT duration) +JointDecisionSingleStep.mlpackage — single-step joint (for streaming) +vocab.json — SentencePiece vocabulary (array format) +metadata.json — model dimensions and export configuration ``` Usage: ```bash -uv run python convert-coreml.py convert \ - --model-id nvidia/parakeet-tdt_ctc-110m \ - --output-dir parakeet_ctc_coreml +# From pretrained (downloads from HuggingFace) +uv run python convert-tdt-coreml.py \ + --output-dir parakeet_tdt_coreml \ + --audio-path audio/trace_15s.wav + +# From local .nemo checkpoint +uv run python convert-tdt-coreml.py \ + --nemo-path ./parakeet-tdt-ctc-110m.nemo \ + --output-dir parakeet_tdt_coreml \ + --audio-path audio/trace_15s.wav + +# Reuse a previously exported mel+encoder +uv run python convert-tdt-coreml.py \ + --reuse-encoder parakeet_tdt_coreml/Preprocessor.mlpackage \ + --output-dir parakeet_tdt_coreml_v2 ``` -Notes: -- Export uses a fixed 15s window (audio_signal shape [1,240000]) to avoid runtime shape issues in CoreML preview. -- Two-model export: - - `parakeet_ctc_mel_encoder.mlpackage` — waveform -> encoder, encoder_length - - `parakeet_ctc_decoder.mlpackage` — encoder -> log_probs +Key differences from the 0.6B export: +- **Fused frontend**: mel spectrogram + encoder are a single `Preprocessor.mlpackage` (0.6B has separate Preprocessor + Encoder) +- **iOS 18 deployment target**: Required for int ops in the encoder's positional encoding +- **Smaller dimensions**: encoderDim=512, decoderHidden=640, decoderLayers=1, vocabSize=1024 -Outputs: +### Using with FluidAudio -- `parakeet_ctc_mel_encoder.mlpackage` — waveform → CTC log_probs + encoder_length -- `metadata.json` — input/output shapes and export configuration +After export, compile the `.mlpackage` files to `.mlmodelc`: -## Next Steps (Swift Integration) +```bash +xcrun coremlcompiler compile Preprocessor.mlpackage output_dir/ +xcrun coremlcompiler compile Decoder.mlpackage output_dir/ +xcrun coremlcompiler compile JointDecisionSingleStep.mlpackage output_dir/ +# Rename to match FluidAudio's expected name +mv output_dir/JointDecisionSingleStep.mlmodelc output_dir/JointDecision.mlmodelc +cp vocab.json output_dir/ +``` + +Then run with: + +```bash +fluidaudiocli transcribe audio.wav --model-version tdt-ctc-110m --model-dir output_dir/ +``` + +## Export CTC (for keyword spotting) + +The `convert-coreml.py` script exports the CTC branch for keyword spotting: + +```bash +uv run python convert-coreml.py convert \ + --model-id nvidia/parakeet-tdt_ctc-110m \ + --output-dir parakeet_ctc_coreml +``` -Once exported and validated: +This produces: +- `parakeet_ctc_mel_encoder.mlpackage` — waveform → encoder features +- `parakeet_ctc_decoder.mlpackage` — encoder → CTC log-probabilities -- Point FluidAudio's CTC loader at the new `parakeet_ctc_mel_encoder.mlpackage` instead of `argmaxinc/ctckit-pro`. -- Re-run the CTC keyword spotter parity checks (NeMo Python vs CoreML) on Kokoro TTS clips. -- When parity is acceptable, re-enable time-aligned CTC boosting in Swift for Argmax-style Custom Vocabulary. +Note: The CTC head is trained as an auxiliary loss and produces blank-dominant outputs. It is not suitable for standalone greedy transcription — use the TDT export for that. diff --git a/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py b/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py new file mode 100644 index 0000000..30a81a2 --- /dev/null +++ b/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py @@ -0,0 +1,565 @@ +#!/usr/bin/env python3 +"""Export hybrid TDT-CTC model (EncDecHybridRNNTCTCBPEModel) to CoreML. + +This exports the TDT components (encoder, RNNT decoder, joint with duration) +instead of the CTC head, which is blank-dominant and unsuitable for greedy +transcription in hybrid models. + +Usage: + uv run python convert-tdt-coreml.py \ + --nemo-path ./parakeet-tdt-ctc-110m-atco-v2.nemo \ + --output-dir parakeet_tdt_coreml \ + --audio-path audio/trace_15s.wav +""" +from __future__ import annotations + +import json +import shutil +from dataclasses import asdict +from pathlib import Path +from typing import Dict, Optional, Tuple + +import coremltools as ct +import numpy as np +import soundfile as sf +import torch +import typer + +import nemo.collections.asr as nemo_asr + +from individual_components import ( + DecoderWrapper, + EncoderWrapper, + ExportSettings, + JointDecisionSingleStep, + JointDecisionWrapper, + JointWrapper, + MelEncoderWrapper, + PreprocessorWrapper, + _coreml_convert, +) + +DEFAULT_MODEL_ID = "nvidia/parakeet-tdt_ctc-110m" +AUTHOR = "Fluid Inference" + + +def _compute_length(seconds: float, sample_rate: int) -> int: + return int(round(seconds * sample_rate)) + + +def _tensor_shape(tensor: torch.Tensor) -> Tuple[int, ...]: + return tuple(int(dim) for dim in tensor.shape) + + +def _save_mlpackage( + model: ct.models.MLModel, path: Path, description: str +) -> None: + try: + model.minimum_deployment_target = ct.target.iOS17 + except Exception: + pass + model.short_description = description + model.author = AUTHOR + path.parent.mkdir(parents=True, exist_ok=True) + model.save(str(path)) + + +def _prepare_audio( + audio_path: Path, sample_rate: int, max_samples: int +) -> Tuple[torch.Tensor, torch.Tensor]: + if not audio_path.exists(): + raise typer.BadParameter(f"Audio not found: {audio_path}") + data, sr = sf.read(str(audio_path), dtype="float32", always_2d=False) + if sr != sample_rate: + raise typer.BadParameter( + f"Audio sample rate {sr} != model rate {sample_rate}" + ) + if data.ndim > 1: + data = data[:, 0] + if data.size == 0: + raise typer.BadParameter("Audio is empty") + original_len = int(data.size) + if data.size < max_samples: + data = np.pad(data, (0, max_samples - data.size)) + elif data.size > max_samples: + data = data[:max_samples] + audio_tensor = torch.from_numpy(data).unsqueeze(0).to(dtype=torch.float32) + length = torch.tensor([min(original_len, max_samples)], dtype=torch.int32) + return audio_tensor, length + + +def _parse_compute_units(name: str) -> ct.ComputeUnit: + normalized = str(name).strip().upper() + mapping = { + "ALL": ct.ComputeUnit.ALL, + "CPU_ONLY": ct.ComputeUnit.CPU_ONLY, + "CPU_AND_GPU": ct.ComputeUnit.CPU_AND_GPU, + "CPU_AND_NE": ct.ComputeUnit.CPU_AND_NE, + "CPU_AND_NEURALENGINE": ct.ComputeUnit.CPU_AND_NE, + } + if normalized not in mapping: + raise typer.BadParameter( + f"Unknown compute units '{name}'. Choose from: " + + ", ".join(mapping.keys()) + ) + return mapping[normalized] + + +def _parse_compute_precision(name: Optional[str]) -> Optional[ct.precision]: + if name is None: + return None + normalized = str(name).strip().upper() + if normalized == "": + return None + mapping = { + "FLOAT32": ct.precision.FLOAT32, + "FLOAT16": ct.precision.FLOAT16, + } + if normalized not in mapping: + raise typer.BadParameter( + f"Unknown compute precision '{name}'. Choose from: " + + ", ".join(mapping.keys()) + ) + return mapping[normalized] + + +def _save_vocabulary(asr_model, output_dir: Path) -> None: + """Save SentencePiece vocabulary as vocab.json (array format).""" + sp = asr_model.tokenizer.tokenizer + vocab_list = [sp.id_to_piece(i) for i in range(sp.get_piece_size())] + vocab_path = output_dir / "vocab.json" + vocab_path.write_text(json.dumps(vocab_list, ensure_ascii=False, indent=2)) + typer.echo( + f"Saved vocabulary ({len(vocab_list)} tokens) to {vocab_path}" + ) + + +app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) + + +@app.command() +def convert( + nemo_path: Optional[Path] = typer.Option( + None, + "--nemo-path", + exists=True, + resolve_path=True, + help="Path to hybrid TDT-CTC .nemo checkpoint", + ), + model_id: str = typer.Option( + DEFAULT_MODEL_ID, + "--model-id", + help="Model identifier when --nemo-path is omitted", + ), + output_dir: Path = typer.Option( + Path("parakeet_tdt_coreml"), + help="Output directory for mlpackage files", + ), + audio_path: Optional[Path] = typer.Option( + None, + "--audio-path", + resolve_path=True, + help="Path to 15s 16kHz WAV for tracing", + ), + max_audio_seconds: float = typer.Option( + 15.0, "--max-audio-seconds", help="Fixed waveform window (seconds)" + ), + mel_encoder_cu: str = typer.Option( + "CPU_AND_NE", + "--mel-encoder-cu", + help="Compute units for fused mel+encoder", + ), + compute_precision: Optional[str] = typer.Option( + None, + "--compute-precision", + help="Export precision: FLOAT32 (default) or FLOAT16", + ), + reuse_encoder: Optional[Path] = typer.Option( + None, + "--reuse-encoder", + resolve_path=True, + help="Path to existing mel+encoder mlpackage to reuse (skips encoder export)", + ), +) -> None: + """Export TDT components from a hybrid TDT-CTC model to CoreML.""" + export_settings = ExportSettings( + output_dir=output_dir, + compute_units=ct.ComputeUnit.CPU_ONLY, + deployment_target=ct.target.iOS18, + compute_precision=_parse_compute_precision(compute_precision), + max_audio_seconds=max_audio_seconds, + max_symbol_steps=1, + ) + + typer.echo(f"Export configuration: {asdict(export_settings)}") + output_dir.mkdir(parents=True, exist_ok=True) + + melenc_cu = _parse_compute_units(mel_encoder_cu) + + # -- Load hybrid model -------------------------------------------------- + typer.echo(f"Loading hybrid TDT-CTC model ({'file' if nemo_path else 'pretrained'})...") + if nemo_path is not None: + asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.restore_from( + str(nemo_path), map_location="cpu" + ) + checkpoint_meta = {"type": "file", "path": str(nemo_path)} + else: + asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained( + model_id, map_location="cpu" + ) + checkpoint_meta = {"type": "pretrained", "model_id": model_id} + asr_model.eval() + + sample_rate = int(asr_model.cfg.preprocessor.sample_rate) + max_samples = _compute_length(max_audio_seconds, sample_rate) + + # -- Probe model dimensions --------------------------------------------- + vocab_size = int(asr_model.tokenizer.vocab_size) + num_extra = int(asr_model.joint.num_extra_outputs) + decoder_hidden = int(asr_model.decoder.pred_hidden) + decoder_layers = int(asr_model.decoder.pred_rnn_layers) + blank_idx = int(asr_model.decoder.blank_idx) + + typer.echo(f" vocab_size={vocab_size}, num_extra={num_extra} (TDT duration bins)") + typer.echo(f" decoder: hidden={decoder_hidden}, layers={decoder_layers}, blank_idx={blank_idx}") + + if num_extra == 0: + typer.echo( + "WARNING: joint.num_extra_outputs=0 -- this model has no TDT duration head. " + "It may be a plain RNNT, not TDT." + ) + + # -- Trace audio -------------------------------------------------------- + if audio_path is None: + candidates = [ + Path(__file__).resolve().parent / "audio" / "trace_15s.wav", + Path(__file__).resolve().parent / "trace_15s.wav", + ] + for c in candidates: + if c.exists(): + audio_path = c + break + if audio_path is None: + raise typer.BadParameter("Provide --audio-path (15s 16kHz WAV)") + typer.echo(f"Trace audio: {audio_path}") + audio_tensor, audio_length = _prepare_audio(audio_path, sample_rate, max_samples) + + # -- Wrap components ---------------------------------------------------- + preprocessor = PreprocessorWrapper(asr_model.preprocessor.eval()) + encoder = EncoderWrapper(asr_model.encoder.eval()) + decoder = DecoderWrapper(asr_model.decoder.eval()) + joint = JointWrapper(asr_model.joint.eval()) + + decoder_export_flag = getattr(asr_model.decoder, "_rnnt_export", False) + asr_model.decoder._rnnt_export = True + + try: + # -- Reference forward pass ----------------------------------------- + with torch.inference_mode(): + mel_ref, mel_length_ref = preprocessor(audio_tensor, audio_length) + mel_length_ref = mel_length_ref.to(dtype=torch.int32) + encoder_ref, encoder_length_ref = encoder(mel_ref, mel_length_ref) + encoder_length_ref = encoder_length_ref.to(dtype=torch.int32) + + mel_ref = mel_ref.clone() + mel_length_ref = mel_length_ref.clone() + encoder_ref = encoder_ref.clone() + encoder_length_ref = encoder_length_ref.clone() + + targets = torch.full( + (1, export_settings.max_symbol_steps), + fill_value=blank_idx, + dtype=torch.int32, + ) + target_lengths = torch.tensor( + [export_settings.max_symbol_steps], dtype=torch.int32 + ) + zero_state = torch.zeros( + decoder_layers, 1, decoder_hidden, dtype=torch.float32 + ) + + with torch.inference_mode(): + decoder_ref, h_ref, c_ref = decoder( + targets, target_lengths, zero_state, zero_state + ) + joint_ref = joint(encoder_ref, decoder_ref) + + decoder_ref = decoder_ref.clone() + h_ref = h_ref.clone() + c_ref = c_ref.clone() + joint_ref = joint_ref.clone() + + typer.echo(f" encoder_ref shape: {_tensor_shape(encoder_ref)}") + typer.echo(f" decoder_ref shape: {_tensor_shape(decoder_ref)}") + typer.echo(f" joint_ref shape: {_tensor_shape(joint_ref)}") + + # -- 1. Fused Mel+Encoder ------------------------------------------- + mel_encoder_path = output_dir / "Preprocessor.mlpackage" + if reuse_encoder is not None: + typer.echo(f"Reusing existing mel+encoder from: {reuse_encoder}") + if mel_encoder_path.exists(): + shutil.rmtree(mel_encoder_path) + shutil.copytree(reuse_encoder, mel_encoder_path) + typer.echo(f" Copied to {mel_encoder_path}") + else: + typer.echo("Tracing fused mel+encoder...") + mel_encoder = MelEncoderWrapper(preprocessor, encoder).cpu().eval() + traced_mel_encoder = torch.jit.trace( + mel_encoder, + (audio_tensor.cpu(), audio_length.cpu()), + strict=False, + ) + traced_mel_encoder.eval() + + mel_encoder_model = _coreml_convert( + traced_mel_encoder, + inputs=[ + ct.TensorType( + name="audio_signal", + shape=(1, max_samples), + dtype=np.float32, + ), + ct.TensorType( + name="audio_length", shape=(1,), dtype=np.int32 + ), + ], + outputs=[ + ct.TensorType(name="encoder", dtype=np.float32), + ct.TensorType(name="encoder_length", dtype=np.int32), + ], + settings=export_settings, + compute_units_override=melenc_cu, + ) + _save_mlpackage( + mel_encoder_model, + mel_encoder_path, + f"Parakeet TDT-CTC Mel+Encoder ({max_audio_seconds:.0f}s window)", + ) + + # -- 2. Decoder (LSTM prediction network) -------------------------- + typer.echo("Tracing decoder (RNNT prediction network)...") + traced_decoder = torch.jit.trace( + decoder, + (targets, target_lengths, zero_state, zero_state), + strict=False, + ) + traced_decoder.eval() + + decoder_model = _coreml_convert( + traced_decoder, + inputs=[ + ct.TensorType( + name="targets", + shape=_tensor_shape(targets), + dtype=np.int32, + ), + ct.TensorType( + name="target_length", shape=(1,), dtype=np.int32 + ), + ct.TensorType( + name="h_in", + shape=_tensor_shape(zero_state), + dtype=np.float32, + ), + ct.TensorType( + name="c_in", + shape=_tensor_shape(zero_state), + dtype=np.float32, + ), + ], + outputs=[ + ct.TensorType(name="decoder", dtype=np.float32), + ct.TensorType(name="h_out", dtype=np.float32), + ct.TensorType(name="c_out", dtype=np.float32), + ], + settings=export_settings, + compute_units_override=ct.ComputeUnit.CPU_ONLY, + ) + decoder_path = output_dir / "Decoder.mlpackage" + _save_mlpackage( + decoder_model, + decoder_path, + "Parakeet TDT-CTC decoder (RNNT prediction network)", + ) + + # -- 3. JointDecision (full T x U grid) ---------------------------- + typer.echo("Tracing joint decision head...") + joint_decision = JointDecisionWrapper( + joint, vocab_size=vocab_size, num_extra=num_extra + ) + traced_joint_decision = torch.jit.trace( + joint_decision, + (encoder_ref, decoder_ref), + strict=False, + ) + traced_joint_decision.eval() + + joint_decision_model = _coreml_convert( + traced_joint_decision, + inputs=[ + ct.TensorType( + name="encoder", + shape=_tensor_shape(encoder_ref), + dtype=np.float32, + ), + ct.TensorType( + name="decoder", + shape=_tensor_shape(decoder_ref), + dtype=np.float32, + ), + ], + outputs=[ + ct.TensorType(name="token_id", dtype=np.int32), + ct.TensorType(name="token_prob", dtype=np.float32), + ct.TensorType(name="duration", dtype=np.int32), + ], + settings=export_settings, + compute_units_override=ct.ComputeUnit.CPU_ONLY, + ) + joint_decision_path = output_dir / "JointDecision.mlpackage" + _save_mlpackage( + joint_decision_model, + joint_decision_path, + "Parakeet TDT-CTC joint + decision head", + ) + + # -- 4. Single-step JointDecision (for streaming) ------------------ + typer.echo("Tracing single-step joint decision...") + jd_single = JointDecisionSingleStep( + joint, vocab_size=vocab_size, num_extra=num_extra + ) + enc_step = encoder_ref[:, :, :1].contiguous() + dec_step = decoder_ref[:, :, :1].contiguous() + traced_jd_single = torch.jit.trace( + jd_single, (enc_step, dec_step), strict=False + ) + traced_jd_single.eval() + + jd_single_model = _coreml_convert( + traced_jd_single, + inputs=[ + ct.TensorType( + name="encoder_step", + shape=(1, enc_step.shape[1], 1), + dtype=np.float32, + ), + ct.TensorType( + name="decoder_step", + shape=(1, dec_step.shape[1], 1), + dtype=np.float32, + ), + ], + outputs=[ + ct.TensorType(name="token_id", dtype=np.int32), + ct.TensorType(name="token_prob", dtype=np.float32), + ct.TensorType(name="duration", dtype=np.int32), + ct.TensorType(name="top_k_ids", dtype=np.int32), + ct.TensorType(name="top_k_logits", dtype=np.float32), + ], + settings=export_settings, + compute_units_override=ct.ComputeUnit.CPU_ONLY, + ) + jd_single_path = output_dir / "JointDecisionSingleStep.mlpackage" + _save_mlpackage( + jd_single_model, + jd_single_path, + "Parakeet TDT-CTC single-step joint decision", + ) + + # -- Save vocabulary ----------------------------------------------- + _save_vocabulary(asr_model, output_dir) + + # -- Metadata ------------------------------------------------------- + enc_dim = int(encoder_ref.shape[1]) + dec_dim = int(decoder_ref.shape[1]) + + metadata: Dict[str, object] = { + "model_id": model_id, + "model_type": "hybrid_tdt_ctc", + "sample_rate": sample_rate, + "max_audio_seconds": max_audio_seconds, + "max_audio_samples": max_samples, + "max_symbol_steps": export_settings.max_symbol_steps, + "vocab_size": vocab_size, + "blank_id": blank_idx, + "joint_extra_outputs": num_extra, + "duration_bins": [0, 1, 2, 3, 4] if num_extra == 5 else list(range(num_extra)), + "encoder_dim": enc_dim, + "decoder_dim": dec_dim, + "decoder_hidden": decoder_hidden, + "decoder_layers": decoder_layers, + "checkpoint": checkpoint_meta, + "coreml": { + "compute_units": export_settings.compute_units.name, + "compute_precision": ( + export_settings.compute_precision.name + if export_settings.compute_precision is not None + else "FLOAT32" + ), + }, + "components": { + "preprocessor": { + "inputs": { + "audio_signal": [1, max_samples], + "audio_length": [1], + }, + "outputs": { + "encoder": list(_tensor_shape(encoder_ref)), + "encoder_length": [1], + }, + "path": mel_encoder_path.name, + }, + "decoder": { + "inputs": { + "targets": list(_tensor_shape(targets)), + "target_length": [1], + "h_in": list(_tensor_shape(zero_state)), + "c_in": list(_tensor_shape(zero_state)), + }, + "outputs": { + "decoder": list(_tensor_shape(decoder_ref)), + "h_out": list(_tensor_shape(h_ref)), + "c_out": list(_tensor_shape(c_ref)), + }, + "path": decoder_path.name, + }, + "joint_decision": { + "inputs": { + "encoder": list(_tensor_shape(encoder_ref)), + "decoder": list(_tensor_shape(decoder_ref)), + }, + "outputs": { + "token_id": "int32", + "token_prob": "float32", + "duration": "int32", + }, + "path": joint_decision_path.name, + }, + "joint_decision_single_step": { + "inputs": { + "encoder_step": [1, enc_dim, 1], + "decoder_step": [1, dec_dim, 1], + }, + "outputs": { + "token_id": [1, 1, 1], + "token_prob": [1, 1, 1], + "duration": [1, 1, 1], + "top_k_ids": "int32", + "top_k_logits": "float32", + }, + "path": jd_single_path.name, + }, + }, + } + + metadata_path = output_dir / "metadata.json" + metadata_path.write_text(json.dumps(metadata, indent=2)) + typer.echo(f"\nExport complete. Metadata: {metadata_path}") + typer.echo(f"Output directory: {output_dir}") + + finally: + asr_model.decoder._rnnt_export = decoder_export_flag + + +if __name__ == "__main__": + app() diff --git a/models/stt/parakeet-tdt-ctc-110m/coreml/individual_components.py b/models/stt/parakeet-tdt-ctc-110m/coreml/individual_components.py new file mode 100644 index 0000000..a3478ef --- /dev/null +++ b/models/stt/parakeet-tdt-ctc-110m/coreml/individual_components.py @@ -0,0 +1,227 @@ +#!/usr/bin/env python3 +"""Wrapper modules for exporting hybrid TDT-CTC model components to CoreML. + +Adapted from the pure-TDT 0.6B export pipeline to support +EncDecHybridRNNTCTCBPEModel (parakeet-tdt_ctc-110m and similar). + +The hybrid model exposes the same .preprocessor, .encoder, .decoder (LSTM +prediction network), and .joint (joint network with TDT duration outputs) +as a pure TDT model. The only loading difference is the NeMo model class. +""" +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Optional, Tuple + +import coremltools as ct +import torch + + +@dataclass +class ExportSettings: + output_dir: Path + compute_units: ct.ComputeUnit + deployment_target: Optional[ct.target.iOS17] + compute_precision: Optional[ct.precision] + max_audio_seconds: float + max_symbol_steps: int + + +# --------------------------------------------------------------------------- +# Component wrappers – thin shims that normalise NeMo I/O for tracing +# --------------------------------------------------------------------------- + +class PreprocessorWrapper(torch.nn.Module): + def __init__(self, module: torch.nn.Module) -> None: + super().__init__() + self.module = module + + def forward( + self, audio_signal: torch.Tensor, length: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + mel, mel_length = self.module( + input_signal=audio_signal, length=length.to(dtype=torch.long) + ) + return mel, mel_length + + +class EncoderWrapper(torch.nn.Module): + def __init__(self, module: torch.nn.Module) -> None: + super().__init__() + self.module = module + + def forward( + self, features: torch.Tensor, length: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + encoded, encoded_lengths = self.module( + audio_signal=features, length=length.to(dtype=torch.long) + ) + return encoded, encoded_lengths + + +class DecoderWrapper(torch.nn.Module): + def __init__(self, module: torch.nn.Module) -> None: + super().__init__() + self.module = module + + def forward( + self, + targets: torch.Tensor, + target_lengths: torch.Tensor, + h_in: torch.Tensor, + c_in: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state = [h_in, c_in] + decoder_output, _, new_state = self.module( + targets=targets.to(dtype=torch.long), + target_length=target_lengths.to(dtype=torch.long), + states=state, + ) + return decoder_output, new_state[0], new_state[1] + + +class JointWrapper(torch.nn.Module): + def __init__(self, module: torch.nn.Module) -> None: + super().__init__() + self.module = module + + def forward( + self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor + ) -> torch.Tensor: + # Input: encoder_outputs [B, D, T], decoder_outputs [B, D, U] + # Transpose to [B, T, D] / [B, U, D] for projection layers + encoder_outputs = encoder_outputs.transpose(1, 2) + decoder_outputs = decoder_outputs.transpose(1, 2) + + enc_proj = self.module.enc(encoder_outputs) + dec_proj = self.module.pred(decoder_outputs) + + # Explicit broadcasting along T and U + x = enc_proj.unsqueeze(2) + dec_proj.unsqueeze(1) # [B, T, U, D_joint] + x = self.module.joint_net[0](x) # ReLU + x = self.module.joint_net[1](x) # Dropout (no-op in eval) + out = self.module.joint_net[2](x) # Linear -> logits + return out + + +class MelEncoderWrapper(torch.nn.Module): + """Fused waveform -> mel -> encoder.""" + + def __init__( + self, preprocessor: PreprocessorWrapper, encoder: EncoderWrapper + ) -> None: + super().__init__() + self.preprocessor = preprocessor + self.encoder = encoder + + def forward( + self, audio_signal: torch.Tensor, audio_length: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + mel, mel_length = self.preprocessor(audio_signal, audio_length) + encoded, enc_len = self.encoder(mel, mel_length.to(dtype=torch.int32)) + return encoded, enc_len + + +class JointDecisionWrapper(torch.nn.Module): + """Joint + decision head: token_id, token_prob, duration. + + Splits joint logits into token logits ([..., :vocab+1]) and duration + logits ([..., -num_extra:]), applies softmax / argmax. + """ + + def __init__( + self, joint: JointWrapper, vocab_size: int, num_extra: int + ) -> None: + super().__init__() + self.joint = joint + self.vocab_with_blank = int(vocab_size) + 1 + self.num_extra = int(num_extra) + + def forward( + self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor + ): + logits = self.joint(encoder_outputs, decoder_outputs) + token_logits = logits[..., : self.vocab_with_blank] + duration_logits = logits[..., -self.num_extra :] + + token_ids = torch.argmax(token_logits, dim=-1).to(dtype=torch.int32) + token_probs_all = torch.softmax(token_logits, dim=-1) + token_prob = torch.gather( + token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) + ).squeeze(-1) + + duration = torch.argmax(duration_logits, dim=-1).to(dtype=torch.int32) + return token_ids, token_prob, duration + + +class JointDecisionSingleStep(torch.nn.Module): + """Single-step variant for streaming: [1, D, 1] -> scalars.""" + + def __init__( + self, + joint: JointWrapper, + vocab_size: int, + num_extra: int, + top_k: int = 64, + ) -> None: + super().__init__() + self.joint = joint + self.vocab_with_blank = int(vocab_size) + 1 + self.num_extra = int(num_extra) + self.top_k = int(top_k) + + def forward( + self, encoder_step: torch.Tensor, decoder_step: torch.Tensor + ): + logits = self.joint(encoder_step, decoder_step) # [1, 1, 1, V+extra] + token_logits = logits[..., : self.vocab_with_blank] + duration_logits = logits[..., -self.num_extra :] + + token_ids = torch.argmax(token_logits, dim=-1, keepdim=False).to( + dtype=torch.int32 + ) + token_probs_all = torch.softmax(token_logits, dim=-1) + token_prob = torch.gather( + token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) + ).squeeze(-1) + duration = torch.argmax(duration_logits, dim=-1, keepdim=False).to( + dtype=torch.int32 + ) + + topk_logits, topk_ids_long = torch.topk( + token_logits, + k=min(self.top_k, token_logits.shape[-1]), + dim=-1, + ) + topk_ids = topk_ids_long.to(dtype=torch.int32) + return token_ids, token_prob, duration, topk_ids, topk_logits + + +# --------------------------------------------------------------------------- +# Conversion helper +# --------------------------------------------------------------------------- + +def _coreml_convert( + traced: torch.jit.ScriptModule, + inputs, + outputs, + settings: ExportSettings, + compute_units_override: Optional[ct.ComputeUnit] = None, +) -> ct.models.MLModel: + cu = ( + compute_units_override + if compute_units_override is not None + else settings.compute_units + ) + kwargs = { + "convert_to": "mlprogram", + "inputs": inputs, + "outputs": outputs, + "compute_units": cu, + } + if settings.deployment_target is not None: + kwargs["minimum_deployment_target"] = settings.deployment_target + if settings.compute_precision is not None: + kwargs["compute_precision"] = settings.compute_precision + return ct.convert(traced, **kwargs) diff --git a/models/stt/parakeet-tdt-ctc-110m/coreml/pyproject.toml b/models/stt/parakeet-tdt-ctc-110m/coreml/pyproject.toml index cd0da45..56d5f55 100644 --- a/models/stt/parakeet-tdt-ctc-110m/coreml/pyproject.toml +++ b/models/stt/parakeet-tdt-ctc-110m/coreml/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "parakeet-tdt-ctc-coreml" version = "0.1.0" -description = "CoreML export tools for nvidia/parakeet-tdt_ctc-110m (CTC branch)" +description = "CoreML export tools for nvidia/parakeet-tdt_ctc-110m (TDT + CTC branches)" readme = "README.md" requires-python = "==3.10.12" dependencies = [ @@ -29,6 +29,7 @@ dependencies = [ [project.scripts] parakeet-tdt-ctc-coreml = "convert-coreml:app" +parakeet-tdt-coreml = "convert-tdt-coreml:app" [build-system] requires = ["hatchling"] @@ -37,6 +38,8 @@ build-backend = "hatchling.build" [tool.hatch.build.targets.wheel] include = [ "convert-coreml.py", + "convert-tdt-coreml.py", + "individual_components.py", "README.md", "audio/**", ] From aaebfe91c96c2f18b347cf0151d168f4ffbc9491 Mon Sep 17 00:00:00 2001 From: miro Date: Mon, 16 Mar 2026 19:13:20 +0000 Subject: [PATCH 2/4] =?UTF-8?q?fix:=20address=20PR=20review=20=E2=80=94=20?= =?UTF-8?q?duration=20slicing=20bug=20and=20iOS=20deployment=20target?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Replace logits[..., -self.num_extra:] with logits[..., self.vocab_with_blank:] to fix Python -0: slicing returning all logits when num_extra == 0 - Guard duration argmax with num_extra > 0 check, return zeros otherwise - Upgrade num_extra == 0 warning to error since TDT export is invalid without duration head - Fix _save_mlpackage: set iOS18 deployment target (matching export), remove unnecessary try/except --- .../coreml/convert-tdt-coreml.py | 11 ++++------- .../coreml/individual_components.py | 18 ++++++++++++------ 2 files changed, 16 insertions(+), 13 deletions(-) diff --git a/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py b/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py index 30a81a2..25e0574 100644 --- a/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py +++ b/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py @@ -54,10 +54,7 @@ def _tensor_shape(tensor: torch.Tensor) -> Tuple[int, ...]: def _save_mlpackage( model: ct.models.MLModel, path: Path, description: str ) -> None: - try: - model.minimum_deployment_target = ct.target.iOS17 - except Exception: - pass + model.minimum_deployment_target = ct.target.iOS18 model.short_description = description model.author = AUTHOR path.parent.mkdir(parents=True, exist_ok=True) @@ -224,9 +221,9 @@ def convert( typer.echo(f" decoder: hidden={decoder_hidden}, layers={decoder_layers}, blank_idx={blank_idx}") if num_extra == 0: - typer.echo( - "WARNING: joint.num_extra_outputs=0 -- this model has no TDT duration head. " - "It may be a plain RNNT, not TDT." + raise typer.BadParameter( + "joint.num_extra_outputs=0 -- this model has no TDT duration head. " + "It appears to be a plain RNNT, not TDT. Use the standard RNNT export instead." ) # -- Trace audio -------------------------------------------------------- diff --git a/models/stt/parakeet-tdt-ctc-110m/coreml/individual_components.py b/models/stt/parakeet-tdt-ctc-110m/coreml/individual_components.py index a3478ef..21de3a6 100644 --- a/models/stt/parakeet-tdt-ctc-110m/coreml/individual_components.py +++ b/models/stt/parakeet-tdt-ctc-110m/coreml/individual_components.py @@ -143,7 +143,7 @@ def forward( ): logits = self.joint(encoder_outputs, decoder_outputs) token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] + duration_logits = logits[..., self.vocab_with_blank :] token_ids = torch.argmax(token_logits, dim=-1).to(dtype=torch.int32) token_probs_all = torch.softmax(token_logits, dim=-1) @@ -151,7 +151,10 @@ def forward( token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) ).squeeze(-1) - duration = torch.argmax(duration_logits, dim=-1).to(dtype=torch.int32) + if self.num_extra > 0: + duration = torch.argmax(duration_logits, dim=-1).to(dtype=torch.int32) + else: + duration = torch.zeros_like(token_ids) return token_ids, token_prob, duration @@ -176,7 +179,7 @@ def forward( ): logits = self.joint(encoder_step, decoder_step) # [1, 1, 1, V+extra] token_logits = logits[..., : self.vocab_with_blank] - duration_logits = logits[..., -self.num_extra :] + duration_logits = logits[..., self.vocab_with_blank :] token_ids = torch.argmax(token_logits, dim=-1, keepdim=False).to( dtype=torch.int32 @@ -185,9 +188,12 @@ def forward( token_prob = torch.gather( token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1) ).squeeze(-1) - duration = torch.argmax(duration_logits, dim=-1, keepdim=False).to( - dtype=torch.int32 - ) + if self.num_extra > 0: + duration = torch.argmax(duration_logits, dim=-1, keepdim=False).to( + dtype=torch.int32 + ) + else: + duration = torch.zeros_like(token_ids) topk_logits, topk_ids_long = torch.topk( token_logits, From 73c97bf53d96ed25258e4e59dcbde4c60553db96 Mon Sep 17 00:00:00 2001 From: miro Date: Mon, 16 Mar 2026 19:26:49 +0000 Subject: [PATCH 3/4] fix: resolve dependency conflicts in pyproject.toml - Bump fsspec 2024.9.0 -> 2024.12.0 (required by nemo-toolkit 2.3.1) - Bump datasets 3.1.0 -> 3.3.2 (compatible with new fsspec) - Add missing transitive deps: editdistance, pyannote.metrics, ipython --- .../coreml/pyproject.toml | 7 +- .../stt/parakeet-tdt-ctc-110m/coreml/uv.lock | 1531 +++++++++++------ 2 files changed, 983 insertions(+), 555 deletions(-) diff --git a/models/stt/parakeet-tdt-ctc-110m/coreml/pyproject.toml b/models/stt/parakeet-tdt-ctc-110m/coreml/pyproject.toml index 56d5f55..7a6f351 100644 --- a/models/stt/parakeet-tdt-ctc-110m/coreml/pyproject.toml +++ b/models/stt/parakeet-tdt-ctc-110m/coreml/pyproject.toml @@ -22,9 +22,12 @@ dependencies = [ "jiwer==3.1.0", "librosa==0.10.1", "pyannote.core==5.0.0", + "pyannote.metrics==3.2.1", + "ipython>=8.0", "webdataset==0.2.86", - 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Verified all 4 components export at spec version 8 (iOS 17) and inference produces correct transcription via FluidAudio CLI. --- models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py b/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py index 25e0574..c1deb7c 100644 --- a/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py +++ b/models/stt/parakeet-tdt-ctc-110m/coreml/convert-tdt-coreml.py @@ -54,7 +54,7 @@ def _tensor_shape(tensor: torch.Tensor) -> Tuple[int, ...]: def _save_mlpackage( model: ct.models.MLModel, path: Path, description: str ) -> None: - model.minimum_deployment_target = ct.target.iOS18 + model.minimum_deployment_target = ct.target.iOS17 model.short_description = description model.author = AUTHOR path.parent.mkdir(parents=True, exist_ok=True) @@ -182,7 +182,7 @@ def convert( export_settings = ExportSettings( output_dir=output_dir, compute_units=ct.ComputeUnit.CPU_ONLY, - deployment_target=ct.target.iOS18, + deployment_target=ct.target.iOS17, compute_precision=_parse_compute_precision(compute_precision), max_audio_seconds=max_audio_seconds, max_symbol_steps=1,