diff --git a/models/stt/qwen3-forced-aligner-0.6b/coreml/README.md b/models/stt/qwen3-forced-aligner-0.6b/coreml/README.md new file mode 100644 index 0000000..17ab155 --- /dev/null +++ b/models/stt/qwen3-forced-aligner-0.6b/coreml/README.md @@ -0,0 +1,252 @@ +# Qwen3-ForcedAligner-0.6B → CoreML + +CoreML conversion of [Qwen/Qwen3-ForcedAligner-0.6B](https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B) for on-device forced alignment on Apple platforms. + +## Model Overview + +Qwen3-ForcedAligner is a **non-autoregressive (NAR)** forced alignment model that takes audio + text and outputs per-word timestamps. It uses the same `Qwen3ASRForConditionalGeneration` architecture as Qwen3-ASR but runs inference differently — a single prefill pass instead of autoregressive decode. + +- **Parameters:** 0.6B +- **Languages:** 11 (Chinese, English, Cantonese, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish) +- **Max audio:** 5 minutes +- **Timestamp resolution:** 80ms segments +- **License:** Apache 2.0 + +## Architecture + +Two audio encoder approaches are available. The inference script auto-detects which +to use based on which `.mlpackage` files are present. + +### Split Encoder (higher accuracy) + +``` +Qwen3ASRForConditionalGeneration + └── thinker + ├── audio_tower (24-layer Transformer, 1024 dim) + │ ├── conv frontend → forced_aligner_audio_conv.mlpackage + │ └── transformer + projection → forced_aligner_audio_transformer.mlpackage + ├── model (28-layer Qwen3 decoder, 1024 dim) → forced_aligner_decoder_prefill.mlpackage + │ └── embed_tokens → forced_aligner_embedding.mlpackage + └── lm_head (1024 → 5000) → forced_aligner_lm_head.mlpackage +``` + +The audio encoder is split into two CoreML models to preserve cross-chunk attention. +Conv runs per-chunk, then all conv outputs are concatenated and passed through the +transformer in a single call with full bidirectional attention across all frames. +This matches the native PyTorch behavior and gives the best accuracy (4.4ms AAS). + +### Monolithic Encoder (faster, simpler) + +``` +Qwen3ASRForConditionalGeneration + └── thinker + ├── audio_tower (24-layer Transformer, 1024 dim) → forced_aligner_audio_encoder.mlpackage + ├── model (28-layer Qwen3 decoder, 1024 dim) → forced_aligner_decoder_prefill.mlpackage + │ └── embed_tokens → forced_aligner_embedding.mlpackage + └── lm_head (1024 → 5000) → forced_aligner_lm_head.mlpackage +``` + +The entire audio encoder (conv + transformer + projection) is a single CoreML model +that processes each 100-frame mel chunk independently. This is faster (one model call +per chunk, no concatenation step) but each chunk's 13 output frames only see their own +context — no cross-chunk attention. This reduces accuracy (20.7ms AAS) but may be +acceptable depending on the use case. + +### Which to use? + +| | Split Encoder | Monolithic Encoder | +|---|---|---| +| Models | `audio_conv` + `audio_transformer` | `audio_encoder` | +| Size | ~1.1GB combined | ~604MB | +| AAS (mean boundary error) | **4.4 ms** | 20.7 ms | +| % within 20ms | **95.4%** | 90.7% | +| Cross-chunk attention | Yes | No | +| Model calls (audio) | N conv + 1 transformer | N encoder | +| Best for | Accuracy-critical alignment | Latency-sensitive / real-time | + +The inference script (`run_coreml_inference.py`) checks for `audio_conv` + `audio_transformer` +first. If found, it uses the split approach. Otherwise it falls back to the monolithic +`audio_encoder` if present. + +### Key Differences from Qwen3-ASR-0.6B + +| | ASR-0.6B | ForcedAligner-0.6B | +|---|---|---| +| Audio encoder layers | 18 | **24** | +| Audio encoder dim | 896 | **1024** | +| Audio encoder heads | 14 | **16** | +| Vocab size | 151,936 | **152,064** | +| RoPE | standard | **interleaved mrope** | +| Inference | autoregressive | **NAR (single prefill)** | +| Output | text tokens | **ms timestamps** | + +## Input/Output Shapes + +### Split Encoder + +#### Audio Conv (per-chunk) +``` +Input: mel_input [1, 128, 100] float32 (128 mel bins, 100 frames = 1 window) +Output: conv_features [1, 13, 1024] float32 (13 frames after 8x conv downsampling) +``` + +#### Audio Transformer (all chunks concatenated) +``` +Input: features [1, 256, 1024] float32 (padded concatenated conv features) +Output: audio_embeddings [1, 256, 1024] float32 (trim to actual frame count) +``` + +### Monolithic Encoder + +#### Audio Encoder (per-chunk) +``` +Input: mel_input [1, 128, 100] float32 (128 mel bins, 100 frames = 1 window) +Output: audio_embeddings [1, 13, 1024] float32 (13 frames, trim for short last chunk) +``` + +### Token Embedding +``` +Input: input_ids [1, seq_len] int32 (seq_len ∈ [1, 1024]) +Output: embeddings [1, seq_len, 1024] float32 +``` + +### Decoder Prefill (NAR) +``` +Input: hidden_states [1, 1024, 1024] float32 (full sequence) + position_cos [1, 1024, 128] float32 (RoPE cos) + position_sin [1, 1024, 128] float32 (RoPE sin) +Output: output_hidden [1, 1024, 1024] float32 +``` + +### LM Head +``` +Input: hidden_states [1, seq_len, 1024] float32 (seq_len ∈ [1, 1024]) +Output: logits [1, seq_len, 5000] float32 (raw timestamp values, NOT vocab tokens) +``` + +> **Note:** The LM head output dim is 5000 (not vocab_size 152064). The ForcedAligner +> predicts raw timestamp values via argmax, where each value × 80ms = absolute time. +> 5000 × 80ms = 400s, covering up to ~6.7 minutes of audio. + +## Inference Pipeline + +Steps 1-3 differ depending on encoder approach. Steps 4-11 are shared. + +### Split Encoder (steps 1-3) +``` +1. Audio → Whisper mel spectrogram → [1, 128, T] +2. Chunk mel into 100-frame windows → Audio Conv (per-chunk) → conv features +3. Concatenate all conv features → pad to 256 → Audio Transformer → audio embeddings +``` + +### Monolithic Encoder (steps 1-3) +``` +1. Audio → Whisper mel spectrogram → [1, 128, T] +2. Chunk mel into 100-frame windows → Audio Encoder (per-chunk) → embeddings +3. Concatenate per-chunk embeddings (trim last chunk to actual frames) +``` + +### Shared (steps 4-11) +``` +4. Tokenize text with delimiters between words +5. Build input_ids: ... word1 word2 ... +6. Embed: audio embeddings + text token embeddings → concatenated sequence +7. Compute MRoPE cos/sin → Decoder prefill (single pass) → hidden states +8. LM head → logits +9. argmax at timestamp_token_id positions → raw ms values +10. Fix monotonicity (LIS algorithm) → final timestamps +11. Scale: ms = raw_value * 80 (timestamp_segment_time) +``` + +## Conversion + +```bash +# Install dependencies +uv pip install torch coremltools transformers typer soundfile + +# Clone Qwen3-ASR source (required for model classes) +git clone https://github.com/QwenLM/Qwen3-ASR.git /path/to/qwen3-asr + +# Convert split encoder (default — higher accuracy) +uv run python convert-coreml.py + +# Convert monolithic encoder (faster) +uv run python convert-coreml.py --components audio_encoder embedding decoder_prefill lm_head + +# Convert all components (both encoder approaches) +uv run python convert-coreml.py --components audio_conv audio_transformer audio_encoder embedding decoder_prefill lm_head +``` + +## Benchmarking + +```bash +# Generate PyTorch reference timestamps from cached test-clean +uv run python compare-models.py --num-files 10 --output results/pytorch_reference.json + +# Single file mode +uv run python compare-models.py --audio-file audio.wav --text "hello world" --language English +``` + +### Parity Metrics (3 LibriSpeech test-clean samples, 54 word boundaries) + +#### Split Encoder + +| Metric | Value | Notes | +|--------|-------|-------| +| AAS (mean boundary error) | 4.4 ms | lower is better | +| Max boundary error | 160 ms | single position, 2 segments | +| % within 20ms | 95.4% | | +| % within 80ms (1 segment) | 99.1% | 80ms = 1 timestamp segment | +| % within 160ms (2 segments) | 100.0% | | +| PyTorch latency (avg) | ~4736 ms | CPU, includes first-run warmup | +| CoreML latency (avg) | ~2781 ms | ALL compute units | + +Per-sample results: +- Long (28 words): 1.4ms AAS, 98.2% within 20ms +- Short (8 words): 10.0ms AAS, 87.5% within 20ms +- Medium (18 words): 6.7ms AAS, 94.4% within 20ms + +#### Monolithic Encoder + +| Metric | Value | Notes | +|--------|-------|-------| +| AAS (mean boundary error) | 20.7 ms | ~5x worse than split | +| % within 20ms | 90.7% | | +| % within 80ms (1 segment) | 92.6% | | +| % within 160ms (2 segments) | 96.3% | | + +The accuracy gap is caused by each chunk's 13 frames only attending to themselves +in the transformer, missing cross-chunk context that the native PyTorch encoder provides. + +## Special Tokens + +| Token | ID | Purpose | +|-------|-----|---------| +| `<\|audio_start\|>` | 151669 | Start of audio embeddings | +| `<\|audio_end\|>` | 151670 | End of audio embeddings | +| `<\|audio_pad\|>` | 151676 | Audio embedding placeholder | +| `` | 151705 | Timestamp prediction position | + +## LM Head Architecture + +The ForcedAligner's LM head is **not** the same as the ASR model's: + +| | ASR LM Head | ForcedAligner LM Head | +|---|---|---| +| Output dim | 151,936 (vocab tokens) | **5,000** (raw timestamp values) | +| Purpose | Next-token prediction | Timestamp regression via argmax | +| Decoding | argmax → token ID → text | argmax → raw_value × 80ms → time | + +The embedding table is still 152,064 tokens (shared architecture), but the LM head +projects to 5,000 outputs — enough for timestamps up to 400 seconds at 80ms resolution. + +## Known Issues + +See [problems_encountered.md](./problems_encountered.md) for detailed conversion journal. + +## References + +- **Model:** [Qwen/Qwen3-ForcedAligner-0.6B](https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B) +- **Paper:** [arXiv:2601.21337](https://arxiv.org/abs/2601.21337) +- **Source:** [QwenLM/Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR) +- **Community request:** [FluidAudio#49](https://github.com/FluidInference/FluidAudio/issues/49) diff --git a/models/stt/qwen3-forced-aligner-0.6b/coreml/SWIFT_INTEGRATION_BUGS.md b/models/stt/qwen3-forced-aligner-0.6b/coreml/SWIFT_INTEGRATION_BUGS.md new file mode 100644 index 0000000..aac9bbd --- /dev/null +++ b/models/stt/qwen3-forced-aligner-0.6b/coreml/SWIFT_INTEGRATION_BUGS.md @@ -0,0 +1,160 @@ +# Qwen3-ForcedAligner-0.6B CoreML — Swift Integration Bugs & Fixes + +Documented issues encountered while integrating the Qwen3-ForcedAligner-0.6B CoreML int8 models into FluidAudio (Swift). These are pitfalls that any future CoreML integration should watch for. + +--- + +## Bug 1: MLMultiArray Non-Contiguous Strides (Critical) + +**Symptom**: Decoder output logits were completely wrong. At position 21, Python showed `[15.27, 9.05, 7.61, ...]` (argmax=0), Swift showed `[-5.58, -6.20, -5.19, ...]` (argmax=168). All timestamps were 10x too high (13440-24960ms for 1.4s audio). + +**Root Cause**: CoreML `MLMultiArray` outputs can have non-contiguous memory layouts with padding between rows. The decoder output shape was `[1, 1024, 5000]` but actual strides were `[5128192, 5008, 1]` — the stride for the sequence dimension was **5008**, not 5000 (the vocab dimension). There are 8 padding elements between each row. + +The Swift code assumed contiguous layout: +```swift +// WRONG: assumes contiguous memory +let offset = i * vocabDim // vocabDim=5000, but actual stride=5008 +``` + +**Fix**: Always use `strides[].intValue` from the MLMultiArray: +```swift +// CORRECT: use actual stride +let logitsStride = logits.strides[1].intValue // 5008 +let logitsPtr = logits.dataPointer.bindMemory( + to: Float.self, capacity: logits.strides[0].intValue +) +for i in 0..= 1000: mel = 15 + 27/ln(6.4) * ln(f/1000) (logarithmic) +``` + +Slaney normalization: `2 / (f_high - f_low)` per filterbank band (area normalization). + +**Fix**: Created `ForcedAlignerMelSpectrogram` with Slaney mel scale and normalization, separate from the existing HTK-based `WhisperMelSpectrogram`. + +**Lesson**: "Whisper mel spectrogram" is not a single thing. The mel scale and normalization depend on the upstream library and model. Always verify which variant the model was trained with. + +--- + +## Bug 4: Missing STFT Center Padding + +**Symptom**: After fixing the mel scale, values were closer but still wrong. Swift mel[0,:5] = `[-0.217, 0.007, ...]` vs Python `[-0.285, -0.208, ...]`. + +**Root Cause**: PyTorch's `torch.stft()` uses `center=True` by default, which reflect-pads the audio by `nFFT/2` on each side before computing the STFT. The Swift implementation had no center padding. + +Without center padding, the first frame window starts at sample 0 and only sees the right half of data, leading to different spectral content for early frames. + +**Fix**: Added reflect padding before STFT: +```swift +private static func reflectPad(_ input: [Float], padLen: Int) -> [Float] { + let n = input.count + var result = [Float](repeating: 0, count: padLen + n + padLen) + // Left reflect: input[padLen], input[padLen-1], ..., input[1] + for i in 0.. (cos: [Float], sin: [Float]) { + let lastValidPos = max(contentLen - 1, 0) + for p in 0.. Tuple[List[AlignmentResult], float]: + """Run PyTorch forced alignment, return results and latency in ms.""" + start = time.perf_counter() + results = aligner.align(audio=audio_path, text=text, language=language) + elapsed_ms = (time.perf_counter() - start) * 1000 + + items = [] + for item in results[0]: + items.append(AlignmentResult( + text=item.text, + start_time_ms=item.start_time * 1000, + end_time_ms=item.end_time * 1000, + )) + return items, elapsed_ms + + +def compute_parity( + ref: List[AlignmentResult], + hyp: List[AlignmentResult], + ref_latency_ms: float, + hyp_latency_ms: float, +) -> ParityMetrics: + """Compute parity metrics between reference (PyTorch) and hypothesis (CoreML).""" + if len(ref) != len(hyp): + typer.echo(f" WARNING: word count mismatch: ref={len(ref)}, hyp={len(hyp)}") + + n = min(len(ref), len(hyp)) + errors = [] + for i in range(n): + errors.append(abs(ref[i].start_time_ms - hyp[i].start_time_ms)) + errors.append(abs(ref[i].end_time_ms - hyp[i].end_time_ms)) + + errors = np.array(errors) + return ParityMetrics( + num_words=n, + aas_ms=float(np.mean(errors)), + max_error_ms=float(np.max(errors)), + pct_within_20ms=float(np.mean(errors <= 20.0) * 100), + pct_within_50ms=float(np.mean(errors <= 50.0) * 100), + pytorch_latency_ms=ref_latency_ms, + coreml_latency_ms=hyp_latency_ms, + speedup=ref_latency_ms / hyp_latency_ms if hyp_latency_ms > 0 else 0, + ) + + +def load_librispeech_samples( + test_clean_dir: Path, num_files: int +) -> List[Tuple[Path, str]]: + """Load audio+transcript pairs from LibriSpeech test-clean.""" + samples = [] + trans_files = sorted(test_clean_dir.rglob("*.trans.txt")) + + for trans_file in trans_files: + with open(trans_file) as f: + for line in f: + parts = line.strip().split(" ", 1) + if len(parts) != 2: + continue + audio_id, text = parts + audio_path = trans_file.parent / f"{audio_id}.flac" + if audio_path.exists(): + samples.append((audio_path, text)) + if len(samples) >= num_files: + return samples + + return samples + + +def print_parity_report(metrics: ParityMetrics, label: str = "") -> None: + """Print a formatted parity report.""" + prefix = f"[{label}] " if label else "" + typer.echo(f"\n{prefix}Parity Report ({metrics.num_words} words):") + typer.echo(f" AAS (mean boundary error): {metrics.aas_ms:.1f} ms") + typer.echo(f" Max boundary error: {metrics.max_error_ms:.1f} ms") + typer.echo(f" Within 20ms: {metrics.pct_within_20ms:.1f}%") + typer.echo(f" Within 50ms: {metrics.pct_within_50ms:.1f}%") + typer.echo(f" PyTorch latency: {metrics.pytorch_latency_ms:.0f} ms") + typer.echo(f" CoreML latency: {metrics.coreml_latency_ms:.0f} ms") + typer.echo(f" Speedup: {metrics.speedup:.2f}x") + + +@app.command() +def compare( + audio_file: Optional[Path] = typer.Option( + None, "--audio-file", help="Single audio file for comparison" + ), + text: Optional[str] = typer.Option( + None, "--text", help="Transcript for single file mode" + ), + language: str = typer.Option( + "English", "--language", help="Language for alignment" + ), + audio_dir: Optional[Path] = typer.Option( + None, "--audio-dir", + help="LibriSpeech test-clean directory for batch comparison" + ), + num_files: int = typer.Option( + 10, "--num-files", help="Number of files for batch mode" + ), + model_id: str = typer.Option( + "Qwen/Qwen3-ForcedAligner-0.6B", "--model-id", + help="HuggingFace model ID for PyTorch reference" + ), + output: Optional[Path] = typer.Option( + None, "--output", help="Output JSON file for results" + ), +) -> None: + """Compare PyTorch and CoreML forced alignment outputs.""" + + # Determine test samples + if audio_file and text: + samples = [(audio_file, text)] + elif audio_dir: + samples = load_librispeech_samples(audio_dir, num_files) + typer.echo(f"Loaded {len(samples)} samples from {audio_dir}") + else: + # Default: use cached test-clean + default_dir = Path.home() / "Library" / "Application Support" / "FluidAudio" / "Datasets" / "LibriSpeech" / "test-clean" + if default_dir.exists(): + samples = load_librispeech_samples(default_dir, num_files) + typer.echo(f"Loaded {len(samples)} samples from cached test-clean") + else: + typer.echo("ERROR: No audio source specified and test-clean not cached.") + typer.echo(" Use --audio-file + --text, or --audio-dir, or run:") + typer.echo(" swift run fluidaudio download --dataset librispeech-test-clean") + raise typer.Exit(1) + + # Load PyTorch reference + typer.echo(f"\nLoading PyTorch model: {model_id}") + aligner = load_pytorch_aligner(model_id) + + # Run PyTorch on all samples + typer.echo(f"\nRunning PyTorch alignment on {len(samples)} samples...") + pytorch_results = [] + for audio_path, transcript in samples: + items, latency = run_pytorch_alignment( + aligner, str(audio_path), transcript, language + ) + pytorch_results.append((items, latency, audio_path, transcript)) + typer.echo(f" {audio_path.name}: {len(items)} words, {latency:.0f}ms") + + # TODO: Load CoreML model and run comparison + # For now, just save PyTorch reference results as ground truth + typer.echo("\n--- PyTorch reference results saved ---") + typer.echo("CoreML comparison will be added after conversion is validated.") + + # Save reference results + if output: + results_data = { + "model_id": model_id, + "language": language, + "num_samples": len(samples), + "samples": [], + } + for items, latency, audio_path, transcript in pytorch_results: + results_data["samples"].append({ + "audio": str(audio_path), + "transcript": transcript, + "latency_ms": latency, + "alignments": [ + { + "text": item.text, + "start_time_ms": item.start_time_ms, + "end_time_ms": item.end_time_ms, + } + for item in items + ], + }) + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(results_data, indent=2)) + typer.echo(f"\nResults written to {output}") + + # Summary stats + total_words = sum(len(items) for items, _, _, _ in pytorch_results) + total_latency = sum(lat for _, lat, _, _ in pytorch_results) + typer.echo(f"\nSummary:") + typer.echo(f" Total words aligned: {total_words}") + typer.echo(f" Total PyTorch time: {total_latency:.0f}ms") + typer.echo(f" Avg per-sample: {total_latency / len(samples):.0f}ms") + + +if __name__ == "__main__": + app() diff --git a/models/stt/qwen3-forced-aligner-0.6b/coreml/convert-coreml.py b/models/stt/qwen3-forced-aligner-0.6b/coreml/convert-coreml.py new file mode 100644 index 0000000..0a501a1 --- /dev/null +++ b/models/stt/qwen3-forced-aligner-0.6b/coreml/convert-coreml.py @@ -0,0 +1,704 @@ +#!/usr/bin/env python3 +"""CLI for exporting Qwen3-ForcedAligner-0.6B components to CoreML. + +Architecture: + Qwen3ASRForConditionalGeneration + └── thinker + ├── audio_tower → AudioEncoderFullWrapper → forced_aligner_audio_encoder.mlpackage + ├── model → PrefillDecoderWrapper → forced_aligner_decoder_prefill.mlpackage + │ ├── embed_tokens → TextEmbeddingWrapper → forced_aligner_embedding.mlpackage + │ └── norm (fused into lm_head) + └── lm_head → LMHeadWrapper → forced_aligner_lm_head.mlpackage + +Key difference from Qwen3-ASR: this is NAR (non-autoregressive). +The full input (audio + text with tokens) is processed in a single +prefill pass. No KV cache, no decode loop. Logits at timestamp positions are +argmax'd to produce ms-resolution timestamps. + +Usage: + uv run python convert-coreml.py + uv run python convert-coreml.py --components audio_encoder + uv run python convert-coreml.py --output-dir ./build/forced-aligner +""" +from __future__ import annotations + +import importlib.util +import json +import sys +import types +from pathlib import Path +from typing import Dict, List, Optional + +import coremltools as ct +import numpy as np +import torch +import typer + +from individual_components import ( + AudioConvWrapper, + AudioEncoderFullWrapper, + AudioTransformerWrapper, + ExportSettings, + LMHeadWrapper, + PrefillDecoderWrapper, + TextEmbeddingWrapper, + _coreml_convert, +) + +DEFAULT_MODEL_ID = "Qwen/Qwen3-ForcedAligner-0.6B" +AUTHOR = "Fluid Inference" +SAMPLE_RATE = 16000 +NUM_MEL_BINS = 128 + +app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) + + +def _load_qwen3_asr_modules(): + """Load Qwen3-ASR configuration and modeling modules via importlib. + + Bypasses qwen_asr/__init__.py which imports heavy inference dependencies + (nagisa, soynlp, qwen-omni-utils) not needed for CoreML conversion. + """ + qwen_asr_path = Path(__file__).resolve().parents[5] / "qwen3-asr" + if not qwen_asr_path.exists(): + raise FileNotFoundError( + f"qwen3-asr source not found at {qwen_asr_path}\n" + "Clone it: git clone https://github.com/QwenLM/Qwen3-ASR.git qwen3-asr" + ) + + tb_dir = qwen_asr_path / "qwen_asr" / "core" / "transformers_backend" + + for pkg_name, pkg_path in [ + ("qwen_asr", qwen_asr_path / "qwen_asr"), + ("qwen_asr.core", qwen_asr_path / "qwen_asr" / "core"), + ("qwen_asr.core.transformers_backend", tb_dir), + ]: + if pkg_name not in sys.modules: + mod = types.ModuleType(pkg_name) + mod.__path__ = [str(pkg_path)] + mod.__package__ = pkg_name + sys.modules[pkg_name] = mod + + config_fqn = "qwen_asr.core.transformers_backend.configuration_qwen3_asr" + spec = importlib.util.spec_from_file_location( + config_fqn, tb_dir / "configuration_qwen3_asr.py" + ) + config_mod = importlib.util.module_from_spec(spec) + sys.modules[config_fqn] = config_mod + spec.loader.exec_module(config_mod) + + model_fqn = "qwen_asr.core.transformers_backend.modeling_qwen3_asr" + spec2 = importlib.util.spec_from_file_location( + model_fqn, tb_dir / "modeling_qwen3_asr.py" + ) + model_mod = importlib.util.module_from_spec(spec2) + sys.modules[model_fqn] = model_mod + spec2.loader.exec_module(model_mod) + + return config_mod, model_mod + + +def _load_model(model_id: str): + """Load Qwen3-ForcedAligner model via transformers.""" + typer.echo(f"Loading model: {model_id}") + + config_mod, model_mod = _load_qwen3_asr_modules() + typer.echo(" Loaded Qwen3-ASR source modules (bypassed heavy deps)") + + from transformers import AutoConfig, AutoModel + + # Patch ROPE_INIT_FUNCTIONS for transformers 5.x compatibility. + from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS + if "default" not in ROPE_INIT_FUNCTIONS: + def _default_rope_init(config, device=None, **kwargs): + base = config.rope_theta + dim = config.head_dim + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64, device=device).float() / dim) + ) + return inv_freq, 1.0 + + ROPE_INIT_FUNCTIONS["default"] = _default_rope_init + typer.echo(" Patched ROPE_INIT_FUNCTIONS: added 'default' rope type") + + AutoConfig.register("qwen3_asr", config_mod.Qwen3ASRConfig) + AutoConfig.register("qwen3_asr_audio_encoder", config_mod.Qwen3ASRAudioEncoderConfig) + AutoModel.register(config_mod.Qwen3ASRConfig, model_mod.Qwen3ASRForConditionalGeneration) + typer.echo(" Registered Qwen3ASR custom classes with AutoConfig/AutoModel") + + config = AutoConfig.from_pretrained(model_id, trust_remote_code=True) + + def _ensure_attr(cfg, attr, default): + if not hasattr(cfg, attr): + setattr(cfg, attr, default) + + _ensure_attr(config, "pad_token_id", None) + if hasattr(config, "thinker_config"): + _ensure_attr(config.thinker_config, "pad_token_id", None) + + model = AutoModel.from_pretrained( + model_id, + config=config, + torch_dtype=torch.float32, + trust_remote_code=True, + ) + model.eval() + typer.echo(f" Model loaded. Type: {type(model).__name__}") + + # Log ForcedAligner-specific config + typer.echo(f" timestamp_token_id: {getattr(config, 'timestamp_token_id', 'N/A')}") + typer.echo(f" timestamp_segment_time: {getattr(config, 'timestamp_segment_time', 'N/A')}ms") + + return model + + +def _get_audio_encoder(model): + if hasattr(model, "thinker"): + return model.thinker.audio_tower + return model.audio_tower + + +def _get_text_model(model): + if hasattr(model, "thinker"): + return model.thinker.model + return model.model + + +def _get_lm_head(model): + if hasattr(model, "thinker"): + return model.thinker.lm_head + return model.lm_head + + +def _get_text_norm(model): + return _get_text_model(model).norm + + +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)) + typer.echo(f" Saved: {path}") + + +# --------------------------------------------------------------------------- +# Audio Encoder Conversion +# --------------------------------------------------------------------------- + +def convert_audio_encoder(model, settings: ExportSettings) -> Path: + """Convert the audio encoder (24-layer, 1024 dim) to CoreML.""" + typer.echo("\n=== Converting Audio Encoder ===") + + audio_encoder = _get_audio_encoder(model) + audio_encoder.eval() + + wrapper = AudioEncoderFullWrapper(audio_encoder) + wrapper.eval() + + # Single window: 100 mel frames (n_window * 2) + max_mel_frames = 100 + mel_input = torch.randn(1, NUM_MEL_BINS, max_mel_frames, dtype=torch.float32) + + typer.echo(f" Trace input shape: {mel_input.shape}") + + with torch.inference_mode(): + ref_output = wrapper(mel_input) + typer.echo(f" Reference output shape: {ref_output.shape}") + + mel_input = mel_input.clone() + + typer.echo(" Tracing audio encoder...") + traced = torch.jit.trace(wrapper, (mel_input,), strict=False) + traced.eval() + + inputs = [ + ct.TensorType( + name="mel_input", + shape=(1, NUM_MEL_BINS, max_mel_frames), + dtype=np.float32, + ), + ] + outputs = [ + ct.TensorType(name="audio_features", dtype=np.float32), + ] + + typer.echo(" Converting to CoreML...") + coreml_model = _coreml_convert( + traced, inputs, outputs, settings, + compute_units_override=settings.compute_units, + compute_precision_override=None, # default FP16 + ) + + path = settings.output_dir / "forced_aligner_audio_encoder.mlpackage" + _save_mlpackage(coreml_model, path, "Qwen3-ForcedAligner audio encoder (24 layers, 1024 dim)") + + return path + + +# --------------------------------------------------------------------------- +# Audio Conv Conversion (split encoder: conv frontend only) +# --------------------------------------------------------------------------- + +def convert_audio_conv(model, settings: ExportSettings) -> Path: + """Convert the audio encoder conv frontend (no transformer) to CoreML.""" + typer.echo("\n=== Converting Audio Conv (split encoder) ===") + + audio_encoder = _get_audio_encoder(model) + audio_encoder.eval() + + wrapper = AudioConvWrapper(audio_encoder) + wrapper.eval() + + max_mel_frames = 100 + mel_input = torch.randn(1, NUM_MEL_BINS, max_mel_frames, dtype=torch.float32) + + typer.echo(f" Trace input shape: {mel_input.shape}") + + with torch.inference_mode(): + ref_output = wrapper(mel_input) + typer.echo(f" Reference output shape: {ref_output.shape}") + + mel_input = mel_input.clone() + + typer.echo(" Tracing audio conv...") + traced = torch.jit.trace(wrapper, (mel_input,), strict=False) + traced.eval() + + inputs = [ + ct.TensorType( + name="mel_input", + shape=(1, NUM_MEL_BINS, max_mel_frames), + dtype=np.float32, + ), + ] + outputs = [ + ct.TensorType(name="conv_features", dtype=np.float32), + ] + + typer.echo(" Converting to CoreML...") + coreml_model = _coreml_convert( + traced, inputs, outputs, settings, + compute_units_override=settings.compute_units, + compute_precision_override=None, # default FP16 + ) + + path = settings.output_dir / "forced_aligner_audio_conv.mlpackage" + _save_mlpackage(coreml_model, path, "Qwen3-ForcedAligner audio conv frontend (3x stride-2 conv)") + + return path + + +# --------------------------------------------------------------------------- +# Audio Transformer Conversion (split encoder: transformer + projection) +# --------------------------------------------------------------------------- + +def convert_audio_transformer(model, settings: ExportSettings) -> Path: + """Convert the audio encoder transformer + projection to CoreML. + + This takes concatenated conv features from multiple chunks and runs + the 24-layer transformer with full bidirectional attention, matching + the native encoder's cross-chunk attention behavior. + """ + typer.echo("\n=== Converting Audio Transformer (split encoder) ===") + + audio_encoder = _get_audio_encoder(model) + audio_encoder.eval() + + wrapper = AudioTransformerWrapper(audio_encoder) + wrapper.eval() + + AUDIO_SEQ = AudioTransformerWrapper.AUDIO_TRANSFORMER_SEQ_LEN + hidden_size = 1024 + features = torch.randn(1, AUDIO_SEQ, hidden_size, dtype=torch.float32) + + typer.echo(f" Trace input shape: {features.shape}") + typer.echo(f" Max audio frames: {AUDIO_SEQ}") + + with torch.inference_mode(): + ref_output = wrapper(features) + typer.echo(f" Reference output shape: {ref_output.shape}") + + features = features.clone() + + typer.echo(" Tracing audio transformer...") + traced = torch.jit.trace(wrapper, (features,), strict=False) + traced.eval() + + inputs = [ + ct.TensorType( + name="features", + shape=(1, AUDIO_SEQ, hidden_size), + dtype=np.float32, + ), + ] + outputs = [ + ct.TensorType(name="audio_embeddings", dtype=np.float32), + ] + + typer.echo(" Converting to CoreML...") + coreml_model = _coreml_convert( + traced, inputs, outputs, settings, + compute_units_override=settings.compute_units, + compute_precision_override=ct.precision.FLOAT32, + ) + + path = settings.output_dir / "forced_aligner_audio_transformer.mlpackage" + _save_mlpackage( + coreml_model, path, + f"Qwen3-ForcedAligner audio transformer (24 layers, bidirectional, max {AUDIO_SEQ} frames)" + ) + + return path + + +# --------------------------------------------------------------------------- +# Text Embedding Conversion +# --------------------------------------------------------------------------- + +def convert_embedding(model, settings: ExportSettings) -> Path: + """Convert the token embedding layer to CoreML.""" + typer.echo("\n=== Converting Token Embedding ===") + + text_model = _get_text_model(model) + wrapper = TextEmbeddingWrapper(text_model) + wrapper.eval() + + seq_len = 32 + input_ids = torch.zeros(1, seq_len, dtype=torch.int32) + + with torch.inference_mode(): + ref_output = wrapper(input_ids) + typer.echo(f" Reference output shape: {ref_output.shape}") + + input_ids = input_ids.clone() + + typer.echo(" Tracing embedding layer...") + traced = torch.jit.trace(wrapper, (input_ids,), strict=False) + traced.eval() + + inputs = [ + ct.TensorType( + name="input_ids", + shape=(1, ct.RangeDim(1, settings.max_seq_length)), + dtype=np.int32, + ), + ] + outputs = [ + ct.TensorType(name="embeddings", dtype=np.float32), + ] + + typer.echo(" Converting to CoreML...") + coreml_model = _coreml_convert( + traced, inputs, outputs, settings, + compute_units_override=settings.compute_units, + ) + + path = settings.output_dir / "forced_aligner_embedding.mlpackage" + _save_mlpackage(coreml_model, path, "Qwen3-ForcedAligner token embedding (152064 vocab → 1024 dim)") + + return path + + +# --------------------------------------------------------------------------- +# LM Head Conversion +# --------------------------------------------------------------------------- + +def convert_lm_head(model, settings: ExportSettings) -> Path: + """Convert the LM head (norm + linear) to CoreML. + + For ForcedAligner, the LM head processes the FULL sequence at once + (not token by token). The logits at timestamp positions are argmax'd + to get ms-resolution timestamps. + """ + typer.echo("\n=== Converting LM Head ===") + + lm_head = _get_lm_head(model) + norm = _get_text_norm(model) + wrapper = LMHeadWrapper(lm_head, norm) + wrapper.eval() + + hidden_size = 1024 + # Realistic magnitude for tracing: hidden states have values in ~[-300, 300] + hidden_states = torch.randn(1, 1, hidden_size, dtype=torch.float32) * 200.0 + + with torch.inference_mode(): + ref_output = wrapper(hidden_states) + typer.echo(f" Reference output shape: {ref_output.shape}") + typer.echo(f" Trace input range: [{hidden_states.min():.1f}, {hidden_states.max():.1f}]") + + hidden_states = hidden_states.clone() + + typer.echo(" Tracing LM head...") + traced = torch.jit.trace(wrapper, (hidden_states,), strict=False) + traced.eval() + + # Use RangeDim for seq_len since this processes full sequences + inputs = [ + ct.TensorType( + name="hidden_states", + shape=(1, ct.RangeDim(1, settings.max_seq_length), hidden_size), + dtype=np.float32, + ), + ] + outputs = [ + ct.TensorType(name="logits", dtype=np.float32), + ] + + typer.echo(" Converting to CoreML...") + coreml_model = _coreml_convert( + traced, inputs, outputs, settings, + compute_units_override=settings.compute_units, + compute_precision_override=ct.precision.FLOAT32, + ) + + path = settings.output_dir / "forced_aligner_lm_head.mlpackage" + _save_mlpackage(coreml_model, path, "Qwen3-ForcedAligner LM head (norm + linear → 5000 timestamp values)") + + return path + + +# --------------------------------------------------------------------------- +# Prefill Decoder Conversion (NAR — single pass) +# --------------------------------------------------------------------------- + +def convert_decoder_prefill(model, settings: ExportSettings) -> Path: + """Convert the full decoder stack for NAR prefill. + + Unlike Qwen3-ASR which needs autoregressive decode with KV cache, + the ForcedAligner processes everything in one shot: + 1. Audio embeddings + text tokens → concatenated sequence + 2. Single prefill pass → hidden states for all positions + 3. LM head → logits → argmax at timestamp positions → ms timestamps + + No KV cache management needed. + """ + typer.echo("\n=== Converting Decoder Prefill (NAR) ===") + + text_model = _get_text_model(model) + num_layers = len(text_model.layers) + hidden_size = 1024 + head_dim = 128 + + wrapper = PrefillDecoderWrapper(text_model) + wrapper.eval() + + PREFILL_SEQ = PrefillDecoderWrapper.PREFILL_SEQ_LEN + typer.echo(f" {num_layers} layers, prefill seq_len={PREFILL_SEQ}") + + hidden_states = torch.randn(1, PREFILL_SEQ, hidden_size, dtype=torch.float32) + position_cos = torch.randn(1, PREFILL_SEQ, head_dim, dtype=torch.float32) + position_sin = torch.randn(1, PREFILL_SEQ, head_dim, dtype=torch.float32) + + with torch.inference_mode(): + ref_output = wrapper(hidden_states, position_cos, position_sin) + typer.echo(f" Output hidden: {ref_output.shape}") + + trace_inputs = ( + hidden_states.clone(), + position_cos.clone(), + position_sin.clone(), + ) + + typer.echo(" Tracing decoder prefill...") + traced = torch.jit.trace(wrapper, trace_inputs, strict=False) + traced.eval() + + inputs = [ + ct.TensorType( + name="hidden_states", + shape=(1, PREFILL_SEQ, hidden_size), + dtype=np.float32, + ), + ct.TensorType( + name="position_cos", + shape=(1, PREFILL_SEQ, head_dim), + dtype=np.float32, + ), + ct.TensorType( + name="position_sin", + shape=(1, PREFILL_SEQ, head_dim), + dtype=np.float32, + ), + ] + outputs = [ + ct.TensorType(name="output_hidden", dtype=np.float32), + ] + + typer.echo(" Converting to CoreML...") + coreml_model = _coreml_convert( + traced, inputs, outputs, settings, + compute_units_override=settings.compute_units, + compute_precision_override=ct.precision.FLOAT32, + ) + + path = settings.output_dir / "forced_aligner_decoder_prefill.mlpackage" + _save_mlpackage( + coreml_model, path, + f"Qwen3-ForcedAligner decoder prefill ({num_layers} layers, NAR)" + ) + + return path + + +# --------------------------------------------------------------------------- +# Metadata +# --------------------------------------------------------------------------- + +def write_metadata( + settings: ExportSettings, + component_paths: Dict[str, object], + model_id: str, +) -> Path: + metadata = { + "model_id": model_id, + "architecture": "Qwen3ASRForConditionalGeneration", + "inference_mode": "NAR (non-autoregressive prefill-only)", + "sample_rate": SAMPLE_RATE, + "num_mel_bins": NUM_MEL_BINS, + "max_audio_seconds": settings.max_audio_seconds, + "max_seq_length": settings.max_seq_length, + "audio_encoder": { + "n_window": 50, + "n_window_infer": 800, + "mel_window_size": 100, + "conv_downsample_factor": 8, + "d_model": 1024, + "output_dim": 1024, + "num_layers": 24, + "num_heads": 16, + "ffn_dim": 4096, + }, + "text_decoder": { + "hidden_size": 1024, + "intermediate_size": 3072, + "num_layers": 28, + "num_attention_heads": 16, + "num_kv_heads": 8, + "head_dim": 128, + "vocab_size": 152064, # embedding table vocab + "lm_head_output_dim": 5000, # timestamp prediction dim (NOT vocab) + "rope_theta": 1000000, + "rope_interleaved": True, + "mrope_section": [24, 20, 20], + }, + "special_tokens": { + "audio_start_token_id": 151669, + "audio_end_token_id": 151670, + "audio_token_id": 151676, + "timestamp_token_id": 151705, + "timestamp_segment_time_ms": 80, + }, + "components": component_paths, + "export_settings": { + "compute_units": settings.compute_units.name, + "compute_precision": ( + settings.compute_precision.name + if settings.compute_precision is not None + else "FLOAT32" + ), + }, + } + + path = settings.output_dir / "metadata.json" + path.write_text(json.dumps(metadata, indent=2, default=str)) + typer.echo(f"\nMetadata written to {path}") + return path + + +# --------------------------------------------------------------------------- +# Main CLI +# --------------------------------------------------------------------------- + +@app.command() +def convert( + model_id: str = typer.Option(DEFAULT_MODEL_ID, "--model-id", help="HuggingFace model ID"), + output_dir: Path = typer.Option( + Path("build/forced-aligner"), + "--output-dir", + help="Output directory for CoreML packages", + ), + components: Optional[str] = typer.Option( + None, + "--components", + help="Comma-separated: audio_encoder,embedding,lm_head,decoder_prefill. Default: all.", + ), + max_seq_length: int = typer.Option(1024, "--max-seq-length", help="Max sequence length for decoder"), + max_audio_seconds: float = typer.Option(300.0, "--max-audio-seconds", help="Max audio duration (5 min)"), + no_ane: bool = typer.Option( + False, "--no-ane", + help="Target CPU+GPU only (exclude ANE).", + ), + no_optimize: bool = typer.Option( + False, "--no-optimize", + help="Skip MIL optimization passes.", + ), +) -> None: + """Export Qwen3-ForcedAligner-0.6B components to CoreML.""" + + target_units = ct.ComputeUnit.CPU_AND_GPU if no_ane else ct.ComputeUnit.ALL + settings = ExportSettings( + output_dir=output_dir, + compute_units=target_units, + deployment_target=ct.target.iOS17, + compute_precision=None, + max_audio_seconds=max_audio_seconds, + max_seq_length=max_seq_length, + ) + + output_dir.mkdir(parents=True, exist_ok=True) + + typer.echo("Export configuration:") + typer.echo(f" Model: {model_id}") + typer.echo(f" Output: {output_dir}") + typer.echo(f" Max seq length: {max_seq_length}") + typer.echo(f" Max audio seconds: {max_audio_seconds}") + if no_ane: + typer.echo(f" Compute units: CPU_AND_GPU (no ANE)") + + if components is not None: + convert_list = [c.strip() for c in components.split(",")] + else: + convert_list = ["audio_conv", "audio_transformer", "embedding", "lm_head", "decoder_prefill"] + + typer.echo(f" Components: {convert_list}") + + model = _load_model(model_id) + + component_paths: Dict[str, object] = {} + + if "audio_encoder" in convert_list: + path = convert_audio_encoder(model, settings) + component_paths["audio_encoder"] = {"path": path.name} + + if "audio_conv" in convert_list: + path = convert_audio_conv(model, settings) + component_paths["audio_conv"] = {"path": path.name} + + if "audio_transformer" in convert_list: + path = convert_audio_transformer(model, settings) + component_paths["audio_transformer"] = {"path": path.name} + + if "embedding" in convert_list: + path = convert_embedding(model, settings) + component_paths["embedding"] = {"path": path.name} + + if "lm_head" in convert_list: + path = convert_lm_head(model, settings) + component_paths["lm_head"] = {"path": path.name} + + if "decoder_prefill" in convert_list: + path = convert_decoder_prefill(model, settings) + component_paths["decoder_prefill"] = {"path": path.name, "num_layers": 28} + + write_metadata(settings, component_paths, model_id) + + typer.echo("\n=== Conversion complete ===") + + +if __name__ == "__main__": + app() diff --git a/models/stt/qwen3-forced-aligner-0.6b/coreml/individual_components.py b/models/stt/qwen3-forced-aligner-0.6b/coreml/individual_components.py new file mode 100644 index 0000000..e738e45 --- /dev/null +++ b/models/stt/qwen3-forced-aligner-0.6b/coreml/individual_components.py @@ -0,0 +1,415 @@ +#!/usr/bin/env python3 +"""Wrapper modules for Qwen3-ForcedAligner-0.6B CoreML export. + +Architecture overview: + Qwen3ASRForConditionalGeneration + └── thinker: Qwen3ASRThinkerForConditionalGeneration + ├── audio_tower: Qwen3ASRAudioEncoder (24 layers, 1024 dim) + ├── model: Qwen3ASRThinkerTextModel (28-layer Qwen3 LLM) + └── lm_head: Linear(1024, 152064) + +Key differences from Qwen3-ASR-0.6B: + - Audio encoder: 24 layers (vs 18), d_model=1024 (vs 896), 16 heads (vs 14) + - Vocab size: 152,064 (vs 151,936) + - RoPE: interleaved mrope with section [24, 20, 20] (vs standard) + - Inference: NAR prefill-only (vs autoregressive decode) + - Special token: timestamp_token_id=151705, timestamp_segment_time=80ms +""" +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Optional, Tuple + +import coremltools as ct +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class AnemllRMSNorm(nn.Module): + """ANEMLL-style RMSNorm using native LayerNorm for better CoreML precision. + + Standard RMSNorm is decomposed by coremltools into pow → mean → rsqrt → mul, + each step losing FP16 precision. Concatenating [x, -x] forces mean to zero, + making LayerNorm mathematically equivalent to RMSNorm. + + Reference: https://huggingface.co/blog/anemll/anemll-style-rms-ane + """ + + def __init__(self, weight: torch.Tensor, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(weight.clone()) + self.eps = eps + self.dim = weight.shape[0] + + def forward(self, x: torch.Tensor) -> torch.Tensor: + doubled = torch.cat([x, -x], dim=-1) + normed = F.layer_norm(doubled, [doubled.shape[-1]], eps=self.eps) + normed = normed[..., : self.dim] + return normed * self.weight + + +def patch_rms_norms(module: nn.Module) -> None: + """Replace all Qwen3RMSNorm instances with AnemllRMSNorm.""" + for name, child in list(module.named_children()): + class_name = type(child).__name__ + if class_name == "AnemllRMSNorm": + continue + if "RMSNorm" in class_name and hasattr(child, "weight"): + eps = getattr(child, "variance_epsilon", getattr(child, "eps", 1e-6)) + replacement = AnemllRMSNorm(child.weight.data, eps=eps) + setattr(module, name, replacement) + else: + patch_rms_norms(child) + + +@dataclass +class ExportSettings: + output_dir: Path + compute_units: ct.ComputeUnit + deployment_target: Optional[ct.target] + compute_precision: Optional[ct.precision] + max_audio_seconds: float + max_seq_length: int + + +# --------------------------------------------------------------------------- +# Audio Encoder Wrapper +# --------------------------------------------------------------------------- + +class AudioConvWrapper(nn.Module): + """Audio encoder conv frontend: mel → conv downsample → positional embedding. + + Processes a single 100-frame mel window through the conv layers. + The transformer layers are separated into AudioTransformerWrapper + to enable cross-chunk attention matching the native encoder. + + Input: + - mel_input: [1, 128, 100] mel spectrogram (single window) + + Output: + - features: [1, 13, 1024] conv features with positional embedding + """ + + def __init__(self, audio_encoder: nn.Module) -> None: + super().__init__() + self.conv2d1 = audio_encoder.conv2d1 + self.conv2d2 = audio_encoder.conv2d2 + self.conv2d3 = audio_encoder.conv2d3 + self.conv_out = audio_encoder.conv_out + self.positional_embedding = audio_encoder.positional_embedding + + def forward(self, mel_input: torch.Tensor) -> torch.Tensor: + # mel_input: [1, 128, 100] + x = mel_input.unsqueeze(1) # [1, 1, 128, 100] + + # Conv downsampling (3 layers, stride 2 each → 8x reduction) + x = F.gelu(self.conv2d1(x)) + x = F.gelu(self.conv2d2(x)) + x = F.gelu(self.conv2d3(x)) + # x: [1, 480, 17, 13] + b, c, f, t = x.size() + x = x.permute(0, 3, 1, 2).contiguous().view(b, t, c * f) + x = self.conv_out(x) # [1, 13, 1024] + + # Positional embedding (each chunk gets positions 0..12, matching native) + pos_emb = self.positional_embedding(t) # [13, 1024] + x = x + pos_emb.unsqueeze(0).to(x.dtype) + + return x # [1, 13, 1024] + + +class AudioTransformerWrapper(nn.Module): + """Audio encoder transformer + projection: conv features → final embeddings. + + Takes concatenated conv features from multiple chunks and runs them through + the 24-layer transformer with full bidirectional attention + projection. + This matches the native encoder behavior where frames within a window + attend to each other. + + Input: + - features: [1, N, 1024] concatenated conv features (N = total frames across chunks) + + Output: + - embeddings: [1, N, 1024] final audio embeddings + """ + + AUDIO_TRANSFORMER_SEQ_LEN = 256 # max frames (covers ~19.6 chunks × 13 ≈ 255) + + def __init__(self, audio_encoder: nn.Module) -> None: + super().__init__() + self.layers = audio_encoder.layers + self.ln_post = audio_encoder.ln_post + self.proj1 = audio_encoder.proj1 + self.proj2 = audio_encoder.proj2 + self.num_layers = len(self.layers) + + def forward(self, features: torch.Tensor) -> torch.Tensor: + # features: [1, N, 1024] + hs = features.squeeze(0) # [N, 1024] + seq_len = hs.shape[0] + cu_seqlens = torch.tensor([0, seq_len], dtype=torch.int32, device=hs.device) + + for layer in self.layers: + layer_outputs = layer(hs, cu_seqlens=cu_seqlens) + hs = layer_outputs[0] + + hs = self.ln_post(hs) + hs = self.proj1(hs) + hs = F.gelu(hs) + hs = self.proj2(hs) # [N, 1024] + return hs.unsqueeze(0) # [1, N, 1024] + + +class AudioEncoderFullWrapper(nn.Module): + """Full audio encoder: mel → conv downsample → transformer → projection. + + DEPRECATED: Use AudioConvWrapper + AudioTransformerWrapper instead. + This wrapper processes each chunk independently through the transformer, + missing cross-chunk attention. Kept for backward compatibility. + + Input: + - mel_input: [1, 128, T] mel spectrogram (128 bins, T frames) + + Output: + - features: [1, T', 1024] where T' = output frames after 8x conv downsampling + """ + + def __init__(self, audio_encoder: nn.Module) -> None: + super().__init__() + self.conv2d1 = audio_encoder.conv2d1 + self.conv2d2 = audio_encoder.conv2d2 + self.conv2d3 = audio_encoder.conv2d3 + self.conv_out = audio_encoder.conv_out + self.positional_embedding = audio_encoder.positional_embedding + self.layers = audio_encoder.layers + self.ln_post = audio_encoder.ln_post + self.proj1 = audio_encoder.proj1 + self.proj2 = audio_encoder.proj2 + + def forward(self, mel_input: torch.Tensor) -> torch.Tensor: + # mel_input: [1, 128, T] + x = mel_input.unsqueeze(1) # [1, 1, 128, T] + + # Conv downsampling (3 layers, stride 2 each → 8x reduction) + x = F.gelu(self.conv2d1(x)) + x = F.gelu(self.conv2d2(x)) + x = F.gelu(self.conv2d3(x)) + # x: [1, 480, F', T'] where F'=17, T'=ceil(T/8) + b, c, f, t = x.size() + x = x.permute(0, 3, 1, 2).contiguous().view(b, t, c * f) + x = self.conv_out(x) # [1, T', 1024] + + # Positional embedding + pos_emb = self.positional_embedding(t) # [T', 1024] + x = x + pos_emb.unsqueeze(0).to(x.dtype) + + # Transformer layers — flat sequence, full attention (no windowing) + hs = x.squeeze(0) # [T', 1024] + seq_len = hs.shape[0] + cu_seqlens = torch.tensor([0, seq_len], dtype=torch.int32, device=hs.device) + + for layer in self.layers: + layer_outputs = layer(hs, cu_seqlens=cu_seqlens) + hs = layer_outputs[0] + + hs = self.ln_post(hs) + hs = self.proj1(hs) + hs = F.gelu(hs) + hs = self.proj2(hs) # [T', 1024] + return hs.unsqueeze(0) # [1, T', 1024] + + +# --------------------------------------------------------------------------- +# Text Embedding Wrapper +# --------------------------------------------------------------------------- + +class TextEmbeddingWrapper(nn.Module): + """Token embedding layer. + + Input: + - input_ids: [1, seq_len] int32 + + Output: + - embeddings: [1, seq_len, 1024] + """ + + def __init__(self, text_model: nn.Module) -> None: + super().__init__() + self.embed_tokens = text_model.embed_tokens + + def forward(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids.long()) + + +# --------------------------------------------------------------------------- +# LM Head Wrapper +# --------------------------------------------------------------------------- + +class LMHeadWrapper(nn.Module): + """LM head: hidden states → logits over vocab. + + Input: + - hidden_states: [1, seq_len, 1024] + + Output: + - logits: [1, seq_len, 5000] (raw timestamp values, NOT vocab tokens) + """ + + def __init__(self, lm_head: nn.Module, norm: nn.Module) -> None: + super().__init__() + self.norm = norm + self.lm_head = lm_head + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hs = self.norm(hidden_states) + return self.lm_head(hs) + + +# --------------------------------------------------------------------------- +# Prefill Decoder Wrapper (NAR — single forward pass) +# --------------------------------------------------------------------------- + +class PrefillDecoderWrapper(nn.Module): + """Full decoder stack for NAR prefill (single forward pass, no autoregressive loop). + + The ForcedAligner processes the entire sequence (audio embeddings + text tokens + with markers) in one prefill call. No KV cache growth, no decode loop. + + Input: + - hidden_states: [1, PREFILL_SEQ_LEN, 1024] + - position_cos: [1, PREFILL_SEQ_LEN, 128] + - position_sin: [1, PREFILL_SEQ_LEN, 128] + + Output: + - output_hidden: [1, PREFILL_SEQ_LEN, 1024] + """ + + PREFILL_SEQ_LEN = 1024 # max sequence length for forced alignment + + def __init__(self, text_model: nn.Module) -> None: + super().__init__() + self.layers = text_model.layers + self.num_layers = len(self.layers) + + # Baked causal mask: [1, 1, N, N] — each position attends to itself and all previous + N = self.PREFILL_SEQ_LEN + mask = torch.full((N, N), -1e9, dtype=torch.float32) + for i in range(N): + mask[i, : i + 1] = 0.0 + self.register_buffer("_causal_mask", mask.view(1, 1, N, N)) + + def forward( + self, + hidden_states: torch.Tensor, + position_cos: torch.Tensor, + position_sin: torch.Tensor, + ) -> torch.Tensor: + cos = position_cos.unsqueeze(1) # [1, 1, N, 128] + sin = position_sin.unsqueeze(1) + attention_mask = self._causal_mask + + for i in range(self.num_layers): + layer = self.layers[i] + hidden_states = self._layer_forward( + layer, hidden_states, cos, sin, attention_mask + ) + + return hidden_states + + @staticmethod + def _layer_forward( + layer: nn.Module, + hidden_states: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + attention_mask: torch.Tensor, + ) -> torch.Tensor: + attn = layer.self_attn + + residual = hidden_states + hs = layer.input_layernorm(hidden_states) + + input_shape = hs.shape[:-1] + hidden_shape = (*input_shape, -1, attn.head_dim) + + q = attn.q_norm(attn.q_proj(hs).view(hidden_shape)).transpose(1, 2) + k = attn.k_norm(attn.k_proj(hs).view(hidden_shape)).transpose(1, 2) + v = attn.v_proj(hs).view(hidden_shape).transpose(1, 2) + + # RoPE + q = (q * cos) + (PrefillDecoderWrapper._rotate_half(q) * sin) + k = (k * cos) + (PrefillDecoderWrapper._rotate_half(k) * sin) + + # GQA: expand KV heads (8 → 16) + num_groups = attn.num_key_value_groups + k_expanded = k.repeat_interleave(num_groups, dim=1) + v_expanded = v.repeat_interleave(num_groups, dim=1) + + # Scaled dot-product attention + attn_weights = torch.matmul(q, k_expanded.transpose(2, 3)) * attn.scaling + attn_weights = attn_weights + attention_mask + attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) + attn_output = torch.matmul(attn_weights, v_expanded) + attn_output = attn_output.transpose(1, 2).reshape(*input_shape, -1) + attn_output = attn.o_proj(attn_output) + + hs = residual + attn_output + + # MLP + residual = hs + hs = layer.post_attention_layernorm(hs) + hs = layer.mlp(hs) + hs = residual + hs + + return hs + + @staticmethod + def _rotate_half(x: torch.Tensor) -> torch.Tensor: + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# --------------------------------------------------------------------------- +# Conversion helper +# --------------------------------------------------------------------------- + +def _coreml_convert( + traced: torch.jit.ScriptModule, + inputs, + outputs, + settings: ExportSettings, + compute_units_override: Optional[ct.ComputeUnit] = None, + compute_precision_override: Optional[ct.precision] = None, + no_optimize: bool = False, +) -> ct.models.MLModel: + cu = compute_units_override if compute_units_override is not None else settings.compute_units + cp = compute_precision_override if compute_precision_override is not None else settings.compute_precision + kwargs = { + "convert_to": "mlprogram", + "inputs": inputs, + "outputs": outputs, + "compute_units": cu, + } + print(f"Converting with compute_units={cu}, compute_precision={cp}, no_optimize={no_optimize}") + if settings.deployment_target is not None: + kwargs["minimum_deployment_target"] = settings.deployment_target + if cp is not None: + kwargs["compute_precision"] = cp + if no_optimize: + minimal_passes = [ + "common::sanitize_input_output_names", + "common::dedup_op_and_var_names", + "common::dead_code_elimination", + "common::const_elimination", + "common::noop_elimination", + "common::update_output_dtypes", + "common::topological_reorder", + "common::canonicalize_inplace_pattern", + ] + kwargs["pass_pipeline"] = ct.PassPipeline( + pass_names=minimal_passes, pipeline_name="minimal" + ) + return ct.convert(traced, **kwargs) diff --git a/models/stt/qwen3-forced-aligner-0.6b/coreml/problems_encountered.md b/models/stt/qwen3-forced-aligner-0.6b/coreml/problems_encountered.md new file mode 100644 index 0000000..7acb5db --- /dev/null +++ b/models/stt/qwen3-forced-aligner-0.6b/coreml/problems_encountered.md @@ -0,0 +1,149 @@ +# Problems Encountered — Qwen3-ForcedAligner-0.6B CoreML Conversion + +Tracking all issues, bugs, trials, and what worked during conversion. +Status: ✅ RESOLVED | ⚠️ PARTIAL | ❌ ABANDONED | 🔵 IN PROGRESS | ⬜ NOT YET TESTED + +--- + +## 1. [✅] RoPE Layout — Interleaved MRoPE vs Standard + +**Context:** The ForcedAligner uses `interleaved: true` with `mrope_section: [24, 20, 20]`, +while Qwen3-ASR uses standard non-interleaved RoPE. + +**Investigation:** Traced PyTorch source — `interleaved: true` in config refers to MRoPE +frequency interleaving across T/H/W dimensions, NOT the `rotate_half` layout. Both PyTorch +and our wrapper use the same concatenated halves `rotate_half`: `[-x2, x1]`. + +**Verification:** With `attn_implementation="eager"`, feeding identical merged embeddings +and cos/sin to the CoreML decoder produces max diff of only 0.0013 vs PyTorch (effectively +identical). The RoPE layout was never the issue. + +**Status:** Resolved. RoPE is correctly implemented. + +--- + +## 2. [✅] Audio Encoder — Cross-Chunk Attention + +**Context:** ForcedAligner encoder is 24 layers / 1024 dim vs ASR's 18 layers / 896 dim. + +**Initial approach:** Monolithic `AudioEncoderFullWrapper` processing one 100-frame mel +window at a time, with each chunk going through conv + transformer independently. + +**Discovery:** The native PyTorch encoder processes conv outputs from ALL chunks through +the transformer together with full bidirectional attention. Processing chunks independently +missed cross-chunk attention, causing max diff of ~2.1 in audio embeddings (despite +per-chunk conv diff being only ~0.08). This was the root cause of the 20.7ms AAS error. + +**Fix:** Split encoder into two CoreML models: +- `AudioConvWrapper`: mel → conv downsample + positional embedding (per-chunk, [1,128,100] → [1,13,1024]) +- `AudioTransformerWrapper`: concatenated conv features → 24-layer transformer + projection ([1,256,1024] → [1,256,1024]) + +At inference time, all chunk conv outputs are concatenated and fed through the transformer +together, matching the native cross-chunk attention behavior. + +**Result:** AAS improved from 20.7ms → 4.4ms, within-20ms from 90.7% → 95.4%, +within-80ms from 92.6% → 99.1%. + +**Status:** Resolved. + +--- + +## 3. [✅] LM Head Output Dim — 5000, NOT vocab_size + +**Context:** Expected LM head output to be vocab_size (152,064) based on config.json. +Actual lm_head.weight shape is `[5000, 1024]`. + +**Discovery:** The ForcedAligner does NOT predict vocab tokens — it predicts raw +timestamp values via argmax. Each output class represents a timestamp bin: +`value × 80ms = absolute time`. With 5000 bins × 80ms = 400 seconds, this covers +up to ~6.7 minutes of audio. + +The embedding table IS 152,064 tokens (shared architecture with ASR), but the LM head +is a separate projection to 5000 timestamp classes. Config's `vocab_size` refers to +the embedding, not the LM head. + +**Impact:** CoreML output shape is correctly `[1, seq, 5000]`. No fix needed — +this was a documentation/understanding issue, not a conversion bug. Updated README, +metadata, and wrapper docstrings. + +--- + +## 4. [🔵] Prefill Sequence Length + +**Context:** ForcedAligner processes up to 5 minutes of audio. With ~100 mel frames +per window and text tokens, sequences could reach 1000+ tokens. Using PREFILL_SEQ_LEN=1024 +as initial value. + +**Risk:** Fixed-shape prefill may need to be larger for long audio. If 1024 isn't enough, +we'll need to increase or implement chunked prefill. + +**Status:** Need to measure typical sequence lengths on test data. + +--- + +## 5. [✅] FP16 Overflow in LM Head + +**Context:** The ASR conversion hit a bug where RMSNorm computed x^2 = 300^2 = 90,000 +which overflows FP16 max (65,504), producing all-zero logits. Fix was FLOAT32 precision. + +**Result:** Using `compute_precision=FLOAT32` for LM head and decoder prefill from +the start. No overflow issues observed. LM head produces correct logits with output +dim 5000 (timestamp values, not vocab tokens). + +--- + +## 6. [⬜] ANE Compilation Time + +**Context:** Kokoro TTS showed ANE compilation taking 60-90s for 15s models. +The ForcedAligner decoder (28 layers, 1024 hidden) is a large model. + +**Risk:** First-run ANE compilation could take minutes. Need to measure and document. + +**Status:** Not yet measured. + +--- + +## 7. [✅] End-to-End Parity Verification + +**Context:** Need to verify that the full CoreML pipeline (audio conv → audio transformer → +embedding → decoder prefill → LM head → timestamp extraction) produces correct timestamps +compared to PyTorch reference. + +**Method:** Ran 3 LibriSpeech test-clean samples through both PyTorch `Qwen3ForcedAligner` +and the 5-model CoreML pipeline. Compared per-word start/end timestamps. + +**Results (54 word boundaries):** +- AAS (mean boundary error): 4.4ms +- Max error: 160ms (single position) +- Within 20ms: 95.4% +- Within 80ms (1 segment): 99.1% +- Within 160ms (2 segments): 100.0% + +**Per-component analysis:** +- Decoder: max diff 0.0013 vs PyTorch (essentially identical) +- Audio conv: max diff ~0.08 per chunk (FP16 precision) +- Audio transformer: enables cross-chunk attention matching native behavior +- LM head cos/sin: max diff 1.5e-5 (essentially identical) + +**Conclusion:** Conversion is functionally correct with near-identical timestamps. +The remaining few mismatches (< 5%) are at the model's resolution limit (80ms) and +are caused by accumulated FP16 precision differences in the audio conv. + +**Status:** Resolved. Parity is excellent for production use. + +--- + +## 8. [✅] Audio Encoder Last Chunk Padding + +**Context:** When mel length is not a multiple of 100, the last chunk needs to be +padded to 100 for the fixed-shape CoreML model. But the conv output for a padded +chunk produces spurious frames. + +**Example:** Last chunk of 62 mel frames → 8 real output frames. Padded to 100 frames +→ 13 output frames → 5 extra spurious frames. + +**Fix:** Calculate expected output frames per chunk using the conv stride formula +`out = (in - 1) // 2 + 1` applied 3 times, then trim the conv output to the +expected count. + +**Status:** Resolved. diff --git a/models/stt/qwen3-forced-aligner-0.6b/coreml/pyproject.toml b/models/stt/qwen3-forced-aligner-0.6b/coreml/pyproject.toml new file mode 100644 index 0000000..736107d --- /dev/null +++ b/models/stt/qwen3-forced-aligner-0.6b/coreml/pyproject.toml @@ -0,0 +1,23 @@ +[project] +name = "qwen3-forced-aligner-coreml" +version = "0.1.0" +description = "Qwen3-ForcedAligner-0.6B CoreML conversion" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "coremltools>=8.1", + "torch>=2.4.0", + "transformers>=4.57.6,<5.0.0", + "numpy>=1.26.4", + "soundfile>=0.13.1", + "huggingface-hub>=0.33.1", + "typer>=0.16.0", + "safetensors>=0.5.3", +] + +[tool.hatch.build.targets.wheel] +only-include = ["convert-coreml.py", "individual_components.py", "compare-models.py"] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/models/stt/qwen3-forced-aligner-0.6b/coreml/run_coreml_inference.py b/models/stt/qwen3-forced-aligner-0.6b/coreml/run_coreml_inference.py new file mode 100644 index 0000000..c918e27 --- /dev/null +++ b/models/stt/qwen3-forced-aligner-0.6b/coreml/run_coreml_inference.py @@ -0,0 +1,803 @@ +#!/usr/bin/env python3 +"""End-to-end CoreML inference for Qwen3-ForcedAligner-0.6B. + +Replicates the PyTorch ForcedAligner pipeline using the 4 converted CoreML models: + 1. Audio Encoder: mel → audio embeddings + 2. Token Embedding: input_ids → text embeddings + 3. Decoder Prefill: merged embeddings + RoPE → hidden states (NAR, single pass) + 4. LM Head: hidden states → logits → argmax → timestamps + +Usage: + uv run python run_coreml_inference.py --audio-file audio.wav --text "hello world" + uv run python run_coreml_inference.py --compare-pytorch # compare against PyTorch reference +""" +from __future__ import annotations + +import json +import math +import time +from dataclasses import dataclass +from pathlib import Path +from typing import List, Optional, Tuple + +import coremltools as ct +import numpy as np +import soundfile as sf +import torch +import typer + +app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) + +# Constants from metadata / model config +SAMPLE_RATE = 16000 +NUM_MEL_BINS = 128 +MEL_WINDOW_SIZE = 100 # frames per encoder window +HIDDEN_SIZE = 1024 +HEAD_DIM = 128 +PREFILL_SEQ_LEN = 1024 +TIMESTAMP_TOKEN_ID = 151705 +TIMESTAMP_SEGMENT_TIME_MS = 80 +AUDIO_START_TOKEN_ID = 151669 +AUDIO_END_TOKEN_ID = 151670 +AUDIO_PAD_TOKEN_ID = 151676 +ROPE_THETA = 1000000.0 +MROPE_SECTION = [24, 20, 20] # T, H, W interleaving +CONV_DOWNSAMPLE_FACTOR = 8 +N_WINDOW = 50 +ATTENTION_SCALING = 1.0 # default rope type +AUDIO_TRANSFORMER_SEQ_LEN = 256 # max frames for audio transformer + + +BUILD_DIR = Path(__file__).parent / "build" / "forced-aligner" + + +@dataclass +class AlignmentResult: + text: str + start_ms: float + end_ms: float + + +# --------------------------------------------------------------------------- +# Mel Spectrogram (reuse transformers WhisperFeatureExtractor) +# --------------------------------------------------------------------------- + +def compute_mel(audio: np.ndarray, sr: int = SAMPLE_RATE) -> np.ndarray: + """Compute 128-bin log-mel spectrogram using WhisperFeatureExtractor. + + Returns: [128, T] float32 numpy array. + """ + from transformers import WhisperFeatureExtractor + + extractor = WhisperFeatureExtractor( + feature_size=NUM_MEL_BINS, + sampling_rate=sr, + padding_value=0.0, + ) + features = extractor(audio, sampling_rate=sr, return_tensors="np") + mel = features["input_features"][0] # [128, T] + return mel.astype(np.float32) + + +# --------------------------------------------------------------------------- +# MRoPE Position Computation +# --------------------------------------------------------------------------- + +def compute_rope_inv_freq() -> np.ndarray: + """Compute inv_freq for RoPE: 1 / (theta^(2i/d)) for i in [0, d/2).""" + dim = HEAD_DIM + inv_freq = 1.0 / ( + ROPE_THETA ** (np.arange(0, dim, 2, dtype=np.float64) / dim) + ) + return inv_freq.astype(np.float32) # [64] + + +def apply_interleaved_mrope(freqs_3d: np.ndarray, mrope_section: list) -> np.ndarray: + """Apply interleaved MRoPE: reorganize from [T,H,W] grids to interleaved layout. + + Args: + freqs_3d: [3, seq_len, head_dim//2] (T, H, W frequency grids) + mrope_section: [24, 20, 20] + + Returns: + freqs: [seq_len, head_dim//2] (interleaved) + """ + freqs_t = freqs_3d[0].copy() # start with T grid + for dim_idx, offset in enumerate((1, 2), start=1): # H=1, W=2 + length = mrope_section[dim_idx] * 3 + # Select every 3rd element starting at offset + idx = slice(offset, length, 3) + freqs_t[..., idx] = freqs_3d[dim_idx, ..., idx] + return freqs_t + + +def compute_mrope_cos_sin( + position_ids_3d: np.ndarray, # [3, seq_len] +) -> Tuple[np.ndarray, np.ndarray]: + """Compute MRoPE cos/sin embeddings for prefill. + + Args: + position_ids_3d: [3, seq_len] position IDs for T, H, W grids + + Returns: + cos: [1, seq_len, head_dim] float32 + sin: [1, seq_len, head_dim] float32 + """ + inv_freq = compute_rope_inv_freq() # [64] + seq_len = position_ids_3d.shape[1] + + # freqs[d, s, f] = position_ids_3d[d, s] * inv_freq[f] + # Shape: [3, seq_len, 64] + freqs_3d = np.zeros((3, seq_len, len(inv_freq)), dtype=np.float32) + for d in range(3): + freqs_3d[d] = np.outer(position_ids_3d[d].astype(np.float32), inv_freq) + + # Apply interleaved mrope + freqs = apply_interleaved_mrope(freqs_3d, MROPE_SECTION) # [seq_len, 64] + + # Duplicate: [seq_len, 64] → [seq_len, 128] + emb = np.concatenate([freqs, freqs], axis=-1) + + cos = np.cos(emb) * ATTENTION_SCALING # [seq_len, 128] + sin = np.sin(emb) * ATTENTION_SCALING + + return cos[np.newaxis], sin[np.newaxis] # [1, seq_len, 128] + + +def compute_position_ids(attention_mask: np.ndarray) -> np.ndarray: + """Compute 3D position IDs from attention mask. + + For forced alignment (no padding), this is simply [0, 1, 2, ..., seq_len-1] + replicated for all 3 dimensions. + + Args: + attention_mask: [seq_len] int + + Returns: + position_ids: [3, seq_len] int + """ + positions = np.cumsum(attention_mask.astype(np.float32)) - 1 + positions = np.maximum(positions, 0).astype(np.int64) + # All 3 grids get the same positions for forced alignment + return np.stack([positions, positions, positions], axis=0) + + +# --------------------------------------------------------------------------- +# Text Processing (simplified for English) +# --------------------------------------------------------------------------- + +def tokenize_for_alignment(text: str, language: str = "English"): + """Tokenize text and create aligner input with delimiters. + + Returns: + word_list: list of words + input_text: formatted text with audio placeholders and timestamps + """ + # Simple space-based tokenization for English + words = [] + for seg in text.split(): + # Clean: keep only letters, numbers, apostrophes + cleaned = "".join(ch for ch in seg if ch.isalnum() or ch == "'") + if cleaned: + words.append(cleaned) + + input_text = "".join(words) + "" + input_text = "<|audio_start|><|audio_pad|><|audio_end|>" + input_text + + return words, input_text + + +def fix_timestamp(data: np.ndarray) -> List[int]: + """Fix non-monotonic timestamps using LIS (Longest Increasing Subsequence). + + Replicates Qwen3ForceAlignProcessor.fix_timestamp(). + """ + data = data.tolist() + n = len(data) + + # LIS with parent tracking + dp = [1] * n + parent = [-1] * n + + for i in range(1, n): + for j in range(i): + if data[j] <= data[i] and dp[j] + 1 > dp[i]: + dp[i] = dp[j] + 1 + parent[i] = j + + max_length = max(dp) + max_idx = dp.index(max_length) + + lis_indices = [] + idx = max_idx + while idx != -1: + lis_indices.append(idx) + idx = parent[idx] + lis_indices.reverse() + + is_normal = [False] * n + for idx in lis_indices: + is_normal[idx] = True + + result = data.copy() + i = 0 + + while i < n: + if not is_normal[i]: + j = i + while j < n and not is_normal[j]: + j += 1 + + anomaly_count = j - i + + if anomaly_count <= 2: + left_val = None + for k in range(i - 1, -1, -1): + if is_normal[k]: + left_val = result[k] + break + + right_val = None + for k in range(j, n): + if is_normal[k]: + right_val = result[k] + break + + for k in range(i, j): + if left_val is None: + result[k] = right_val + elif right_val is None: + result[k] = left_val + else: + result[k] = left_val if (k - (i - 1)) <= ((j) - k) else right_val + + else: + left_val = None + for k in range(i - 1, -1, -1): + if is_normal[k]: + left_val = result[k] + break + + right_val = None + for k in range(j, n): + if is_normal[k]: + right_val = result[k] + break + + if left_val is not None and right_val is not None: + step = (right_val - left_val) / (anomaly_count + 1) + for k in range(i, j): + result[k] = left_val + step * (k - i + 1) + elif left_val is not None: + for k in range(i, j): + result[k] = left_val + elif right_val is not None: + for k in range(i, j): + result[k] = right_val + + i = j + else: + i += 1 + + return [int(res) for res in result] + + +# --------------------------------------------------------------------------- +# CoreML Model Loading +# --------------------------------------------------------------------------- + +class CoreMLAligner: + """Manages the 4 CoreML models for forced alignment inference.""" + + def __init__(self, model_dir: Path): + typer.echo(f"Loading CoreML models from {model_dir}...") + + # Try split encoder (audio_conv + audio_transformer) first, fall back to monolithic + conv_path = model_dir / "forced_aligner_audio_conv.mlpackage" + transformer_path = model_dir / "forced_aligner_audio_transformer.mlpackage" + monolithic_path = model_dir / "forced_aligner_audio_encoder.mlpackage" + + if conv_path.exists() and transformer_path.exists(): + self.audio_conv = ct.models.MLModel(str(conv_path)) + self.audio_transformer = ct.models.MLModel(str(transformer_path)) + self.audio_encoder = None + self._use_split_encoder = True + typer.echo(" ✓ Audio conv (split encoder)") + typer.echo(" ✓ Audio transformer (split encoder)") + elif monolithic_path.exists(): + self.audio_encoder = ct.models.MLModel(str(monolithic_path)) + self.audio_conv = None + self.audio_transformer = None + self._use_split_encoder = False + typer.echo(" ✓ Audio encoder (monolithic)") + else: + raise FileNotFoundError( + f"No audio encoder found in {model_dir}. " + "Need either audio_conv+audio_transformer or audio_encoder." + ) + + self.embedding = ct.models.MLModel( + str(model_dir / "forced_aligner_embedding.mlpackage") + ) + typer.echo(" ✓ Token embedding") + + self.decoder = ct.models.MLModel( + str(model_dir / "forced_aligner_decoder_prefill.mlpackage") + ) + typer.echo(" ✓ Decoder prefill") + + self.lm_head = ct.models.MLModel( + str(model_dir / "forced_aligner_lm_head.mlpackage") + ) + typer.echo(" ✓ LM head") + + # Load the HF processor for tokenization + self._init_processor() + + def _init_processor(self): + """Initialize HF processor for tokenization + feature extraction.""" + import sys + import types + import importlib.util + + qwen_asr_path = Path(__file__).resolve().parents[5] / "qwen3-asr" + if not qwen_asr_path.exists(): + raise FileNotFoundError(f"qwen3-asr not found at {qwen_asr_path}") + + tb_dir = qwen_asr_path / "qwen_asr" / "core" / "transformers_backend" + + for pkg_name, pkg_path in [ + ("qwen_asr", qwen_asr_path / "qwen_asr"), + ("qwen_asr.core", qwen_asr_path / "qwen_asr" / "core"), + ("qwen_asr.core.transformers_backend", tb_dir), + ]: + if pkg_name not in sys.modules: + mod = types.ModuleType(pkg_name) + mod.__path__ = [str(pkg_path)] + mod.__package__ = pkg_name + sys.modules[pkg_name] = mod + + config_fqn = "qwen_asr.core.transformers_backend.configuration_qwen3_asr" + if config_fqn not in sys.modules: + spec = importlib.util.spec_from_file_location( + config_fqn, tb_dir / "configuration_qwen3_asr.py" + ) + config_mod = importlib.util.module_from_spec(spec) + sys.modules[config_fqn] = config_mod + spec.loader.exec_module(config_mod) + else: + config_mod = sys.modules[config_fqn] + + proc_fqn = "qwen_asr.core.transformers_backend.processing_qwen3_asr" + if proc_fqn not in sys.modules: + spec2 = importlib.util.spec_from_file_location( + proc_fqn, tb_dir / "processing_qwen3_asr.py" + ) + proc_mod = importlib.util.module_from_spec(spec2) + sys.modules[proc_fqn] = proc_mod + spec2.loader.exec_module(proc_mod) + else: + proc_mod = sys.modules[proc_fqn] + + from transformers import AutoConfig, AutoProcessor + try: + AutoConfig.register("qwen3_asr", config_mod.Qwen3ASRConfig) + except Exception: + pass + try: + AutoProcessor.register(config_mod.Qwen3ASRConfig, proc_mod.Qwen3ASRProcessor) + except Exception: + pass + + # Full Qwen3ASRProcessor with feature extractor + tokenizer + self.processor = AutoProcessor.from_pretrained( + "Qwen/Qwen3-ForcedAligner-0.6B", + fix_mistral_regex=True, + ) + typer.echo(f" ✓ HF processor ({type(self.processor).__name__})") + + def align( + self, + audio_path: str, + text: str, + language: str = "English", + ) -> Tuple[List[AlignmentResult], float]: + """Run forced alignment using CoreML models. + + Returns: (alignments, latency_ms) + """ + start_time = time.perf_counter() + + # 1. Load audio + audio, sr = sf.read(audio_path) + if sr != SAMPLE_RATE: + raise ValueError(f"Expected {SAMPLE_RATE}Hz, got {sr}Hz") + if len(audio.shape) > 1: + audio = audio.mean(axis=1) + + # 2. Tokenize text with timestamp delimiters + word_list, input_text = tokenize_for_alignment(text, language) + + # 3. Use HF processor to get input_ids + mel features + inputs = self.processor( + text=[input_text], + audio=[audio], + return_tensors="np", + padding=True, + ) + input_ids = inputs["input_ids"][0].astype(np.int64) # [seq_len] + input_features = inputs["input_features"] # [1, 128, T] + feature_attention_mask = inputs.get("feature_attention_mask", None) + if feature_attention_mask is not None: + feature_len = int(feature_attention_mask[0].sum()) + else: + feature_len = input_features.shape[2] + + typer.echo(f" Input IDs: {input_ids.shape}, Features: {input_features.shape}") + + # 4. Run audio encoder on mel + mel_features = input_features[0, :, :feature_len] # [128, feature_len] + if self._use_split_encoder: + audio_embeddings = self._run_split_audio_encoder(mel_features) + else: + audio_embeddings = self._run_audio_encoder(mel_features) + typer.echo(f" Audio embeddings: {audio_embeddings.shape}") + + # 5. Run token embedding + text_embeddings = self._run_embedding(input_ids) # [1, seq_len, 1024] + typer.echo(f" Text embeddings: {text_embeddings.shape}") + + # 6. Merge: replace audio_pad positions with audio features + audio_mask = (input_ids == AUDIO_PAD_TOKEN_ID) + num_audio_pads = audio_mask.sum() + typer.echo(f" Audio pads: {num_audio_pads}, Audio features: {audio_embeddings.shape[0]}") + + merged = text_embeddings[0].copy() # [seq_len, 1024] + + # Scatter audio embeddings into pad positions + pad_indices = np.where(audio_mask)[0] + n_audio = min(len(pad_indices), audio_embeddings.shape[0]) + for i in range(n_audio): + merged[pad_indices[i]] = audio_embeddings[i] + + seq_len = merged.shape[0] + typer.echo(f" Merged sequence: {seq_len}") + + # 7. Pad/truncate to PREFILL_SEQ_LEN + if seq_len > PREFILL_SEQ_LEN: + typer.echo(f" WARNING: seq_len {seq_len} > PREFILL_SEQ_LEN {PREFILL_SEQ_LEN}, truncating") + merged = merged[:PREFILL_SEQ_LEN] + input_ids = input_ids[:PREFILL_SEQ_LEN] + seq_len = PREFILL_SEQ_LEN + + padded = np.zeros((PREFILL_SEQ_LEN, HIDDEN_SIZE), dtype=np.float32) + padded[:seq_len] = merged + + # 8. Compute MRoPE position embeddings + attention_mask = np.zeros(PREFILL_SEQ_LEN, dtype=np.int32) + attention_mask[:seq_len] = 1 + position_ids_3d = compute_position_ids(attention_mask) # [3, PREFILL_SEQ_LEN] + cos, sin = compute_mrope_cos_sin(position_ids_3d) # [1, PREFILL_SEQ_LEN, 128] + + # 9. Run decoder prefill + decoder_output = self._run_decoder( + padded[np.newaxis], # [1, PREFILL_SEQ_LEN, 1024] + cos.astype(np.float32), + sin.astype(np.float32), + ) + typer.echo(f" Decoder output: {decoder_output.shape}") + + # 10. Run LM head on actual sequence positions only + hidden_states = decoder_output[0, :seq_len] # [seq_len, 1024] + logits = self._run_lm_head(hidden_states[np.newaxis]) # [1, seq_len, 5000] + typer.echo(f" Logits: {logits.shape}") + + # 11. Extract timestamps at positions + output_ids = np.argmax(logits[0], axis=-1) # [seq_len] + timestamp_mask = (input_ids[:seq_len] == TIMESTAMP_TOKEN_ID) + masked_output_ids = output_ids[timestamp_mask] + timestamp_ms = masked_output_ids * TIMESTAMP_SEGMENT_TIME_MS + + typer.echo(f" Timestamp tokens: {timestamp_mask.sum()}, Raw values: {masked_output_ids[:10]}...") + + # 12. Fix monotonicity + timestamp_fixed = fix_timestamp(timestamp_ms) + + # 13. Parse into word-level alignments + alignments = [] + for i, word in enumerate(word_list): + start_ms = timestamp_fixed[i * 2] + end_ms = timestamp_fixed[i * 2 + 1] + alignments.append(AlignmentResult( + text=word, + start_ms=float(start_ms), + end_ms=float(end_ms), + )) + + elapsed_ms = (time.perf_counter() - start_time) * 1000 + return alignments, elapsed_ms + + def _run_audio_encoder(self, mel: np.ndarray) -> np.ndarray: + """Run audio encoder on mel features, chunking into 100-frame windows. + + Replicates PyTorch's Qwen3ASRAudioEncoder.forward() chunking: + - Split mel into n_window*2=100 frame chunks + - Last chunk keeps its actual length (not padded to 100) + - Conv stride-2 x3 layers: output_len = (input_len - 1) // 2 + 1, repeated 3x + - Trim last chunk's output to the correct frame count + + Args: + mel: [128, T] mel spectrogram + + Returns: + audio_features: [N, 1024] concatenated audio embeddings + """ + T = mel.shape[1] + all_features = [] + + # Process in 100-frame chunks + for start in range(0, T, MEL_WINDOW_SIZE): + end = min(start + MEL_WINDOW_SIZE, T) + actual_chunk_len = end - start + chunk = mel[:, start:end] # [128, chunk_len] + + # Compute expected output frames for this chunk + # Conv stride-2, 3 layers: out = (in - 1) // 2 + 1, repeated 3x + expected_out = actual_chunk_len + for _ in range(3): + expected_out = (expected_out - 1) // 2 + 1 + + # Pad to exactly MEL_WINDOW_SIZE for CoreML (fixed input shape) + if chunk.shape[1] < MEL_WINDOW_SIZE: + pad_width = MEL_WINDOW_SIZE - chunk.shape[1] + chunk = np.pad(chunk, ((0, 0), (0, pad_width)), mode='constant') + + # [1, 128, 100] + chunk_input = chunk[np.newaxis].astype(np.float32) + result = self.audio_encoder.predict({"mel_input": chunk_input}) + features = result["audio_features"][0] # [T', 1024] + + # Trim to expected output length (removes padding artifacts) + features = features[:expected_out] + all_features.append(features) + + audio_features = np.concatenate(all_features, axis=0) # [N, 1024] + return audio_features + + def _run_split_audio_encoder(self, mel: np.ndarray) -> np.ndarray: + """Run split audio encoder: conv per chunk, then transformer on all frames. + + This matches the native PyTorch encoder behavior where all conv outputs + are concatenated and processed through the transformer with full + bidirectional attention, enabling cross-chunk attention. + + Args: + mel: [128, T] mel spectrogram + + Returns: + audio_features: [N, 1024] final audio embeddings + """ + T = mel.shape[1] + all_conv_features = [] + chunk_frame_counts = [] + + # Step 1: Run conv on each 100-frame chunk + for start in range(0, T, MEL_WINDOW_SIZE): + end = min(start + MEL_WINDOW_SIZE, T) + actual_chunk_len = end - start + chunk = mel[:, start:end] # [128, chunk_len] + + # Expected output frames for this chunk + expected_out = actual_chunk_len + for _ in range(3): + expected_out = (expected_out - 1) // 2 + 1 + + # Pad to 100 for CoreML fixed input shape + if chunk.shape[1] < MEL_WINDOW_SIZE: + pad_width = MEL_WINDOW_SIZE - chunk.shape[1] + chunk = np.pad(chunk, ((0, 0), (0, pad_width)), mode='constant') + + chunk_input = chunk[np.newaxis].astype(np.float32) # [1, 128, 100] + result = self.audio_conv.predict({"mel_input": chunk_input}) + features = result["conv_features"][0] # [13, 1024] + + # Trim to expected output length + features = features[:expected_out] + all_conv_features.append(features) + chunk_frame_counts.append(expected_out) + + # Step 2: Concatenate all conv features + conv_features = np.concatenate(all_conv_features, axis=0) # [N, 1024] + total_frames = conv_features.shape[0] + + # Step 3: Pad to AUDIO_TRANSFORMER_SEQ_LEN and run transformer + if total_frames > AUDIO_TRANSFORMER_SEQ_LEN: + typer.echo(f" WARNING: audio frames {total_frames} > max {AUDIO_TRANSFORMER_SEQ_LEN}, truncating") + conv_features = conv_features[:AUDIO_TRANSFORMER_SEQ_LEN] + total_frames = AUDIO_TRANSFORMER_SEQ_LEN + + padded = np.zeros((AUDIO_TRANSFORMER_SEQ_LEN, 1024), dtype=np.float32) + padded[:total_frames] = conv_features + + result = self.audio_transformer.predict({ + "features": padded[np.newaxis].astype(np.float32), + }) + audio_embeddings = result["audio_embeddings"][0] # [AUDIO_TRANSFORMER_SEQ_LEN, 1024] + + # Trim to actual frame count + audio_embeddings = audio_embeddings[:total_frames] + return audio_embeddings + + def _run_embedding(self, input_ids: np.ndarray) -> np.ndarray: + """Run token embedding. + + Args: + input_ids: [seq_len] int + + Returns: + embeddings: [1, seq_len, 1024] + """ + ids = input_ids[np.newaxis].astype(np.int32) # [1, seq_len] + result = self.embedding.predict({"input_ids": ids}) + return result["embeddings"] + + def _run_decoder( + self, + hidden_states: np.ndarray, + position_cos: np.ndarray, + position_sin: np.ndarray, + ) -> np.ndarray: + """Run decoder prefill (NAR, single pass). + + Args: + hidden_states: [1, PREFILL_SEQ_LEN, 1024] + position_cos: [1, PREFILL_SEQ_LEN, 128] + position_sin: [1, PREFILL_SEQ_LEN, 128] + + Returns: + output_hidden: [1, PREFILL_SEQ_LEN, 1024] + """ + result = self.decoder.predict({ + "hidden_states": hidden_states.astype(np.float32), + "position_cos": position_cos.astype(np.float32), + "position_sin": position_sin.astype(np.float32), + }) + return result["output_hidden"] + + def _run_lm_head(self, hidden_states: np.ndarray) -> np.ndarray: + """Run LM head to get logits. + + Args: + hidden_states: [1, seq_len, 1024] + + Returns: + logits: [1, seq_len, 5000] + """ + result = self.lm_head.predict({ + "hidden_states": hidden_states.astype(np.float32), + }) + return result["logits"] + + +# --------------------------------------------------------------------------- +# CLI Commands +# --------------------------------------------------------------------------- + +@app.command() +def infer( + audio_file: Path = typer.Option(..., "--audio-file", help="Path to audio file"), + text: str = typer.Option(..., "--text", help="Transcript text"), + language: str = typer.Option("English", "--language", help="Language"), + model_dir: Path = typer.Option(BUILD_DIR, "--model-dir", help="CoreML model directory"), +) -> None: + """Run CoreML forced alignment on a single file.""" + aligner = CoreMLAligner(model_dir) + + typer.echo(f"\nAligning: {audio_file.name}") + typer.echo(f"Text: {text[:80]}...") + + alignments, latency = aligner.align(str(audio_file), text, language) + + typer.echo(f"\nResults ({latency:.0f}ms):") + for a in alignments: + typer.echo(f" {a.text:15s} {a.start_ms:8.1f} - {a.end_ms:8.1f} ms") + + +@app.command() +def compare( + model_dir: Path = typer.Option(BUILD_DIR, "--model-dir", help="CoreML model directory"), + reference_file: Path = typer.Option( + BUILD_DIR / "pytorch_reference.json", + "--reference", + help="PyTorch reference JSON", + ), + num_files: int = typer.Option(3, "--num-files", help="Number of files to compare"), +) -> None: + """Compare CoreML output against PyTorch reference timestamps.""" + if not reference_file.exists(): + typer.echo(f"ERROR: Reference file not found: {reference_file}") + typer.echo("Run PyTorch reference first:") + typer.echo(" uv run python run_coreml_inference.py pytorch-reference") + raise typer.Exit(1) + + with open(reference_file) as f: + ref_data = json.load(f) + + aligner = CoreMLAligner(model_dir) + + # Build a map from filename to full path in test-clean + test_clean = ( + Path.home() / "Library" / "Application Support" / "FluidAudio" + / "Datasets" / "LibriSpeech" / "test-clean" + ) + flac_map = {} + if test_clean.exists(): + for f in test_clean.rglob("*.flac"): + flac_map[f.name] = str(f) + + all_errors = [] + for sample in ref_data[:num_files]: + audio_path = sample["audio"] + # Resolve relative/short paths + if not Path(audio_path).exists(): + fname = Path(audio_path).name + if fname in flac_map: + audio_path = flac_map[fname] + else: + typer.echo(f" WARNING: Cannot find {audio_path}, skipping") + continue + text = sample["text"] + + typer.echo(f"\n=== {Path(audio_path).name} ===") + + coreml_alignments, coreml_latency = aligner.align(audio_path, text) + + ref_alignments = sample["alignments"] + pytorch_latency = sample["latency_ms"] + + n = min(len(ref_alignments), len(coreml_alignments)) + if len(ref_alignments) != len(coreml_alignments): + typer.echo(f" WARNING: word count mismatch: ref={len(ref_alignments)}, coreml={len(coreml_alignments)}") + + sample_errors = [] + for i in range(n): + ref = ref_alignments[i] + hyp = coreml_alignments[i] + start_err = abs(ref["start_ms"] - hyp.start_ms) + end_err = abs(ref["end_ms"] - hyp.end_ms) + sample_errors.extend([start_err, end_err]) + all_errors.extend([start_err, end_err]) + + errors = np.array(sample_errors) + typer.echo(f" Words: {n}") + typer.echo(f" AAS: {errors.mean():.1f}ms") + typer.echo(f" Max error: {errors.max():.1f}ms") + typer.echo(f" Within 20ms: {(errors <= 20).mean() * 100:.1f}%") + typer.echo(f" Within 80ms: {(errors <= 80).mean() * 100:.1f}%") + typer.echo(f" PyTorch: {pytorch_latency:.0f}ms, CoreML: {coreml_latency:.0f}ms") + + # Show per-word comparison for first 5 words + typer.echo(f" Per-word comparison (first 5):") + for i in range(min(5, n)): + ref = ref_alignments[i] + hyp = coreml_alignments[i] + typer.echo( + f" {ref['text']:12s} " + f"PT: {ref['start_ms']:7.1f}-{ref['end_ms']:7.1f} " + f"CM: {hyp.start_ms:7.1f}-{hyp.end_ms:7.1f} " + f"Δ: {abs(ref['start_ms'] - hyp.start_ms):5.1f}/{abs(ref['end_ms'] - hyp.end_ms):5.1f}ms" + ) + + # Overall summary + if all_errors: + errors = np.array(all_errors) + typer.echo(f"\n=== Overall Parity ({len(all_errors)//2} boundaries) ===") + typer.echo(f" AAS (mean boundary error): {errors.mean():.1f}ms") + typer.echo(f" Max error: {errors.max():.1f}ms") + typer.echo(f" Within 20ms: {(errors <= 20).mean() * 100:.1f}%") + typer.echo(f" Within 80ms (1 segment): {(errors <= 80).mean() * 100:.1f}%") + typer.echo(f" Within 160ms (2 segments): {(errors <= 160).mean() * 100:.1f}%") + + +if __name__ == "__main__": + 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