transcribe: swap speaker embedder from resemblyzer to WeSpeaker ONNX

+1

Makefile

··· 51 51 echo "Python 3.14+ detected - installing onnxruntime from nightly feed..."; \ 52 52 $(UV) pip install --pre --no-deps --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ onnxruntime; \ 53 53 fi 54 + @$(VENV_BIN)/python -c "from observe.transcribe.main import WESPEAKER_MODEL_PATH, WESPEAKER_MODEL_SHA256; from observe.utils import compute_file_sha256; actual = compute_file_sha256(WESPEAKER_MODEL_PATH); assert actual == WESPEAKER_MODEL_SHA256, f'WeSpeaker asset hash mismatch: got {actual}, expected {WESPEAKER_MODEL_SHA256}'; print(f'wespeaker asset ok ({actual[:12]}...)')" 54 55 @echo "Installing Playwright browser for sol screenshot..." 55 56 $(VENV_BIN)/playwright install chromium 56 57 @$(MAKE) --no-print-directory skills

observe/transcribe/assets/wespeaker-resnet34-256.onnx

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observe/transcribe/main.py

··· 8 8 2. Audio reduction: Trim long silence gaps for faster processing 9 9 3. Transcription: Dispatch to STT backend (default: whisper) 10 10 4. Enrichment: Extract topics, setting, emotions, and warnings via LLM (optional) 11 - 5. Embeddings: Generate voice embeddings for each sentence using resemblyzer 11 + 5. Embeddings: Generate voice embeddings for each sentence using wespeaker-resnet34 12 12 6. Output: JSONL format compatible with format_audio() in observe/hear.py 13 13 14 14 Output files: ··· 39 39 40 40 Platform optimizations (Whisper): 41 41 - CUDA GPU: Uses float16 for GPU-optimized inference 42 - - Apple Silicon: Uses int8 for Whisper (~2x faster), MPS for embeddings (~16x faster) 42 + - Apple hosts: Uses int8 for Whisper on CPU and CoreML/CPU for embeddings 43 43 - Other CPU: Uses int8 for best performance 44 44 """ 45 45 ··· 50 50 import json 51 51 import logging 52 52 import os 53 + import platform 53 54 import time 54 55 from pathlib import Path 55 56 56 57 import numpy as np 58 + import onnxruntime as ort 57 59 58 60 from observe.transcribe import ( 59 61 BACKEND_REGISTRY, 60 62 get_backend, 61 63 ) 62 64 from observe.transcribe import transcribe as stt_transcribe 63 - from observe.transcribe.utils import is_apple_silicon 64 65 from observe.transcribe.whisper import DEFAULT_COMPUTE, DEFAULT_DEVICE, DEFAULT_MODEL 65 66 from observe.utils import SAMPLE_RATE, get_segment_key, load_audio 66 67 from observe.vad import ( ··· 101 102 # Minimum statement duration for embedding (seconds) 102 103 MIN_STATEMENT_DURATION = 0.3 103 104 105 + # WeSpeaker embedder asset 106 + EMBEDDER_NAME = "wespeaker-resnet34-256" 107 + WESPEAKER_MODEL_SHA256 = ( 108 + "5ef208a9da1453335308a6b6f4e6dfbd7e183a38b604de0a57664f45d257fe94" 109 + ) 110 + WESPEAKER_MODEL_PATH = Path(__file__).parent / "assets" / "wespeaker-resnet34-256.onnx" 111 + 104 112 # Number of recent entity names to load for transcription context 105 113 ENTITY_NAMES_LIMIT = 40 106 114 107 - # Module-level voice encoder cache 108 - _voice_encoder = None 109 - 115 + # Module-level embedder cache 116 + _embedder_session: ort.InferenceSession | None = None 110 117 111 - def _get_optimal_encoder_device() -> str: 112 - """Get optimal device for VoiceEncoder (resemblyzer). 113 118 114 - On Apple Silicon, MPS provides ~16x speedup over CPU for embeddings. 119 + def _select_onnx_providers() -> list[str]: 120 + """Return the ONNX Runtime provider list for this host. 115 121 116 - Returns: 117 - Device string: "mps" on Apple Silicon with MPS available, "cpu" otherwise 122 + Darwin (any arch) prefers CoreML with CPU fallback; elsewhere, CPU only. 118 123 """ 119 - if is_apple_silicon(): 120 - try: 121 - import torch 124 + if platform.system() == "Darwin": 125 + return ["CoreMLExecutionProvider", "CPUExecutionProvider"] 126 + return ["CPUExecutionProvider"] 122 127 123 - if torch.backends.mps.is_available(): 124 - return "mps" 125 - except ImportError: 126 - pass 127 - return "cpu" 128 128 129 + def _get_embedder_session() -> ort.InferenceSession: 130 + """Return a cached ONNX InferenceSession for the WeSpeaker encoder.""" 131 + global _embedder_session 129 132 130 - def _get_voice_encoder(whisper_device: str = "cpu"): 131 - """Get or create VoiceEncoder with caching. 133 + if _embedder_session is None: 134 + if not WESPEAKER_MODEL_PATH.is_file(): 135 + raise FileNotFoundError( 136 + f"WeSpeaker model asset not found at {WESPEAKER_MODEL_PATH}. " 137 + "Run `make install` to verify the bundled asset." 138 + ) 139 + providers = _select_onnx_providers() 140 + start = time.monotonic() 141 + _embedder_session = ort.InferenceSession( 142 + str(WESPEAKER_MODEL_PATH), 143 + providers=providers, 144 + ) 145 + elapsed = time.monotonic() - start 146 + logging.info( 147 + "wespeaker session loaded providers=%s elapsed=%.2fs", 148 + _embedder_session.get_providers(), 149 + elapsed, 150 + ) 132 151 133 - Args: 134 - whisper_device: Device used by whisper (affects encoder device selection) 152 + return _embedder_session 135 153 136 - Returns: 137 - VoiceEncoder instance 138 - """ 139 - global _voice_encoder 140 - from resemblyzer import VoiceEncoder 154 + 155 + def _compute_wespeaker_features(audio: np.ndarray, sample_rate: int) -> np.ndarray: 156 + """Compute Kaldi-style fbank features for the bundled WeSpeaker encoder.""" 157 + import kaldi_native_fbank as knf 158 + 159 + if sample_rate != SAMPLE_RATE: 160 + raise ValueError( 161 + f"WeSpeaker embedder requires {SAMPLE_RATE} Hz audio, got {sample_rate}" 162 + ) 141 163 142 - if _voice_encoder is not None: 143 - return _voice_encoder 164 + opts = knf.FbankOptions() 165 + opts.frame_opts.samp_freq = float(sample_rate) 166 + opts.frame_opts.dither = 0.0 167 + opts.frame_opts.snip_edges = False 168 + opts.frame_opts.frame_length_ms = 25.0 169 + opts.frame_opts.frame_shift_ms = 10.0 170 + opts.mel_opts.num_bins = 80 171 + opts.energy_floor = 0.0 172 + opts.use_energy = False 144 173 145 - # VoiceEncoder: use MPS on Apple Silicon for ~16x speedup, otherwise CPU 146 - # (CUDA auto-detection handled by resemblyzer when device=None) 147 - if whisper_device == "cuda": 148 - encoder_device = None # Let resemblyzer auto-detect CUDA 149 - else: 150 - encoder_device = _get_optimal_encoder_device() 174 + fbank = knf.OnlineFbank(opts) 175 + scaled = (audio.astype(np.float32) * 32768.0).tolist() 176 + fbank.accept_waveform(float(sample_rate), scaled) 177 + fbank.input_finished() 151 178 152 - logging.info("Loading resemblyzer VoiceEncoder...") 153 - t0 = time.perf_counter() 154 - _voice_encoder = VoiceEncoder(device=encoder_device) 155 - logging.info( 156 - f" VoiceEncoder loaded in {time.perf_counter() - t0:.2f}s " 157 - f"(device={_voice_encoder.device})" 158 - ) 179 + frames = [fbank.get_frame(i) for i in range(fbank.num_frames_ready)] 180 + if not frames: 181 + return np.zeros((0, 80), dtype=np.float32) 159 182 160 - return _voice_encoder 183 + return np.stack(frames, axis=0).astype(np.float32) 161 184 162 185 163 186 def _get_jsonl_path(audio_path: Path) -> Path: ··· 227 250 audio: np.ndarray, 228 251 statements: list[dict], 229 252 sample_rate: int, 230 - whisper_device: str = "cpu", 231 253 ) -> dict[str, np.ndarray] | None: 232 254 """Generate voice embeddings for each statement. 233 255 ··· 235 257 audio: Audio buffer (float32, mono) 236 258 statements: List of statements 237 259 sample_rate: Sample rate in Hz 238 - whisper_device: Device used by whisper (affects encoder device selection) 239 260 240 261 Returns: 241 262 Dict with embedding data or None on error: 242 263 - embeddings: (N, 256) float32 array 243 264 - statement_ids: (N,) int32 array of statement IDs 265 + - encoder: 0-d array naming the embedder 244 266 """ 245 267 try: 246 - voice_encoder = _get_voice_encoder(whisper_device) 247 - 268 + session = _get_embedder_session() 248 269 audio_duration = len(audio) / sample_rate 270 + input_name = session.get_inputs()[0].name 271 + output_name = session.get_outputs()[0].name 249 272 250 273 # Filter statements with valid timestamps and sufficient duration 251 274 # Defensive: handle None timestamps, clamp to audio bounds ··· 290 313 continue 291 314 292 315 try: 293 - emb = voice_encoder.embed_utterance(stmt_audio) 294 - embeddings.append(emb) 316 + feats = _compute_wespeaker_features(stmt_audio, sample_rate) 317 + if feats.shape[0] == 0: 318 + skipped += 1 319 + continue 320 + emb = session.run([output_name], {input_name: feats[None, :, :]})[0] 321 + embeddings.append(emb[0].astype(np.float32)) 295 322 statement_ids.append(stmt["id"]) 296 323 except Exception: 324 + logging.exception( 325 + "wespeaker embedding failed for statement %s", stmt["id"] 326 + ) 297 327 skipped += 1 298 328 continue 299 329 ··· 309 339 ) 310 340 311 341 return { 312 - "embeddings": np.array(embeddings, dtype=np.float32), 313 - "statement_ids": np.array(statement_ids, dtype=np.int32), 342 + "embeddings": np.stack(embeddings, axis=0).astype(np.float32), 343 + "statement_ids": np.asarray(statement_ids, dtype=np.int32), 344 + "encoder": np.array(EMBEDDER_NAME), 314 345 } 315 346 316 - except Exception as e: 317 - logging.error(f"Embedding failed: {e}", exc_info=True) 347 + except Exception: 348 + logging.exception("failed to load WeSpeaker embedder") 318 349 return None 319 350 320 351 ··· 577 608 578 609 # Generate embeddings before timestamp restoration 579 610 # Use reduced audio buffer if available for consistent timestamps 580 - embeddings_data = _embed_statements( 581 - stt_buffer, statements, SAMPLE_RATE, model_info.get("device", "cpu") 582 - ) 611 + embeddings_data = _embed_statements(stt_buffer, statements, SAMPLE_RATE) 583 612 584 613 # Restore original timestamps if audio was reduced (non-Gemini backends only) 585 614 # Gemini with chunks already has timestamps in original audio time

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pyproject.toml

··· 75 75 # Audio processing 76 76 "soundfile", 77 77 "faster-whisper>=1.0.0", 78 - "resemblyzer", 79 - "webrtcvad-wheels>=2.0.12", 78 + "onnxruntime>=1.19", 79 + "kaldi-native-fbank>=1.22", 80 80 81 81 # Media processing 82 82 "opencv-python-headless", ··· 109 109 apps = ["*/templates/*.html", "*/talent/*.md"] 110 110 think = ["*.md", "*.json", "templates/*.md", "policies/*.toml"] 111 111 talent = ["*.md", "*.py"] 112 - observe = ["*.md", "categories/*.md", "transcribe/*.md"] 112 + observe = ["*.md", "categories/*.md", "transcribe/*.md", "transcribe/assets/*.onnx"] 113 113 convey = [ 114 114 "templates/*.html", 115 115 "static/*", ··· 131 131 exclude = ["tests", ".venv", "scratch", "logs", "build", "dist"] 132 132 ignore_missing_imports = true 133 133 ignore_errors = true 134 - 135 - [tool.uv] 136 - # resemblyzer -> librosa -> numba: force modern version (old numba only supports Python <3.10) 137 - constraint-dependencies = ["numba>=0.60"] 138 - # resemblyzer -> webrtcvad: abandoned (2017), uses pkg_resources removed in setuptools 82. 139 - # Replace with maintained fork webrtcvad-wheels (same Python module, pkg_resources fix since 2.0.12). 140 - override-dependencies = ["webrtcvad>=99; python_version < '0'"] 141 134 142 135 [tool.pytest.ini_options] 143 136 addopts = "--import-mode=importlib"

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talent/journal/references/config.md

··· 188 188 - **CUDA GPU**: Uses `float16` for GPU-optimized inference 189 189 - **CPU (including Apple Silicon)**: Uses `int8` for ~2x faster inference and significantly faster model loading 190 190 191 - Voice embeddings (resemblyzer) also auto-detect the best device: MPS on Apple Silicon (~16x faster), CUDA when available, or CPU fallback. 191 + Voice embeddings (wespeaker-resnet34) use CoreML with CPU fallback on Darwin and CPU-only elsewhere. 192 192 193 193 CLI flags can override settings: `--backend` selects the backend, `--cpu` forces CPU mode with int8 (Whisper only), `--model MODEL` overrides the Whisper model. 194 194

+7 -1

tests/test_transcribe.py

··· 22 22 build_statement, 23 23 build_statements_from_acoustic, 24 24 ) 25 + from observe.transcribe.main import EMBEDDER_NAME 25 26 from observe.utils import load_audio 26 27 27 28 ··· 371 372 372 373 embeddings = np.random.randn(5, 256).astype(np.float32) 373 374 statement_ids = np.array([1, 2, 3, 4, 5], dtype=np.int32) 375 + encoder = np.array(EMBEDDER_NAME) 374 376 375 377 np.savez_compressed( 376 - npz_path, embeddings=embeddings, statement_ids=statement_ids 378 + npz_path, 379 + embeddings=embeddings, 380 + statement_ids=statement_ids, 381 + encoder=encoder, 377 382 ) 378 383 379 384 loaded = np.load(npz_path) 380 385 np.testing.assert_array_almost_equal(loaded["embeddings"], embeddings) 381 386 np.testing.assert_array_equal(loaded["statement_ids"], statement_ids) 387 + assert loaded["encoder"].item() == EMBEDDER_NAME 382 388 383 389 def test_statement_ids_are_unique(self): 384 390 """Statement IDs should be unique."""

+78

tests/test_transcribe_embedder.py

··· 1 + # SPDX-License-Identifier: AGPL-3.0-only 2 + # Copyright (c) 2026 sol pbc 3 + 4 + """Tests for the bundled WeSpeaker embedder.""" 5 + 6 + import platform 7 + 8 + import numpy as np 9 + import pytest 10 + 11 + from observe.transcribe.main import ( 12 + EMBEDDER_NAME, 13 + _embed_statements, 14 + _get_embedder_session, 15 + _select_onnx_providers, 16 + ) 17 + 18 + 19 + def test_session_loads() -> None: 20 + session = _get_embedder_session() 21 + inputs = session.get_inputs() 22 + outputs = session.get_outputs() 23 + 24 + assert inputs[0].name == "feats" 25 + assert inputs[0].shape == ["B", "T", 80] 26 + assert outputs[0].name == "embs" 27 + assert outputs[0].shape == ["B", 256] 28 + 29 + 30 + def test_embed_synthetic_shape_and_provenance() -> None: 31 + audio = np.zeros(3 * 16000, dtype=np.float32) 32 + result = _embed_statements( 33 + audio, 34 + [{"id": 1, "start": 0.0, "end": 3.0, "text": "x"}], 35 + 16000, 36 + ) 37 + 38 + assert result is not None 39 + assert result["embeddings"].shape == (1, 256) 40 + assert result["embeddings"].dtype == np.float32 41 + assert result["statement_ids"].tolist() == [1] 42 + assert result["encoder"].item() == EMBEDDER_NAME 43 + 44 + 45 + def test_embed_determinism() -> None: 46 + rng = np.random.default_rng(42) 47 + audio = (rng.normal(0.0, 0.01, 3 * 16000)).astype(np.float32) 48 + statements = [{"id": 1, "start": 0.0, "end": 3.0, "text": "x"}] 49 + 50 + first = _embed_statements(audio, statements, 16000) 51 + second = _embed_statements(audio, statements, 16000) 52 + 53 + assert first is not None 54 + assert second is not None 55 + 56 + first_embedding = first["embeddings"][0] 57 + second_embedding = second["embeddings"][0] 58 + cosine = float( 59 + np.dot(first_embedding, second_embedding) 60 + / (np.linalg.norm(first_embedding) * np.linalg.norm(second_embedding)) 61 + ) 62 + assert cosine >= 0.99 63 + 64 + 65 + @pytest.mark.parametrize( 66 + ("system_name", "expected"), 67 + [ 68 + ("Darwin", ["CoreMLExecutionProvider", "CPUExecutionProvider"]), 69 + ("Linux", ["CPUExecutionProvider"]), 70 + ], 71 + ) 72 + def test_provider_selection( 73 + monkeypatch: pytest.MonkeyPatch, 74 + system_name: str, 75 + expected: list[str], 76 + ) -> None: 77 + monkeypatch.setattr(platform, "system", lambda: system_name) 78 + assert _select_onnx_providers() == expected

+40 -1010

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