File size: 52,238 Bytes
f3cb094 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 | """Runtime tests that need no model files: fake graphs stand in for the bundle's sessions."""
from __future__ import annotations
import json
import os
from pathlib import Path
import tempfile
import unittest
from unittest import mock
import numpy as np
from scyllasband.contract import (BundleError, CONTEXT_INPUTS, DURATION_INPUTS, GRAPH_CONTRACT, GRAPH_CONTRACT_V4, PITCH_INPUTS,
VECTOR_INPUTS, VECTOR_INPUTS_V4, VOCODER_INPUTS, validate_bundle_layout)
from scyllasband.delivery import CONTROLS, DEFAULT_RANGES, delivery_tensors, resolve_delivery, with_defaults
from scyllasband.engine import Engine, OverlongError, Sentence, balanced_context
from scyllasband.events import frame_events, frames_to_durations, modifier_bits, sentence_types
from scyllasband.g2p import punctuated_segments
from scyllasband.planner import parse_group_lines, plan_records, records_from_text, split_for_retry, split_sentences
from scyllasband.streaming import StreamOptions, synthesize_plan_stream
VOCAB = ["<pad>", "<unk>", "<sil>", "<pause_comma>", "<pause_semicolon>", "<pause_colon>", "<pause_dash>", "<ellipsis>", "<end_stmt>",
"<end_question>", "<end_exclaim>", "a", "b", "h", "l", "o", "w", "ˈ", "ː"]
PHONE_TO_ID = {p: i for i, p in enumerate(VOCAB)}
# The v4 manifest controls of the singing release, as the fake bundle declares them (tests copy and adjust them).
V4_DELIVERY_MODES = dict(enabled=True, input="delivery_mode_id", ids=dict(spoken=0, sung=1, rap=2, ballad=3), default="spoken",
singing=dict(modes=["sung", "ballad"], sampler="heun", steps=8, decoder="bigvgan",
melody=dict(layout="note_phase_presence", midi_center=60, midi_scale=24),
pitch_predictor="bypassed", pitch_soft_limit="bypassed"))
V4_DECODERS = dict(vocos=dict(component="vocoder", samples_per_frame=512, output_samples="latent_frames * 512 - 256"),
bigvgan=dict(component="vocoder_bigvgan", samples_per_frame=512, output_samples="latent_frames * 512", required=False))
class PunctuationTest(unittest.TestCase):
def test_segments_carry_their_punctuation(self):
self.assertEqual(punctuated_segments("Wait — what was that? I heard it, too."),
[("Wait", ["<pause_dash>"]), ("what was that", ["<end_question>"]), ("I heard it", ["<pause_comma>"]),
("too", ["<end_stmt>"])])
def test_stacked_marks_stay_on_the_previous_phrase(self):
self.assertEqual(punctuated_segments("for Death, — He kindly stopped —"),
[("for Death", ["<pause_comma>", "<pause_dash>"]), ("He kindly stopped", ["<pause_dash>"])])
def test_a_sentence_end_wins_over_other_stacked_marks(self):
self.assertEqual(punctuated_segments("Really?! she asked."), [("Really", ["<end_question>"]), ("she asked", ["<end_stmt>"])])
def test_long_phrases_split_between_words_without_punctuation(self):
segments = punctuated_segments("one two three four five six.", max_chars=10)
self.assertEqual([s for s, _ in segments], ["one two", "three four", "five six"])
self.assertEqual([t for _, t in segments], [[], [], ["<end_stmt>"]])
class FakeG2PTest(unittest.TestCase):
def test_layout_word_starts_and_punctuation_silences(self):
engine = _fake_engine()
result = engine.phonemize("hello bob, ah.", language="en_us")
phones = result["phones"]
self.assertEqual(phones[0], "<sil>")
self.assertEqual(phones[-1], "<sil>")
self.assertIn("<pause_comma>", phones)
comma = phones.index("<pause_comma>")
self.assertEqual(phones[comma + 1], "<sil>")
self.assertEqual([phones[i - 1] for i in result["word_starts"]], ["<sil>", "<sil>"]) # each later word follows a <sil>
self.assertEqual(len(result["word_boundary_candidates"]), 1) # only between hello and bob
class DurationAndEventTest(unittest.TestCase):
def test_rounding_matches_training(self):
phones = ["<sil>", "ˈ", "a", "<pause_comma>", "<sil>", "b", "<sil>"]
self.assertEqual(frames_to_durations([0.4, 3, 0.2, 5, 2.5, 3.5, 0.0], phones), [0, 0, 1, 0, 2, 4, 0])
def test_pause_scale_moves_only_silences(self):
phones = ["<sil>", "a", "<sil>", "b", "<sil>"]
self.assertEqual(frames_to_durations([4, 4, 0.4, 4, 2], phones, pause_scale=1.3), [5, 4, 1, 4, 3])
self.assertEqual(frames_to_durations([4, 4, 0.4, 4, 2], phones, pause_scale=0.7), [3, 4, 0, 4, 1])
def test_events(self):
phones = ["<sil>", "ˈ", "a", "b", "<pause_comma>", "<sil>", "h", "ː", "<end_stmt>", "<sil>"]
durations = [2, 0, 3, 2, 0, 3, 2, 0, 0, 2]
bits = modifier_bits(PHONE_TO_ID)
events = frame_events(phones, durations, word_starts=[6], following=[], token_to_id=PHONE_TO_ID, bits=bits)
boundary = events["expanded_boundary_event_ids"]
self.assertTrue((boundary[7:10] == PHONE_TO_ID["<pause_comma>"]).all()) # the silence after the comma
self.assertTrue((boundary[10:12] == len(PHONE_TO_ID)).all()) # word start: next word's first phone
self.assertTrue((boundary[12:14] == PHONE_TO_ID["<end_stmt>"]).all()) # the final silence
modifier = events["expanded_modifier_event_ids"]
self.assertTrue((modifier[2:5] == bits["ˈ"]).all()) # stress -> next phone
self.assertTrue((modifier[10:12] == bits["ː"]).all()) # length -> preceding phone
np.testing.assert_allclose(events["expanded_phone_phase"][2:5], [1 / 6, 0.5, 5 / 6], rtol=1e-6)
types = events["expanded_sentence_type_ids"]
self.assertTrue((types[:-2] == 1).all()) # up to the statement end
self.assertTrue((types[-2:] == 0).all()) # the silence after it belongs to the next sentence (none here)
def test_zero_frame_silence_moves_the_event_to_the_next_phone(self):
phones = ["<sil>", "a", "<pause_comma>", "<sil>", "b", "<sil>"]
events = frame_events(phones, [1, 2, 0, 0, 2, 1], word_starts=[4], following=[], token_to_id=PHONE_TO_ID, bits={})
self.assertEqual(events["expanded_boundary_event_ids"].tolist(), [0, 0, 0, PHONE_TO_ID["<pause_comma>"], 0, 0])
def test_sentence_type_reads_the_following_context(self):
self.assertEqual(sentence_types(["a", "<pause_comma>", "b"], ["c", "<end_question>"]), [2, 2, 2])
self.assertEqual(sentence_types(["a"], []), [0])
class ContextTest(unittest.TestCase):
def test_balanced_context(self):
span, segments = balanced_context(list(range(10)), [99] * 4, list(range(20, 30)), 10)
self.assertEqual(segments, [0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
self.assertEqual(span[:3], [7, 8, 9])
self.assertEqual(span[-3:], [20, 21, 22])
def test_spare_room_goes_to_the_longer_side(self):
span, segments = balanced_context([1, 2], [9], list(range(20, 30)), 8)
self.assertEqual(segments, [0, 0, 1, 2, 2, 2, 2, 2])
class NormalizerTest(unittest.TestCase):
def test_decorative_asterisks_are_not_spoken(self):
from scyllasband.text_normalizer import normalize_spoken_text
self.assertEqual(normalize_spoken_text("* * * * * The Pool of Tears. It was *very* odd.*", language="en_gb"),
"The Pool of Tears. It was very odd.")
def test_letters_glued_to_digits_are_split(self):
from scyllasband.text_normalizer import normalize_spoken_text
self.assertEqual(normalize_spoken_text("Whisper Large V3 beat v3.5.", language="en_us"),
"Whisper Large vee three beat vee three point five.")
self.assertEqual(normalize_spoken_text("MP3 in 4K, H2O, Python3 and COVID19.", language="en_us"),
"em pee three in four kay, aitch two oh, Python three and COVID nineteen.")
self.assertEqual(normalize_spoken_text("La V3 y el MP3.", language="es"), "La uve tres y el eme pe tres.")
self.assertEqual(normalize_spoken_text("test_voice2.py", language="en_us"), "test_voice2.py")
def test_english_decades_clock_times_and_years(self):
from scyllasband.text_normalizer import normalize_spoken_text
self.assertEqual(normalize_spoken_text("The 1990s, the '80s and her 20s.", language="en_us"),
"The nineteen nineties, the eighties and her twenties.")
self.assertEqual(normalize_spoken_text("100s of 4s, two MP3s, Route 66's and 1.5s.", language="en_us"),
"hundreds of fours, two em pee threes, Route sixty six's and one point five seconds.")
self.assertEqual(normalize_spoken_text("68x faster on a 4x4 grid, 1.5x, 20ms, 24kHz, 1 GB and 60fps.", language="en_us"),
"sixty eight times faster on a four by four grid, one point five times, twenty milliseconds, "
"twenty four kilohertz, one gigabyte and sixty frames per second.")
self.assertEqual(normalize_spoken_text("At 10am, 5 PM or 7:05 AM.", language="en_us"),
"At ten ay em, five pee em or seven oh five ay em.")
self.assertEqual(normalize_spoken_text("On March 4, 1990 and 3/4/2005.", language="en_us"),
"On March fourth, nineteen ninety and March fourth, two thousand five.")
class LexiconTest(unittest.TestCase):
def test_entries_and_matching(self):
from scyllasband.lexicon import LETTERS, WORDS, Lexicon, initialism_phones, split_phones
self.assertEqual(initialism_phones("GPU", LETTERS["en_us"]), "dʒˌiːpˌiːjˈuː")
spelled = "".join(t for table in LETTERS.values() for t in table.values()) + "".join(p for w in WORDS.values() for p in w.values())
vocabulary = {ch: 1 for ch in spelled.replace("{s}", "") + "ˈˌzsˈɪlə"}
self.assertEqual(split_phones("ɡˈɪt", vocabulary), ("ɡ", "ˈ", "ɪ", "t"))
self.assertIsNone(split_phones("ɡˈɪt§", vocabulary))
lexicon = Lexicon(vocabulary, {"en_us": {"Scylla": "sˈɪlə"}})
self.assertEqual("".join(lexicon.lookup("GPUs", "en_us", initialisms=True)), "dʒˌiːpˌiːjˈuːz")
self.assertEqual("".join(lexicon.lookup("Git", "en_us", initialisms=True)), "ɡˈɪt")
self.assertEqual("".join(lexicon.lookup("scylla", "en_us", initialisms=True)), "sˈɪlə")
self.assertIsNone(lexicon.lookup("GPU", "en_us", initialisms=False)) # text written all in capitals
self.assertIsNone(lexicon.lookup("US", "en_us", initialisms=True))
self.assertIsNone(lexicon.lookup("GPU", "es", initialisms=True))
def test_native_lexicon_table_is_current(self):
import importlib.util
path = Path(__file__).resolve().parents[1] / "libscyllasband" / "tools" / "generate_lexicon.py"
spec = importlib.util.spec_from_file_location("generate_lexicon", path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
self.assertEqual(module.OUT.read_text(encoding="utf-8"), module.render())
class InitialismTest(unittest.TestCase):
def test_names_stay_in_their_sentence_and_times_can_end_one(self):
from scyllasband.text_normalizer import normalize_spoken_text
self.assertEqual(normalize_spoken_text("D.J. Stumpy met J.K. Rowling.", language="en_us"), "dee jay Stumpy met jay kay Rowling.")
self.assertEqual(normalize_spoken_text("We left at 5 p.m. Then it rained.", language="en_us"),
"We left at five pee em. Then it rained.")
self.assertEqual(normalize_spoken_text("She lives in the U.S.", language="en_us"), "She lives in the you ess.")
class PlannerTest(unittest.TestCase):
def test_sentence_split(self):
self.assertEqual(split_sentences('You. Are. Not. It. "Really?!" she asked... Fine'),
["You.", "Are.", "Not.", "It.", '"Really?!"', "she asked...", "Fine"])
def test_retry_split_prefers_clause_punctuation(self):
self.assertEqual(split_for_retry("one two three, four five six seven"), ["one two three,", "four five six seven"])
def test_retry_split_leaves_each_piece_a_minimum_share(self):
self.assertEqual(split_for_retry("Well, one two three four five six"), ["Well,", "one two three four five six"])
self.assertEqual(split_for_retry("Well, one two three four five six", min_share=0.25), ["Well, one two", "three four five six"])
def test_chains_follow_voice_language_and_delivery(self):
records = parse_group_lines("[scylla:en_us] Hi there. How are you?\n[ink:en_gb:intensity=1,pace=-0.5] Fine. [scylla] Good.",
default_voice=None, default_language=None)
plan = plan_records(records, resolve_language=lambda voice, language: language or "en_us", normalize=False)
self.assertEqual([c.chain_id for c in plan.chunks], ["chain_001", "chain_001", "chain_002", "chain_003"])
self.assertEqual(plan.chunks[2].delivery, dict(intensity=1.0, pace=-0.5, pitch=None))
self.assertIsNone(plan.chunks[0].delivery)
def test_delivery_fields_or_string_not_both(self):
plan = plan_records(records_from_text("Hi.", voice="scylla", language=None, pace=-1), resolve_language=lambda v, l: "en_us")
self.assertEqual(plan.chunks[0].delivery, dict(intensity=None, pace=-1.0, pitch=None))
with self.assertRaisesRegex(ValueError, "not both"):
plan_records(records_from_text("Hi.", voice="scylla", language=None, delivery="pace=1", pace=-1),
resolve_language=lambda v, l: "en_us")
with self.assertRaisesRegex(ValueError, "emotion comes from the text"):
plan_records([dict(text="Hi.", voice="scylla", emotion="joy")], resolve_language=lambda v, l: "en_us")
class DeliveryTest(unittest.TestCase):
def test_defaults(self):
self.assertEqual(resolve_delivery(None), dict.fromkeys(CONTROLS))
self.assertEqual(resolve_delivery(""), resolve_delivery("default"))
self.assertEqual(with_defaults(resolve_delivery("pace=-1"), dict(pace=0.5, pitch=1)), dict(intensity=0.0, pace=-1.0, pitch=1.0))
values, present = delivery_tensors(with_defaults({}, None))
self.assertEqual(present[0].tolist(), [True, True, False, False, False])
self.assertEqual(values[0].tolist(), [0, 0, 0, 0, 0])
def test_values_are_z_over_five_and_clamped_into_the_ranges(self):
resolved = resolve_delivery("intensity=5,pace=-3,pitch=2.5")
self.assertEqual(resolved, dict(intensity=2.0, pace=-2.0, pitch=2.0))
self.assertEqual(resolve_delivery({"pace": 1}, ranges=dict(pace=[-0.5, 0.5]))["pace"], 0.5)
values, _ = delivery_tensors(dict(intensity=1.0, pace=-2.0, pitch=0.0))
np.testing.assert_allclose(values[0, :2], [0.2, -0.4], rtol=1e-6)
for low, high in DEFAULT_RANGES.values():
self.assertTrue(low <= 0.0 <= high)
def test_v2_axes_and_unknown_keys_are_rejected(self):
for spec in ("energy=2", "tension=2,pace=1", {"whisper": False}, {"emotion": "joy"}):
with self.assertRaisesRegex(ValueError, "intensity, pace and pitch"):
resolve_delivery(spec)
with self.assertRaisesRegex(ValueError, "only intensity"):
resolve_delivery("tempo=1")
with self.assertRaisesRegex(ValueError, "must be a number"):
resolve_delivery("pace=fast")
class ContractTest(unittest.TestCase):
def test_valid_and_invalid_bundles(self):
with tempfile.TemporaryDirectory() as tmp:
bundle = _fake_bundle(Path(tmp))
summary = validate_bundle_layout(bundle)
self.assertEqual(summary["buckets"], [8, 16])
manifest = json.loads((bundle / "manifest.json").read_text())
manifest["components"]["vector_estimator"]["inputs"] = list(VECTOR_INPUTS[:-1])
(bundle / "manifest.json").write_text(json.dumps(manifest))
with self.assertRaises(BundleError):
validate_bundle_layout(bundle)
def test_v4_bundles_take_the_mode_input_and_optional_decoders(self):
with tempfile.TemporaryDirectory() as tmp:
bundle = _fake_bundle(Path(tmp), v4=True)
summary = validate_bundle_layout(bundle)
self.assertEqual((summary["graph_contract"], summary["singing"]), (GRAPH_CONTRACT_V4, True))
manifest = json.loads((bundle / "manifest.json").read_text())
# A v3 flow under the v4 contract (or the reverse) is rejected.
broken = json.loads(json.dumps(manifest))
broken["components"]["vector_estimator"]["inputs"] = list(VECTOR_INPUTS)
(bundle / "manifest.json").write_text(json.dumps(broken))
with self.assertRaisesRegex(BundleError, "vector_estimator"):
validate_bundle_layout(bundle)
broken = json.loads(json.dumps(manifest))
broken["controls"]["graph_input_contract"] = GRAPH_CONTRACT
(bundle / "manifest.json").write_text(json.dumps(broken))
with self.assertRaisesRegex(BundleError, "vector_estimator"):
validate_bundle_layout(bundle)
# The second decoder need not exist for every backend, but a bucket may not name a component that is absent.
tolerant = json.loads(json.dumps(manifest))
tolerant["components"]["vocoder_bigvgan_8"]["artifacts"] = {}
(bundle / "manifest.json").write_text(json.dumps(tolerant))
self.assertTrue(validate_bundle_layout(bundle)["singing"])
del tolerant["components"]["vocoder_bigvgan"]
(bundle / "manifest.json").write_text(json.dumps(tolerant))
with self.assertRaisesRegex(BundleError, "vocoder_bigvgan"):
validate_bundle_layout(bundle)
def test_v3_bundles_validate_without_the_mode_input(self):
with tempfile.TemporaryDirectory() as tmp:
summary = validate_bundle_layout(_fake_bundle(Path(tmp)))
self.assertEqual((summary["graph_contract"], summary["singing"]), (GRAPH_CONTRACT, False))
class LiteRTThreadsTest(unittest.TestCase):
def test_default_threads_follow_the_native_runtime(self):
from unittest import mock
from scyllasband import litert
with mock.patch.object(litert.os, "sched_getaffinity", return_value=set(range(16)), create=True):
self.assertEqual(litert.default_threads(), 4)
with mock.patch.object(litert.os, "sched_getaffinity", return_value={0, 1}, create=True):
self.assertEqual(litert.default_threads(), 2)
def test_interpreters_get_the_default_unless_threads_are_given(self):
from unittest import mock
from scyllasband import litert
made = []
class Interpreter:
def __init__(self, model_path, num_threads):
made.append(num_threads)
spec = {"artifacts": {"litert": {"path": "g2p.tflite"}}, "inputs": []}
with mock.patch.object(litert, "_interpreter_class", return_value=Interpreter), \
mock.patch.object(litert, "LiteRTSession"), mock.patch.object(litert, "default_threads", return_value=3):
litert.litert_session_factory(Path("."))("g2p", spec)
litert.litert_session_factory(Path("."), threads=8)("g2p", spec)
self.assertEqual(made, [3, 8])
class OnnxProvidersTest(unittest.TestCase):
def test_compute_units_pick_execution_providers(self):
from scyllasband.engine import onnx_providers
cpu, cuda = ["CPUExecutionProvider"], ["CUDAExecutionProvider", "CPUExecutionProvider"]
for value in (None, "", "auto", "cpu"):
self.assertEqual(onnx_providers(value)("vector_estimator_256"), cpu)
per_graph = onnx_providers("gpu,g2p=cpu")
self.assertEqual(per_graph("vector_estimator_256"), cuda)
self.assertEqual(per_graph("g2p"), cpu)
self.assertEqual(onnx_providers("cuda")("vocoder"), cuda)
self.assertEqual(onnx_providers({"vocoder": "gpu"})("vocoder"), cuda)
with self.assertRaises(ValueError):
onnx_providers("ane")
def test_a_cuda_session_that_falls_back_to_the_cpu_is_an_error(self):
import sys
import types
from unittest import mock
from scyllasband.engine import OnnxSession, _preload_cuda
class Session:
def __init__(self, path, options, providers):
pass
def get_providers(self):
return ["CPUExecutionProvider"]
def get_inputs(self):
return []
fake = types.SimpleNamespace(SessionOptions=lambda: types.SimpleNamespace(), InferenceSession=Session,
preload_dlls=lambda: None)
_preload_cuda.cache_clear()
with mock.patch.dict(sys.modules, {"onnxruntime": fake}):
OnnxSession(Path("g2p.onnx")) # CPU only: nothing to check
with self.assertRaisesRegex(RuntimeError, r"\.\[cuda\]"):
OnnxSession(Path("g2p.onnx"), providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
_preload_cuda.cache_clear()
def test_gpu_requests_use_the_webgpu_bundle(self):
import argparse
from scyllasband.cli import _default_bundle, wants_gpu
self.assertTrue(wants_gpu("gpu") and wants_gpu("cuda") and wants_gpu("cpu,vector_estimator=gpu"))
self.assertFalse(wants_gpu(None) or wants_gpu("cpu") or wants_gpu("gpu_like=cpu"))
with tempfile.TemporaryDirectory() as tmp:
for flavor in ("onnx", "webgpu"):
Path(tmp, flavor).mkdir()
Path(tmp, flavor, "manifest.json").write_text("{}")
args = lambda units: argparse.Namespace(models_dir=tmp, backend="onnx", compute_units=units)
self.assertEqual(_default_bundle(args("gpu")).name, "webgpu")
self.assertEqual(_default_bundle(args(None)).name, "onnx")
class EngineTest(unittest.TestCase):
def test_bucket_padding_prefix_and_decode(self):
engine = _fake_engine()
sentence = Sentence(phones=["<sil>", "a", "b", "<end_stmt>", "<sil>"], word_starts=[], before_ids=[11, 12], after_ids=[13])
delivery = engine.delivery_inputs("scylla", None)[:2]
result = engine.synthesize_sentence(sentence, voice="scylla", language="en_us", delivery=delivery, steps=2,
rng=np.random.default_rng(0), prefix=np.ones((24, 3), np.float32))
self.assertEqual(result.latents.shape, (24, sum(result.durations)))
self.assertEqual(result.bucket, 8)
self.assertEqual(result.metadata["prefix_frames"], 3)
calls = engine._fake.calls
self.assertTrue(all(call["noise"].shape == (1, 24, 8) for call in calls if call["name"] == "vector_estimator_8"))
audio = engine.decode(result.latents, voice="scylla", language="en_us", left=np.zeros((24, 20), np.float32))
self.assertEqual(audio.shape, (result.latents.shape[1] * 512,))
def test_decode_gives_left_context_way_to_long_sentences(self):
engine = _fake_engine()
left = np.zeros((24, 20), np.float32)
for frames in (15, 16): # 16 fills the largest bucket, so its last 256 samples are padding
audio = engine.decode(np.zeros((24, frames), np.float32), voice="scylla", language="en_us", left=left)
self.assertEqual(audio.shape, (frames * 512,))
self.assertEqual(engine._fake.calls[-1]["latents"].shape, (1, 24, 16))
self.assertEqual(int(engine._fake.calls[-1]["latent_mask"].sum()), frames)
def test_fused_flow_replaces_the_step_loop(self):
engine = _fake_engine(apple=True)
sentence = Sentence(phones=["<sil>", "a", "b", "<end_stmt>", "<sil>"], word_starts=[])
delivery = engine.delivery_inputs("scylla", None)[:2]
fused = engine.synthesize_sentence(sentence, voice="scylla", language="en_us", delivery=delivery, steps=8, sampler="heun",
rng=np.random.default_rng(0))
names = [call["name"] for call in engine._fake.calls]
self.assertEqual(names.count("vector_flow_8"), 1)
self.assertFalse(any(name.startswith("vector_estimator") for name in names))
self.assertNotIn("time", engine._fake.calls[names.index("vector_flow_8")])
self.assertTrue(fused.metadata["fused_flow"])
engine._fake.calls.clear()
stepped = engine.synthesize_sentence(sentence, voice="scylla", language="en_us", delivery=delivery, steps=2, sampler="heun",
rng=np.random.default_rng(0))
names = [call["name"] for call in engine._fake.calls]
self.assertEqual(names.count("vector_estimator_8"), 4)
self.assertFalse(stepped.metadata["fused_flow"])
def test_g2p_runs_the_narrowest_bucket_that_fits(self):
plain, bucketed = _fake_engine(), _fake_engine(apple=True)
self.assertEqual(bucketed.phonemize("hello", language="en_us")["phones"], plain.phonemize("hello", language="en_us")["phones"])
call = next(c for c in bucketed._fake.calls if c["name"].startswith("g2p"))
self.assertEqual((call["name"], call["text"].shape), ("g2p_16", (1, 16)))
bucketed.phonemize("hello hello hello hello", language="en_us")
self.assertEqual(bucketed._fake.calls[-1]["name"], "g2p_32")
def test_overlong(self):
engine = _fake_engine(frames_per_phone=9.0)
sentence = Sentence(phones=["<sil>", "a", "b", "a", "<sil>"], word_starts=[])
with self.assertRaises(OverlongError):
engine.synthesize_sentence(sentence, voice="scylla", language="en_us", delivery=engine.delivery_inputs("scylla", None)[:2],
steps=1)
def test_short_targets_leave_intensity_and_pace_unset(self):
engine = _fake_engine()
delivery = engine.delivery_inputs("scylla", dict(intensity=1.0, pace=-1.0))[:2]
short = Sentence(phones=["<sil>", "ˈ", "a", "b", "a", "<end_stmt>", "<sil>"], word_starts=[]) # 3 spoken phones
long = Sentence(phones=["<sil>", "a", "b", "a", "b", "<end_stmt>", "<sil>"], word_starts=[]) # 4
for sentence, expected in ((short, [False] * 5), (long, [True, True, False, False, False])):
engine._fake.calls.clear()
engine.synthesize_sentence(sentence, voice="scylla", language="en_us", delivery=delivery, steps=1, rng=np.random.default_rng(0))
for call in engine._fake.calls:
if "delivery_present" in call:
self.assertEqual(call["delivery_present"][0].tolist(), expected, call["name"])
def test_pace_moves_pauses_even_when_unset(self):
engine = _fake_engine(frames_per_phone=4.0)
sentence = Sentence(phones=["<sil>", "a", "b", "<end_stmt>", "<sil>"], word_starts=[])
normal = engine.durations(sentence, voice=0, language=0, delivery=engine.delivery_inputs("scylla", None)[:2])
slow = engine.durations(sentence, voice=0, language=0, delivery=engine.delivery_inputs("scylla", dict(pace=-2.0))[:2])
self.assertEqual(normal, [4, 4, 4, 0, 4])
self.assertEqual(slow, [5, 4, 4, 0, 5]) # 4 x exp(0.26), the words left as the graph gives them
def test_pitch_contour_feeds_the_flow_melody(self):
engine = _fake_engine()
sentence = Sentence(phones=["<sil>", "a", "b", "a", "b", "<end_stmt>", "<sil>"], word_starts=[])
result = engine.synthesize_sentence(sentence, voice="scylla", language="en_us", delivery=(*engine.delivery_inputs("scylla", None)[:2], 1.5),
steps=1, rng=np.random.default_rng(0), pitch_noise=np.full((2, 6), 0.25, np.float32))
calls = engine._fake.calls
pitch = [c for c in calls if c["name"] == "pitch_estimator_8"]
self.assertEqual(len(pitch), engine.pitch_steps)
self.assertEqual(pitch[0]["noise"].shape, (1, 2, 8))
self.assertEqual(pitch[0]["span_context_hidden"].shape, (1, 384))
flow = next(c for c in calls if c["name"] == "vector_estimator_8")
melody = flow["expanded_melody"]
self.assertEqual(melody.shape, (1, 8, 3))
self.assertTrue((melody[0, 6:] == 0).all()) # bucket padding
contour = np.full((2, 6), 0.25 * (1 - 1 / engine.pitch_steps) ** engine.pitch_steps, np.float32)
np.testing.assert_allclose(melody[0, :6], engine.melody(contour, "scylla", 1.5), rtol=1e-5)
self.assertEqual(result.metadata["pitch_shift"], 1.5)
def test_melody_soft_limit_and_voicing(self):
engine = _fake_engine()
low, high = engine.pitch["edges"]["scylla"]
contour = np.array([[(high + 5 - 10) / 6, (low - 5 - 10) / 6, 0.0], [1.0, 1.0, -1.0]], np.float32)
melody = engine.melody(contour, "scylla")
st = melody[:2, 0] * 24 + 16.65
self.assertTrue(high - 2 < st[0] < high and low < st[1] < low + 2)
self.assertEqual(melody[:, 2].tolist(), [1.0, 1.0, 0.0])
self.assertEqual(float(melody[2, 0]), 0.0) # unvoiced: no pitch
def _spoken(self, engine, **kwargs):
sentence = Sentence(phones=["<sil>", "a", "b", "a", "b", "<end_stmt>", "<sil>"], word_starts=[3], before_ids=[11], after_ids=[12])
delivery = (*engine.delivery_inputs("scylla", dict(intensity=0.5))[:2], 1.0)
return engine.synthesize_sentence(sentence, voice="scylla", language="en_us", delivery=delivery, steps=2, sampler="heun",
rng=np.random.default_rng(7), prefix=np.ones((24, 2), np.float32), **kwargs)
def test_v3_speech_feeds_exactly_the_v3_inputs_and_v4_adds_mode_zero(self):
v3, v4 = _fake_engine(), _fake_engine(v4=True)
spoken_v3, spoken_v4 = self._spoken(v3), self._spoken(v4)
flows_v3 = [c for c in v3._fake.calls if c["name"].startswith("vector_estimator")]
flows_v4 = [c for c in v4._fake.calls if c["name"].startswith("vector_estimator")]
self.assertEqual(len(flows_v3), 4)
for call in flows_v3:
self.assertEqual(set(call) - {"name"}, set(VECTOR_INPUTS))
self.assertFalse(any("delivery_mode_id" in call for call in v3._fake.calls))
self.assertNotIn("delivery_mode", spoken_v3.metadata)
# Spoken on v4: the same graphs are called with the same values, plus delivery_mode_id = 0 on the flow.
self.assertEqual([c["name"] for c in v3._fake.calls], [c["name"] for c in v4._fake.calls])
for call in flows_v4:
self.assertEqual(set(call) - {"name"}, set(VECTOR_INPUTS_V4))
self.assertEqual(call["delivery_mode_id"].tolist(), [0])
self.assertEqual(call["delivery_mode_id"].dtype, np.int64)
for a, b in zip(v3._fake.calls, v4._fake.calls):
for key in a:
if key != "name":
np.testing.assert_array_equal(a[key], b[key], key)
np.testing.assert_array_equal(spoken_v3.latents, spoken_v4.latents)
self.assertEqual(spoken_v4.metadata["delivery_mode"], 0)
self.assertEqual(spoken_v4.metadata["melody_source"], "pitch_predictor")
def test_a_score_melody_replaces_the_pitch_predictor(self):
engine = _fake_engine(v4=True)
melody = np.zeros((6, 3), np.float32)
melody[1:5] = [[2 / 24, 0.5, 1.0]] * 4
result = self._spoken(engine, melody=melody, delivery_mode=3)
names = [c["name"] for c in engine._fake.calls]
self.assertFalse(any(n.startswith("pitch_") for n in names))
flow = next(c for c in engine._fake.calls if c["name"].startswith("vector_estimator"))
np.testing.assert_array_equal(flow["expanded_melody"][0, :6], melody)
self.assertTrue((flow["expanded_melody"][0, 6:] == 0).all())
self.assertEqual(flow["delivery_mode_id"].tolist(), [3])
self.assertEqual((result.metadata["delivery_mode"], result.metadata["melody_source"]), (3, "score"))
with self.assertRaisesRegex(ValueError, r"\[6, 3\]"):
self._spoken(engine, melody=np.zeros((5, 3), np.float32))
with self.assertRaisesRegex(ValueError, "v4"):
self._spoken(_fake_engine(), delivery_mode=1)
def test_guidance_contrasts_the_melody_and_mode_against_off(self):
engine = _fake_engine(v4=True)
melody = np.full((6, 3), 0.5, np.float32)
self._spoken(engine, melody=melody, delivery_mode=1, guidance=2.0)
flows = [c for c in engine._fake.calls if c["name"].startswith("vector_estimator")]
self.assertEqual(len(flows), 8) # two evaluations per Heun stage
self.assertEqual([c["delivery_mode_id"].tolist()[0] for c in flows], [1, 0] * 4)
self.assertTrue((flows[1]["expanded_melody"] == 0).all() and (flows[0]["expanded_melody"][0, :6] == 0.5).all())
def test_bigvgan_decoder_needs_no_spare_frame(self):
engine = _fake_engine(v4=True)
for frames in (9, 16):
audio = engine.decode(np.zeros((24, frames), np.float32), voice="scylla", language="en_us", decoder="bigvgan")
self.assertEqual(audio.shape, (frames * 512,))
self.assertEqual(engine._fake.calls[-1]["name"], "vocoder_bigvgan")
self.assertEqual(int(engine._fake.calls[-1]["latent_mask"].sum()), frames)
engine.decode(np.zeros((24, 7), np.float32), voice="scylla", language="en_us", decoder="bigvgan")
self.assertEqual(engine._fake.calls[-1]["name"], "vocoder_bigvgan_8")
engine.decode(np.zeros((24, 7), np.float32), voice="scylla", language="en_us")
self.assertEqual(engine._fake.calls[-1]["name"], "vocoder_8") # vocos keeps its spare frame: 7 + 1 fits 8
engine.decode(np.zeros((24, 8), np.float32), voice="scylla", language="en_us")
self.assertEqual(engine._fake.calls[-1]["name"], "vocoder")
with self.assertRaisesRegex(ValueError, "Unknown decoder"):
_fake_engine().decode(np.zeros((24, 4), np.float32), voice="scylla", language="en_us", decoder="bigvgan")
class DecoderWindowTest(unittest.TestCase):
"""Windowed decoding (controls.decoders.<name>.window), as libscyllasband does it: with a BigVGAN stand-in whose
samples are their frame's first latent channel, windows stitched from the frames each keeps equal one whole decode."""
@staticmethod
def _engine(window=None, *, single_bucket=False):
tmp = tempfile.TemporaryDirectory()
bundle = _fake_bundle(Path(tmp.name), v4=True)
manifest = json.loads((bundle / "manifest.json").read_text(encoding="utf-8"))
if window is not None:
manifest["controls"]["decoders"]["bigvgan"]["window"] = window
if single_bucket: # BigVGAN only at the 8-frame bucket
for entry in manifest["controls"]["target_buckets"]["buckets"]:
if entry["latent_frames"] != 8:
entry.pop("vocoder_bigvgan")
del manifest["components"]["vocoder_bigvgan"]
(bundle / "manifest.json").write_text(json.dumps(manifest), encoding="utf-8")
graphs = _FakeGraphs(1.0)
graphs.bigvgan_by_frame = True
engine = Engine(bundle, session_factory=graphs.factory)
engine._fake, engine._tmp = graphs, tmp
return engine
@staticmethod
def _latents(frames, base):
latents = np.full((24, frames), 0.5, np.float32)
latents[0] = base + np.arange(frames, dtype=np.float32) / 100
return latents
def test_windows_stitch_to_the_whole_decode(self):
whole, windowed = self._engine(), self._engine(dict(frames=8, context=2))
self.assertEqual(windowed.decoder_windows, {"bigvgan": (8, 2)})
latents = self._latents(13, 0.1)
want = whole.decode(latents, voice="scylla", language="en_us", decoder="bigvgan")
got = windowed.decode(latents, voice="scylla", language="en_us", decoder="bigvgan")
np.testing.assert_array_equal(got, want)
self.assertEqual([c["name"] for c in windowed._fake.calls], ["vocoder_bigvgan_8"] * 3) # keeps [0, 6), [6, 10), [10, 13)
left = self._latents(5, 0.7)
np.testing.assert_array_equal(windowed.decode(latents, voice="scylla", language="en_us", left=left, decoder="bigvgan"),
whole.decode(latents, voice="scylla", language="en_us", left=left, decoder="bigvgan"))
short = self._latents(5, 0.3)
windowed._fake.calls.clear()
np.testing.assert_array_equal(windowed.decode(short, voice="scylla", language="en_us", decoder="bigvgan"),
whole.decode(short, voice="scylla", language="en_us", decoder="bigvgan"))
self.assertEqual([c["name"] for c in windowed._fake.calls], ["vocoder_bigvgan_8"])
# Vocos is unchanged by a BigVGAN window.
np.testing.assert_array_equal(windowed.decode(latents, voice="scylla", language="en_us"), whole.decode(latents, voice="scylla", language="en_us"))
def test_a_single_bucket_decodes_any_length(self):
engine = self._engine(dict(frames=8, context=2), single_bucket=True)
latents = self._latents(13, 0.1)
np.testing.assert_array_equal(engine.decode(latents, voice="scylla", language="en_us", decoder="bigvgan"),
self._engine().decode(latents, voice="scylla", language="en_us", decoder="bigvgan"))
with self.assertRaises(OverlongError): # without its window the one bucket refuses longer decodes
self._engine(single_bucket=True).decode(latents, voice="scylla", language="en_us", decoder="bigvgan")
def test_bad_windows_and_the_environment(self):
with self.assertRaisesRegex(ValueError, "leave frames to keep"):
self._engine(dict(frames=8, context=4))
with mock.patch.dict(os.environ, {"SCYLLASBAND_DECODER_WINDOW": "bigvgan=8:3"}):
self.assertEqual(self._engine().decoder_windows, {"bigvgan": (8, 3)})
with mock.patch.dict(os.environ, {"SCYLLASBAND_DECODER_WINDOW": "bigvgan=0"}):
self.assertEqual(self._engine(dict(frames=8, context=2)).decoder_windows, {})
with mock.patch.dict(os.environ, {"SCYLLASBAND_DECODER_WINDOW": "vocos=8"}), self.assertRaisesRegex(ValueError, "whole frames"):
self._engine()
class StreamingTargetTest(unittest.TestCase):
"""Fake durations: one frame per phone and silence, none for punctuation ("ab." 4 frames, "ab ab." 7); trained
target range 6 to 12 frames."""
def targets(self, records, frames_per_phone: float = 1.0):
engine = _fake_engine(frames_per_phone, chunking=dict(min_target_frames=6, max_target_frames=12),
span_conditioning=dict(context_max_phones=64, context_phones_each_side=8))
plan = plan_records(records, resolve_language=lambda voice, language: "en_us")
events = [e for e in synthesize_plan_stream(engine, plan, StreamOptions(steps=1, seed=0)) if e.type == "audio_chunk"]
return [(e.chunk.chunk_id, e.chunk.text, e.metadata["latent_frames"]) for e in events]
def test_short_sentences_join_the_following_ones_until_long_enough(self):
self.assertEqual(self.targets(records_from_text("ab. ab. ab ab ab. ab ab.", voice="scylla", language=None)),
[("chunk_0001+chunk_0002", "ab. ab.", 7), ("chunk_0003", "ab ab ab.", 10), ("chunk_0004", "ab ab.", 7)])
def test_a_short_last_sentence_joins_the_one_before(self):
self.assertEqual(self.targets(records_from_text("ab ab. ab.", voice="scylla", language=None)),
[("chunk_0001+chunk_0002", "ab ab. ab.", 10)])
def test_a_short_first_sentence_takes_the_next_word_rather_than_speak_alone(self):
# "ab." (4 frames) cannot take all of the next sentence (13); it takes its first word, and the rest is one target.
self.assertEqual(self.targets(records_from_text("ab. ab ab ab ab.", voice="scylla", language=None)),
[("chunk_0001+chunk_0002.1", "ab. ab", 7), ("chunk_0002.2", "ab ab ab.", 10)])
def test_later_targets_fill_toward_the_trained_maximum(self):
self.assertEqual(self.targets(records_from_text("ab. ab. ab. ab. ab. ab. ab. ab.", voice="scylla", language=None)),
[("chunk_0001+chunk_0002", "ab. ab.", 7), ("chunk_0003+chunk_0005", "ab. ab. ab.", 10),
("chunk_0006+chunk_0008", "ab. ab. ab.", 10)])
def test_text_without_punctuation_splits_between_words(self):
self.assertEqual(self.targets(records_from_text("ab ab ab ab ab ab ab ab", voice="scylla", language=None)),
[("chunk_0001.1", "ab ab", 7), ("chunk_0001.2", "ab ab ab", 10), ("chunk_0001.3", "ab ab ab", 10)])
def test_slow_delivery_never_exceeds_the_trained_maximum(self):
frames = [f for _, _, f in self.targets(records_from_text("ab. ab. ab. ab. ab. ab.", voice="scylla", language=None),
frames_per_phone=2.0)]
self.assertEqual(frames, [8] * 6)
def test_short_paragraphs_and_records_join_and_chains_never_do(self):
paragraphs = records_from_text("ab.\n\nab.", voice="scylla", language=None)
records = [dict(text="ab.", voice="scylla"), dict(text="ab.", voice="scylla")]
chains = [dict(text="ab.", voice="scylla"), dict(text="ab.", voice="scylla", delivery="intensity=1")]
self.assertEqual([text for _, text, _ in self.targets(paragraphs)], ["ab. ab."])
self.assertEqual([text for _, text, _ in self.targets(records)], ["ab. ab."])
self.assertEqual([text for _, text, _ in self.targets(chains)], ["ab.", "ab."])
def test_a_long_enough_paragraph_ends_its_target(self):
self.assertEqual([text for _, text, _ in self.targets(records_from_text("ab ab. ab ab.\n\nab ab.", voice="scylla",
language=None))],
["ab ab.", "ab ab.", "ab ab."])
# --- fakes --------------------------------------------------------------------------------------------------------------
class _FakeGraphs:
"""Graph stand-ins with the bundle's input names. The vocoder returns frames * 512 - 256 samples, like the real one;
the BigVGAN decoder of a v4 bundle returns frames * 512."""
def __init__(self, frames_per_phone: float):
self.frames_per_phone, self.calls = frames_per_phone, []
self.bigvgan_by_frame = False # BigVGAN samples = their frame's first latent channel (0 where masked)
def factory(self, name, spec):
graphs = self
class Session:
def run(self, feeds):
graphs.calls.append(dict(feeds, name=name))
if name.startswith("g2p"):
return _fake_g2p_logits(feeds["text"])
if name.startswith("vector_flow"):
return feeds["noise"] * 0.5
if name == "duration_predictor":
return np.full((1, feeds["span_phone_ids"].shape[1]), graphs.frames_per_phone, np.float32)
if name == "vector_context_encoder":
return np.zeros((1, 512), np.float32)
if name == "pitch_context_encoder":
return np.zeros((1, 384), np.float32)
if name.startswith("pitch_estimator"):
return -feeds["noise"]
if name.startswith("vector_estimator"):
return -feeds["noise"]
frames = feeds["latents"].shape[-1]
if name.startswith("vocoder_bigvgan"):
if graphs.bigvgan_by_frame:
values = np.where(feeds["latent_mask"][0], feeds["latents"][0, 0], 0.0).astype(np.float32)
return np.repeat(values, 512)[None]
return np.ones((1, frames * 512), np.float32) * 0.2
return np.ones((1, frames * 512 - 256), np.float32) * 0.1
return Session()
# G2P fake: characters map to phones of the same letter, spaces to the word-boundary symbol.
_PHONEME_SYMBOLS = {0: "<pad>", 1: "<end>", 2: " ", 3: "a", 4: "b", 5: "h", 6: "l", 7: "o", 8: "w"}
_TEXT_SYMBOLS = {"<pad>": 0, "<end>": 1, "<en_us>": 2, " ": 3, "a": 4, "b": 5, "h": 6, "l": 7, "o": 8, "w": 9, "e": 10}
def _fake_g2p_logits(encoded: np.ndarray) -> np.ndarray:
inverse = {v: k for k, v in _TEXT_SYMBOLS.items()}
phone_of = {s: i for i, s in _PHONEME_SYMBOLS.items()}
frames = []
for token in encoded[0].tolist():
char = inverse.get(int(token), "<pad>")
if char in ("<pad>", "<en_us>"):
continue
if char == "<end>":
frames.append(1)
break
frames.append(phone_of.get(char, phone_of.get("a")) if char != "e" else phone_of["o"])
frames.append(0) # CTC blank between characters so repeats survive
logits = np.full((1, len(frames), len(_PHONEME_SYMBOLS)), -10.0, np.float32)
for index, symbol in enumerate(frames):
logits[0, index, symbol] = 10.0
return logits
def _fake_bundle(root: Path, *, apple: bool = False, controls: dict | None = None, v4: bool = False) -> Path:
"""``apple``: also the optional fused-flow graphs and narrower G2P inputs of the Core ML and Core AI bundles.
``controls``: manifest controls to replace. ``v4``: the singing contract (the flow takes delivery_mode_id, delivery
modes and decoders are declared, every bucket has a BigVGAN decoder)."""
bundle = root / "bundle"
(bundle / "assets/g2p").mkdir(parents=True)
(bundle / "onnx").mkdir()
flow_inputs = list(VECTOR_INPUTS_V4 if v4 else VECTOR_INPUTS)
write = lambda rel, value: (bundle / rel).write_text(json.dumps(value)) # noqa: E731
write("assets/phone_vocab.json", dict(token_to_id=PHONE_TO_ID))
write("assets/voices.json", [dict(id="scylla", index=0)])
write("assets/languages.json", [dict(id="en_us", index=0)])
write("assets/g2p/export_config.json", dict(input_granularity="phrase", chunk_max_chars=100))
write("assets/g2p/tokenizer.json", dict(text_symbols=_TEXT_SYMBOLS, phoneme_symbols={str(k): v for k, v in _PHONEME_SYMBOLS.items()},
char_repeats=1, lowercase=True, text_pad_index=0, phoneme_pad_index=0, phoneme_end_index=1))
write("assets/g2p/language_map.json", {"en_us": "en_us"})
components = {}
for name, inputs in (("g2p", ["text"]), ("duration_predictor", list(DURATION_INPUTS)), ("vector_context_encoder", list(CONTEXT_INPUTS)),
("pitch_context_encoder", list(CONTEXT_INPUTS)), ("vector_estimator_8", flow_inputs),
("pitch_estimator_8", list(PITCH_INPUTS)), ("vocoder_8", list(VOCODER_INPUTS)),
("vector_estimator", flow_inputs), ("pitch_estimator", list(PITCH_INPUTS)), ("vocoder", list(VOCODER_INPUTS)),
*((("vocoder_bigvgan_8", list(VOCODER_INPUTS)), ("vocoder_bigvgan", list(VOCODER_INPUTS))) if v4 else ())):
(bundle / f"onnx/{name}.onnx").write_bytes(b"")
components[name] = dict(artifacts=dict(onnx=dict(path=f"onnx/{name}.onnx", format="onnx")), inputs=inputs, outputs=["out"])
manifest = dict(contract_version="1.0.0", audio=dict(sample_rate=24000, latent_dim=24, latent_hop_length=512, hop_length=256),
assets=dict(phone_vocab="assets/phone_vocab.json", voice_index="assets/voices.json", language_index="assets/languages.json",
g2p_config="assets/g2p/export_config.json", g2p_tokenizer="assets/g2p/tokenizer.json",
g2p_language_map="assets/g2p/language_map.json"),
components=components,
voices=[dict(id="scylla", languages=["en_us"], default_language="en_us")],
controls=dict(graph_input_contract=GRAPH_CONTRACT, word_boundaries=dict(enabled=True, g2p_output_symbol=" "),
fixed_shapes=dict(g2p_text_tokens=64), span_conditioning=dict(context_max_phones=16, context_phones_each_side=4),
prefix_conditioning=dict(max_frames=6), decoding=dict(context_frames=4),
delivery=dict(z_scale=5.0, z_limit=2.5, min_spoken_phones=4, ranges=dict(DEFAULT_RANGES),
defaults=dict(scylla=dict(intensity=0.0, pace=0.0, pitch=0.0))),
pace_pauses=dict(phone="<sil>", per_sd=-0.13),
pitch=dict(channels=2, context_encoder="pitch_context_encoder", sampler=dict(default="euler"),
steps=dict(default=2), center=10.0, scale=6.0, melody_center=16.65, melody_scale=24.0,
voiced_threshold=0.5, knee=2.0, edges=dict(scylla=[5.0, 25.0])),
target_buckets=dict(buckets=[dict(latent_frames=8, vector_estimator="vector_estimator_8", vocoder="vocoder_8",
pitch_estimator="pitch_estimator_8"),
dict(latent_frames=16, vector_estimator="vector_estimator", vocoder="vocoder",
pitch_estimator="pitch_estimator")])))
if apple:
fused_inputs = [n for n in flow_inputs if n != "time"]
for name, inputs in (("vector_flow_8", fused_inputs), ("vector_flow", fused_inputs), ("g2p_16", ["text"]), ("g2p_32", ["text"])):
(bundle / f"onnx/{name}.onnx").write_bytes(b"")
components[name] = dict(artifacts=dict(onnx=dict(path=f"onnx/{name}.onnx", format="onnx")), inputs=inputs, outputs=["out"])
manifest["controls"]["fused_flow"] = dict(sampler="heun", steps=8)
manifest["controls"]["g2p_buckets"] = [dict(text_tokens=16, component="g2p_16"), dict(text_tokens=32, component="g2p_32")]
for entry, flow in zip(manifest["controls"]["target_buckets"]["buckets"], ("vector_flow_8", "vector_flow")):
entry["vector_flow"] = flow
if v4:
manifest["controls"]["graph_input_contract"] = GRAPH_CONTRACT_V4
manifest["controls"]["delivery_modes"] = json.loads(json.dumps(V4_DELIVERY_MODES))
manifest["controls"]["decoders"] = json.loads(json.dumps(V4_DECODERS))
for entry, decoder in zip(manifest["controls"]["target_buckets"]["buckets"], ("vocoder_bigvgan_8", "vocoder_bigvgan")):
entry["vocoder_bigvgan"] = decoder
manifest["controls"].update(controls or {})
write("manifest.json", manifest)
return bundle
def _fake_engine(frames_per_phone: float = 1.0, *, apple: bool = False, v4: bool = False, **controls) -> Engine:
tmp = tempfile.TemporaryDirectory()
bundle = _fake_bundle(Path(tmp.name), apple=apple, controls=controls, v4=v4)
graphs = _FakeGraphs(frames_per_phone)
engine = Engine(bundle, session_factory=graphs.factory)
engine._fake, engine._tmp = graphs, tmp
return engine
if __name__ == "__main__":
unittest.main()
|