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311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 311fd09 127a1e8 | 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 | """Frozen dual codec: native SSL teacher -> semantic Q1, acoustic residual RVQ.
Each independent lane retains both external-rate teacher audio and full-file
resampled codec audio. Packing external hop samples into the second channel is
lossless and only aligns the two sample grids for the existing batch scheduler.
The teacher sees 20-second lanes, including the same 10-second real left context
as the acoustic encoder. It retains native bidirectional attention within a lane.
"""
import json
import time
from types import SimpleNamespace
import numpy as np
import torch
from torch.nn import functional as F
from zonos3.codec.data_processing.independent_batcher import IndependentBatcher
from zonos3.codec.data_processing.torch_utils import compile_fixed
from zonos3.codec.hybrid10_dual_encoding.inference import extract_ssl
from zonos3.codec.hybrid10_dual_encoding.model import MossCodec
from zonos3.codec.resample_exact import resample_exact
from zonos3.codec.s1dac.losses import SemanticTeacher
class MossAutoencoder:
def __init__(
self,
args,
*,
generator_path: str,
teacher_dir: str,
step: int,
codebook_sizes: list[int],
name: str,
checkpoint_sha256: str,
device: str,
):
torch.set_num_threads(1)
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
self.device = torch.device(device)
if self.device.type != "cuda":
raise RuntimeError("Production encoding requires an allocated GPU")
torch.cuda.set_device(self.device)
self.receipt = {
"step": step,
"codebook_sizes": list(codebook_sizes),
"output_name": name,
"sha256": checkpoint_sha256,
"checkpoint": generator_path,
}
self.teacher_dir = teacher_dir
state = torch.load(generator_path, map_location="cpu", weights_only=True, mmap=True)
cfg = state["config"]["model"]
self.cfg = cfg
assert state["step"] == self.receipt["step"]
self.codebook_sizes = self.receipt["codebook_sizes"]
assert self.codebook_sizes == [cfg["semantic_codebook_size"]] + [cfg["residual_codebook_size"]] * 9
assert cfg["residual_codebooks"] == 9 and cfg["frame_rate"] == 15 and cfg["dual_encoding"]
assert (
cfg["sample_rate"],
cfg["internal_sample_rate"],
cfg["internal_hop_length"],
cfg["external_samples_per_frame"],
) == (44100, 44160, 2944, 2940)
model = MossCodec(cfg)
model.load_state_dict(state["generator"], strict=True, assign=True)
model.eval().requires_grad_(False)
self.resample = model.to_internal.cpu()
# Both semantic modules are required for token encoding.
del model.decoder, model.to_external, model.to_internal
self.model = model.to(self.device)
self.teacher = (
SemanticTeacher(teacher_dir, layer=state["config"]["teacher"]["layer"], dtype=torch.bfloat16)
.to(self.device)
.eval()
.requires_grad_(False)
)
del state
self.sample_rate = 44100
self.feature_dim = 10
self.name = self.receipt["output_name"]
self.batch_size = args.hybrid_batch_size
self.chunk_seconds = args.hybrid_chunk_seconds
self.context_seconds = args.hybrid_context_seconds
self.execution = (
"cuda_graph_acoustic_encoder_native_ssl_and_dual_rvq"
if args.hybrid_cuda_graph
else "eager_native_ssl_and_dual_rvq"
)
self.packer = IndependentBatcher(
torch=torch,
device=self.device,
sample_rate=44160,
hop_length=2944,
feature_dim=10,
prepare=self._prepare,
run=self._run,
batch_size=self.batch_size,
chunk_seconds=self.chunk_seconds,
continuation_context_seconds=self.context_seconds,
window=32,
output_dtype=np.int16,
label=self.name,
input_dtype=torch.float32,
input_channels=2,
)
self.optimized = None
print(json.dumps(dict(event="model_loaded", **self.metadata())), flush=True)
with torch.inference_mode():
g = torch.Generator(device=self.device).manual_seed(728)
warmup = (
torch.randn((self.batch_size, 1, self.packer.chunk_samples), generator=g, device=self.device) * 0.05
)
expected = self._encoder(warmup).clone()
self.optimized = compile_fixed(
torch,
self._encoder,
warmup,
enabled=args.hybrid_cuda_graph,
cache_name=self.name,
strategy="cuda_graph",
)
assert torch.equal(self.optimized(warmup) if self.optimized else self._encoder(warmup), expected), (
"Graph altered acoustic latent"
)
for scale in [0.05, 0.1]:
packed = (
torch.randn((self.batch_size, 2, self.packer.chunk_samples), generator=g, device=self.device)
* scale
)
assert torch.equal(self._run(packed), self._codes(packed)), "Graph/native dual tokens disagree"
report = {
"event": "inference_validation_passed",
"step": self.receipt["step"],
"codebooks": 10,
"codebook_sizes": self.codebook_sizes,
"graph_token_agreement": 1.0,
"native_quantize_dual": True,
"teacher_layer": 16,
"batch_size": self.batch_size,
"chunk_seconds": self.chunk_seconds,
"peak_gib": torch.cuda.max_memory_allocated() / 1024**3,
}
print(json.dumps(report), flush=True)
def _external(self, packed):
b = packed.shape[0]
return packed[:, 1:2].reshape(b, 1, -1, 2944)[..., :2940].reshape(b, 1, -1)
def _encoder(self, audio):
with torch.autocast("cuda", dtype=torch.bfloat16):
return self.model.encoder(audio)
def _dual(self, packed, optimized):
# Uses the exact CPU feature preprocessor and frozen teacher from training.
audio = packed[:, 0:1].contiguous()
with torch.autocast("cuda", dtype=torch.bfloat16):
ssl = extract_ssl(self._external(packed), self.teacher)
latent = self.optimized(audio) if optimized and self.optimized is not None else self.model.encoder(audio)
result = self.model.quantize_dual(latent, ssl)
return result["codes"].transpose(1, 2)
def _codes(self, packed):
return self._dual(packed, False)
def _run(self, packed):
return self._dual(packed, True)
def _prepare(self, audio, sample_rate):
if sample_rate != 44100:
raise ValueError("Expected 44100 Hz")
audio = np.asarray(audio, dtype=np.float32)
if audio.ndim != 2 or audio.shape[0] != 1 or not audio.shape[-1]:
raise ValueError("Expected nonempty mono audio")
if not np.isfinite(audio).all():
raise ValueError("Nonfinite source audio")
with torch.inference_mode():
wave = torch.from_numpy(np.ascontiguousarray(audio)).unsqueeze(0)
frames = (wave.shape[-1] + 2939) // 2940
internal = resample_exact(wave, self.resample)
internal = F.pad(internal, (0, frames * 2944 - internal.shape[-1]))
external = F.pad(wave, (0, frames * 2940 - wave.shape[-1])).reshape(1, 1, frames, 2940)
external = F.pad(external, (0, 4)).reshape(1, 1, frames * 2944)
return torch.cat([internal, external], dim=1)
def metadata(self):
return {
"name": self.name,
"model": "s1_s2_dac/hybrid_dual_encoding",
"checkpoint_step": self.receipt["step"],
"checkpoint_sha256": self.receipt["sha256"],
"checkpoint": self.receipt["checkpoint"],
"sample_rate": 44100,
"internal_sample_rate": 44160,
"hop_length": 2940,
"frame_rate": 15,
"n_codebooks": 10,
"codebook_sizes": self.codebook_sizes,
"semantic_codebook_index": 0,
"representation": "discrete_codes",
"layout": ["time", "codebook"],
"output_dtype": np.dtype(np.int16).str,
"batch_size": self.batch_size,
"chunk_seconds": self.chunk_seconds,
"continuation_context_seconds": self.context_seconds,
"resample_policy": "full_file_before_hop_aligned_chunking",
"long_file_policy": "overlap_left_context_then_discard_context_tokens",
"precision": "fp32_weights_bf16_encoder_teacher_semantic_modules_fp32_rvq",
"execution": self.execution,
"ssl_teacher_layer": 16,
"ssl_teacher": self.teacher_dir,
"ssl_preprocessing": "native_training_44100_to_16000_fbank_stride2",
"ssl_context_policy": "native_bidirectional_teacher_on_independent_padded_lanes",
}
def reference_metadata(self, audio, sample_rate, output):
expected = (audio.shape[-1] + 2939) // 2940
if output.shape != (expected, 10):
raise ValueError(f"Incorrect token shape {output.shape}, expected {(expected, 10)}")
if output.min() < 0 or np.any(output >= np.asarray(self.codebook_sizes)[None, :]):
raise ValueError("Invalid codebook index")
return {
"audio_samples": int(audio.shape[-1]),
"audio_sample_rate": sample_rate,
"checkpoint_step": self.receipt["step"],
}
def encode_stream(self, samples):
count = 0
seconds = 0.0
start = time.monotonic()
for sample, codes in self.packer.encode_stream(samples):
if sample.error is None:
self.reference_metadata(sample.audio, sample.sample_rate, codes)
seconds += sample.audio.shape[-1] / sample.sample_rate
count += 1
if count % 100 == 0:
print(
json.dumps(
{
"event": "encoding_progress",
"files": count,
"audio_hours": seconds / 3600,
"audio_seconds_per_wall_second": seconds / (time.monotonic() - start),
}
),
flush=True,
)
yield sample, codes
def encode(self, audio, sample_rate):
return next(iter(self.encode_stream([SimpleNamespace(audio=audio, sample_rate=sample_rate, error=None)])))[1]
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