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b522cc2 | 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 | """High-level API for Ming-3.0-tts-tiny multi-task inference."""
from __future__ import annotations
import os
from typing import Any, Optional, Sequence
from packaging.version import Version
from .task_protocol import (
FORMAL_CFG,
FORMAL_FLOW_STEPS,
FORMAL_SIGMA,
FORMAL_TEMPERATURE,
configure_release_environment,
podcast_frame_bounds_to_decode_steps,
podcast_generation_frame_bounds,
resolve_generation_seed,
resolve_task_request,
validate_release_bundle,
validate_release_engine,
)
REQUIRED_TRANSFORMERS = "4.46.3"
def resolve_model_dir(model_path: str) -> str:
"""Return a local checkpoint directory, downloading a Hub repo id."""
if os.path.isdir(model_path):
return model_path
from huggingface_hub import snapshot_download
return snapshot_download(repo_id=model_path)
def require_transformers() -> None:
import transformers
if Version(transformers.__version__) != Version(REQUIRED_TRANSFORMERS):
raise RuntimeError(
f"Ming-3.0-tts-tiny requires transformers=={REQUIRED_TRANSFORMERS}, "
f"found {transformers.__version__}; install requirements-tiny.txt"
)
class MingTinyTTS:
"""Load one Ming-3.0-tts-tiny checkpoint once and run every released task.
Use :meth:`from_pretrained` rather than constructing the low-level engine
directly. Task-specific conditioning and prompt policies are resolved by
this class so callers never need to pick a speaker mode or serialize
structured-control JSON by hand.
"""
def __init__(self, engine: Any) -> None:
self.engine = engine
@property
def sample_rate(self) -> int:
return self.engine.sample_rate
@classmethod
def from_pretrained(
cls,
model_path: str,
*,
device: str = "cuda:0",
dtype: str = "bf16",
moe_backend: str = "auto",
runtime_state_dtype: str = "auto",
) -> "MingTinyTTS":
"""Load a local checkpoint directory or a Hugging Face repo id."""
if dtype != "bf16":
raise ValueError("Ming-3.0-tts-tiny public runtime requires dtype='bf16'")
if moe_backend not in {"auto", "loop"}:
raise ValueError(
"Ming-3.0-tts-tiny public runtime requires moe_backend='auto' or 'loop'"
)
if runtime_state_dtype not in {"auto", "stable_fp32"}:
raise ValueError(
"Ming-3.0-tts-tiny public runtime requires runtime_state_dtype="
"'auto' or 'stable_fp32'"
)
require_transformers()
configure_release_environment()
model_dir = resolve_model_dir(model_path)
validate_release_bundle(model_dir)
from .inference import MingTinyEngine
engine = MingTinyEngine(
model_dir,
device=device,
dtype=dtype,
moe_backend=moe_backend,
runtime_state_dtype=runtime_state_dtype,
)
validate_release_engine(engine)
return cls(engine)
def generate(
self,
*,
task: str,
text: str,
output_path: Optional[str] = None,
prompt_wav: Optional[str | Sequence[str]] = None,
prompt_text: Optional[str | Sequence[str]] = None,
target_language: Optional[str] = None,
instruction: Optional[str] = None,
dialect: Optional[str] = None,
emotion: Optional[str] = None,
style: Optional[str] = None,
speed: Optional[str] = None,
pitch: Optional[str] = None,
volume: Optional[str] = None,
ip: Optional[str] = None,
seed: Optional[int] = None,
cfg_scale: float = FORMAL_CFG,
flow_steps: int = FORMAL_FLOW_STEPS,
sigma: float = FORMAL_SIGMA,
temperature: float = FORMAL_TEMPERATURE,
min_decode_steps: Optional[int] = None,
max_decode_steps: Optional[int] = None,
stop_threshold: Optional[float] = None,
min_generation_frames: Optional[int] = None,
max_generation_frames: Optional[int] = None,
log_every_n_steps: int = 0,
):
request = resolve_task_request(
task,
text=text,
prompt_wav=prompt_wav,
prompt_text=prompt_text,
target_language=target_language,
instruction=instruction,
dialect=dialect,
emotion=emotion,
style=style,
speed=speed,
pitch=pitch,
volume=volume,
ip=ip,
)
seed = resolve_generation_seed(request.task, seed)
self._validate_reference_paths(request.prompt_wav)
decode_alignment_override = None
if request.task == "podcast":
if min_decode_steps is not None or max_decode_steps is not None:
raise ValueError(
"Podcast uses 12.5 Hz generation-frame bounds; do not "
"pass decode-step bounds directly"
)
if (min_generation_frames is None) != (max_generation_frames is None):
raise ValueError(
"min_generation_frames and max_generation_frames must be paired"
)
if min_generation_frames is None:
min_generation_frames, max_generation_frames = (
podcast_generation_frame_bounds(request.text)
)
geometry = getattr(self.engine, "prompt_geometry", None)
if not isinstance(geometry, dict):
raise RuntimeError("Podcast requires checkpoint prompt geometry")
min_decode_steps, max_decode_steps = podcast_frame_bounds_to_decode_steps(
min_generation_frames,
max_generation_frames,
int(geometry["flow_patch_size"]),
)
alignments = getattr(self.engine, "task_decode_alignment", None)
decode_alignment_override = (
alignments.get("podcast") if isinstance(alignments, dict) else None
)
if decode_alignment_override not in {"old_shift", "unshifted"}:
raise RuntimeError(
"Podcast requires task_decode_alignment.podcast in config.json"
)
elif min_generation_frames is not None or max_generation_frames is not None:
raise ValueError("generation-frame bounds are Podcast-only")
output = self.engine.synthesize(
request.text,
instruction=request.instruction,
prompt_wav=request.prompt_wav,
prompt_text=request.prompt_text,
target_language=request.target_language,
include_target_language=request.include_target_language,
conditioning=request.conditioning,
assistant_instruction=request.assistant_instruction,
allow_mixed_rml=request.allow_mixed_rml,
decode_alignment_override=decode_alignment_override,
seed=seed,
cfg_scale=cfg_scale,
flow_steps=flow_steps,
sigma=sigma,
temperature=temperature,
min_decode_steps=min_decode_steps,
max_decode_steps=max_decode_steps,
stop_threshold=stop_threshold,
log_every_n_steps=log_every_n_steps,
)
self._validate_output_contract(request, output)
if output_path is not None:
from .inference import save_waveform
save_waveform(output_path, output.waveform, self.engine.sample_rate)
return output
@staticmethod
def _validate_reference_paths(
prompt_wav: Optional[str | tuple[str, ...]],
) -> None:
if prompt_wav is None:
return
paths = (prompt_wav,) if isinstance(prompt_wav, str) else prompt_wav
for path in paths:
if not os.path.isfile(path):
raise FileNotFoundError(path)
@staticmethod
def _validate_output_contract(request, output) -> None:
speaker_count = (
len(request.prompt_wav) if request.conditioning == "multi_speaker" else None
)
expected = {
"prompt_latent": (1, 1, True),
"multi_speaker": (speaker_count, speaker_count, True),
"speaker_only": (1, 1, False),
"masked_speaker": (1, 0, False),
"zero_speaker": (1, 0, False),
"no_rml": (0, 0, False),
"prompt_latent_no_rml": (0, 0, True),
}
slots, rows, needs_prompt_latent = expected[request.conditioning]
actual_slots = getattr(output, "speaker_slot_count", None)
actual_rows = getattr(output, "campplus_speaker_rows", None)
actual_prompt_steps = getattr(output, "prompt_latent_steps", None)
valid_slots = isinstance(actual_slots, int) and not isinstance(
actual_slots, bool
)
valid_rows = isinstance(actual_rows, int) and not isinstance(actual_rows, bool)
if (
not valid_slots
or not valid_rows
or actual_slots != slots
or actual_rows != rows
):
raise RuntimeError(
f"task {request.task} conditioning proof mismatch: "
f"slots={actual_slots}/{slots}, CampPlus={actual_rows}/{rows}"
)
if (
not isinstance(actual_prompt_steps, int)
or isinstance(actual_prompt_steps, bool)
or actual_prompt_steps < 0
):
raise RuntimeError(
f"task {request.task} returned invalid prompt latent provenance: "
f"{actual_prompt_steps!r}"
)
if needs_prompt_latent and actual_prompt_steps <= 0:
raise RuntimeError(
f"task {request.task} produced no AudioVAE prompt continuation"
)
if not needs_prompt_latent and actual_prompt_steps != 0:
raise RuntimeError(
f"task {request.task} unexpectedly used AudioVAE prompt continuation"
)
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