import os # very top of app.py, before importing tensorflow/jax/magenta stuff try: import pylibsrtp # This is the key: call srtp_init via cffi binding from pylibsrtp import _binding _binding.lib.srtp_init() print("SRTP init OK (pre-TF)", flush=True) except Exception as e: print("SRTP init failed early:", e, flush=True) # ---- Space mode gating (place above any JAX import!) ---- SPACE_MODE = os.getenv("SPACE_MODE") if SPACE_MODE is None: try: import jax SPACE_MODE = "serve" if any(getattr(d, "platform", "") in ("gpu","cuda","rocm") for d in jax.devices()) else "template" except Exception: SPACE_MODE = "template" if SPACE_MODE != "serve": # In template mode, force JAX to CPU so it won't try to load CUDA plugins os.environ.setdefault("JAX_PLATFORMS", "cpu") else: # Only set GPU-friendly XLA flags when we actually intend to serve on GPU os.environ.setdefault( "XLA_FLAGS", " ".join([ "--xla_gpu_enable_triton_gemm=true", "--xla_gpu_enable_latency_hiding_scheduler=true", "--xla_gpu_autotune_level=2", ]) ) # Optional: persist JAX compile cache across restarts (reduces warmup time) os.environ.setdefault("JAX_CACHE_DIR", "/home/appuser/.cache/jax") import jax # ✅ Valid choices include: "default", "high", "highest", "tensorfloat32", "float32", etc. # TF32 is the sweet spot on Ampere/Ada GPUs for ~1.1–1.3× matmul speedups. try: jax.config.update("jax_default_matmul_precision", "tensorfloat32") except Exception: jax.config.update("jax_default_matmul_precision", "high") # older alias # Initialize the on-disk compilation cache (best-effort) try: from jax.experimental.compilation_cache import compilation_cache as cc cc.initialize_cache(os.environ["JAX_CACHE_DIR"]) except Exception: pass # -------------------------------------------------------------------- from magenta_rt import system, audio as au import numpy as np from fastapi import FastAPI, UploadFile, File, Form, Body, HTTPException, Response, Request, WebSocket, WebSocketDisconnect, Query from fastapi.responses import JSONResponse, FileResponse, HTMLResponse import tempfile, io, base64, math, threading from fastapi.middleware.cors import CORSMiddleware from contextlib import contextmanager import soundfile as sf from math import gcd from scipy.signal import resample_poly from utils import ( match_loudness_to_reference, stitch_generated, hard_trim_seconds, apply_micro_fades, make_bar_aligned_context, take_bar_aligned_tail, resample_and_snap, wav_bytes_base64 ) from jam_worker import JamWorker, JamParams, JamChunk from one_shot_generation import generate_loop_continuation_with_mrt, generate_style_only_with_mrt import uuid, threading import logging import gradio as gr from typing import Optional, Union, Literal import json, asyncio, base64 import time from starlette.websockets import WebSocketState try: from uvicorn.protocols.utils import ClientDisconnected # uvicorn >= 0.20 except Exception: class ClientDisconnected(Exception): # fallback pass import re, tarfile from pathlib import Path from huggingface_hub import snapshot_download, HfApi from pydantic import BaseModel from model_management import CheckpointManager, AssetManager, ModelSelector, ModelSelect # from fastrtc_magenta import create_magenta_stream, FASTRTC_AVAILABLE def _gpu_probe() -> dict: """ Returns: { "ok": bool, "backend": str | None, # "gpu" | "cpu" | "tpu" | None "has_gpu": bool, "devices": list[str], # e.g. ["gpu:0", "gpu:1"] "error": str | None, } """ try: import jax try: backend = jax.default_backend() # "gpu", "cpu", "tpu" except Exception: from jax.lib import xla_bridge backend = getattr(xla_bridge.get_backend(), "platform", None) try: devices = jax.devices() has_gpu = any(getattr(d, "platform", "") in ("gpu", "cuda", "rocm") for d in devices) dev_list = [f"{getattr(d, 'platform', '?')}:{getattr(d, 'id', '?')}" for d in devices] return {"ok": True, "backend": backend, "has_gpu": has_gpu, "devices": dev_list, "error": None} except Exception as e: return {"ok": False, "backend": backend, "has_gpu": False, "devices": [], "error": f"jax.devices failed: {e}"} except Exception as e: return {"ok": False, "backend": None, "has_gpu": False, "devices": [], "error": f"jax import failed: {e}"} # ---- Finetune assets (mean & centroids) -------------------------------------- # _FINETUNE_REPO_DEFAULT = os.getenv("MRT_ASSETS_REPO", "thepatch/magenta-ft") _ASSETS_REPO_ID: str | None = None _MEAN_EMBED: np.ndarray | None = None # shape (D,) dtype float32 _CENTROIDS: np.ndarray | None = None # shape (K, D) dtype float32 # _STEP_RE = re.compile(r"(?:^|/)checkpoint_(\d+)(?:/|\.tar\.gz|\.tgz)?$") # Create instances (these don't modify globals) asset_manager = AssetManager() model_selector = ModelSelector(CheckpointManager(), asset_manager) def _sync_assets_globals_from_manager(): # Keeps /model/config in sync with what the asset manager has global _MEAN_EMBED, _CENTROIDS, _ASSETS_REPO_ID _MEAN_EMBED = asset_manager.mean_embed _CENTROIDS = asset_manager.centroids _ASSETS_REPO_ID = asset_manager.assets_repo_id def _any_jam_running() -> bool: with jam_lock: return any(w.is_alive() for w in jam_registry.values()) def _stop_all_jams(timeout: float = 5.0): with jam_lock: for sid, w in list(jam_registry.items()): if w.is_alive(): w.stop() w.join(timeout=timeout) jam_registry.pop(sid, None) async def send_json_safe(ws: WebSocket, obj) -> bool: """Try to send. Returns False if the socket is (or becomes) closed.""" if ws.client_state == WebSocketState.DISCONNECTED or ws.application_state == WebSocketState.DISCONNECTED: return False try: await ws.send_text(json.dumps(obj)) return True except (WebSocketDisconnect, ClientDisconnected, RuntimeError): return False except Exception: return False # --- Patch T5X mesh helpers for GPUs on JAX >= 0.7 (coords present, no core_on_chip) --- def _patch_t5x_for_gpu_coords(): try: import jax from t5x import partitioning as _t5x_part old_bounds = getattr(_t5x_part, "bounds_from_last_device", None) old_getcoords = getattr(_t5x_part, "get_coords", None) def _bounds_from_last_device_gpu_safe(last_device): # TPU: coords + core_on_chip core = getattr(last_device, "core_on_chip", None) coords = getattr(last_device, "coords", None) if coords is not None and core is not None: x, y, z = coords return x + 1, y + 1, z + 1, core + 1 # Non-TPU (or GPU lacking core_on_chip): hosts x local_devices return jax.host_count(), jax.local_device_count() def _get_coords_gpu_safe(device): core = getattr(device, "core_on_chip", None) coords = getattr(device, "coords", None) if coords is not None and core is not None: return (*coords, core) # Fallback that works on CPU/GPU return (device.process_index, device.id % jax.local_device_count()) _t5x_part.bounds_from_last_device = _bounds_from_last_device_gpu_safe _t5x_part.get_coords = _get_coords_gpu_safe import logging; logging.info("Patched t5x.partitioning for GPU coords without core_on_chip.") except Exception as e: import logging; logging.exception("t5x GPU-coords patch failed: %s", e) def _patch_magenta_rt_asset_fetch(): """ Patch magenta_rt.asset._fetch_single_hf to handle None response gracefully. Prevents AttributeError when network timeouts occur. """ try: from magenta_rt import asset import logging # Save original function _original_fetch = asset._fetch_single_hf def _fetch_single_hf_safe(*args, **kwargs): """Wrapper that fixes the None response.status_code bug""" try: return _original_fetch(*args, **kwargs) except Exception as e: # This is the bug fix: check if response exists before accessing it response = getattr(e, 'response', None) if response is not None and hasattr(response, 'status_code'): if response.status_code == 429: # Original code's rate-limit handling would go here logging.warning("Rate limited by HuggingFace Hub") raise # For all other cases (including timeout with no response), re-raise logging.error(f"HuggingFace download failed: {e}") raise # Apply the patch asset._fetch_single_hf = _fetch_single_hf_safe logging.info("Patched magenta_rt.asset._fetch_single_hf for safer error handling.") except Exception as e: logging.exception("magenta_rt asset fetch patch failed: %s", e) # Call the patch immediately at import time (before MagentaRT init) _patch_t5x_for_gpu_coords() _patch_magenta_rt_asset_fetch() jam_registry: dict[str, JamWorker] = {} jam_lock = threading.Lock() @contextmanager def mrt_overrides(mrt, **kwargs): """Temporarily set attributes on MRT if they exist; restore after.""" old = {} try: for k, v in kwargs.items(): if hasattr(mrt, k): old[k] = getattr(mrt, k) setattr(mrt, k, v) yield finally: for k, v in old.items(): setattr(mrt, k, v) # loudness utils try: import pyloudnorm as pyln _HAS_LOUDNORM = True except Exception: _HAS_LOUDNORM = False def build_style_vector( mrt, *, text_styles: list[str] | None = None, text_weights: list[float] | None = None, loop_embed: np.ndarray | None = None, loop_weight: float | None = None, mean_weight: float | None = None, centroid_weights: list[float] | None = None, ) -> np.ndarray: """ Returns a single style embedding combining: - loop embedding (optional) - one or more text style embeddings (optional) - mean finetune embedding (optional) - centroid embeddings (optional) All weights are normalized so they sum to 1 if > 0. """ comps: list[np.ndarray] = [] weights: list[float] = [] # loop component if loop_embed is not None and (loop_weight or 0) > 0: comps.append(loop_embed.astype(np.float32, copy=False)) weights.append(float(loop_weight)) # text components if text_styles: for i, s in enumerate(text_styles): s = s.strip() if not s: continue w = 1.0 if text_weights and i < len(text_weights): try: w = float(text_weights[i]) except: w = 1.0 if w <= 0: continue e = mrt.embed_style(s) comps.append(e.astype(np.float32, copy=False)) weights.append(w) # mean finetune if mean_weight and (_MEAN_EMBED is not None) and mean_weight > 0: comps.append(_MEAN_EMBED) weights.append(float(mean_weight)) # centroid components if centroid_weights and _CENTROIDS is not None: K = _CENTROIDS.shape[0] for k, w in enumerate(centroid_weights[:K]): try: w = float(w) except: w = 0.0 if w <= 0: continue comps.append(_CENTROIDS[k]) weights.append(w) if not comps: # fallback: neutral style if nothing provided return mrt.embed_style("") wsum = sum(weights) if wsum <= 0: return mrt.embed_style("") weights = [w/wsum for w in weights] # weighted sum out = np.zeros_like(comps[0], dtype=np.float32) for w, e in zip(weights, comps): out += w * e.astype(np.float32, copy=False) return out # ---------------------------- # FastAPI app with lazy, thread-safe model init # ---------------------------- app = FastAPI() app.add_middleware( CORSMiddleware, allow_origins=["*"], # or lock to your domain(s) allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) _MRT = None _MRT_LOCK = threading.Lock() _PROGRESS = {} _PROGRESS_LOCK = threading.Lock() _GENERATE_COUNTER = 0 _GENERATE_COUNTER_LOCK = threading.Lock() def _light_reset_mrt(mrt): """ Lightweight reset that clears accumulated state without full recompilation. Should take <1 second instead of 30 seconds. """ import logging logging.info("Performing light MRT reset after prolonged use...") try: # 1. Clear JAX device arrays (but not compiled functions) import jax for device in jax.devices(): # Force garbage collection on device try: device.clear_memory() # If available in your JAX version except AttributeError: pass # 2. Clear any MRT-level caches attrs_to_clear = ['_last_state', '_generation_cache', '_style_cache'] for attr in attrs_to_clear: if hasattr(mrt, attr): setattr(mrt, attr, None) # 3. Clear codec internal state codec_attrs = [ '_encode_state', '_decode_state', '_encoder_cache', '_decoder_cache', '_buffer', '_frame_buffer' ] for attr in codec_attrs: if hasattr(mrt.codec, attr): setattr(mrt.codec, attr, None) # 4. Force Python garbage collection import gc gc.collect() # 5. If style model has cache, clear it if hasattr(mrt, 'style_model'): if hasattr(mrt.style_model, 'clear_cache'): mrt.style_model.clear_cache() # Clear any embedding caches for attr in ['_embed_cache', '_text_cache']: if hasattr(mrt.style_model, attr): setattr(mrt.style_model, attr, None) logging.info("Light reset complete") return True except Exception as e: logging.warning(f"Light reset partially failed (non-fatal): {e}") return False def _progress_update(req_id: str, n: int, total: int, stage: str = "generating"): if not req_id: return with _PROGRESS_LOCK: _PROGRESS[req_id] = { "n": int(n), "total": int(total), "percent": int(round(100.0 * max(0, min(n, total)) / max(1, total))), "stage": stage, "ts": time.time(), } def _progress_done(req_id: str): if not req_id: return with _PROGRESS_LOCK: st = _PROGRESS.get(req_id, {}) total = st.get("total") or st.get("n") or 1 _PROGRESS[req_id] = {"n": total, "total": total, "percent": 100, "stage": "done", "ts": time.time()} def _progress_get(req_id: str): with _PROGRESS_LOCK: return _PROGRESS.get(req_id, {"percent": 0, "stage": "pending"}) def get_mrt(): global _MRT if _MRT is None: with _MRT_LOCK: if _MRT is None: ckpt_dir = CheckpointManager.resolve_checkpoint_dir() # uses MRT_CKPT_REPO/STEP if present _MRT = system.MagentaRT( tag=os.getenv("MRT_SIZE", "large"), guidance_weight=1.0, device="gpu", checkpoint_dir=ckpt_dir, lazy=False ) # If no assets loaded yet, and a repo is configured, load them now. if asset_manager.mean_embed is None and asset_manager.centroids is None: repo = os.getenv("MRT_ASSETS_REPO") or os.getenv("MRT_CKPT_REPO") if repo: asset_manager.load_finetune_assets_from_hf(repo, None) _sync_assets_globals_from_manager() return _MRT _WARMED = False _WARMUP_LOCK = threading.Lock() def _generate_pink_noise(n_samples: int, channels: int = 2) -> np.ndarray: """ Generate pink (1/f) noise using the Voss-McCartney algorithm approximation. Pink noise is more representative of real audio spectrally than silence, which helps avoid tainting JAX compilation cache with silence patterns. """ # Simple approximation: filter white noise white = np.random.randn(n_samples, channels).astype(np.float32) # Apply simple 1/f rolloff via cumulative sum + normalization pink = np.cumsum(white, axis=0) # High-pass to remove DC drift pink = pink - np.mean(pink, axis=0, keepdims=True) # Normalize to reasonable level (-20 dBFS ish) peak = np.max(np.abs(pink)) if peak > 0: pink = pink * (0.1 / peak) return pink.astype(np.float32) def _mrt_warmup(): """ Build a minimal, bar-aligned PINK NOISE context and run one 2s generate_chunk to trigger XLA JIT & autotune so first real request is fast. KEY FIX: Uses pink noise instead of silence for warmup! Pure silence can taint JAX compilation cache with silence patterns, leading to intermittent silence gaps in generated audio. """ global _WARMED with _WARMUP_LOCK: if _WARMED: return try: mrt = get_mrt() # --- derive timing from model config --- codec_fps = float(mrt.codec.frame_rate) ctx_seconds = float(mrt.config.context_length_frames) / codec_fps sr = int(mrt.sample_rate) # We'll align to 120 BPM, 4/4, and generate one ~2s chunk bpm = 120.0 beats_per_bar = 4 # --- build a PINK NOISE stereo context of ctx_seconds --- samples = int(max(1, round(ctx_seconds * sr))) warmup_audio = _generate_pink_noise(samples, channels=2) logging.info("Warmup using pink noise instead of silence (%d samples)", samples) with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp: sf.write(tmp.name, warmup_audio, sr, subtype="PCM_16") tmp_path = tmp.name try: # Load as Waveform and take a tail of exactly ctx_seconds loop = au.Waveform.from_file(tmp_path).resample(sr).as_stereo() ctx_tail = take_bar_aligned_tail(loop, bpm, beats_per_bar, ctx_seconds) # Tokens for context window tokens_full = mrt.codec.encode(ctx_tail).astype(np.int32) tokens = tokens_full[:, :mrt.config.decoder_codec_rvq_depth] context_tokens = make_bar_aligned_context( tokens, bpm=bpm, fps=float(mrt.codec.frame_rate), ctx_frames=mrt.config.context_length_frames, beats_per_bar=beats_per_bar, ) # Init state and a basic style vector (text token is fine) state = mrt.init_state() state.context_tokens = context_tokens style_vec = mrt.embed_style("warmup") # --- one throwaway chunk (~2s) --- _wav, _state = mrt.generate_chunk(state=state, style=style_vec) logging.info("MagentaRT warmup complete (pink noise context).") finally: try: os.unlink(tmp_path) except Exception: pass _WARMED = True except Exception as e: # Never crash on warmup errors; log and continue serving logging.exception("MagentaRT warmup failed (continuing without warmup): %s", e) # ---------------------------- # startup and model selection # ---------------------------- @app.on_event("startup") def _boot(): # 1) Load finetune assets up front (only if envs are present) repo = os.getenv("MRT_ASSETS_REPO") or os.getenv("MRT_CKPT_REPO") if repo: ok, msg = asset_manager.load_finetune_assets_from_hf(repo, None) _sync_assets_globals_from_manager() # keep /model/config in sync logging.info("Startup asset load from %s: %s", repo, "ok" if ok else msg) else: logging.info("Startup asset load: no repo env set; skipping.") # 2) Start warmup in the background (unchanged behavior) if os.getenv("MRT_WARMUP", "1") != "0": threading.Thread(target=_mrt_warmup, name="mrt-warmup", daemon=True).start() @app.get("/model/status") def model_status(): mrt = get_mrt() return { "tag": getattr(mrt, "_tag", "unknown"), "using_checkpoint_dir": True, "codec_frame_rate": float(mrt.codec.frame_rate), "decoder_rvq_depth": int(mrt.config.decoder_codec_rvq_depth), "context_seconds": float(mrt.config.context_length), "chunk_seconds": float(mrt.config.chunk_length), "crossfade_seconds": float(mrt.config.crossfade_length), "selected_step": os.getenv("MRT_CKPT_STEP"), "repo": os.getenv("MRT_CKPT_REPO"), } @app.post("/model/swap") def model_swap(step: int = Form(...)): # stop any active jam if you want to be strict (not shown) os.environ["MRT_CKPT_STEP"] = str(step) global _MRT with _MRT_LOCK: _MRT = None # force re-create on next get_mrt() # optionally pre-warm here by calling get_mrt() return {"reloaded": True, "step": step} @app.post("/model/assets/load") def model_assets_load(repo_id: str = Form(None)): global _MEAN_EMBED, _CENTROIDS, _ASSETS_REPO_ID ok, msg = asset_manager.load_finetune_assets_from_hf(repo_id, get_mrt()) # Sync globals after loading _MEAN_EMBED = asset_manager.mean_embed _CENTROIDS = asset_manager.centroids _ASSETS_REPO_ID = asset_manager.assets_repo_id return {"ok": ok, "message": msg, "repo_id": _ASSETS_REPO_ID, "mean": _MEAN_EMBED is not None, "centroids": None if _CENTROIDS is None else int(_CENTROIDS.shape[0])} @app.get("/model/assets/status") def model_assets_status(): d = None try: d = int(get_mrt().style_model.config.embedding_dim) except Exception: pass return { "repo_id": _ASSETS_REPO_ID, "mean_loaded": _MEAN_EMBED is not None, "centroids_loaded": False if _CENTROIDS is None else True, "centroid_count": None if _CENTROIDS is None else int(_CENTROIDS.shape[0]), "embedding_dim": d, } @app.get("/model/config") def model_config(): """ Lightweight config snapshot: - never calls get_mrt() (no model build / no downloads) - never calls snapshot_download() - reports whether a model instance is currently loaded in memory - best-effort local checkpoint presence (no network) """ # Read-only snapshot of in-memory model presence with _MRT_LOCK: loaded = (_MRT is not None) size = os.getenv("MRT_SIZE", "large") repo = os.getenv("MRT_CKPT_REPO") rev = os.getenv("MRT_CKPT_REV", "main") step = os.getenv("MRT_CKPT_STEP") assets = os.getenv("MRT_ASSETS_REPO") # Use CheckpointManager for local cache probe (no network) local_ckpt = None if step: try: from pathlib import Path import re step_escaped = re.escape(str(step)) candidates = [] for root in ("/home/appuser/.cache/mrt_ckpt/extracted", "/home/appuser/.cache/mrt_ckpt/repo"): p = Path(root) if not p.exists(): continue # Look for exact "checkpoint_" directories anywhere under these roots for d in p.rglob(f"checkpoint_{step}"): if d.is_dir(): candidates.append(str(d)) local_ckpt = candidates[0] if candidates else None except Exception: local_ckpt = None return { "size": size, "repo": repo, "revision": rev, "selected_step": step, "assets_repo": assets, # in-memory + local cache hints (no network, no model build) "loaded": loaded, "active_jam": _any_jam_running(), "local_checkpoint_dir": local_ckpt, # None if not found locally # steering assets currently resident in memory "mean_loaded": (_MEAN_EMBED is not None), "centroids_loaded": (_CENTROIDS is not None), "centroid_count": (None if _CENTROIDS is None else int(_CENTROIDS.shape[0])), "warmup_done": bool(_WARMED), } @app.get("/model/checkpoints") def model_checkpoints(repo_id: str, revision: str = "main"): steps = CheckpointManager.list_ckpt_steps(repo_id, revision) return {"repo": repo_id, "revision": revision, "steps": steps, "latest": (steps[-1] if steps else None)} @app.post("/model/select") def model_select(req: ModelSelect): """ Swap model/checkpoint/assets. If req.prewarm is True, run the full bar-aligned warmup (_mrt_warmup) synchronously so we only report warmed once the new model is actually ready. """ global _MRT, _MEAN_EMBED, _CENTROIDS, _ASSETS_REPO_ID, _WARMED # 1) Validate the request (no side-effects) success, validation_result = model_selector.validate_selection(req) if not success: if "error" in validation_result: raise HTTPException(status_code=400, detail=validation_result["error"]) return {"ok": False, **validation_result} # Augment response surface validation_result["active_jam"] = _any_jam_running() # Dry-run path if req.dry_run: return {"ok": True, "dry_run": True, **validation_result} if isinstance(req.step, str) and req.step.lower() == "none": # user asked for stock base # Clear any resident finetune assets so /model/config reflects "no assets" asset_manager.mean_embed = None asset_manager.centroids = None asset_manager.assets_repo_id = None # keep module-level mirrors in sync _MEAN_EMBED = None _CENTROIDS = None _ASSETS_REPO_ID = None # 2) Handle jam policy if _any_jam_running(): if req.stop_active: _stop_all_jams() else: raise HTTPException(status_code=409, detail="A jam is running; retry with stop_active=true") # 3) Compute environment changes (no mutation yet) env_changes = model_selector.prepare_env_changes(req, validation_result) # Keep current env for rollback old_env = { "MRT_SIZE": os.getenv("MRT_SIZE"), "MRT_CKPT_REPO": os.getenv("MRT_CKPT_REPO"), "MRT_CKPT_REV": os.getenv("MRT_CKPT_REV"), "MRT_CKPT_STEP": os.getenv("MRT_CKPT_STEP"), "MRT_ASSETS_REPO": os.getenv("MRT_ASSETS_REPO"), } try: # 4) Apply env atomically for key, value in env_changes.items(): if value is None: os.environ.pop(key, None) else: os.environ[key] = str(value) # 5) Force rebuild of the model and reset warmup state with _MRT_LOCK: _MRT = None with _WARMUP_LOCK: _WARMED = False # ← critical: don't leak previous model's warmed state # 6) Load finetune assets if requested (mean/centroids) if req.sync_assets and validation_result.get("assets_repo"): ok, msg = asset_manager.load_finetune_assets_from_hf( validation_result["assets_repo"], None # don't implicitly instantiate model here; we'll do it below ) if ok: _MEAN_EMBED = asset_manager.mean_embed _CENTROIDS = asset_manager.centroids _ASSETS_REPO_ID = asset_manager.assets_repo_id else: logging.warning("Asset sync skipped/failed: %s", msg) # 7) Prewarm behavior: # - If prewarm=True, run the *real* bar-aligned warmup synchronously. # - This will instantiate the new MRT and set _WARMED=True on success. if req.prewarm: _mrt_warmup() # builds MRT internally via get_mrt(), runs generate_chunk, sets _WARMED # Optional: if you want to always ensure MRT exists (even without prewarm), uncomment: # else: # _ = get_mrt() return { "ok": True, **validation_result, "warmup_done": bool(_WARMED), } except Exception as e: # 8) Roll back env on failure for k, v in old_env.items(): if v is None: os.environ.pop(k, None) else: os.environ[k] = v # Also reset model pointer & warmed flag to a safe state with _MRT_LOCK: _MRT = None with _WARMUP_LOCK: _WARMED = False logging.exception("Model select failed: %s", e) raise HTTPException(status_code=500, detail=f"Model select failed: {e}") # ---------------------------- # one-shot generation # ---------------------------- @app.post("/generate/reset") def generate_reset(): """ Manually trigger a light reset of the generation system. Useful if user notices quality degradation without full restart. """ global _GENERATE_COUNTER with _GENERATE_COUNTER_LOCK: _GENERATE_COUNTER = 0 # Reset counter mrt = get_mrt() success = _light_reset_mrt(mrt) return { "reset": success, "message": "Light reset complete" if success else "Reset partially completed", "counter_reset": True } @app.get("/progress") def progress(request_id: str): return _progress_get(request_id) @app.post("/generate") def generate( loop_audio: UploadFile = File(...), bpm: float = Form(...), bars: int = Form(8), beats_per_bar: int = Form(4), styles: str = Form("acid house"), style_weights: str = Form(""), loop_weight: float = Form(1.0), loudness_mode: str = Form("auto"), loudness_headroom_db: float = Form(1.0), guidance_weight: float = Form(5.0), temperature: float = Form(1.1), topk: int = Form(40), target_sample_rate: int | None = Form(None), intro_bars_to_drop: int = Form(0), request_id: str = Form(None), force_reset: bool = Form(False), # NEW: Manual reset trigger ): global _GENERATE_COUNTER req_id = request_id or str(uuid.uuid4()) tmp_path = None try: # Check if we need a periodic reset with _GENERATE_COUNTER_LOCK: _GENERATE_COUNTER += 1 gen_count = _GENERATE_COUNTER # Every 1 generations, do a light reset # (Or if user explicitly requests it) if gen_count % 1 == 0 or force_reset: logging.info(f"[Generate {req_id}] Triggering light reset (generation #{gen_count})") mrt = get_mrt() _light_reset_mrt(mrt) # 0) Read file -> tmp wav data = loop_audio.file.read() if not data: # finalize progress as error and return with _PROGRESS_LOCK: _PROGRESS[req_id] = { "percent": 100, "stage": "error", "error": "Empty file", "ts": time.time(), } return {"error": "Empty file", "request_id": req_id} with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: tmp.write(data) tmp_path = tmp.name # 1) Parse styles + weights extra_styles = [s.strip() for s in (styles.split(",") if styles else []) if s.strip()] weights = [float(x) for x in style_weights.split(",")] if style_weights else None # 2) Get model and apply per-request overrides mrt = get_mrt() with mrt_overrides( mrt, guidance_weight=guidance_weight, temperature=temperature, topk=topk, ): # progress callback (called from the generator loop) def on_chunk(i, total): _progress_update(req_id, i, total, stage="generating") # 2a) (optional) emit initial 0% once steps are known: # We'll do this inside the generator right after steps is computed. wav, loud_stats = generate_loop_continuation_with_mrt( mrt, input_wav_path=tmp_path, bpm=bpm, extra_styles=extra_styles, style_weights=weights, bars=bars, beats_per_bar=beats_per_bar, loop_weight=loop_weight, loudness_mode=loudness_mode, loudness_headroom_db=loudness_headroom_db, intro_bars_to_drop=intro_bars_to_drop, progress_cb=on_chunk, ) # 3) Post-process stages (optional: expose sub-stages for nicer UI) # Mark "postprocess" before we resample/snap/encode. st = _PROGRESS_GET(req_id) if False else None # (placeholder so lints don't complain) _progress_update( req_id, _progress_get(req_id).get("total", 1), _progress_get(req_id).get("total", 1), "postprocess", ) # 3a) Determine SR inp_info = sf.info(tmp_path) input_sr = int(inp_info.samplerate) target_sr_val = int(target_sample_rate or input_sr) # 3b) Convert SR + snap to exact bars cur_sr = int(mrt.sample_rate) x = wav.samples if wav.samples.ndim == 2 else wav.samples[:, None] seconds_per_bar = (60.0 / float(bpm)) * int(beats_per_bar) expected_secs = float(bars) * seconds_per_bar # (optional) sub-stage _progress_update(req_id, _progress_get(req_id).get("total", 1), _progress_get(req_id).get("total", 1), "resample_and_snap") x = resample_and_snap(x, cur_sr=cur_sr, target_sr=target_sr_val, seconds=expected_secs) # 3c) Encode WAV -> base64 _progress_update(req_id, _progress_get(req_id).get("total", 1), _progress_get(req_id).get("total", 1), "encode") audio_b64, total_samples, channels = wav_bytes_base64(x, target_sr_val) loop_duration_seconds = total_samples / float(target_sr_val) # 4) Metadata metadata = { "bpm": int(round(bpm)), "bars": int(bars), "beats_per_bar": int(beats_per_bar), "styles": extra_styles, "style_weights": weights, "loop_weight": loop_weight, "loudness": loud_stats, "sample_rate": int(target_sr_val), "channels": int(channels), "crossfade_seconds": mrt.config.crossfade_length, "total_samples": int(total_samples), "seconds_per_bar": seconds_per_bar, "loop_duration_seconds": loop_duration_seconds, "guidance_weight": guidance_weight, "temperature": temperature, "topk": topk, } _progress_done(req_id) return {"audio_base64": audio_b64, "metadata": metadata, "request_id": req_id} except Exception as e: # Flip to error state so the UI stops polling and can show a message with _PROGRESS_LOCK: _PROGRESS[req_id] = { "percent": 100, "stage": "error", "error": str(e), "ts": time.time(), } # Re-raise so FastAPI returns a 500 (or your exception handler formats it) raise finally: # Clean up temp file if tmp_path: try: os.unlink(tmp_path) except Exception: pass # new endpoint to return a bar-aligned chunk without the need for combined audio @app.post("/generate_style") def generate_style( bpm: float = Form(...), bars: int = Form(8), beats_per_bar: int = Form(4), styles: str = Form("warmup"), style_weights: str = Form(""), guidance_weight: float = Form(1.1), temperature: float = Form(1.1), topk: int = Form(40), target_sample_rate: int | None = Form(None), intro_bars_to_drop: int = Form(0), ): """ Style-only, bar-aligned generation (no input audio). Seeds with 10s of silent context; outputs exactly `bars` at the requested BPM. """ mrt = get_mrt() # Override sampling knobs just for this request with mrt_overrides(mrt, guidance_weight=guidance_weight, temperature=temperature, topk=topk): wav, _ = generate_style_only_with_mrt( mrt, bpm=bpm, bars=bars, beats_per_bar=beats_per_bar, styles=styles, style_weights=style_weights, intro_bars_to_drop=intro_bars_to_drop, ) # Determine target SR (defaults to model SR = 48k) cur_sr = int(mrt.sample_rate) target_sr = int(target_sample_rate or cur_sr) x = wav.samples if wav.samples.ndim == 2 else wav.samples[:, None] seconds_per_bar = (60.0 / float(bpm)) * int(beats_per_bar) expected_secs = float(bars) * seconds_per_bar # Snap exactly to musical length at the requested sample rate x = resample_and_snap(x, cur_sr=cur_sr, target_sr=target_sr, seconds=expected_secs) audio_b64, total_samples, channels = wav_bytes_base64(x, target_sr) metadata = { "bpm": int(round(bpm)), "bars": int(bars), "beats_per_bar": int(beats_per_bar), "styles": [s.strip() for s in (styles.split(",") if styles else []) if s.strip()], "style_weights": [float(y) for y in style_weights.split(",")] if style_weights else None, "sample_rate": int(target_sr), "channels": int(channels), "crossfade_seconds": mrt.config.crossfade_length, "seconds_per_bar": seconds_per_bar, "loop_duration_seconds": total_samples / float(target_sr), "guidance_weight": guidance_weight, "temperature": temperature, "topk": topk, } return {"audio_base64": audio_b64, "metadata": metadata} # ---------------------------- # the 'keep jamming' button # ---------------------------- @app.post("/jam/start") def jam_start( loop_audio: UploadFile = File(...), bpm: float = Form(...), bars_per_chunk: int = Form(4), beats_per_bar: int = Form(4), styles: str = Form(""), style_weights: str = Form(""), loop_weight: float = Form(1.0), # NEW steering params: mean: float = Form(0.0), centroid_weights: str = Form(""), loudness_mode: str = Form("auto"), loudness_headroom_db: float = Form(1.0), guidance_weight: float = Form(1.1), temperature: float = Form(1.1), topk: int = Form(40), target_sample_rate: int | None = Form(None), ): asset_manager.ensure_assets_loaded(get_mrt()) # enforce single active jam per GPU with jam_lock: for sid, w in list(jam_registry.items()): if w.is_alive(): raise HTTPException(status_code=429, detail="A jam is already running. Try again later.") # read input + prep context/style (reuse your existing code) data = loop_audio.file.read() if not data: raise HTTPException(status_code=400, detail="Empty file") with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: tmp.write(data); tmp_path = tmp.name mrt = get_mrt() loop = au.Waveform.from_file(tmp_path).resample(mrt.sample_rate).as_stereo() # build tail context + style vec (tail-biased) codec_fps = float(mrt.codec.frame_rate) ctx_seconds = float(mrt.config.context_length_frames) / codec_fps loop_tail = take_bar_aligned_tail(loop, bpm, beats_per_bar, ctx_seconds) # Parse client style fields (preserves your semantics) text_list = [s.strip() for s in (styles.split(",") if styles else []) if s.strip()] try: tw = [float(x) for x in style_weights.split(",")] if style_weights else [] except ValueError: tw = [] try: cw = [float(x) for x in centroid_weights.split(",")] if centroid_weights else [] except ValueError: cw = [] # Compute loop-tail embed once (same as before) loop_tail_embed = mrt.embed_style(loop_tail) # Build final style vector: # - identical to your previous mix when mean==0 and cw is empty # - otherwise includes mean and centroid components (weights auto-normalized) style_vec = build_style_vector( mrt, text_styles=text_list, text_weights=tw, loop_embed=loop_tail_embed, loop_weight=float(loop_weight), mean_weight=float(mean), centroid_weights=cw, ).astype(np.float32, copy=False) # target SR (default input SR) inp_info = sf.info(tmp_path) input_sr = int(inp_info.samplerate) target_sr = int(target_sample_rate or input_sr) params = JamParams( bpm=bpm, beats_per_bar=beats_per_bar, bars_per_chunk=bars_per_chunk, target_sr=target_sr, loudness_mode=loudness_mode, headroom_db=loudness_headroom_db, style_vec=style_vec, ref_loop=loop_tail, # For loudness matching combined_loop=loop, # NEW: Full loop for context setup guidance_weight=guidance_weight, temperature=temperature, topk=topk ) worker = JamWorker(mrt, params) sid = str(uuid.uuid4()) with jam_lock: jam_registry[sid] = worker worker.start() return {"session_id": sid} @app.get("/jam/next") def jam_next(session_id: str): """ Get the next sequential chunk in the jam session. This ensures chunks are delivered in order without gaps. The new JamWorker returns raw WAV bytes in chunk.audio_bytes. We convert to base64 here for backward compatibility with existing clients. """ with jam_lock: worker = jam_registry.get(session_id) if worker is None or not worker.is_alive(): raise HTTPException(status_code=404, detail="Session not found") # Get the next sequential chunk (this blocks until ready) chunk = worker.get_next_chunk() if chunk is None: raise HTTPException(status_code=408, detail="Chunk not ready within timeout") # Convert raw WAV bytes to base64 for backward compatibility audio_base64 = base64.b64encode(chunk.audio_bytes).decode("utf-8") return { "chunk": { "index": chunk.index, "audio_base64": audio_base64, "metadata": chunk.metadata } } @app.post("/jam/consume") def jam_consume(session_id: str = Form(...), chunk_index: int = Form(...)): """ Mark a chunk as consumed by the frontend. This helps the worker manage its buffer and generation flow. """ with jam_lock: worker = jam_registry.get(session_id) if worker is None or not worker.is_alive(): raise HTTPException(status_code=404, detail="Session not found") worker.mark_chunk_consumed(chunk_index) return {"consumed": chunk_index} @app.post("/jam/stop") def jam_stop(session_id: str = Body(..., embed=True)): with jam_lock: worker = jam_registry.get(session_id) if worker is None: raise HTTPException(status_code=404, detail="Session not found") worker.stop() worker.join(timeout=5.0) if worker.is_alive(): # It’s daemon=True, so it won’t block process exit, but report it print(f"⚠️ JamWorker {session_id} did not stop within timeout") with jam_lock: jam_registry.pop(session_id, None) return {"stopped": True} @app.post("/jam/stop_all") def jam_stop_all(): """Force stop all active jam sessions (nuclear option for cleanup)""" stopped_sessions = [] with jam_lock: for session_id, worker in list(jam_registry.items()): if worker.is_alive(): worker.stop() worker.join(timeout=2.0) stopped_sessions.append(session_id) jam_registry.pop(session_id, None) return {"stopped_sessions": stopped_sessions, "count": len(stopped_sessions)} @app.post("/jam/update") def jam_update( session_id: str = Form(...), # knobs guidance_weight: Optional[float] = Form(None), temperature: Optional[float] = Form(None), topk: Optional[int] = Form(None), # styles styles: str = Form(""), style_weights: str = Form(""), loop_weight: Optional[float] = Form(None), use_current_mix_as_style: bool = Form(False), # NEW steering mean: Optional[float] = Form(None), centroid_weights: str = Form(""), ): asset_manager.ensure_assets_loaded(get_mrt()) with jam_lock: worker = jam_registry.get(session_id) if worker is None or not worker.is_alive(): raise HTTPException(status_code=404, detail="Session not found") # 1) fast knob updates if any(v is not None for v in (guidance_weight, temperature, topk)): worker.update_knobs( guidance_weight=guidance_weight, temperature=temperature, topk=topk ) # 2) rebuild style only if asked wants_style_update = ( use_current_mix_as_style or (styles.strip() != "") or (mean is not None) or (centroid_weights.strip() != "") ) if not wants_style_update: return {"ok": True} # --- parse inputs (robust) --- text_list = [s.strip() for s in (styles.split(",") if styles else []) if s.strip()] try: tw = [float(x) for x in style_weights.split(",")] if style_weights else [] except ValueError: tw = [] try: cw = [float(x) for x in centroid_weights.split(",")] if centroid_weights else [] except ValueError: cw = [] # Clamp centroid weights to available centroids (if loaded) max_c = 0 if _CENTROIDS is None else int(_CENTROIDS.shape[0]) if max_c and len(cw) > max_c: cw = cw[:max_c] # Snapshot minimal state under lock with worker._lock: combined_loop = worker.params.combined_loop if use_current_mix_as_style else None lw = None if use_current_mix_as_style: lw = 1.0 if (loop_weight is None) else float(loop_weight) mrt = worker.mrt # Heavy work OUTSIDE the lock loop_embed = None if combined_loop is not None: loop_embed = mrt.embed_style(combined_loop) style_vec = build_style_vector( mrt, text_styles=text_list, text_weights=tw, loop_embed=loop_embed, # None => ignored by builder loop_weight=lw, # None => ignored by builder mean_weight=(None if mean is None else float(mean)), centroid_weights=cw, # [] => ignored by builder ).astype(np.float32, copy=False) # Swap atomically with worker._lock: worker.params.style_vec = style_vec return {"ok": True} @app.post("/jam/reseed") def jam_reseed(session_id: str = Form(...), loop_audio: UploadFile = File(None)): with jam_lock: worker = jam_registry.get(session_id) if worker is None or not worker.is_alive(): raise HTTPException(status_code=404, detail="Session not found") # Option 1: use uploaded new combined bounce from the app (preferred) if loop_audio is not None: data = loop_audio.file.read() if not data: raise HTTPException(status_code=400, detail="Empty file") with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: tmp.write(data); path = tmp.name wav = au.Waveform.from_file(path).resample(worker.mrt.sample_rate).as_stereo() else: # Option 2: reseed from the original combined_loop # (Usually better to reseed from the Swift-side combined mix you upload.) combined_loop = worker.params.combined_loop if combined_loop is None: raise HTTPException(status_code=400, detail="No combined_loop available to reseed from. Please upload loop_audio.") wav = combined_loop.as_stereo().resample(worker.mrt.sample_rate) worker.reseed_from_waveform(wav) return {"ok": True} @app.post("/jam/reseed_splice") def jam_reseed_splice( session_id: str = Form(...), anchor_bars: float = Form(2.0), # how much of the original to re-inject combined_audio: UploadFile = File(None), # optional: Swift supplies the current combined mix ): """ Seamless reseed by splicing tokens at the next chunk boundary. The new jam_worker implementation: - Keeps the *first* `anchor_bars` worth of context from the original loop - Keeps the *remainder* from the CURRENT state.context_tokens (no re-encoding!) - This ensures the tail of the spliced context is token-identical to what MagentaRT was already working with, avoiding codec round-trip artifacts. If combined_audio is provided, it will be parsed but the splice itself uses current tokens to avoid re-encoding artifacts. """ worker = jam_registry.get(session_id) if worker is None or not worker.is_alive(): raise HTTPException(status_code=404, detail="Session not found") # If combined_audio is provided, parse it (future use) # but the reseed_splice itself doesn't need it anymore recent_wav = None if combined_audio is not None: data = combined_audio.file.read() if data: with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: tmp.write(data) path = tmp.name recent_wav = au.Waveform.from_file(path).resample(worker.mrt.sample_rate).as_stereo() # Perform the splice reseed # New signature: reseed_splice(recent_wav: Optional, anchor_bars: float) # recent_wav is now optional - if None, uses current state tokens worker.reseed_splice(recent_wav, anchor_bars=float(anchor_bars)) return {"ok": True, "anchor_bars": float(anchor_bars)} @app.get("/jam/status") def jam_status(session_id: str): with jam_lock: worker = jam_registry.get(session_id) if worker is None: raise HTTPException(status_code=404, detail="Session not found") running = worker.is_alive() # Snapshot safely - use new attribute names from updated jam_worker.py with worker._lock: last_generated = int(worker.idx) # _next_to_deliver is the index we're ABOUT to deliver, so last delivered is one less last_delivered = int(worker._next_to_deliver) - 1 queued = len(worker._outbox) # Changed from outbox to _outbox buffer_ahead = last_generated - max(0, last_delivered) p = worker.params spb = p.beats_per_bar * (60.0 / p.bpm) chunk_secs = p.bars_per_chunk * spb return { "running": running, "last_generated_index": last_generated, # Last chunk that finished generating "last_delivered_index": last_delivered, # Last chunk sent to frontend "buffer_ahead": buffer_ahead, # How many chunks ahead we are "queued_chunks": queued, # Total chunks in outbox "bpm": p.bpm, "beats_per_bar": p.beats_per_bar, "bars_per_chunk": p.bars_per_chunk, "seconds_per_bar": spb, "chunk_duration_seconds": chunk_secs, "target_sample_rate": p.target_sr, "last_chunk_started_at": worker.last_chunk_started_at, "last_chunk_completed_at": worker.last_chunk_completed_at, } @app.get("/health") def health(): # 1) Template mode → not ready (encourage duplication on GPU) if SPACE_MODE != "serve": return JSONResponse( status_code=503, content={ "ok": False, "status": "template_mode", "message": "This Space is a GPU template. Duplicate it and select an L40s/A100-class runtime to use the API.", "mode": SPACE_MODE, }, ) # 2) Runtime hardware probe probe = _gpu_probe() if not probe["ok"] or not probe["has_gpu"] or probe.get("backend") != "gpu": return JSONResponse( status_code=503, content={ "ok": False, "status": "gpu_unavailable", "message": "GPU is not visible to JAX. Select a GPU runtime (e.g., L40s) to serve.", "probe": probe, "mode": SPACE_MODE, }, ) # 3) Ready; include operational hints warmed = bool(_WARMED) with jam_lock: active_jams = sum(1 for w in jam_registry.values() if w.is_alive()) return { "ok": True, "status": "ready" if warmed else "initializing", "mode": SPACE_MODE, "warmed": warmed, "active_jams": active_jams, "probe": probe, } @app.middleware("http") async def log_requests(request: Request, call_next): rid = request.headers.get("X-Request-ID", "-") print(f"📥 {request.method} {request.url.path}?{request.url.query} [rid={rid}]") try: response = await call_next(request) except Exception as e: print(f"💥 exception for {request.url.path} [rid={rid}]: {e}") raise print(f"📤 {response.status_code} {request.url.path} [rid={rid}]") return response # ---------------------------- # websockets route (rt-mode only) # ---------------------------- @app.websocket("/ws/jam") async def ws_jam(websocket: WebSocket): """ Real-time streaming WebSocket endpoint for MagentaRT. This route operates in 'rt' mode only - for bar-aligned jam sessions, use the HTTP endpoints (/jam/start, /jam/chunk, etc.) instead. The server handles crossfading internally via MagentaRTState, so clients can simply play back chunks sequentially without additional crossfade logic. """ await websocket.accept() binary_audio = False async def send_json(obj): return await send_json_safe(websocket, obj) try: while True: raw = await websocket.receive_text() msg = json.loads(raw) mtype = msg.get("type") # --- START (rt-mode only) --- if mtype == "start": binary_audio = bool(msg.get("binary_audio", False)) params = msg.get("params", {}) or {} # Initialize MagentaRT state mrt = get_mrt() state = mrt.init_state() # Build silent context (10s) tokens codec_fps = float(mrt.codec.frame_rate) ctx_seconds = float(mrt.config.context_length_frames) / codec_fps sr = int(mrt.sample_rate) samples = int(max(1, round(ctx_seconds * sr))) silent = au.Waveform(np.zeros((samples, 2), np.float32), sr) tokens = mrt.codec.encode(silent).astype(np.int32)[:, :mrt.config.decoder_codec_rvq_depth] state.context_tokens = tokens # Parse params (including steering) asset_manager.ensure_assets_loaded(get_mrt()) styles_str = params.get("styles", "warmup") or "" style_weights_str = params.get("style_weights", "") or "" mean_w = float(params.get("mean", 0.0) or 0.0) cw_str = str(params.get("centroid_weights", "") or "") text_list = [s.strip() for s in styles_str.split(",") if s.strip()] try: text_w = [float(x) for x in style_weights_str.split(",")] if style_weights_str else [] except ValueError: text_w = [] try: cw = [float(x) for x in cw_str.split(",") if x.strip() != ""] except ValueError: cw = [] # Clamp centroid weights to available centroids if _CENTROIDS is not None and len(cw) > int(_CENTROIDS.shape[0]): cw = cw[: int(_CENTROIDS.shape[0])] # Build initial style vector (no loop_embed in rt mode) style_vec = build_style_vector( mrt, text_styles=text_list, text_weights=text_w, loop_embed=None, loop_weight=None, mean_weight=mean_w, centroid_weights=cw, ) # Stash rt session fields on the websocket object websocket._mrt = mrt websocket._state = state websocket._style_cur = style_vec websocket._style_tgt = style_vec websocket._style_ramp_s = float(params.get("style_ramp_seconds", 0.0)) websocket._rt_mean = mean_w websocket._rt_centroid_weights = cw websocket._rt_running = True websocket._rt_sr = sr websocket._rt_topk = int(params.get("topk", 40)) websocket._rt_temp = float(params.get("temperature", 1.1)) websocket._rt_guid = float(params.get("guidance_weight", 1.1)) websocket._pace = params.get("pace", "asap") # "realtime" | "asap" # Report whether steering assets were loaded assets_ok = (_MEAN_EMBED is not None) or (_CENTROIDS is not None) await send_json({"type": "started", "mode": "rt", "steering_assets": "loaded" if assets_ok else "none"}) # Kick off the ~2s streaming loop async def _rt_loop(): try: mrt = websocket._mrt chunk_secs = (mrt.config.chunk_length_frames * mrt.config.frame_length_samples) / float(mrt.sample_rate) target_next = time.perf_counter() while websocket._rt_running: mrt.guidance_weight = websocket._rt_guid mrt.temperature = websocket._rt_temp mrt.topk = websocket._rt_topk # Ramp style toward target ramp = float(getattr(websocket, "_style_ramp_s", 0.0) or 0.0) if ramp <= 0.0: websocket._style_cur = websocket._style_tgt else: step = min(1.0, chunk_secs / ramp) websocket._style_cur = websocket._style_cur + step * (websocket._style_tgt - websocket._style_cur) # Generate chunk (crossfading handled internally by MagentaRT) wav, new_state = mrt.generate_chunk(state=websocket._state, style=websocket._style_cur) websocket._state = new_state x = wav.samples.astype(np.float32, copy=False) buf = io.BytesIO() sf.write(buf, x, mrt.sample_rate, subtype="FLOAT", format="WAV") ok = True if binary_audio: try: await websocket.send_bytes(buf.getvalue()) ok = await send_json({"type": "chunk_meta", "metadata": {"sample_rate": mrt.sample_rate}}) except Exception: ok = False else: b64 = base64.b64encode(buf.getvalue()).decode("utf-8") ok = await send_json({"type": "chunk", "audio_base64": b64, "metadata": {"sample_rate": mrt.sample_rate}}) if not ok: break if getattr(websocket, "_pace", "asap") == "realtime": t1 = time.perf_counter() target_next += chunk_secs sleep_s = max(0.0, target_next - t1 - 0.02) if sleep_s > 0: await asyncio.sleep(sleep_s) except asyncio.CancelledError: pass except Exception: pass websocket._rt_task = asyncio.create_task(_rt_loop()) # --- UPDATE (rt-mode knobs & style) --- elif mtype == "update": if not hasattr(websocket, "_rt_running"): await send_json({"type":"error","error":"Session not started"}); continue websocket._rt_temp = float(msg.get("temperature", websocket._rt_temp)) websocket._rt_topk = int(msg.get("topk", websocket._rt_topk)) websocket._rt_guid = float(msg.get("guidance_weight", websocket._rt_guid)) # Steering fields if "mean" in msg and msg["mean"] is not None: try: websocket._rt_mean = float(msg["mean"]) except: websocket._rt_mean = 0.0 if "centroid_weights" in msg: cw = [w.strip() for w in str(msg["centroid_weights"]).split(",") if w.strip() != ""] try: websocket._rt_centroid_weights = [float(x) for x in cw] except: websocket._rt_centroid_weights = [] # Styles / text weights (optional, comma-separated) styles_str = msg.get("styles", None) style_weights_str = msg.get("style_weights", "") text_list = [s for s in (styles_str.split(",") if styles_str else []) if s.strip()] text_w = [float(x) for x in style_weights_str.split(",")] if style_weights_str else [] asset_manager.ensure_assets_loaded(get_mrt()) websocket._style_tgt = build_style_vector( websocket._mrt, text_styles=text_list, text_weights=text_w, loop_embed=None, loop_weight=None, mean_weight=float(websocket._rt_mean), centroid_weights=websocket._rt_centroid_weights, ) # Optionally allow live changes to ramp: if "style_ramp_seconds" in msg: try: websocket._style_ramp_s = float(msg["style_ramp_seconds"]) except: pass await send_json({"type":"status","updated":"rt-knobs+style"}) # --- BUFFER STATUS (from frontend for adaptive pacing - acknowledged silently) --- elif mtype == "buffer_status": # Frontend reports its buffer level; could be used for adaptive pacing # For now we just acknowledge receipt without action pass # --- STOP --- elif mtype == "stop": websocket._rt_running = False task = getattr(websocket, "_rt_task", None) if task is not None: task.cancel() try: await task except asyncio.CancelledError: pass await send_json({"type":"stopped"}) break # --- PING/PONG --- elif mtype == "ping": await send_json({"type":"pong"}) else: await send_json({"type":"error","error":f"Unknown type {mtype}"}) except WebSocketDisconnect: pass except Exception as e: try: await send_json({"type":"error","error":str(e)}) except Exception: pass finally: # Ensure streaming loop is stopped if hasattr(websocket, "_rt_running"): websocket._rt_running = False task = getattr(websocket, "_rt_task", None) if task is not None: task.cancel() try: await asyncio.wait_for(task, timeout=1.0) except: pass try: if websocket.client_state != WebSocketState.DISCONNECTED: await websocket.close() except Exception: pass # --- FastRTC Gradio Integration (only in serve mode) --- if SPACE_MODE == "serve": try: from fastrtc_magenta import create_magenta_stream, FASTRTC_AVAILABLE if FASTRTC_AVAILABLE: magenta_stream = create_magenta_stream( get_mrt_fn=get_mrt, build_style_fn=build_style_vector, asset_manager=asset_manager, concurrency_limit=1, time_limit=3600, ) magenta_stream.mount(app, path="/rtc") app = gr.mount_gradio_app(app, magenta_stream.ui, path="/gradio") print("✓ FastRTC Gradio interface available at /gradio") except Exception as e: print(f"⚠ FastRTC integration skipped: {e}") @app.get("/ping") def ping(): return {"ok": True} @app.get("/", response_class=Response) def read_root(): """Root endpoint that explains what this API does""" try: html_file = Path(__file__).parent / "documentation.html" html_content = html_file.read_text(encoding='utf-8') except FileNotFoundError: # Fallback if file is missing html_content = """

MagentaRT Research API

Documentation file not found. Please check documentation.html

""" return Response(content=html_content, media_type="text/html") @app.get("/lil_demo_540p.mp4") def demo_video(): return FileResponse(Path(__file__).parent / "lil_demo_540p.mp4", media_type="video/mp4") @app.get("/tester", response_class=HTMLResponse) def tester(): html_path = Path(__file__).parent / "magentaRT_rt_tester.html" return HTMLResponse( html_path.read_text(encoding="utf-8"), headers={"Cache-Control": "no-store"} # avoid sticky caches while iterating ) @app.get("/magenta_prompts.js") def magenta_prompts_js(): js_path = Path(__file__).parent / "magenta_prompts.js" return FileResponse(js_path, media_type="text/javascript", headers={"Cache-Control": "no-store"})