import os # Useful XLA GPU optimizations (harmless if a flag is unknown) 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 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 import uuid, threading import logging import gradio as gr from typing import Optional 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 from pathlib import Path def _resolve_checkpoint_dir() -> str | None: repo_id = os.getenv("MRT_CKPT_REPO") if not repo_id: return None step = os.getenv("MRT_CKPT_STEP") # e.g., "1863001" from huggingface_hub import snapshot_download allow = None if step: base = f"checkpoint_{step}" # include everything under the step *including dotfiles* allow = [ f"{base}/**", # all regular files f"{base}/**/.*", # dotfiles like .zarray / .zattrs f"{base}/**/.zarray", f"{base}/**/.zattrs", ] local = snapshot_download( repo_id=repo_id, repo_type="model", revision=os.getenv("MRT_CKPT_REV", "main"), local_dir="/home/appuser/.cache/mrt_ckpt/repo", local_dir_use_symlinks=False, allow_patterns=allow or ["*"], # whole repo if no step provided ) root = Path(local) if step: step_dir = root / f"checkpoint_{step}" # sanity check: make sure dotfiles arrived if not any(step_dir.rglob(".zarray")): raise RuntimeError( f"Checkpoint appears incomplete (no .zarray files under {step_dir}). " "Ensure allow_patterns includes dotfiles or re-upload preserving dotfiles." ) return str(step_dir) # otherwise pick latest checkpoint_* directory step_dirs = [d for d in root.iterdir() if d.is_dir() and re.match(r"checkpoint_\\d+$", d.name)] if step_dirs: pick = max(step_dirs, key=lambda d: int(d.name.split('_')[-1])) if not any(pick.rglob(".zarray")): raise RuntimeError( f"Checkpoint appears incomplete (no .zarray files under {pick})." ) return str(pick) return str(root) 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) # Call the patch immediately at import time (before MagentaRT init) _patch_t5x_for_gpu_coords() def create_documentation_interface(): """Create a Gradio interface for documentation and transparency""" with gr.Blocks(title="MagentaRT Research API", theme=gr.themes.Soft()) as interface: gr.Markdown( r""" # 🎵 MagentaRT Live Music Generation Research API **Research-only implementation for iOS/web app development** This API uses Google's [MagentaRT](https://github.com/magenta/magenta-realtime) to generate continuous music either as **bar-aligned chunks over HTTP** or as **low-latency realtime chunks via WebSocket**. """ ) with gr.Tabs(): # ------------------------------------------------------------------ # About & current status # ------------------------------------------------------------------ with gr.Tab("📖 About & Status"): gr.Markdown( r""" ## What this is We're exploring AI‑assisted loop‑based music creation that can run on GPUs (not just TPUs) and stream to apps in realtime. ### Implemented backends - **HTTP (bar‑aligned):** `/generate`, `/jam/start`, `/jam/next`, `/jam/stop`, `/jam/update`, etc. - **WebSocket (realtime):** `ws://…/ws/jam` with `mode="rt"` (Colab‑style continuous chunks). New in this build. ## What we learned (GPU notes) - **L40S 48GB:** comfortably **faster than realtime** → we added a `pace: "realtime"` switch so the server doesn’t outrun playback. - **L4 24GB:** **consistently just under realtime**; even with pre‑roll buffering, TF32/JAX tunings, reduced chunk size, and the **base** checkpoint, we still see eventual under‑runs. - **Implication:** For production‑quality realtime, aim for ~**40GB VRAM** per user/session (e.g., **A100 40GB**, or MIG slices ≈ **35–40GB** on newer parts). Smaller GPUs can demo, but sustained realtime is not reliable. ## Model / audio specs - **Model:** MagentaRT (T5X; decoder RVQ depth = 16) - **Audio:** 48 kHz stereo, 2.0 s chunks by default, 40 ms crossfade - **Context:** 10 s rolling context window """ ) # ------------------------------------------------------------------ # HTTP API # ------------------------------------------------------------------ with gr.Tab("🔧 API (HTTP)"): gr.Markdown( r""" ### Single Generation ```bash curl -X POST \ "$HOST/generate" \ -F "loop_audio=@drum_loop.wav" \ -F "bpm=120" \ -F "bars=8" \ -F "styles=acid house,techno" \ -F "guidance_weight=5.0" \ -F "temperature=1.1" ``` ### Continuous Jamming (bar‑aligned, HTTP) ```bash # 1) Start a session echo $(curl -s -X POST "$HOST/jam/start" \ -F "loop_audio=@loop.wav" \ -F "bpm=120" \ -F "bars_per_chunk=8") | jq . # → {"session_id":"…"} # 2) Pull next chunk (repeat) curl "$HOST/jam/next?session_id=$SESSION" # 3) Stop curl -X POST "$HOST/jam/stop" \ -H "Content-Type: application/json" \ -d '{"session_id":"'$SESSION'"}' ``` ### Common parameters - **bpm** *(int)* – beats per minute - **bars / bars_per_chunk** *(int)* – musical length - **styles** *(str)* – comma‑separated text prompts (mixed internally) - **guidance_weight** *(float)* – style adherence (CFG weight) - **temperature / topk** – sampling controls - **intro_bars_to_drop** *(int, /generate)* – generate-and-trim intro """ ) # ------------------------------------------------------------------ # WebSocket API: realtime (‘rt’ mode) # ------------------------------------------------------------------ with gr.Tab("🧩 API (WebSocket • rt mode)"): gr.Markdown( r""" Connect to `wss://…/ws/jam` and send a **JSON control stream**. In `rt` mode the server emits ~2 s WAV chunks (or binary frames) continuously. ### Start (client → server) ```jsonc { "type": "start", "mode": "rt", "binary_audio": false, // true → raw WAV bytes + separate chunk_meta "params": { "styles": "heavy metal", // or "jazz, hiphop" "style_weights": "1.0,1.0", // optional, auto‑normalized "temperature": 1.1, "topk": 40, "guidance_weight": 1.1, "pace": "realtime", // "realtime" | "asap" (default) "max_decode_frames": 50 // 50≈2.0s; try 36–45 on smaller GPUs } } ``` ### Server events (server → client) - `{"type":"started","mode":"rt"}` – handshake - `{"type":"chunk","audio_base64":"…","metadata":{…}}` – base64 WAV - `metadata.sample_rate` *(int)* – usually 48000 - `metadata.chunk_frames` *(int)* – e.g., 50 - `metadata.chunk_seconds` *(float)* – frames / 25.0 - `metadata.crossfade_seconds` *(float)* – typically 0.04 - `{"type":"chunk_meta","metadata":{…}}` – sent **after** a binary frame when `binary_audio=true` - `{"type":"status",…}`, `{"type":"error",…}`, `{"type":"stopped"}` ### Update (client → server) ```jsonc { "type": "update", "styles": "jazz, hiphop", "style_weights": "1.0,0.8", "temperature": 1.2, "topk": 64, "guidance_weight": 1.0, "pace": "realtime", // optional live flip "max_decode_frames": 40 // optional; <= 50 } ``` ### Stop / ping ```json {"type":"stop"} {"type":"ping"} ``` ### Browser quick‑start (schedules seamlessly with 25–40 ms crossfade) ```html ``` ### Python client (async) ```python import asyncio, json, websockets, base64, soundfile as sf, io async def run(url): async with websockets.connect(url) as ws: await ws.send(json.dumps({"type":"start","mode":"rt","binary_audio":False, "params": {"styles":"warmup","temperature":1.1,"topk":40,"guidance_weight":1.1,"pace":"realtime"}})) while True: msg = json.loads(await ws.recv()) if msg.get("type") == "chunk": wav = base64.b64decode(msg["audio_base64"]) # bytes of a WAV x, sr = sf.read(io.BytesIO(wav), dtype="float32") print("chunk", x.shape, sr) elif msg.get("type") in ("stopped","error"): break asyncio.run(run("wss://YOUR_SPACE/ws/jam")) ``` """ ) # ------------------------------------------------------------------ # Performance & hardware guidance # ------------------------------------------------------------------ with gr.Tab("📊 Performance & Hardware"): gr.Markdown( r""" ### Current observations - **L40S 48GB** → faster than realtime. Use `pace:"realtime"` to avoid client over‑buffering. - **L4 24GB** → slightly **below** realtime even with pre‑roll buffering, TF32/Autotune, smaller chunks (`max_decode_frames`), and the **base** checkpoint. ### Practical guidance - For consistent realtime, target **~40GB VRAM per active stream** (e.g., **A100 40GB**, or MIG slices ≈ **35–40GB** on newer GPUs). - Keep client‑side **overlap‑add** (25–40 ms) for seamless chunk joins. - Prefer **`pace:"realtime"`** once playback begins; use **ASAP** only to build a short pre‑roll if needed. - Optional knob: **`max_decode_frames`** (default **50** ≈ 2.0 s). Reducing to **36–45** can lower per‑chunk latency/VRAM, but doesn’t increase frames/sec throughput. ### Concurrency This research build is designed for **one active jam per GPU**. Concurrency would require GPU partitioning (MIG) or horizontal scaling with a session scheduler. """ ) # ------------------------------------------------------------------ # Changelog & legal # ------------------------------------------------------------------ with gr.Tab("🗒️ Changelog & Legal"): gr.Markdown( r""" ### Recent changes - New **WebSocket realtime** route: `/ws/jam` (`mode:"rt"`) - Added server pacing flag: `pace: "realtime" | "asap"` - Exposed `max_decode_frames` for shorter chunks on smaller GPUs - Client test page now does proper **overlap‑add** crossfade between chunks ### Licensing This project uses MagentaRT under: - **Code:** Apache 2.0 - **Model weights:** CC‑BY 4.0 Please review the MagentaRT repo for full terms. """ ) gr.Markdown( r""" --- **🔬 Research Project** | **📱 iOS/Web Development** | **🎵 Powered by MagentaRT** """ ) return interface 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 # ---------------------------- # Main generation (single combined style vector) # ---------------------------- def generate_loop_continuation_with_mrt( mrt, input_wav_path: str, bpm: float, extra_styles=None, style_weights=None, bars: int = 8, beats_per_bar: int = 4, loop_weight: float = 1.0, loudness_mode: str = "auto", loudness_headroom_db: float = 1.0, intro_bars_to_drop: int = 0, # <— NEW ): # Load & prep (unchanged) loop = au.Waveform.from_file(input_wav_path).resample(mrt.sample_rate).as_stereo() # Use tail for context (your recent change) codec_fps = float(mrt.codec.frame_rate) ctx_seconds = float(mrt.config.context_length_frames) / codec_fps loop_for_context = take_bar_aligned_tail(loop, bpm, beats_per_bar, ctx_seconds) tokens_full = mrt.codec.encode(loop_for_context).astype(np.int32) tokens = tokens_full[:, :mrt.config.decoder_codec_rvq_depth] # Bar-aligned token window (unchanged) 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 ) state = mrt.init_state() state.context_tokens = context_tokens # STYLE embed (optional: switch to loop_for_context if you want stronger “recent” bias) loop_embed = mrt.embed_style(loop_for_context) embeds, weights = [loop_embed], [float(loop_weight)] if extra_styles: for i, s in enumerate(extra_styles): if s.strip(): embeds.append(mrt.embed_style(s.strip())) w = style_weights[i] if (style_weights and i < len(style_weights)) else 1.0 weights.append(float(w)) wsum = float(sum(weights)) or 1.0 weights = [w / wsum for w in weights] combined_style = np.sum([w * e for w, e in zip(weights, embeds)], axis=0).astype(loop_embed.dtype) # --- Length math --- seconds_per_bar = beats_per_bar * (60.0 / bpm) total_secs = bars * seconds_per_bar drop_bars = max(0, int(intro_bars_to_drop)) drop_secs = min(drop_bars, bars) * seconds_per_bar # clamp to <= bars gen_total_secs = total_secs + drop_secs # generate extra # Chunk scheduling to cover gen_total_secs chunk_secs = mrt.config.chunk_length_frames * mrt.config.frame_length_samples / mrt.sample_rate # ~2.0 steps = int(math.ceil(gen_total_secs / chunk_secs)) + 1 # pad then trim # Generate chunks = [] for _ in range(steps): wav, state = mrt.generate_chunk(state=state, style=combined_style) chunks.append(wav) # Stitch continuous audio stitched = stitch_generated(chunks, mrt.sample_rate, mrt.config.crossfade_length).as_stereo() # Trim to generated length (bars + dropped bars) stitched = hard_trim_seconds(stitched, gen_total_secs) # 👉 Drop the intro bars if drop_secs > 0: n_drop = int(round(drop_secs * stitched.sample_rate)) stitched = au.Waveform(stitched.samples[n_drop:], stitched.sample_rate) # Final exact-length trim to requested bars out = hard_trim_seconds(stitched, total_secs) # Final polish AFTER drop out = out.peak_normalize(0.95) apply_micro_fades(out, 5) # Loudness match to input (after drop) so bar 1 sits right out, loud_stats = match_loudness_to_reference( ref=loop, target=out, method=loudness_mode, headroom_db=loudness_headroom_db ) return out, loud_stats def generate_style_only_with_mrt( mrt, bpm: float, bars: int = 8, beats_per_bar: int = 4, styles: str = "warmup", style_weights: str = "", intro_bars_to_drop: int = 0, ): """ Style-only, bar-aligned generation using a silent context (no input audio). Returns: (au.Waveform out, dict loud_stats_or_None) """ # ---- Build a 10s silent context, tokenized for the model ---- codec_fps = float(mrt.codec.frame_rate) ctx_seconds = float(mrt.config.context_length_frames) / codec_fps sr = int(mrt.sample_rate) silent = au.Waveform(np.zeros((int(round(ctx_seconds * sr)), 2), np.float32), sr) tokens_full = mrt.codec.encode(silent).astype(np.int32) tokens = tokens_full[:, :mrt.config.decoder_codec_rvq_depth] state = mrt.init_state() state.context_tokens = tokens # ---- Style vector (text prompts only, normalized weights) ---- prompts = [s.strip() for s in (styles.split(",") if styles else []) if s.strip()] if not prompts: prompts = ["warmup"] sw = [float(x) for x in style_weights.split(",")] if style_weights else [] embeds, weights = [], [] for i, p in enumerate(prompts): embeds.append(mrt.embed_style(p)) weights.append(sw[i] if i < len(sw) else 1.0) wsum = float(sum(weights)) or 1.0 weights = [w / wsum for w in weights] style_vec = np.sum([w * e for w, e in zip(weights, embeds)], axis=0).astype(np.float32) # ---- Target length math ---- seconds_per_bar = beats_per_bar * (60.0 / bpm) total_secs = bars * seconds_per_bar drop_bars = max(0, int(intro_bars_to_drop)) drop_secs = min(drop_bars, bars) * seconds_per_bar gen_total_secs = total_secs + drop_secs # ~2.0s chunk length from model config chunk_secs = (mrt.config.chunk_length_frames * mrt.config.frame_length_samples) / float(mrt.sample_rate) # Generate enough chunks to cover total, plus a pad chunk for crossfade headroom steps = int(math.ceil(gen_total_secs / chunk_secs)) + 1 chunks = [] for _ in range(steps): wav, state = mrt.generate_chunk(state=state, style=style_vec) chunks.append(wav) # Stitch & trim to exact musical length stitched = stitch_generated(chunks, mrt.sample_rate, mrt.config.crossfade_length).as_stereo() stitched = hard_trim_seconds(stitched, gen_total_secs) if drop_secs > 0: n_drop = int(round(drop_secs * stitched.sample_rate)) stitched = au.Waveform(stitched.samples[n_drop:], stitched.sample_rate) out = hard_trim_seconds(stitched, total_secs) out = out.peak_normalize(0.95) apply_micro_fades(out, 5) return out, None # loudness stats not applicable (no reference) # ---------------------------- # 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() def get_mrt(): global _MRT if _MRT is None: with _MRT_LOCK: if _MRT is None: ckpt_dir = _resolve_checkpoint_dir() # ← points to checkpoint_1863001 _MRT = system.MagentaRT( tag=os.getenv("MRT_SIZE", "large"), # keep 'large' if finetuned from large guidance_weight=5.0, device="gpu", checkpoint_dir=ckpt_dir, # ← uses your finetune lazy=False, ) return _MRT _WARMED = False _WARMUP_LOCK = threading.Lock() def _mrt_warmup(): """ Build a minimal, bar-aligned silent context and run one 2s generate_chunk to trigger XLA JIT & autotune so first real request is fast. """ 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 silent, stereo context of ctx_seconds --- import numpy as np, soundfile as sf samples = int(max(1, round(ctx_seconds * sr))) silent = np.zeros((samples, 2), dtype=np.float32) with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp: sf.write(tmp.name, silent, 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() seconds_per_bar = beats_per_bar * (60.0 / bpm) 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.") 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) # Kick it off in the background on server start @app.on_event("startup") def _kickoff_warmup(): 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("/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), # <— NEW ): # Read file data = loop_audio.file.read() if not data: return {"error": "Empty file"} with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: tmp.write(data) tmp_path = tmp.name # Parse styles + weights extra_styles = [s 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 mrt = get_mrt() # warm once, in this worker thread # Temporarily override MRT inference knobs for this request with mrt_overrides(mrt, guidance_weight=guidance_weight, temperature=temperature, topk=topk): 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, # <— pass through ) # 1) Figure out the desired SR inp_info = sf.info(tmp_path) input_sr = int(inp_info.samplerate) target_sr = int(target_sample_rate or input_sr) # 2) Convert to target 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 x = resample_and_snap(x, cur_sr=cur_sr, target_sr=target_sr, seconds=expected_secs) # 3) Encode WAV once (no extra write) audio_b64, total_samples, channels = wav_bytes_base64(x, target_sr) loop_duration_seconds = total_samples / float(target_sr) # 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), "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, } return {"audio_base64": audio_b64, "metadata": metadata} # 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), 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), ): # 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) # style vec = normalized mix of loop_tail + extra styles embeds, weights = [mrt.embed_style(loop_tail)], [float(loop_weight)] extra = [s for s in (styles.split(",") if styles else []) if s.strip()] sw = [float(x) for x in style_weights.split(",")] if style_weights else [] for i, s in enumerate(extra): embeds.append(mrt.embed_style(s.strip())) weights.append(sw[i] if i < len(sw) else 1.0) wsum = sum(weights) or 1.0 weights = [w / wsum for w in weights] style_vec = np.sum([w * e for w, e in zip(weights, embeds)], axis=0).astype(embeds[0].dtype) # 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. """ 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") return { "chunk": { "index": chunk.index, "audio_base64": chunk.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/update") # consolidated def jam_update( session_id: str = Form(...), # knobs (all optional) guidance_weight: Optional[float] = Form(None), temperature: Optional[float] = Form(None), topk: Optional[int] = Form(None), # styles (all optional) styles: str = Form(""), style_weights: str = Form(""), loop_weight: Optional[float] = Form(None), # None means "don’t change" use_current_mix_as_style: bool = Form(False), ): 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) Apply knob updates (atomic under lock) 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) Apply style updates only if requested wants_style_update = use_current_mix_as_style or (styles.strip() != "") if wants_style_update: embeds, weights = [], [] # optional: include current mix as a style component if use_current_mix_as_style and worker.params.combined_loop is not None: lw = 1.0 if loop_weight is None else float(loop_weight) embeds.append(worker.mrt.embed_style(worker.params.combined_loop)) weights.append(lw) # extra text styles extra = [s for s in (styles.split(",") if styles else []) if s.strip()] sw = [float(x) for x in style_weights.split(",")] if style_weights else [] for i, s in enumerate(extra): embeds.append(worker.mrt.embed_style(s.strip())) weights.append(sw[i] if i < len(sw) else 1.0) if embeds: # only swap if we actually built something wsum = sum(weights) or 1.0 weights = [w / wsum for w in weights] style_vec = np.sum([w * e for w, e in zip(weights, embeds)], axis=0).astype(np.float32) # install 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 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 what we’ve been streaming (the model side) # (Usually better to reseed from the Swift-side “combined” mix you trust.) s = getattr(worker, "_stream", None) if s is None or s.shape[0] == 0: raise HTTPException(status_code=400, detail="No internal stream to reseed from") wav = au.Waveform(s.astype(np.float32, copy=False), int(worker.mrt.sample_rate)).as_stereo() 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), # preferred: Swift supplies the current combined mix ): worker = jam_registry.get(session_id) if worker is None or not worker.is_alive(): raise HTTPException(status_code=404, detail="Session not found") # Build a waveform to reseed from wav = None if combined_audio is not None: data = combined_audio.file.read() if not data: raise HTTPException(status_code=400, detail="Empty combined_audio") 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: # Fallback: reseed from the model’s internal stream (less ideal than the Swift-side bounce) s = getattr(worker, "_stream", None) if s is None or s.shape[0] == 0: raise HTTPException(status_code=400, detail="No audio available to reseed from") wav = au.Waveform(s.astype(np.float32, copy=False), int(worker.mrt.sample_rate)).as_stereo() # Perform the splice reseed worker.reseed_splice(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 with worker._lock: last_generated = int(worker.idx) last_delivered = int(worker._last_delivered_index) queued = len(worker.outbox) buffer_ahead = last_generated - 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(): return {"ok": True} @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 # ---------------------------- def _combine_styles(mrt, styles_str: str = "", weights_str: str = ""): extra = [s.strip() for s in (styles_str or "").split(",") if s.strip()] if not extra: return mrt.embed_style("warmup") sw = [float(x) for x in (weights_str or "").split(",") if x.strip()] embeds, weights = [], [] for i, s in enumerate(extra): embeds.append(mrt.embed_style(s)) weights.append(sw[i] if i < len(sw) else 1.0) wsum = sum(weights) or 1.0 weights = [w/wsum for w in weights] import numpy as np return np.sum([w*e for w, e in zip(weights, embeds)], axis=0).astype(np.float32) @app.websocket("/ws/jam") async def ws_jam(websocket: WebSocket): await websocket.accept() sid = None worker = None binary_audio = False mode = "rt" # or "bar" # NEW: capture ws in closure 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 --- if mtype == "start": binary_audio = bool(msg.get("binary_audio", False)) mode = msg.get("mode", "bar") params = msg.get("params", {}) or {} sid = msg.get("session_id") # attach or create if sid: with jam_lock: worker = jam_registry.get(sid) if worker is None or not worker.is_alive(): await send_json({"type":"error","error":"Session not found"}) continue else: # optionally accept base64 loop and start a new worker (bar-mode) if mode == "bar": loop_b64 = msg.get("loop_audio_b64") if not loop_b64: await send_json({"type":"error","error":"loop_audio_b64 required for mode=bar when no session_id"}) continue loop_bytes = base64.b64decode(loop_b64) # mimic /jam/start with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: tmp.write(loop_bytes); tmp_path = tmp.name # build JamParams similar to /jam/start mrt = get_mrt() model_sr = int(mrt.sample_rate) # typically 48000 # Defaults for WS: raw loudness @ model SR, unless overridden by client: target_sr = int(params.get("target_sr", model_sr)) loudness_mode = params.get("loudness_mode", "none") headroom_db = float(params.get("headroom_db", 1.0)) loop = au.Waveform.from_file(tmp_path).resample(mrt.sample_rate).as_stereo() codec_fps = float(mrt.codec.frame_rate) ctx_seconds = float(mrt.config.context_length_frames) / codec_fps bpm = float(params.get("bpm", 120.0)) bpb = int(params.get("beats_per_bar", 4)) loop_tail = take_bar_aligned_tail(loop, bpm, bpb, ctx_seconds) # style vector (loop + extra styles) embeds, weights = [mrt.embed_style(loop_tail)], [float(params.get("loop_weight", 1.0))] extra = [s for s in (params.get("styles","").split(",")) if s.strip()] sw = [float(x) for x in params.get("style_weights","").split(",") if x.strip()] for i, s in enumerate(extra): embeds.append(mrt.embed_style(s.strip())) weights.append(sw[i] if i < len(sw) else 1.0) wsum = sum(weights) or 1.0 weights = [w/wsum for w in weights] style_vec = np.sum([w*e for w, e in zip(weights, embeds)], axis=0).astype(np.float32) # target SR fallback: input SR inp_info = sf.info(tmp_path) target_sr = int(params.get("target_sr", int(inp_info.samplerate))) # Build JamParams for WS bar-mode jp = JamParams( bpm=bpm, beats_per_bar=bpb, bars_per_chunk=int(params.get("bars_per_chunk", 8)), target_sr=target_sr, loudness_mode=loudness_mode, headroom_db=headroom_db, style_vec=style_vec, ref_loop=None if loudness_mode == "none" else loop_tail, # disable match by default combined_loop=loop, guidance_weight=float(params.get("guidance_weight", 1.1)), temperature=float(params.get("temperature", 1.1)), topk=int(params.get("topk", 40)), ) worker = JamWorker(get_mrt(), jp) sid = str(uuid.uuid4()) with jam_lock: # single active jam per GPU, mirroring /jam/start for _sid, w in list(jam_registry.items()): if w.is_alive(): await send_json({"type":"error","error":"A jam is already running"}) worker = None; sid = None break if worker is not None: jam_registry[sid] = worker worker.start() else: # mode == "rt" (Colab-style, no loop context) # seed a fresh state with a silent context like warmup mrt = get_mrt() state = mrt.init_state() 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 websocket._mrt = mrt websocket._state = state websocket._style = _combine_styles(mrt, params.get("styles","warmup"), params.get("style_weights","")) 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" await send_json({"type":"started","mode":"rt"}) # kick off a background task to stream ~2s chunks 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: # read knobs (already set by update) mrt.guidance_weight = websocket._rt_guid mrt.temperature = websocket._rt_temp mrt.topk = websocket._rt_topk wav, new_state = mrt.generate_chunk(state=websocket._state, style=websocket._style) 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") # send bytes / json best-effort 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: # client went away — exit cleanly break # pacing (use captured flag from start) 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: # normal on stop/close — just exit pass except Exception: # don't try to send an error; socket may be closed pass websocket._rt_task = asyncio.create_task(_rt_loop()) continue # skip the “bar-mode started” message below await send_json({"type":"started","session_id": sid, "mode": mode}) # if we’re in bar-mode, begin pushing chunks as they arrive if mode == "bar" and worker is not None: async def _pump(): while True: if not worker.is_alive(): break chunk = worker.get_next_chunk(timeout=60.0) if chunk is None: continue if binary_audio: await websocket.send_bytes(base64.b64decode(chunk.audio_base64)) await send_json({"type":"chunk_meta","index":chunk.index,"metadata":chunk.metadata}) else: await send_json({"type":"chunk","index":chunk.index, "audio_base64":chunk.audio_base64,"metadata":chunk.metadata}) asyncio.create_task(_pump()) # --- UPDATES (bar or rt) --- elif mtype == "update": if mode == "bar": if not sid: await send_json({"type":"error","error":"No session_id yet"}); return # fan values straight into your existing HTTP handler: res = jam_update( session_id=sid, guidance_weight=msg.get("guidance_weight"), temperature=msg.get("temperature"), topk=msg.get("topk"), styles=msg.get("styles",""), style_weights=msg.get("style_weights",""), loop_weight=msg.get("loop_weight"), use_current_mix_as_style=bool(msg.get("use_current_mix_as_style", False)), ) await send_json({"type":"status", **res}) # {"ok": True} else: # rt-mode: there’s no JamWorker; update the local knobs/state 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)) if ("styles" in msg) or ("style_weights" in msg): websocket._style = _combine_styles( websocket._mrt, msg.get("styles", ""), msg.get("style_weights", "") ) await send_json({"type":"status","updated":"rt-knobs"}) elif mtype == "consume" and mode == "bar": with jam_lock: worker = jam_registry.get(msg.get("session_id")) if worker is not None: worker.mark_chunk_consumed(int(msg.get("chunk_index", -1))) elif mtype == "reseed" and mode == "bar": with jam_lock: worker = jam_registry.get(msg.get("session_id")) if worker is None or not worker.is_alive(): await send_json({"type":"error","error":"Session not found"}); continue loop_b64 = msg.get("loop_audio_b64") if not loop_b64: await send_json({"type":"error","error":"loop_audio_b64 required"}); continue loop_bytes = base64.b64decode(loop_b64) with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: tmp.write(loop_bytes); path = tmp.name wav = au.Waveform.from_file(path).resample(worker.mrt.sample_rate).as_stereo() worker.reseed_from_waveform(wav) await send_json({"type":"status","reseeded":True}) elif mtype == "reseed_splice" and mode == "bar": with jam_lock: worker = jam_registry.get(msg.get("session_id")) if worker is None or not worker.is_alive(): await send_json({"type":"error","error":"Session not found"}); continue anchor = float(msg.get("anchor_bars", 2.0)) b64 = msg.get("combined_audio_b64") if b64: data = base64.b64decode(b64) 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() worker.reseed_splice(wav, anchor_bars=anchor) else: # fallback: model-side stream splice worker.reseed_splice(worker.params.combined_loop, anchor_bars=anchor) await send_json({"type":"status","splice":anchor}) elif mtype == "stop": if mode == "rt": 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 # <- add this if you want to end the socket after stop elif mtype == "ping": await send_json({"type":"pong"}) else: await send_json({"type":"error","error":f"Unknown type {mtype}"}) except WebSocketDisconnect: # best-effort cleanup for bar-mode sessions started within this socket (optional) pass except Exception as e: try: await send_json({"type":"error","error":str(e)}) except Exception: pass finally: try: if websocket.client_state != WebSocketState.DISCONNECTED: await websocket.close() except Exception: pass @app.get("/ping") def ping(): return {"ok": True} @app.get("/", response_class=Response) def read_root(): """Root endpoint that explains what this API does""" html_content = """ MagentaRT Research API

🎵 MagentaRT Research API

Purpose: AI music generation for iOS/web app research using Google's MagentaRT.

Available Endpoints

WebSocket Quick Start (rt mode)

Connect to wss://<your-space>/ws/jam and send:

{
  "type": "start",
  "mode": "rt",
  "binary_audio": false,
  "params": {
    "styles": "warmup",
    "temperature": 1.1,
    "topk": 40,
    "guidance_weight": 1.1,
    "pace": "realtime",          // or "asap" to bootstrap quickly
    "max_decode_frames": 50      // default ~2.0s; try 36–45 on smaller GPUs
  }
}

Update parameters live:

{
  "type": "update",
  "styles": "jazz, hiphop",
  "style_weights": "1.0,0.8",
  "temperature": 1.2,
  "topk": 64,
  "guidance_weight": 1.0,
  "pace": "realtime",
  "max_decode_frames": 40
}

Stop:

{"type":"stop"}

Notes

Licensing: Uses MagentaRT (Apache 2.0 + CC-BY 4.0). Users are responsible for outputs.

See /docs for full API details and client examples.

""" return Response(content=html_content, media_type="text/html")