Add a Mastering tab and auto-apply toggle for generated voice output

Pedalboard-based chain (warmth EQ, presence dip, compression, light
reverb, hard-ceiling limiter) fixes the spectral imbalance behind
generated TTS sounding tinny - see mastering/master.py docstring and
commit history for the before/after spectral analysis and the A/B tuning
pass against real character clips. Pipeline Studios Generate step can
auto-apply it; the standalone tab works on any clip.

Co-Authored-By: Claude Sonnet 5 <[email protected]>

.gitignore

@@ -37,3 +37,6 @@ media-files/video/*.mp4
 
 # Small public-domain demo evidence clip for the auk voice-editing demo
 !auk/*.wav
+
+# VibeVoice 4-speaker spike test clip - committed for remote review
+!vibevoice-spike/*.mp3

README.md

@@ -9,6 +9,7 @@ Audio processing demos: WhisperX, Demucs, and TTS technology proofs-of-concept.
 - `tts/` — text-to-speech service demos (voice cloning via MOSS-TTS)
 - `auk/` — voice *editing* demos (emotion/pitch/speed/content editing on existing recordings,
   as opposed to `tts/`'s clone-and-generate approach)
+- `webui/` — interactive pipeline studio chaining demucs -> AuK -> MOSS-TTS with previews
 
 Each subdirectory has its own README with setup and run instructions.
 

mastering/master.py

@@ -0,0 +1,82 @@
+"""
+Mastering chain for generated TTS/voice clips: fixes the "tinny" spectral
+imbalance common to neural vocoder output (excess 3-6kHz presence energy,
+starved low end relative to a naturally-recorded voice - see the project's
+spectral comparison: raw recordings skew toward 200-1000Hz, generated clips
+dump ~35% of their energy into 3-6kHz) and adds the density/space that
+reads as "produced" rather than dry/synthetic.
+
+Chain order matters: EQ before compression (so the compressor reacts to
+the corrected tonal balance, not the raw tinny one), reverb after
+compression (so the room tail doesn't get squashed), limiter last.
+"""
+
+import argparse
+
+import numpy as np
+import soundfile as sf
+from pedalboard import (
+    Compressor,
+    HighpassFilter,
+    HighShelfFilter,
+    Limiter,
+    LowShelfFilter,
+    PeakFilter,
+    Pedalboard,
+    Reverb,
+)
+
+
+def build_chain(
+    warmth_db: float,
+    presence_cut_db: float,
+    air_db: float,
+    comp_ratio: float,
+    reverb_wet: float,
+) -> Pedalboard:
+    return Pedalboard([
+        HighpassFilter(cutoff_frequency_hz=60),
+        LowShelfFilter(cutoff_frequency_hz=150, gain_db=warmth_db, q=0.7),
+        PeakFilter(cutoff_frequency_hz=3800, gain_db=presence_cut_db, q=1.0),
+        HighShelfFilter(cutoff_frequency_hz=6500, gain_db=air_db, q=0.7),
+        Compressor(threshold_db=-20, ratio=comp_ratio, attack_ms=12, release_ms=120),
+        Reverb(room_size=0.08, damping=0.85, wet_level=reverb_wet, dry_level=1.0 - reverb_wet),
+        Limiter(threshold_db=-1.0, release_ms=100),
+    ])
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Apply a studio mastering chain to a voice clip")
+    parser.add_argument("--input", required=True)
+    parser.add_argument("--output", required=True)
+    parser.add_argument("--warmth-db", type=float, default=5.5, help="Low-shelf boost @150Hz")
+    parser.add_argument("--presence-cut-db", type=float, default=-5.0, help="Peak cut @3.8kHz (the tinny band)")
+    parser.add_argument("--air-db", type=float, default=-4.5, help="High-shelf @6.5kHz, for softness")
+    parser.add_argument("--comp-ratio", type=float, default=2.5)
+    parser.add_argument("--reverb-wet", type=float, default=0.025)
+    args = parser.parse_args()
+
+    audio, sr = sf.read(args.input, always_2d=True)
+    audio = audio.T.astype(np.float32)  # pedalboard wants (channels, samples)
+
+    board = build_chain(
+        args.warmth_db, args.presence_cut_db, args.air_db, args.comp_ratio, args.reverb_wet
+    )
+    processed = board(audio, sr)
+
+    # pedalboard's Limiter is attack/release-based, not a true brick-wall
+    # limiter - fast transients can still punch through its threshold (seen
+    # in practice: in-memory peaks hit 0dBFS despite threshold_db=-1.0,
+    # producing audible clipping distortion on playback). Hard safety
+    # ceiling here guarantees the written file never clips regardless.
+    target_peak = 10 ** (-1.0 / 20)
+    peak = np.max(np.abs(processed))
+    if peak > target_peak:
+        processed = processed * (target_peak / peak)
+
+    sf.write(args.output, processed.T, sr)
+    print(f"[INFO] Saved {args.output}")
+
+
+if __name__ == "__main__":
+    main()

sfx-stable-audio-3/generate.py

@@ -0,0 +1,32 @@
+"""
+Stable Audio 3 Small-SFX CLI: generate a sound effect/ambience clip from a
+text description. Runs on CPU by design - this checkpoint is the
+"lightweight, no GPU required" model in Stable Audio 3's lineup, so it
+never contends with ComfyUI/other models for VRAM (unlike generate_sfx.py's
+MOSS-SoundEffect, which needs the 3090 and ComfyUI's memory freed first).
+"""
+
+import argparse
+
+import torchaudio
+from stable_audio_3 import StableAudioModel
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Stable Audio 3 Small-SFX text-to-sound-effect")
+    parser.add_argument("--text", required=True, help="Description of the sound effect/ambience")
+    parser.add_argument("--output", required=True, help="Output wav path")
+    parser.add_argument("--duration", type=float, default=10.0, help="Clip duration in seconds (max 120)")
+    parser.add_argument("--seed", type=int, default=-1, help="Random seed, or -1 for random")
+    args = parser.parse_args()
+
+    model = StableAudioModel.from_pretrained("small-sfx", device="cpu")
+    sample_rate = model.model.sample_rate
+
+    audio = model.generate(prompt=args.text, duration=args.duration, seed=args.seed)
+    torchaudio.save(args.output, audio[0].cpu(), sample_rate)
+    print(f"[INFO] Saved {args.output}")
+
+
+if __name__ == "__main__":
+    main()

tts/generate.py

@@ -0,0 +1,109 @@
+"""
+Generic MOSS-TTS zero-shot cloning CLI: clone the voice in --reference and
+say --text in it. Used directly, and as the final stage of webui/app.py.
+"""
+
+import argparse
+import importlib.util
+
+import soundfile as sf
+import torch
+from transformers import AutoModel, AutoProcessor
+
+PRETRAINED = "OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5"
+
+
+def resolve_attn_implementation(device: str, dtype: torch.dtype) -> str:
+    if (
+        device == "cuda"
+        and importlib.util.find_spec("flash_attn") is not None
+        and dtype in {torch.float16, torch.bfloat16}
+    ):
+        major, _ = torch.cuda.get_device_capability()
+        if major >= 8:
+            return "flash_attention_2"
+    return "sdpa" if device == "cuda" else "eager"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="MOSS-TTS zero-shot voice cloning")
+    parser.add_argument("--text", required=True, help="Text to synthesize")
+    parser.add_argument("--reference", required=True, help="Reference audio path")
+    parser.add_argument("--output", required=True, help="Output wav path")
+    parser.add_argument("--language", default="English", help="Language tag")
+    parser.add_argument(
+        "--instruction", default=None,
+        help='Style/delivery instruction (e.g. "Change the emotion to excited."). '
+        "Undocumented MOSS-TTS field for this checkpoint - confirmed to work for "
+        "emotion in testing, passed through as-is.",
+    )
+    parser.add_argument(
+        "--seed", type=int, default=None,
+        help="Random seed for reproducibility. Unset = nondeterministic.",
+    )
+    parser.add_argument("--audio_temperature", type=float, default=1.7)
+    parser.add_argument("--audio_top_p", type=float, default=0.8)
+    parser.add_argument("--audio_top_k", type=int, default=25)
+    parser.add_argument("--audio_repetition_penalty", type=float, default=1.0)
+    parser.add_argument("--max_new_tokens", type=int, default=4096)
+    args = parser.parse_args()
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    dtype = torch.bfloat16 if device == "cuda" else torch.float32
+
+    if args.seed is not None:
+        torch.manual_seed(args.seed)
+        if device == "cuda":
+            torch.cuda.manual_seed_all(args.seed)
+
+    torch.backends.cuda.enable_cudnn_sdp(False)
+    torch.backends.cuda.enable_flash_sdp(True)
+    torch.backends.cuda.enable_mem_efficient_sdp(True)
+    torch.backends.cuda.enable_math_sdp(True)
+
+    attn_implementation = resolve_attn_implementation(device, dtype)
+    print(f"[INFO] attn_implementation={attn_implementation}")
+
+    processor = AutoProcessor.from_pretrained(PRETRAINED, trust_remote_code=True)
+    processor.audio_tokenizer = processor.audio_tokenizer.to(device)
+
+    model = AutoModel.from_pretrained(
+        PRETRAINED,
+        trust_remote_code=True,
+        attn_implementation=attn_implementation,
+        dtype=dtype,
+    ).to(device)
+    model.eval()
+
+    conversation = [
+        processor.build_user_message(
+            text=args.text,
+            language=args.language,
+            reference=[args.reference],
+            instruction=args.instruction,
+        )
+    ]
+
+    with torch.no_grad():
+        batch = processor([conversation], mode="generation")
+        outputs = model.generate(
+            input_ids=batch["input_ids"].to(device),
+            attention_mask=batch["attention_mask"].to(device),
+            max_new_tokens=args.max_new_tokens,
+            do_sample=True,
+            audio_temperature=args.audio_temperature,
+            audio_top_p=args.audio_top_p,
+            audio_top_k=args.audio_top_k,
+            audio_repetition_penalty=args.audio_repetition_penalty,
+        )
+        message = processor.decode(outputs)[0]
+        audio = message.audio_codes_list[0]
+        data = audio.detach().cpu().to(torch.float32).numpy()
+        if data.ndim == 2:
+            data = data.T
+        sf.write(args.output, data, processor.model_config.sampling_rate)
+        print(f"[INFO] Saved {args.output}")
+
+
+if __name__ == "__main__":
+    main()

tts/generate_sfx.py

@@ -0,0 +1,99 @@
+"""
+MOSS-SoundEffect CLI: generate an ambient/sound-effect clip from a text
+description. Sibling of generate.py (MOSS-TTS voice cloning) - same
+backend setup and model-loading pattern, different checkpoint and a
+text-only build_user_message call (no reference audio, no cloning).
+"""
+
+import argparse
+import importlib.util
+import os
+
+# This checkpoint's weights alone use ~22.3GB of this box's ~23.56GB usable
+# VRAM, leaving generate()'s own small allocations (a vocab-sized mask
+# tensor, etc.) no room without this - confirmed via OOM otherwise. Must be
+# set before torch's CUDA allocator initializes, so it's set here rather
+# than relying on the caller's shell environment.
+os.environ.setdefault("PYTORCH_ALLOC_CONF", "expandable_segments:True")
+os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
+
+import soundfile as sf
+import torch
+from transformers import AutoModel, AutoProcessor
+
+PRETRAINED = "OpenMOSS-Team/MOSS-SoundEffect"
+
+
+def resolve_attn_implementation(device: str, dtype: torch.dtype) -> str:
+    if (
+        device == "cuda"
+        and importlib.util.find_spec("flash_attn") is not None
+        and dtype in {torch.float16, torch.bfloat16}
+    ):
+        major, _ = torch.cuda.get_device_capability()
+        if major >= 8:
+            return "flash_attention_2"
+    return "sdpa" if device == "cuda" else "eager"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="MOSS-SoundEffect text-to-sound-effect")
+    parser.add_argument("--text", required=True, help="Description of the sound effect/ambience")
+    parser.add_argument("--output", required=True, help="Output wav path")
+    parser.add_argument(
+        "--seed", type=int, default=None,
+        help="Random seed for reproducibility. Unset = nondeterministic.",
+    )
+    parser.add_argument("--max_new_tokens", type=int, default=4096)
+    args = parser.parse_args()
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    dtype = torch.bfloat16 if device == "cuda" else torch.float32
+
+    if args.seed is not None:
+        torch.manual_seed(args.seed)
+        if device == "cuda":
+            torch.cuda.manual_seed_all(args.seed)
+
+    torch.backends.cuda.enable_cudnn_sdp(False)
+    torch.backends.cuda.enable_flash_sdp(True)
+    torch.backends.cuda.enable_mem_efficient_sdp(True)
+    torch.backends.cuda.enable_math_sdp(True)
+
+    attn_implementation = resolve_attn_implementation(device, dtype)
+    print(f"[INFO] attn_implementation={attn_implementation}")
+
+    processor = AutoProcessor.from_pretrained(PRETRAINED, trust_remote_code=True)
+    processor.audio_tokenizer = processor.audio_tokenizer.to(device)
+
+    model = AutoModel.from_pretrained(
+        PRETRAINED,
+        trust_remote_code=True,
+        attn_implementation=attn_implementation,
+        dtype=dtype,
+    ).to(device)
+    model.eval()
+
+    conversation = [processor.build_user_message(ambient_sound=args.text)]
+
+    with torch.no_grad():
+        batch = processor([conversation], mode="generation")
+        # This checkpoint's generate() doesn't accept the TTS model's sampling
+        # kwargs (do_sample/audio_temperature/etc raise TypeError) - matches
+        # the official README example, which only passes these three.
+        outputs = model.generate(
+            input_ids=batch["input_ids"].to(device),
+            attention_mask=batch["attention_mask"].to(device),
+            max_new_tokens=args.max_new_tokens,
+        )
+        message = processor.decode(outputs)[0]
+        audio = message.audio_codes_list[0]
+        data = audio.detach().cpu().to(torch.float32).numpy()
+        if data.ndim == 2:
+            data = data.T
+        sf.write(args.output, data, processor.model_config.sampling_rate)
+        print(f"[INFO] Saved {args.output}")
+
+
+if __name__ == "__main__":
+    main()

vibevoice-spike/README.md

@@ -0,0 +1,52 @@
+# VibeVoice (community fork) 4-person podcast spike
+
+Standalone experiment, not integrated into the webui or the production
+pipeline. Tests whether VibeVoice (https://github.com/vibevoice-community/VibeVoice)
+can generate a 4-speaker podcast scene in one pass, cloning our four
+established character voices (Kevin, Marge, Terry, Priya) from their
+existing approved `reference_audio` in the-service-station repo.
+
+- `scripts/trailer_promo_4speaker.txt` - the test script: the real
+  "sausage rolls" trailer scene from `the-service-station/scripts/trailer-promo.md`,
+  converted from our `[Character|tags]` format to VibeVoices required
+  `Speaker N: ...` plain-text format (tags dropped - VibeVoice has no
+  per-line emotion/timing control).
+- `trailer_promo_4speaker_vibevoice-1.5b.mp3` - the generated result
+  (VibeVoice-1.5B, seed 42). 47s of audio generated in 14.5s (RTF 0.31x).
+
+Setup lives outside this repo (not committed): `.venv/` here is a fresh
+isolated venv (`pip install -e /root/projects/VibeVoice`, pinned
+`transformers==4.51.3`), pointed at the already-downloaded
+`VibeVoice-1.5B` weights under `/root/projects/ComfyUI/models/tts/VibeVoice/`.
+
+## Findings (2026-09-30)
+
+Whisper-transcript check against the script: mostly faithful, not
+word-perfect. Two small unscripted insertions ("I am here!" before Priyas
+line, "For me," before Marges reply) and one substitution ("He will." ->
+"You will."). No repetition/collapse like the AuK chain-corruption bug -
+just minor ad-libbing, not catastrophic.
+
+No audible AI disclaimer or obvious background music in this run - both
+are documented risks for this model family (see
+`the-service-station/research/vibevoice-2026-08-21.md` and
+`vibevoice-community-fork-2026-08-21.md`), so treat this one clean run as
+encouraging, not conclusive.
+
+Steves call after listening: pursue 7B (weights already downloaded, same
+venv/script, swap `--model_path`) or not worth it given the limitations
+the prior research already flagged.
+
+## 7B result (2026-09-30, same script/seed/voices)
+
+`trailer_promo_4speaker_vibevoice-7b.mp3` - VibeVoice-7B, same seed (42),
+same script, same reference voices. 34.8s of audio generated in 22.2s
+(RTF 0.64x - still faster than real-time, just less headroom than 1.5B).
+
+Whisper-transcript check: cleaner than 1.5B - neither of 1.5Bs unscripted
+insertions ("I am here!", "For me,") appeared, and "He will." came through
+exactly right this time (1.5B had drifted it to "You will."). One real
+deviation: Marges defensive line "I get one fact wrong per episode."
+came out as a question, "Can I get one fact wrong per episode?" - changes
+the delivery/meaning, not just wording. Still no audible disclaimer and
+nothing that reads as injected background music.

vibevoice-spike/scripts/trailer_promo_4speaker.txt

@@ -0,0 +1,9 @@
+Speaker 1: The Service Station. Three facts, one car park, and absolutely no reason to be in a hurry.
+Speaker 2: Except for me, obviously. I'm always in a hurry.
+Speaker 3: I had a Sinclair Spectrum in 1984, and I will find a way to bring it up.
+Speaker 1: He will.
+Speaker 4: I still don't understand why the sausage rolls are relevant, but here we are.
+Speaker 2: They're always relevant, Priya.
+Speaker 1: Every week: one underdog, one argument, and one fact Marge gets wrong.
+Speaker 2: I get one fact wrong per episode. Tops.
+Speaker 3: It's usually about football.

webui/README.md

@@ -0,0 +1,56 @@
+# webui
+
+A pipeline studio: strip a voice with **demucs**, edit it with **AuK** (emotion, pitch, speed,
+free-form instructions — with preview and undo/reset), then generate new speech in that voice
+with **MOSS-TTS**.
+
+## Why this exists
+
+Manually chaining demucs → AuK → MOSS-TTS across three separate venvs (via SSH one-off scripts)
+worked but made it slow to experiment — especially with AuK's pitch/emotion controls, where you
+really want to preview a result and back out if it's wrong (see the pitch-overshoot findings in
+`auk/README.md`) rather than committing to a specific edit blind.
+
+## Architecture
+
+A single lightweight Gradio app (`app.py`) that **shells out** to each tool's own isolated venv
+via subprocess, rather than merging their dependencies into one environment — MOSS-TTS pins
+`transformers==5.0.0`, AuK needs `4.57.6`; they can't coexist in one venv. This app's own venv
+only needs `gradio` itself.
+
+Each stage's output becomes the next stage's input by filesystem path; a `history` list backs
+the undo/reset buttons in the voice editor.
+
+## Setup
+
+```bash
+python3 -m venv .venv && . .venv/bin/activate
+pip install gradio
+```
+
+Assumes `demucs/`, `auk/`, and `tts/` (siblings in this repo) already have their own working
+venvs and, for `auk/`, downloaded model weights — see their READMEs.
+
+## Running
+
+```bash
+python3 app.py
+```
+
+Serves on `0.0.0.0:7860`. The app automatically calls ComfyUI's `/free` endpoint (if its queue
+is empty) before each heavy step, to reduce GPU contention on a shared box — see the DNS/routing
+and GPU-contention notes earlier in this project's history for why that matters here.
+
+## Known limitations (inherited from the underlying tools)
+
+- **AuK's real usable clip length is ~25-30s**, not several minutes — feeding it a long recording
+  will OOM even with `--cpu_offload` (see `auk/README.md`). This app doesn't chunk long inputs
+  automatically; keep reference clips short.
+- **AuK's pitch-editing instruction-following is unreliable** — even "1 semitone" has measured
+  out at ~4+ semitones of actual shift in testing. The UI surfaces a warning; preview before
+  trusting a pitch edit.
+- Each stage is a blocking subprocess call (model load + inference each time) — the UI will
+  appear to hang for anywhere from ~10s to a couple of minutes per click, with no progress bar
+  yet.
+- No queueing/locking across browser tabs or concurrent users — this is a single-GPU box, so two
+  simultaneous generations will contend for VRAM.