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Project · Voice Engineering · 2026-04-22

Milo Voice Cloner

Fine-tuning Qwen3-TTS-1.7B for voice cloning on a local DGX Spark. 25 epochs, 106GB of checkpoints, 8 training runs to understand what actually matters — and a working result.

🟢 v8 LIVE Qwen3-TTS-1.7B 25 epochs lr = 2e-5 RTF 1.15 DGX Spark · GB10 361 training samples

Training Pipeline

The full pipeline from raw audio to a production inference server. Each stage has been rebuilt at least once.

🎙️ RAW CORPUS 365 WAV chunks ~249 MB audio will-corpus-v4 ⚙️ PREPROCESS Whisper STT audio_codes inject 361-entry JSONL 🔥 TRAINING sft_12hz.py lr = 2e-5 DGX Spark 2 GB10 · ~45GB VRAM 12+ hours 💾 CHECKPOINTS 25 × epoch 3.6 GB each full model (no LoRA) 🎧 INFERENCE gen_will_v8.py Qwen3TTSModel RTF 1.15 24 kHz WAV out speaker idx 3000 🚀 VOICE SERVER :8767 endpoint MiloBridge V2 coming soon codec_embedding[3000] speaker embed baked in CE loss 9.7–10.8 ~0.65/layer across 16 MILO VOICE CLONER · TRAINING PIPELINE · v8 Qwen3-TTS-12Hz-1.7B · Full SFT · DGX Spark 2 GB10
Fig 1 — End-to-end pipeline. Dashed border = not yet integrated into production. Teal = active path.
INFERENCE ARCHITECTURE · CHECKPOINT ANATOMY BASE MODEL Qwen3-TTS-12Hz-1.7B text tokenizer LM head (text) codec embedding 16-layer codec head speaker encoder dropped at save ↓ SFT FINE-TUNED CHECKPOINT model.safetensors (3.6 GB) tokenizer_config.json generation_config.json config.json ← talker_config.spk_id: { "will_prowse": 3000 } speaker baked in as embedding load gen_will_v8.py Qwen3TTSModel .from_pretrained(ckpt_dir) tts.generate_custom_voice( text="...", speaker="will_prowse" ) no ref audio needed WAV OUTPUT 24,000 Hz · int16 RTF 1.15 (no flash-attn) ✓ confirmed working
Fig 2 — Checkpoint anatomy and inference call signature. Speaker identity lives at codec_embedding[3000].

What This Is

Milo needs a voice. Not a generic TTS voice — a real one. The plan is to fine-tune a voice model on James's audio and deploy it as the primary TTS for the MiloBridge voice pipeline: real-time, local, zero cloud dependency.

Will Prowse is the test case. His voice is clean, expressive, and he has hundreds of hours of podcast-quality audio publicly available. If the system can clone Will convincingly, it can clone anyone with a decent corpus.

The model is Qwen3-TTS-12Hz-1.7B — Alibaba's open TTS architecture that uses a 12Hz codec and 16-layer codec head to generate audio autoregressively. Fine-tuning injects a new speaker identity into a specific embedding slot rather than modifying the architecture.

Training Configuration — v8

Base Model
Qwen3-TTS-12Hz-1.7B
full SFT (not LoRA)
Learning Rate
2e-5
AdamW, no scheduler
Corpus
361 samples
365 WAV chunks, ~249MB
Epochs
25 (of 50)
training stopped at convergence
Loss (final)
9.7 – 10.8
CE summed across 16 codec layers
Hardware
DGX Spark 2
GB10 · ~45GB usable VRAM
Speaker Slot
codec_embedding[3000]
will_prowse baked in at save
Inference RTF
1.15×
no flash-attn, bfloat16
On the loss numbers: CE loss summed across 16 codec layers looks alarming (~10) but is normal. Per-layer average is ~0.65 — well below the random baseline of ~5 for a 1024-token codec vocabulary. The model converged by epoch 5; epochs 6–24 added no improvement. Don't read too much into the absolute value.

Version History — What Failed and Why

Run LR Epochs Result Status
v1–v2 varied — FasterQwen3TTS (0.6B) + LoRA adapter approach. Wrong architecture — v8 uses full 1.7B model with merged weights, not LoRA delta. dead end
v3 unknown 25 Epoch-3 checkpoint was the only one that ever produced valid audio. Higher epochs degraded. Set the benchmark for what "working" sounds like. reference
v4 — — Data pipeline only — produced train_with_codes.jsonl (361 samples). No training output; used as data source for v5+. data
v5 1e-4 ~10 Catastrophic overfit. Loss collapsed, output became noise. lr=1e-4 is too aggressive for a 361-sample corpus. overfit
v6 1e-5 unknown Produced a 15MB output file but garbage audio. Too conservative — model didn't shift far enough from the base in available epochs. underfit
v7 — — Intermediate experiment, results not documented. skipped
v8 2e-5 25/50 Loss stable 9.7–10.8, no collapse, inference confirmed working. RTF 1.15, 7.28s audio generated clean. Current production candidate. current

The Inference Fix That Took Too Long

Every test before v8 failed at inference with HF repo format mismatch. The training script (sft_12hz.py) copies the base model into each checkpoint directory with shutil.copytree(), but config.json inside retains the original _name_or_path field pointing to the HuggingFace registry slug:

# What was in every checkpoint's config.json: { "_name_or_path": "Qwen/Qwen3-TTS-12Hz-1.7B-Base", ← this caused the HF lookup "tts_model_type": "custom_voice", "spk_id": {"will_prowse": 3000} } # What from_pretrained() does with a slash-name: # → tries HuggingFace API → fails → "format mismatch" # Fix: pass the absolute local directory path directly. # from_pretrained() is smart enough to bypass HF when given a real path.

The correct call — no patches to config.json needed:

from qwen_tts.inference.qwen3_tts_model import Qwen3TTSModel tts = Qwen3TTSModel.from_pretrained( "/home/milo/voice-pipeline/tts/will-lora-v8/checkpoint/checkpoint-epoch-24", device_map="cuda:0", dtype=torch.bfloat16, ) wavs, sr = tts.generate_custom_voice( text="Hey, in today's video I'm going to show you something interesting.", speaker="will_prowse", # must match the key in config.json spk_id ) # → no ref_audio needed — speaker embedding is baked in at index 3000 sf.write("output.wav", wavs[0], sr)
Critical: The old gen_will_v3.py used FasterQwen3TTS (the 0.6B model) with PeftModel.from_pretrained() for LoRA adapter loading. v8 checkpoints are full merged model weights — the LoRA approach is wrong and will produce garbage or fail outright. Use Qwen3TTSModel directly.

Results

Epoch 24 checkpoint loads clean, generates audio without errors. RTF 1.15 without flash-attn means generation is slightly slower than real-time — acceptable, and will drop below 1.0 once flash-attn is installed.

Load time
32.2 s
one-time on server startup
Generation
7.28 s audio
183-char input, 8.4s wall time
RTF
1.15
eager attn. flash-attn expected <1.0
Output format
24,000 Hz WAV
16-bit, mono

The voice quality question is still open — "working" means the pipeline runs without errors and produces valid audio. Whether it actually sounds like Will Prowse is a human judgment call. If it doesn't, the most likely culprit is reference audio quality, not epoch count or learning rate. Fix the corpus, retrain for 5 epochs.

What's Next

  • Evaluate ep-24 audio quality against the v3 ep-3 reference
  • If voice character is wrong: audit will-corpus-v4 for noise, compression artifacts, and speaker consistency
  • If voice character is right: build a FastAPI inference server wrapping gen_will_v8.py, wire into MiloBridge v2 as the primary TTS endpoint
  • Record James's voice corpus (30–60 min, scripted) for Milo's actual production voice
  • LoRA fine-tune Cindy's voice using the same pipeline
The playbook: A reusable voice training guide covering all 6 pipeline stages, checkpoint anatomy, loss interpretation, and how to adapt for a new speaker (James, Cindy) is in ~/clawd/projects/voice-pipeline/VOICE-TRAINING-PLAYBOOK.md.