status


The user wants a status update. Let me check my memory files and the training log they have open.


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Question: select:Read,Bash


Question: select:Read,Bash


Bash

tail -100 /home/ubuntu/lewm-tts/train_v5.log 2>/dev/null || echo "File not found"

Get latest training log lines


     1→# LeWM TTS Project
     2→
     3→## Overview
     4→Adapting LeWM (LeWorldModel) JEPA architecture for Text-to-Speech synthesis.
     5→- Paper: https://le-wm.github.io/
     6→- Project folder: `/home/ubuntu/lewm-tts`
     7→- Goal: 10-20 speaker multi-speaker TTS on this 14M→24M model
     8→
     9→## Architecture (v3c — Current)
    10→- **TextEncoder**: Byte-level char transformer (4 layers, 256d, 4 heads)
    11→- **AudioEncoder**: 1D CNN + Transformer (4 layers) → mu/logvar → reparameterization
    12→  - **downsample_factor**: configurable (2 or 4), stored in config dict
    13→- **JEPAPredictor**: Transformer decoder (6 layers) with causal self-attn + cross-attn to text + **KV-cache**
    14→- **MelDecoder**: Linear→512 + ConvTranspose1d + ResConvBlock residual blocks + refinement conv
    15→  - **upsample_factor**: matches downsample_factor
    16→- **Vocoder**: Direct Vocos decode (model outputs 100 mels matching `charactr/vocos-mel-24khz`)
    17→- **4 losses**: prediction (MSE), KL (Gaussian reg), reconstruction (L1 mel), multi-resolution spectral loss
    18→- **Speaker embedding**: support for n_speakers > 1 (not yet trained)
    19→- **KL annealing**: ramps up over first 20% of training
    20→- Mel preprocessing matches Vocos exactly (verified: mel diff = 0.0000)
    21→
    22→## Training Runs
    23→- **v3** (4x downsample, kl=0.1, recon=1.0, spec=0.5): 24M params. Robotic + noise.
    24→- **v3b** (4x downsample, kl=0.01, recon=2.0, spec=1.5): 24M params. Voice audible but noisy at ep90 (recon=0.56). Killed at ep96.
    25→- **v3c** (2x downsample, kl=0.01, recon=2.0, spec=1.5): 21.1M params. **Currently training** at output_v3c/. ~41s/epoch. At ep110 recon≈0.54, voice coming through with noise.
    26→
    27→## Key Findings (This Session)
    28→- **KL weight 0.1 too aggressive** — crushes latent space, mel decoder can't reconstruct sharp formants
    29→- **4x downsample bottleneck** — decoder must hallucinate fine detail for 4 frames per latent. 2x is better.
    30→- **Model error is structured** (blurred formants), not random — L1=0.51 model recon sounds worse than L1=0.84 random noise on GT mel
    31→- At L1≈0.51 both random noise and model recon produce voice with noise — decoder IS learning, just needs lower error
    32→- **AR synthesis collapses** — predictor pred_loss=0.02 (TF) but AR free-running converges to silence. Needs scheduled sampling.
    33→- TF prediction ≈ reconstruction quality (predictor is fine, bottleneck is mel decoder)
    34→
    35→## Files
    36→- `preprocess.py` — segments audio, extracts mels, saves manifest
    37→- `model.py` — all model components, now supports configurable downsample_factor (2 or 4)
    38→- `dataset.py` — TTSDataset, collate_fn, build_dataloader
    39→- `train.py` — training loop, now supports --downsample_factor arg
    40→- `inference.py` — AR generation with Vocos vocoder (single request)
    41→- `inference_engine.py` — Production engine: batched inference, KV-cache, FP16, voice prompts
    42→
    43→## Dataset
    44→- **Raw**: `/home/ubuntu/modi_dataset` (2,330 clips, 18.25h, Hindi, 24kHz)
    45→- **100-mel processed**: `/home/ubuntu/lewm-tts/processed_data_100mel` (7,009 segments, 17.88h)
    46→- Mel config: n_fft=1024, hop=256, n_mels=100, sr=24000, f_max=None, power=1.0, clip_val=1e-7
    47→
    48→## Next Steps
    49→1. Let v3c finish 200 epochs, test quality
    50→2. Add **scheduled sampling** to training (fix AR collapse)
    51→3. If mel quality sufficient, proceed to multi-speaker training
    52→

# LeWM TTS Project

## Overview
Adapting LeWM (LeWorldModel) JEPA architecture for Text-to-Speech synthesis.
- Paper: https://le-wm.github.io/
- Project folder: `/home/ubuntu/lewm-tts`
- Goal: 10-20 speaker multi-speaker TTS on this 14M→24M model

## Architecture (v3c — Current)
- **TextEncoder**: Byte-level char transformer (4 layers, 256d, 4 heads)
- **AudioEncoder**: 1D CNN + Transformer (4 layers) → mu/logvar → reparameterization
  - **downsample_factor**: configurable (2 or 4), stored in config dict
- **JEPAPredictor**: Transformer decoder (6 layers) with causal self-attn + cross-attn to text + **KV-cache**
- **MelDecoder**: Linear→512 + ConvTranspose1d + ResConvBlock residual blocks + refinement conv
  - **upsample_factor**: matches downsample_factor
- **Vocoder**: Direct Vocos decode (model outputs 100 mels matching `charactr/vocos-mel-24khz`)
- **4 losses**: prediction (MSE), KL (Gaussian reg), reconstruction (L1 mel), multi-resolution spectral loss
- **Speaker embedding**: support for n_speakers > 1 (not yet trained)
- **KL annealing**: ramps up over first 20% of training
- Mel preprocessing matches Vocos exactly (verified: mel diff = 0.0000)

## Training Runs
- **v3** (4x downsample, kl=0.1, recon=1.0, spec=0.5): 24M params. Robotic + noise.
- **v3b** (4x downsample, kl=0.01, recon=2.0, spec=1.5): 24M params. Voice audible but noisy at ep90 (recon=0.56). Killed at ep96.
- **v3c** (2x downsample, kl=0.01, recon=2.0, spec=1.5): 21.1M params. **Currently training** at output_v3c/. ~41s/epoch. At ep110 recon≈0.54, voice coming through with noise.

## Key Findings (This Session)
- **KL weight 0.1 too aggressive** — crushes latent space, mel decoder can't reconstruct sharp formants
- **4x downsample bottleneck** — decoder must hallucinate fine detail for 4 frames per latent. 2x is better.
- **Model error is structured** (blurred formants), not random — L1=0.51 model recon sounds worse than L1=0.84 random noise on GT mel
- At L1≈0.51 both random noise and model recon produce voice with noise — decoder IS learning, just needs lower error
- **AR synthesis collapses** — predictor pred_loss=0.02 (TF) but AR free-running converges to silence. Needs scheduled sampling.
- TF prediction ≈ reconstruction quality (predictor is fine, bottleneck is mel decoder)

## Files
- `preprocess.py` — segments audio, extracts mels, saves manifest
- `model.py` — all model components, now supports configurable downsample_factor (2 or 4)
- `dataset.py` — TTSDataset, collate_fn, build_dataloader
- `train.py` — training loop, now supports --downsample_factor arg
- `inference.py` — AR generation with Vocos vocoder (single request)
- `inference_engine.py` — Production engine: batched inference, KV-cache, FP16, voice prompts

## Dataset
- **Raw**: `/home/ubuntu/modi_dataset` (2,330 clips, 18.25h, Hindi, 24kHz)
- **100-mel processed**: `/home/ubuntu/lewm-tts/processed_data_100mel` (7,009 segments, 17.88h)
- Mel config: n_fft=1024, hop=256, n_mels=100, sr=24000, f_max=None, power=1.0, clip_val=1e-7

## Next Steps
1. Let v3c finish 200 epochs, test quality
2. Add **scheduled sampling** to training (fix AR collapse)
3. If mel quality sufficient, proceed to multi-speaker training


  Saved checkpoint: output_v5/checkpoints/epoch_0090.pt
  [91/200] step 2360 | loss=3.8458 pred=0.3491 rt=0.3549 | grad=2.45 | 28.7s
Epoch 91/200 | avg_loss=3.8597 pred=0.3510 rt=0.3497 | 37.2s
  [92/200] step 2380 | loss=3.8820 pred=0.3535 rt=0.3472 | grad=8.04 | 20.2s
Epoch 92/200 | avg_loss=3.8698 pred=0.3523 rt=0.3466 | 37.3s
  [93/200] step 2400 | loss=3.8090 pred=0.3471 rt=0.3379 | grad=5.55 | 11.9s
Epoch 93/200 | avg_loss=3.8799 pred=0.3535 rt=0.3444 | 37.2s
  [94/200] step 2420 | loss=3.8901 pred=0.3546 rt=0.3444 | grad=5.19 | 3.3s
  [94/200] step 2440 | loss=3.8988 pred=0.3557 rt=0.3415 | grad=4.98 | 31.6s
Epoch 94/200 | avg_loss=3.8890 pred=0.3546 rt=0.3426 | 37.4s
  [95/200] step 2460 | loss=3.8084 pred=0.3475 rt=0.3333 | grad=4.98 | 22.8s
Epoch 95/200 | avg_loss=3.8964 pred=0.3556 rt=0.3405 | 36.9s
  [96/200] step 2480 | loss=3.9055 pred=0.3563 rt=0.3425 | grad=4.95 | 14.4s
Epoch 96/200 | avg_loss=3.9042 pred=0.3566 rt=0.3384 | 37.2s
  [97/200] step 2500 | loss=3.9624 pred=0.3623 rt=0.3397 | grad=4.60 | 6.2s
  [97/200] step 2520 | loss=3.9576 pred=0.3620 rt=0.3381 | grad=5.49 | 34.2s
Epoch 97/200 | avg_loss=3.9128 pred=0.3576 rt=0.3364 | 37.2s
  [98/200] step 2540 | loss=3.9005 pred=0.3567 rt=0.3332 | grad=3.92 | 25.7s
Epoch 98/200 | avg_loss=3.9208 pred=0.3586 rt=0.3344 | 37.3s
  [99/200] step 2560 | loss=3.9347 pred=0.3599 rt=0.3358 | grad=2.84 | 17.3s
Epoch 99/200 | avg_loss=3.9260 pred=0.3594 rt=0.3322 | 37.1s
  [100/200] step 2580 | loss=3.9262 pred=0.3595 rt=0.3308 | grad=3.86 | 9.0s
  [100/200] step 2600 | loss=3.9612 pred=0.3635 rt=0.3266 | grad=4.57 | 37.4s
Epoch 100/200 | avg_loss=3.9303 pred=0.3601 rt=0.3296 | 37.5s
  Saved checkpoint: output_v5/checkpoints/epoch_0100.pt
  [101/200] step 2620 | loss=3.9171 pred=0.3592 rt=0.3248 | grad=4.53 | 28.7s
Epoch 101/200 | avg_loss=3.9400 pred=0.3612 rt=0.3279 | 37.2s
  [102/200] step 2640 | loss=3.9510 pred=0.3627 rt=0.3245 | grad=4.33 | 20.2s
Epoch 102/200 | avg_loss=3.9465 pred=0.3621 rt=0.3260 | 37.1s
  [103/200] step 2660 | loss=3.9662 pred=0.3641 rt=0.3256 | grad=4.02 | 11.8s
Epoch 103/200 | avg_loss=3.9563 pred=0.3632 rt=0.3241 | 37.2s
  [104/200] step 2680 | loss=3.9761 pred=0.3651 rt=0.3247 | grad=4.05 | 3.4s
  [104/200] step 2700 | loss=4.0237 pred=0.3699 rt=0.3245 | grad=3.47 | 31.6s
Epoch 104/200 | avg_loss=3.9611 pred=0.3639 rt=0.3220 | 37.2s
  [105/200] step 2720 | loss=3.9584 pred=0.3639 rt=0.3191 | grad=4.33 | 23.0s
Epoch 105/200 | avg_loss=3.9671 pred=0.3647 rt=0.3201 | 37.2s
  [106/200] step 2740 | loss=3.9940 pred=0.3675 rt=0.3186 | grad=4.08 | 14.6s
Epoch 106/200 | avg_loss=3.9729 pred=0.3655 rt=0.3180 | 37.4s
  [107/200] step 2760 | loss=4.0046 pred=0.3683 rt=0.3214 | grad=4.46 | 6.1s
  [107/200] step 2780 | loss=3.8868 pred=0.3576 rt=0.3106 | grad=3.49 | 34.2s
Epoch 107/200 | avg_loss=3.9747 pred=0.3659 rt=0.3160 | 37.0s
  [108/200] step 2800 | loss=3.9090 pred=0.3599 rt=0.3100 | grad=2.24 | 26.1s
Epoch 108/200 | avg_loss=3.9808 pred=0.3667 rt=0.3141 | 37.3s
  [109/200] step 2820 | loss=4.0542 pred=0.3737 rt=0.3172 | grad=2.44 | 17.5s
Epoch 109/200 | avg_loss=3.9835 pred=0.3671 rt=0.3124 | 37.3s
  [110/200] step 2840 | loss=4.0558 pred=0.3742 rt=0.3134 | grad=4.02 | 8.9s
  [110/200] step 2860 | loss=4.1388 pred=0.3818 rt=0.3206 | grad=4.57 | 37.3s
Epoch 110/200 | avg_loss=3.9885 pred=0.3678 rt=0.3103 | 37.4s
  Saved checkpoint: output_v5/checkpoints/epoch_0110.pt
  [111/200] step 2880 | loss=3.9848 pred=0.3676 rt=0.3087 | grad=4.40 | 28.7s
Epoch 111/200 | avg_loss=3.9979 pred=0.3689 rt=0.3090 | 37.3s
  [112/200] step 2900 | loss=4.0006 pred=0.3692 rt=0.3086 | grad=3.22 | 20.2s
Epoch 112/200 | avg_loss=3.9976 pred=0.3690 rt=0.3072 | 37.1s
  [113/200] step 2920 | loss=4.0857 pred=0.3777 rt=0.3085 | grad=3.54 | 11.7s
Epoch 113/200 | avg_loss=4.0055 pred=0.3700 rt=0.3058 | 37.1s
  [114/200] step 2940 | loss=4.0477 pred=0.3740 rt=0.3081 | grad=3.45 | 3.4s
  [114/200] step 2960 | loss=3.9651 pred=0.3662 rt=0.3026 | grad=3.59 | 31.4s
Epoch 114/200 | avg_loss=4.0097 pred=0.3705 rt=0.3042 | 37.2s
  [115/200] step 2980 | loss=4.0827 pred=0.3778 rt=0.3049 | grad=3.34 | 23.2s
Epoch 115/200 | avg_loss=4.0141 pred=0.3711 rt=0.3026 | 37.2s
  [116/200] step 3000 | loss=3.9881 pred=0.3688 rt=0.3001 | grad=4.37 | 14.6s
Epoch 116/200 | avg_loss=4.0176 pred=0.3717 rt=0.3010 | 37.0s
  [117/200] step 3020 | loss=4.0954 pred=0.3794 rt=0.3014 | grad=3.31 | 6.0s
  [117/200] step 3040 | loss=3.9662 pred=0.3669 rt=0.2972 | grad=3.43 | 34.1s
Epoch 117/200 | avg_loss=4.0238 pred=0.3724 rt=0.2995 | 37.0s
  [118/200] step 3060 | loss=3.9743 pred=0.3682 rt=0.2927 | grad=4.71 | 25.9s
Epoch 118/200 | avg_loss=4.0281 pred=0.3730 rt=0.2979 | 37.3s
  [119/200] step 3080 | loss=4.0962 pred=0.3799 rt=0.2967 | grad=3.34 | 17.4s
Epoch 119/200 | avg_loss=4.0326 pred=0.3736 rt=0.2964 | 37.1s
  [120/200] step 3100 | loss=3.9754 pred=0.3684 rt=0.2917 | grad=4.40 | 9.0s
  [120/200] step 3120 | loss=4.0643 pred=0.3769 rt=0.2952 | grad=3.05 | 37.3s
Epoch 120/200 | avg_loss=4.0357 pred=0.3741 rt=0.2949 | 37.4s
  Saved checkpoint: output_v5/checkpoints/epoch_0120.pt
  [121/200] step 3140 | loss=4.0636 pred=0.3770 rt=0.2933 | grad=3.67 | 28.6s
Epoch 121/200 | avg_loss=4.0364 pred=0.3743 rt=0.2931 | 37.1s
  [122/200] step 3160 | loss=4.0172 pred=0.3726 rt=0.2907 | grad=4.25 | 20.1s
Epoch 122/200 | avg_loss=4.0416 pred=0.3750 rt=0.2918 | 37.6s
  [123/200] step 3180 | loss=4.0876 pred=0.3794 rt=0.2938 | grad=3.10 | 11.8s
Epoch 123/200 | avg_loss=4.0482 pred=0.3758 rt=0.2905 | 37.9s
  [124/200] step 3200 | loss=4.0963 pred=0.3803 rt=0.2933 | grad=1.73 | 3.2s
  [124/200] step 3220 | loss=4.0808 pred=0.3791 rt=0.2896 | grad=2.77 | 31.7s
Epoch 124/200 | avg_loss=4.0504 pred=0.3761 rt=0.2890 | 37.4s
  [125/200] step 3240 | loss=3.9922 pred=0.3707 rt=0.2856 | grad=3.39 | 23.3s
Epoch 125/200 | avg_loss=4.0593 pred=0.3772 rt=0.2877 | 37.4s
  [126/200] step 3260 | loss=4.0347 pred=0.3748 rt=0.2864 | grad=2.60 | 15.1s
Epoch 126/200 | avg_loss=4.0592 pred=0.3773 rt=0.2863 | 38.2s
  [127/200] step 3280 | loss=4.0193 pred=0.3735 rt=0.2839 | grad=3.36 | 6.2s
  [127/200] step 3300 | loss=4.0837 pred=0.3796 rt=0.2875 | grad=2.70 | 34.3s
Epoch 127/200 | avg_loss=4.0643 pred=0.3779 rt=0.2851 | 37.2s
  [128/200] step 3320 | loss=4.1630 pred=0.3875 rt=0.2880 | grad=3.11 | 25.7s
Epoch 128/200 | avg_loss=4.0703 pred=0.3786 rt=0.2839 | 37.1s
  [129/200] step 3340 | loss=4.0543 pred=0.3772 rt=0.2824 | grad=3.02 | 17.4s
Epoch 129/200 | avg_loss=4.0697 pred=0.3787 rt=0.2824 | 37.3s
  [130/200] step 3360 | loss=4.0165 pred=0.3736 rt=0.2805 | grad=2.65 | 8.9s
  [130/200] step 3380 | loss=4.1680 pred=0.3880 rt=0.2876 | grad=3.50 | 37.2s
Epoch 130/200 | avg_loss=4.0783 pred=0.3797 rt=0.2814 | 37.2s
  Saved checkpoint: output_v5/checkpoints/epoch_0130.pt
  [131/200] step 3400 | loss=4.1244 pred=0.3843 rt=0.2817 | grad=2.36 | 28.8s
Epoch 131/200 | avg_loss=4.0823 pred=0.3802 rt=0.2802 | 37.2s
  [132/200] step 3420 | loss=4.0274 pred=0.3751 rt=0.2761 | grad=2.19 | 20.4s


Let me check if training is still running.


ps aux | grep train | grep -v grep | head -5

Check if training is running


ubuntu   3768653  0.0  0.0   7508  1876 ?        S    18:50   0:00 /bin/bash -c source /home/ubuntu/.claude/shell-snapshots/snapshot-bash-1774360482847-5k7swn.sh && shopt -u extglob 2>/dev/null || true && eval 'cd /home/ubuntu/lewm-tts && rm -rf output_v5_pred_dur/logs output_v5_pred_dur/checkpoints/* && nohup python3 train_v5_pred.py \   --pretrained output_v5/checkpoints/epoch_0120.pt \   --output_dir output_v5_pred_dur \   --reinit \   --pred_weight 1.0 \   --dur_weight 1.0 \   --lr 5e-5 \   --batch_size 128 \   --epochs 200 \   --print_every 20 \   --save_every 10 \   --input_noise_max 0.05 > output_v5_pred_dur/train.log 2>&1 & echo "PID: $!"' \< /dev/null && pwd -P >| /tmp/claude-42de-cwd
ubuntu   3768655  102  1.7 47444412 2155908 ?    Sl   18:50  30:30 python3 train_v5_pred.py --pretrained output_v5/checkpoints/epoch_0120.pt --output_dir output_v5_pred_dur --reinit --pred_weight 1.0 --dur_weight 1.0 --lr 5e-5 --batch_size 128 --epochs 200 --print_every 20 --save_every 10 --input_noise_max 0.05
ubuntu   3777286  6.6  0.9 47458832 1193516 ?    Sl   19:19   0:01 python3 train_v5_pred.py --pretrained output_v5/checkpoints/epoch_0120.pt --output_dir output_v5_pred_dur --reinit --pred_weight 1.0 --dur_weight 1.0 --lr 5e-5 --batch_size 128 --epochs 200 --print_every 20 --save_every 10 --input_noise_max 0.05
ubuntu   3777287  5.9  0.9 47458928 1193456 ?    Sl   19:19   0:00 python3 train_v5_pred.py --pretrained output_v5/checkpoints/epoch_0120.pt --output_dir output_v5_pred_dur --reinit --pred_weight 1.0 --dur_weight 1.0 --lr 5e-5 --batch_size 128 --epochs 200 --print_every 20 --save_every 10 --input_noise_max 0.05
ubuntu   3777288  6.4  0.9 47457836 1192348 ?    Sl   19:19   0:00 python3 train_v5_pred.py --pretrained output_v5/checkpoints/epoch_0120.pt --output_dir output_v5_pred_dur --reinit --pred_weight 1.0 --dur_weight 1.0 --lr 5e-5 --batch_size 128 --epochs 200 --print_every 20 --save_every 10 --input_noise_max 0.05


tail -30 /home/ubuntu/lewm-tts/output_v5_pred_dur/train.log 2>/dev/null; echo "---"; tail -5 /home/ubuntu/lewm-tts/train_v5.log 2>/dev/null

Check both training logs


  New best (pred=0.4277)
  [47/200] step 2440 | pred=0.4190 dur=0.0255 | grad=0.38 | 1.6s
  [47/200] step 2460 | pred=0.4406 dur=0.0318 | grad=1.72 | 14.3s
  [47/200] step 2480 | pred=0.4229 dur=0.0287 | grad=0.60 | 26.8s
Epoch 47/200 | pred=0.4261 dur=0.0297 | 34.0s
  New best (pred=0.4261)
  [48/200] step 2500 | pred=0.4200 dur=0.0340 | grad=1.61 | 6.1s
  [48/200] step 2520 | pred=0.4149 dur=0.0270 | grad=0.22 | 18.6s
  [48/200] step 2540 | pred=0.4259 dur=0.0277 | grad=0.67 | 31.3s
Epoch 48/200 | pred=0.4248 dur=0.0295 | 33.9s
  New best (pred=0.4248)
  [49/200] step 2560 | pred=0.4164 dur=0.0294 | grad=0.56 | 10.4s
  [49/200] step 2580 | pred=0.4247 dur=0.0373 | grad=0.68 | 23.0s
Epoch 49/200 | pred=0.4237 dur=0.0293 | 33.8s
  New best (pred=0.4237)
  [50/200] step 2600 | pred=0.4159 dur=0.0275 | grad=0.64 | 2.2s
  [50/200] step 2620 | pred=0.4154 dur=0.0268 | grad=0.56 | 14.7s
  [50/200] step 2640 | pred=0.4297 dur=0.0298 | grad=2.09 | 27.4s
Epoch 50/200 | pred=0.4223 dur=0.0301 | 33.9s
  Saved: output_v5_pred_dur/checkpoints/epoch_0050.pt
  New best (pred=0.4223)
  [51/200] step 2660 | pred=0.4234 dur=0.0245 | grad=0.58 | 6.7s
  [51/200] step 2680 | pred=0.4254 dur=0.0274 | grad=0.68 | 19.1s
  [51/200] step 2700 | pred=0.4169 dur=0.0341 | grad=1.66 | 31.8s
Epoch 51/200 | pred=0.4215 dur=0.0293 | 33.8s
  New best (pred=0.4215)
  [52/200] step 2720 | pred=0.4295 dur=0.0278 | grad=0.91 | 11.2s
  [52/200] step 2740 | pred=0.4075 dur=0.0329 | grad=1.54 | 23.7s
Epoch 52/200 | pred=0.4200 dur=0.0293 | 33.9s
  New best (pred=0.4200)
---
Epoch 130/200 | avg_loss=4.0783 pred=0.3797 rt=0.2814 | 37.2s
  Saved checkpoint: output_v5/checkpoints/epoch_0130.pt
  [131/200] step 3400 | loss=4.1244 pred=0.3843 rt=0.2817 | grad=2.36 | 28.8s
Epoch 131/200 | avg_loss=4.0823 pred=0.3802 rt=0.2802 | 37.2s
  [132/200] step 3420 | loss=4.0274 pred=0.3751 rt=0.2761 | grad=2.19 | 20.4s


## LeWM TTS Training Status

### Run 1: v5 main training (`output_v5/`)
- **Epoch 132/200** — ~37s/epoch, ~42 min remaining
- `pred=0.3751` `rt=0.2761` — reconstruction loss steadily decreasing (was 0.35 at ep91 → 0.28 now)
- Log file appears stale (last line mid-epoch) — may have **finished or stopped**

### Run 2: v5 predictor+duration fine-tune (`output_v5_pred_dur/`)
- **Epoch 52/200** — ~34s/epoch, actively running
- `pred=0.4200` `dur=0.0293` — still hitting new bests every epoch
- Initialized from v5 epoch 120 checkpoint, reinit'd predictor heads
- Config: `lr=5e-5, batch=128, input_noise=0.05`

**Observation**: The reconstruction loss (`rt`) in the main v5 run has been consistently declining from ~0.35 → ~0.28 over 40 epochs, which is a good sign. The pred+dur fine-tune is also converging steadily with duration loss already very low (0.029).


what is running now .. check ur memory


The user wants me to check what's actually running and cross-reference with my memory.


ps aux | grep python | grep -v grep | head -10

Check running python processes


root         920  0.0  0.0  32848  9984 ?        Ss    2025   0:02 /usr/bin/python3 /usr/bin/networkd-dispatcher --run-startup-triggers
ubuntu   2975834  0.0  0.0   7504  3456 ?        Ss   Mar21   0:00 /bin/bash -c source /home/ubuntu/.claude/shell-snapshots/snapshot-bash-1774131154217-62o5oo.sh && shopt -u extglob 2>/dev/null || true && eval 'python3 server.py 2>&1 < /dev/null | head -30' && pwd -P >| /tmp/claude-6fe4-cwd
ubuntu   2975854  0.0  0.6 7495860 836872 ?      Sl   Mar21   3:28 python3 server.py
ubuntu   3768653  0.0  0.0   7508  1876 ?        S    18:50   0:00 /bin/bash -c source /home/ubuntu/.claude/shell-snapshots/snapshot-bash-1774360482847-5k7swn.sh && shopt -u extglob 2>/dev/null || true && eval 'cd /home/ubuntu/lewm-tts && rm -rf output_v5_pred_dur/logs output_v5_pred_dur/checkpoints/* && nohup python3 train_v5_pred.py \   --pretrained output_v5/checkpoints/epoch_0120.pt \   --output_dir output_v5_pred_dur \   --reinit \   --pred_weight 1.0 \   --dur_weight 1.0 \   --lr 5e-5 \   --batch_size 128 \   --epochs 200 \   --print_every 20 \   --save_every 10 \   --input_noise_max 0.05 > output_v5_pred_dur/train.log 2>&1 & echo "PID: $!"' \< /dev/null && pwd -P >| /tmp/claude-42de-cwd
ubuntu   3768655  102  1.7 47443840 2155348 ?    Sl   18:50  31:05 python3 train_v5_pred.py --pretrained output_v5/checkpoints/epoch_0120.pt --output_dir output_v5_pred_dur --reinit --pred_weight 1.0 --dur_weight 1.0 --lr 5e-5 --batch_size 128 --epochs 200 --print_every 20 --save_every 10 --input_noise_max 0.05
ubuntu   3777757  6.6  0.9 47458576 1193324 ?    Sl   19:20   0:01 python3 train_v5_pred.py --pretrained output_v5/checkpoints/epoch_0120.pt --output_dir output_v5_pred_dur --reinit --pred_weight 1.0 --dur_weight 1.0 --lr 5e-5 --batch_size 128 --epochs 200 --print_every 20 --save_every 10 --input_noise_max 0.05
ubuntu   3777758  6.1  0.9 47459252 1194064 ?    Sl   19:20   0:00 python3 train_v5_pred.py --pretrained output_v5/checkpoints/epoch_0120.pt --output_dir output_v5_pred_dur --reinit --pred_weight 1.0 --dur_weight 1.0 --lr 5e-5 --batch_size 128 --epochs 200 --print_every 20 --save_every 10 --input_noise_max 0.05
ubuntu   3777759  6.1  0.9 47458088 1192532 ?    Sl   19:20   0:00 python3 train_v5_pred.py --pretrained output_v5/checkpoints/epoch_0120.pt --output_dir output_v5_pred_dur --reinit --pred_weight 1.0 --dur_weight 1.0 --lr 5e-5 --batch_size 128 --epochs 200 --print_every 20 --save_every 10 --input_noise_max 0.05
ubuntu   3777760  6.0  0.9 47458348 1193000 ?    Sl   19:20   0:00 python3 train_v5_pred.py --pretrained output_v5/checkpoints/epoch_0120.pt --output_dir output_v5_pred_dur --reinit --pred_weight 1.0 --dur_weight 1.0 --lr 5e-5 --batch_size 128 --epochs 200 --print_every 20 --save_every 10 --input_noise_max 0.05
ubuntu   4101220  0.0  0.0   9360  1264 ?        S    Mar10   0:00 /bin/bash -O extglob -c snap=$(command cat <&3) && builtin shopt -s extglob && builtin eval -- "$snap" && { builtin set +u 2>/dev/null || true; builtin eval "${__CURSOR_SANDBOX_ENV_RESTORE:-}" 2>/dev/null; builtin export PWD="$(builtin pwd)"; builtin shopt -s expand_aliases 2>/dev/null; builtin eval "$1"; }; COMMAND_EXIT_CODE=$?; dump_bash_state >&4; builtin exit $COMMAND_EXIT_CODE -- kill -9 4100054 2>/dev/null; sleep 2; source /home/ubuntu/transcripts/venv/bin/activate && cd /home/ubuntu/transcripts && python scripts/validation_audit_ui.py --port 5112 &


My memory is outdated — it still references **v3c** as current. Here's what's **actually running**:

**1 active training job**: `train_v5_pred.py` (predictor + duration fine-tune)
- Output: `output_v5_pred_dur/`
- Pretrained from: `output_v5/checkpoints/epoch_0120.pt` (reinit'd heads)
- Config: `pred_weight=1.0, dur_weight=1.0, lr=5e-5, batch=128, input_noise=0.05`
- Currently at **epoch ~52/200**

**v5 main training** (`train_v5.log`) is **not running** — it stopped at epoch 132/200 (log file you have open). No matching process found.

**Also running**: `server.py` (PID 2975854, started Mar 21) and a validation UI on port 5112.

Want me to update my memory to reflect the v5 training history? And should we check why v5 main training stopped at ep132?


log file


tail -50 /home/ubuntu/lewm-tts/output_v5_pred_dur/train.log 2>/dev/null

Get latest pred_dur training log


Epoch 44/200 | pred=0.4305 dur=0.0301 | 33.7s
  New best (pred=0.4305)
  [45/200] step 2340 | pred=0.4425 dur=0.0278 | grad=0.56 | 5.4s
  [45/200] step 2360 | pred=0.4252 dur=0.0370 | grad=1.00 | 17.9s
  [45/200] step 2380 | pred=0.4224 dur=0.0319 | grad=0.21 | 30.7s
Epoch 45/200 | pred=0.4291 dur=0.0294 | 33.9s
  New best (pred=0.4291)
  [46/200] step 2400 | pred=0.4234 dur=0.0349 | grad=1.90 | 9.8s
  [46/200] step 2420 | pred=0.4420 dur=0.0326 | grad=0.47 | 22.3s
Epoch 46/200 | pred=0.4277 dur=0.0301 | 33.6s
  New best (pred=0.4277)
  [47/200] step 2440 | pred=0.4190 dur=0.0255 | grad=0.38 | 1.6s
  [47/200] step 2460 | pred=0.4406 dur=0.0318 | grad=1.72 | 14.3s
  [47/200] step 2480 | pred=0.4229 dur=0.0287 | grad=0.60 | 26.8s
Epoch 47/200 | pred=0.4261 dur=0.0297 | 34.0s
  New best (pred=0.4261)
  [48/200] step 2500 | pred=0.4200 dur=0.0340 | grad=1.61 | 6.1s
  [48/200] step 2520 | pred=0.4149 dur=0.0270 | grad=0.22 | 18.6s
  [48/200] step 2540 | pred=0.4259 dur=0.0277 | grad=0.67 | 31.3s
Epoch 48/200 | pred=0.4248 dur=0.0295 | 33.9s
  New best (pred=0.4248)
  [49/200] step 2560 | pred=0.4164 dur=0.0294 | grad=0.56 | 10.4s
  [49/200] step 2580 | pred=0.4247 dur=0.0373 | grad=0.68 | 23.0s
Epoch 49/200 | pred=0.4237 dur=0.0293 | 33.8s
  New best (pred=0.4237)
  [50/200] step 2600 | pred=0.4159 dur=0.0275 | grad=0.64 | 2.2s
  [50/200] step 2620 | pred=0.4154 dur=0.0268 | grad=0.56 | 14.7s
  [50/200] step 2640 | pred=0.4297 dur=0.0298 | grad=2.09 | 27.4s
Epoch 50/200 | pred=0.4223 dur=0.0301 | 33.9s
  Saved: output_v5_pred_dur/checkpoints/epoch_0050.pt
  New best (pred=0.4223)
  [51/200] step 2660 | pred=0.4234 dur=0.0245 | grad=0.58 | 6.7s
  [51/200] step 2680 | pred=0.4254 dur=0.0274 | grad=0.68 | 19.1s
  [51/200] step 2700 | pred=0.4169 dur=0.0341 | grad=1.66 | 31.8s
Epoch 51/200 | pred=0.4215 dur=0.0293 | 33.8s
  New best (pred=0.4215)
  [52/200] step 2720 | pred=0.4295 dur=0.0278 | grad=0.91 | 11.2s
  [52/200] step 2740 | pred=0.4075 dur=0.0329 | grad=1.54 | 23.7s
Epoch 52/200 | pred=0.4200 dur=0.0293 | 33.9s
  New best (pred=0.4200)
  [53/200] step 2760 | pred=0.4180 dur=0.0285 | grad=1.02 | 2.9s
  [53/200] step 2780 | pred=0.4268 dur=0.0247 | grad=0.53 | 15.4s
  [53/200] step 2800 | pred=0.4197 dur=0.0267 | grad=1.34 | 28.1s
Epoch 53/200 | pred=0.4186 dur=0.0290 | 33.8s
  New best (pred=0.4186)
  [54/200] step 2820 | pred=0.4187 dur=0.0267 | grad=1.37 | 7.2s
  [54/200] step 2840 | pred=0.4049 dur=0.0249 | grad=0.27 | 19.8s
  [54/200] step 2860 | pred=0.4153 dur=0.0276 | grad=0.87 | 32.4s
Epoch 54/200 | pred=0.4172 dur=0.0292 | 33.8s
  New best (pred=0.4172)


**v5_pred_dur** is at **epoch 54/200**, ~34s/epoch. Still hitting new bests every epoch.

| Metric | Value | Trend |
|--------|-------|-------|
| pred loss | 0.4172 | Slowly decreasing (was ~0.43 at ep40) |
| dur loss | 0.029 | Stable/converged |
| grad norm | ~0.5-1.5 | Healthy |

The file you opened (`train_v5_pred.log`) — is that a different file from `output_v5_pred_dur/train.log`? Want me to check it?


v5_pred_dur is at epoch 54/200, ~34s/epoch. Still hitting new b log?


wc -l /home/ubuntu/lewm-tts/train_v5_pred.log; echo "---"; tail -30 /home/ubuntu/lewm-tts/train_v5_pred.log

Check the train_v5_pred.log file


139 /home/ubuntu/lewm-tts/train_v5_pred.log
---
  [32/200] step 820 | pred=0.5977 | grad=0.43 | 19.8s
Epoch 32/200 | pred=0.6242 | 36.8s
  [33/200] step 840 | pred=0.6167 | grad=0.29 | 11.9s
Epoch 33/200 | pred=0.6242 | 37.0s
  [34/200] step 860 | pred=0.6287 | grad=0.58 | 3.4s
  [34/200] step 880 | pred=0.5971 | grad=0.29 | 34.2s
Epoch 34/200 | pred=0.6177 | 40.6s
  New best (pred=0.6177)
  [35/200] step 900 | pred=0.5977 | grad=0.18 | 22.7s
Epoch 35/200 | pred=0.6148 | 36.9s
  New best (pred=0.6148)
  [36/200] step 920 | pred=0.6137 | grad=0.40 | 14.3s
Epoch 36/200 | pred=0.6131 | 36.9s
  New best (pred=0.6131)
  [37/200] step 940 | pred=0.5883 | grad=0.22 | 6.0s
  [37/200] step 960 | pred=0.6466 | grad=0.40 | 34.2s
Epoch 37/200 | pred=0.6058 | 37.0s
  New best (pred=0.6058)
  [38/200] step 980 | pred=0.6216 | grad=0.31 | 25.6s
Epoch 38/200 | pred=0.6092 | 37.1s
  [39/200] step 1000 | pred=0.6373 | grad=0.51 | 17.3s
Epoch 39/200 | pred=0.6144 | 36.9s
  [40/200] step 1020 | pred=0.5853 | grad=0.32 | 9.1s
  [40/200] step 1040 | pred=0.6260 | grad=0.28 | 36.9s
Epoch 40/200 | pred=0.6077 | 36.9s
  Saved: output_v5_pred/checkpoints/epoch_0040.pt
  [41/200] step 1060 | pred=0.5953 | grad=0.36 | 28.7s
Epoch 41/200 | pred=0.5988 | 37.0s
  New best (pred=0.5988)
  [42/200] step 1080 | pred=0.6237 | grad=0.20 | 20.1s


That's a **different, older run** — `train_v5_pred.log` output to `output_v5_pred/` (no `_dur`). It stopped at epoch 42 with pred=0.60. No duration loss logged — this was a pred-only run before you added duration training.

The **currently active** run is `output_v5_pred_dur/train.log` (epoch 54, pred=0.42, with duration loss). The old `train_v5_pred.log` is dead.


test that genarate soome samples


The user wants to generate some samples from the current training run to test quality. Let me look at what inference scripts are available.


ls /home/ubuntu/lewm-tts/inference*.py /home/ubuntu/lewm-tts/generate*.py /home/ubuntu/lewm-tts/test*.py 2>/dev/null

Find inference/generation scripts


List available checkpoints


Error: Exit code 2
/home/ubuntu/lewm-tts/inference_engine.py
/home/ubuntu/lewm-tts/inference.py
/home/ubuntu/lewm-tts/inference_v5.py


best.pt
epoch_0010.pt
epoch_0020.pt
epoch_0030.pt
epoch_0040.pt
epoch_0050.pt


     1→"""
     2→Inference for LeWM TTS v5 (codec-based JEPA with duration predictor).
     3→Text → duration predict → expand text → JEPA predict → proj_out → EnCodec decode → waveform
     4→"""
     5→
     6→import torch
     7→import torchaudio
     8→import numpy as np
     9→import argparse
    10→from pathlib import Path
    11→
    12→from model_v5 import LeWMTTSv5, length_regulate
    13→
    14→
    15→class LeWMTTSv5Inference:
    16→    def __init__(self, checkpoint_path, device="cuda"):
    17→        self.device = torch.device(device if torch.cuda.is_available() else "cpu")
    18→
    19→        ckpt = torch.load(checkpoint_path, map_location=self.device, weights_only=False)
    20→        config = ckpt["config"]
    21→
    22→        self.model = LeWMTTSv5(config).to(self.device)
    23→        self.model.load_state_dict(ckpt["model"])
    24→        self.model.eval()
    25→
    26→        # EnCodec decoder (frozen)
    27→        from encodec import EncodecModel
    28→        self.codec = EncodecModel.encodec_model_24khz()
    29→        self.codec.set_target_bandwidth(6.0)
    30→        self.codec.eval().to(self.device)
    31→
    32→        self.config = config
    33→        print(f"v5 model loaded from {checkpoint_path}")
    34→
    35→    def text_to_tokens(self, text):
    36→        tokens = list(text.encode("utf-8"))
    37→        return torch.tensor(tokens, dtype=torch.long).unsqueeze(0).to(self.device)
    38→
    39→    @torch.no_grad()
    40→    def synthesize(self, text, duration_scale=1.0, temperature=0.0):
    41→        """AR synthesis with duration predictor for text alignment."""
    42→        text_tokens = self.text_to_tokens(text)
    43→        text_emb = self.model.text_encoder(text_tokens)
    44→
    45→        # Predict durations and expand text
    46→        durations = self.model.predict_durations(text_emb)
    47→        durations = (durations.float() * duration_scale).round().long().clamp(min=1)
    48→        total_frames = durations.sum().item()
    49→        print(f"Text: {len(text)} chars → {total_frames} frames ({total_frames/75:.2f}s)")
    50→
    51→        # Expand text to frame level
    52→        text_emb_expanded = length_regulate(text_emb, durations)  # [1, T_total, d_model]
    53→
    54→        # AR generation
    55→        start_emb = self.model.start_emb.to(self.device)
    56→        cache = self.model.init_ar_cache()
    57→        all_embeddings = [start_emb]
    58→
    59→        # First step
    60→        text_frame = text_emb_expanded[:, 0:1]  # [1, 1, d_model]
    61→        next_emb, cache = self.model.predict_next_cached(start_emb, text_frame, 0, cache)
    62→        if temperature > 0:
    63→            next_emb = next_emb + torch.randn_like(next_emb) * temperature
    64→        all_embeddings.append(next_emb)
    65→
    66→        for step in range(1, total_frames):
    67→            # Get text embedding for this frame
    68→            if step < text_emb_expanded.shape[1]:
    69→                text_frame = text_emb_expanded[:, step:step+1]
    70→            else:
    71→                text_frame = torch.zeros(1, 1, text_emb.shape[-1], device=self.device)
    72→
    73→            next_emb, cache = self.model.predict_next_cached(next_emb, text_frame, step, cache)
    74→            if temperature > 0:
    75→                next_emb = next_emb + torch.randn_like(next_emb) * temperature
    76→            all_embeddings.append(next_emb)
    77→
    78→        # Concat predicted embeddings and project to codec space
    79→        pred_embs = torch.cat(all_embeddings, dim=1)  # [1, T, d_model]
    80→        codec_emb = self.model.proj_out(pred_embs)     # [1, T, 128]
    81→        codec_emb = codec_emb.transpose(1, 2)          # [1, 128, T]
    82→
    83→        # EnCodec decode
    84→        waveform = self.codec.decoder(codec_emb)
    85→        waveform = waveform.squeeze().cpu().numpy()
    86→
    87→        return waveform, 24000
    88→
    89→    @torch.no_grad()
    90→    def reconstruct(self, audio_path):
    91→        """Encode audio → proj_in → proj_out → decode. Tests roundtrip quality."""
    92→        from encodec.utils import convert_audio
    93→        wav, sr = torchaudio.load(audio_path)
    94→        wav = convert_audio(wav, sr, 24000, 1).to(self.device)
    95→
    96→        codec_emb = self.codec.encoder(wav.unsqueeze(0))
    97→        z = self.model.proj_in(codec_emb.transpose(1, 2))
    98→        codec_recon = self.model.proj_out(z).transpose(1, 2)
    99→        waveform = self.codec.decoder(codec_recon).squeeze().cpu().numpy()
   100→        return waveform, 24000
   101→
   102→    @torch.no_grad()
   103→    def reconstruct_codec_only(self, audio_path):
   104→        """Pure EnCodec roundtrip (no JEPA). Baseline quality."""
   105→        from encodec.utils import convert_audio
   106→        wav, sr = torchaudio.load(audio_path)
   107→        wav = convert_audio(wav, sr, 24000, 1).to(self.device)
   108→
   109→        codec_emb = self.codec.encoder(wav.unsqueeze(0))
   110→        waveform = self.codec.decoder(codec_emb).squeeze().cpu().numpy()
   111→        return waveform, 24000
   112→
   113→    @torch.no_grad()
   114→    def reconstruct_with_prediction(self, audio_path, text):
   115→        """Encode audio → proj_in → teacher-forced predict with text alignment → proj_out → decode."""
   116→        from encodec.utils import convert_audio
   117→        from model_v5 import compute_uniform_durations
   118→        wav, sr = torchaudio.load(audio_path)
   119→        wav = convert_audio(wav, sr, 24000, 1).to(self.device)
   120→
   121→        codec_emb = self.codec.encoder(wav.unsqueeze(0))  # [1, 128, T]
   122→        T = codec_emb.shape[2]
   123→        z = self.model.proj_in(codec_emb.transpose(1, 2))  # [1, T, d_model]
   124→
   125→        text_tokens = self.text_to_tokens(text)
   126→        text_emb = self.model.text_encoder(text_tokens)
   127→
   128→        # Compute uniform durations and expand text
   129→        text_len = torch.tensor([text_tokens.shape[1]], device=self.device)
   130→        audio_len = torch.tensor([T], device=self.device)
   131→        durations = compute_uniform_durations(text_len, audio_len)
   132→        text_emb_expanded = length_regulate(text_emb, durations)
   133→        if text_emb_expanded.shape[1] > T:
   134→            text_emb_expanded = text_emb_expanded[:, :T]
   135→        elif text_emb_expanded.shape[1] < T:
   136→            pad = torch.zeros(1, T - text_emb_expanded.shape[1], text_emb.shape[-1],
   137→                            device=self.device)
   138→            text_emb_expanded = torch.cat([text_emb_expanded, pad], dim=1)
   139→
   140→        B = z.shape[0]
   141→        start = self.model.start_emb.expand(B, -1, -1)
   142→        input_emb = torch.cat([start, z[:, :-1]], dim=1)
   143→        predicted = self.model.predictor(input_emb, text_emb_expanded)
   144→
   145→        codec_recon = self.model.proj_out(predicted).transpose(1, 2)
   146→        waveform = self.codec.decoder(codec_recon).squeeze().cpu().numpy()
   147→        return waveform, 24000
   148→
   149→    def save_audio(self, waveform, sr, output_path):
   150→        mx = np.abs(waveform).max()
   151→        if mx > 0:
   152→            waveform = waveform / mx * 0.95
   153→        wav_tensor = torch.from_numpy(waveform).unsqueeze(0)
   154→        torchaudio.save(output_path, wav_tensor, sr)
   155→        print(f"Saved: {output_path} ({len(waveform)/sr:.2f}s)")
   156→
   157→
   158→def main():
   159→    parser = argparse.ArgumentParser()
   160→    parser.add_argument("--checkpoint", required=True)
   161→    parser.add_argument("--text", default=None)
   162→    parser.add_argument("--audio", default=None, help="Audio path for reconstruction")
   163→    parser.add_argument("--mode", choices=["synthesize", "reconstruct", "codec_only", "predict"],
   164→                        default="synthesize")
   165→    parser.add_argument("--output", default="output_v5.wav")
   166→    parser.add_argument("--duration_scale", type=float, default=1.0)
   167→    parser.add_argument("--temperature", type=float, default=0.0)
   168→    args = parser.parse_args()
   169→
   170→    tts = LeWMTTSv5Inference(args.checkpoint)
   171→
   172→    if args.mode == "synthesize":
   173→        assert args.text, "Need --text"
   174→        waveform, sr = tts.synthesize(args.text, args.duration_scale, args.temperature)
   175→    elif args.mode == "reconstruct":
   176→        assert args.audio, "Need --audio"
   177→        waveform, sr = tts.reconstruct(args.audio)
   178→    elif args.mode == "codec_only":
   179→        assert args.audio, "Need --audio"
   180→        waveform, sr = tts.reconstruct_codec_only(args.audio)
   181→    elif args.mode == "predict":
   182→        assert args.audio and args.text, "Need --audio and --text"
   183→        waveform, sr = tts.reconstruct_with_prediction(args.audio, args.text)
   184→
   185→    tts.save_audio(waveform, sr, args.output)
   186→
   187→
   188→if __name__ == "__main__":
   189→    main()
   190→

"""
Inference for LeWM TTS v5 (codec-based JEPA with duration predictor).
Text → duration predict → expand text → JEPA predict → proj_out → EnCodec decode → waveform
"""

import torch
import torchaudio
import numpy as np
import argparse
from pathlib import Path

from model_v5 import LeWMTTSv5, length_regulate

class LeWMTTSv5Inference:
    def __init__(self, checkpoint_path, device="cuda"):
        self.device = torch.device(device if torch.cuda.is_available() else "cpu")

        ckpt = torch.load(checkpoint_path, map_location=self.device, weights_only=False)
        config = ckpt["config"]

        self.model = LeWMTTSv5(config).to(self.device)
        self.model.load_state_dict(ckpt["model"])
        self.model.eval()

        # EnCodec decoder (frozen)
        from encodec import EncodecModel
        self.codec = EncodecModel.encodec_model_24khz()
        self.codec.set_target_bandwidth(6.0)
        self.codec.eval().to(self.device)

        self.config = config
        print(f"v5 model loaded from {checkpoint_path}")

    def text_to_tokens(self, text):
        tokens = list(text.encode("utf-8"))
        return torch.tensor(tokens, dtype=torch.long).unsqueeze(0).to(self.device)

    @torch.no_grad()
    def synthesize(self, text, duration_scale=1.0, temperature=0.0):
        """AR synthesis with duration predictor for text alignment."""
        text_tokens = self.text_to_tokens(text)
        text_emb = self.model.text_encoder(text_tokens)

        # Predict durations and expand text
        durations = self.model.predict_durations(text_emb)
        durations = (durations.float() * duration_scale).round().long().clamp(min=1)
        total_frames = durations.sum().item()
        print(f"Text: {len(text)} chars → {total_frames} frames ({total_frames/75:.2f}s)")

        # Expand text to frame level
        text_emb_expanded = length_regulate(text_emb, durations)  # [1, T_total, d_model]

        # AR generation
        start_emb = self.model.start_emb.to(self.device)
        cache = self.model.init_ar_cache()
        all_embeddings = [start_emb]

        # First step
        text_frame = text_emb_expanded[:, 0:1]  # [1, 1, d_model]
        next_emb, cache = self.model.predict_next_cached(start_emb, text_frame, 0, cache)
        if temperature > 0:
            next_emb = next_emb + torch.randn_like(next_emb) * temperature
        all_embeddings.append(next_emb)

        for step in range(1, total_frames):
            # Get text embedding for this frame
            if step < text_emb_expanded.shape[1]:
                text_frame = text_emb_expanded[:, step:step+1]
            else:
                text_frame = torch.zeros(1, 1, text_emb.shape[-1], device=self.device)

            next_emb, cache = self.model.predict_next_cached(next_emb, text_frame, step, cache)
            if temperature > 0:
                next_emb = next_emb + torch.randn_like(next_emb) * temperature
            all_embeddings.append(next_emb)

        # Concat predicted embeddings and project to codec space
        pred_embs = torch.cat(all_embeddings, dim=1)  # [1, T, d_model]
        codec_emb = self.model.proj_out(pred_embs)     # [1, T, 128]
        codec_emb = codec_emb.transpose(1, 2)          # [1, 128, T]

        # EnCodec decode
        waveform = self.codec.decoder(codec_emb)
        waveform = waveform.squeeze().cpu().numpy()

        return waveform, 24000

    @torch.no_grad()
    def reconstruct(self, audio_path):
        """Encode audio → proj_in → proj_out → decode. Tests roundtrip quality."""
        from encodec.utils import convert_audio
        wav, sr = torchaudio.load(audio_path)
        wav = convert_audio(wav, sr, 24000, 1).to(self.device)

        codec_emb = self.codec.encoder(wav.unsqueeze(0))
        z = self.model.proj_in(codec_emb.transpose(1, 2))
        codec_recon = self.model.proj_out(z).transpose(1, 2)
        waveform = self.codec.decoder(codec_recon).squeeze().cpu().numpy()
        return waveform, 24000

    @torch.no_grad()
    def reconstruct_codec_only(self, audio_path):
        """Pure EnCodec roundtrip (no JEPA). Baseline quality."""
        from encodec.utils import convert_audio
        wav, sr = torchaudio.load(audio_path)
        wav = convert_audio(wav, sr, 24000, 1).to(self.device)

        codec_emb = self.codec.encoder(wav.unsqueeze(0))
        waveform = self.codec.decoder(codec_emb).squeeze().cpu().numpy()
        return waveform, 24000

    @torch.no_grad()
    def reconstruct_with_prediction(self, audio_path, text):
        """Encode audio → proj_in → teacher-forced predict with text alignment → proj_out → decode."""
        from encodec.utils import convert_audio
        from model_v5 import compute_uniform_durations
        wav, sr = torchaudio.load(audio_path)
        wav = convert_audio(wav, sr, 24000, 1).to(self.device)

        codec_emb = self.codec.encoder(wav.unsqueeze(0))  # [1, 128, T]
        T = codec_emb.shape[2]
        z = self.model.proj_in(codec_emb.transpose(1, 2))  # [1, T, d_model]

        text_tokens = self.text_to_tokens(text)
        text_emb = self.model.text_encoder(text_tokens)

        # Compute uniform durations and expand text
        text_len = torch.tensor([text_tokens.shape[1]], device=self.device)
        audio_len = torch.tensor([T], device=self.device)
        durations = compute_uniform_durations(text_len, audio_len)
        text_emb_expanded = length_regulate(text_emb, durations)
        if text_emb_expanded.shape[1] > T:
            text_emb_expanded = text_emb_expanded[:, :T]
        elif text_emb_expanded.shape[1] < T:
            pad = torch.zeros(1, T - text_emb_expanded.shape[1], text_emb.shape[-1],
                            device=self.device)
            text_emb_expanded = torch.cat([text_emb_expanded, pad], dim=1)

        B = z.shape[0]
        start = self.model.start_emb.expand(B, -1, -1)
        input_emb = torch.cat([start, z[:, :-1]], dim=1)
        predicted = self.model.predictor(input_emb, text_emb_expanded)

        codec_recon = self.model.proj_out(predicted).transpose(1, 2)
        waveform = self.codec.decoder(codec_recon).squeeze().cpu().numpy()
        return waveform, 24000

    def save_audio(self, waveform, sr, output_path):
        mx = np.abs(waveform).max()
        if mx > 0:
            waveform = waveform / mx * 0.95
        wav_tensor = torch.from_numpy(waveform).unsqueeze(0)
        torchaudio.save(output_path, wav_tensor, sr)
        print(f"Saved: {output_path} ({len(waveform)/sr:.2f}s)")

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--checkpoint", required=True)
    parser.add_argument("--text", default=None)
    parser.add_argument("--audio", default=None, help="Audio path for reconstruction")
    parser.add_argument("--mode", choices=["synthesize", "reconstruct", "codec_only", "predict"],
                        default="synthesize")
    parser.add_argument("--output", default="output_v5.wav")
    parser.add_argument("--duration_scale", type=float, default=1.0)
    parser.add_argument("--temperature", type=float, default=0.0)
    args = parser.parse_args()

    tts = LeWMTTSv5Inference(args.checkpoint)

    if args.mode == "synthesize":
        assert args.text, "Need --text"
        waveform, sr = tts.synthesize(args.text, args.duration_scale, args.temperature)
    elif args.mode == "reconstruct":
        assert args.audio, "Need --audio"
        waveform, sr = tts.reconstruct(args.audio)
    elif args.mode == "codec_only":
        assert args.audio, "Need --audio"
        waveform, sr = tts.reconstruct_codec_only(args.audio)
    elif args.mode == "predict":
        assert args.audio and args.text, "Need --audio and --text"
        waveform, sr = tts.reconstruct_with_prediction(args.audio, args.text)

    tts.save_audio(waveform, sr, args.output)

if __name__ == "__main__":
    main()


The inference script loads from a checkpoint but the pred_dur checkpoint may have different model state dict keys. Let me check if the checkpoint from pred_dur training is compatible. Let me also check model_v5.py and train_v5_pred.py to understand the model structure.

Actually, the pred_dur training loads from v5 checkpoint and reinitializes - so it should save a compatible checkpoint. Let me just try to generate samples. But I need to check - does the pred_dur checkpoint have the same model structure? The train_v5_pred.py might only train predictor + duration head, so the checkpoint might have a different format.

Let me look at what's actually in the checkpoint to see if the keys match up.


cd /home/ubuntu/lewm-tts && python3 -c "
import torch
ckpt = torch.load('output_v5_pred_dur/checkpoints/best.pt', map_location='cpu', weights_only=False)
print('Keys:', list(ckpt.keys()))
print('Config:', ckpt.get('config', 'N/A'))
" 2>&1 | head -20

Check checkpoint format


ls /home/ubuntu/lewm-tts/processed_data_100mel/*.wav 2>/dev/null | head -3; ls /home/ubuntu/modi_dataset/*.wav 2>/dev/null | head -3; find /home/ubuntu/modi_dataset -name "*.wav" 2>/dev/null | head -3

Find sample audio files for reconstruction test


Keys: ['epoch', 'global_step', 'model', 'config', 'loss']
Config: {'d_model': 256, 'nhead': 4, 'codec_dim': 128, 'text_vocab_size': 256, 'text_encoder_layers': 4, 'predictor_layers': 6, 'dropout': 0.1, 'pred_weight': 1.0, 'roundtrip_weight': 1.0, 'ema_decay': 0.998, 'input_noise': 0.0, 'dur_weight': 1.0}


(Bash completed with no output)


find /home/ubuntu/modi_dataset -name "*.wav" -o -name "*.mp3" -o -name "*.flac" 2>/dev/null | head -3; find /home/ubuntu/lewm-tts -name "*.wav" -not -path "*/output*" 2>/dev/null | head -3

Find audio files


/home/ubuntu/lewm-tts/sample_tf_v4_0.wav
/home/ubuntu/lewm-tts/samples_v5/codec_only_0.wav
/home/ubuntu/lewm-tts/samples_v5/roundtrip_0.wav


python3 -c "
import json
with open('processed_data_100mel/manifest.json') as f:
    manifest = json.load(f)
print(manifest[0])
" 2>&1 | head -5

Check manifest for audio paths


{'id': 0, 'audio_path': '/home/ubuntu/lewm-tts/processed_data_100mel/audio/000000.wav', 'mel_path': '/home/ubuntu/lewm-tts/processed_data_100mel/mels/000000.pt', 'text': 'मेरे प्यारे देशवासियों, नमस्कार। मन की बात में आपका स्वागत है, अभिन', 'duration': 9.52, 'mel_frames': 893}


     1→"""
     2→Train predictor + text encoder + duration predictor with frozen projections.
     3→Loads proj_in/proj_out from a v5 checkpoint (already good at roundtrip).
     4→Text is expanded to frame level via duration predictor — can't be ignored.
     5→"""
     6→
     7→import os
     8→import json
     9→import time
    10→import argparse
    11→import torch
    12→import torch.nn.functional as F
    13→from torch.utils.tensorboard import SummaryWriter
    14→from pathlib import Path
    15→
    16→from model_v5 import (
    17→    LeWMTTSv5, build_model, JEPAPredictor, TextEncoder,
    18→    DurationPredictor, compute_uniform_durations, length_regulate,
    19→)
    20→from dataset_v5 import build_dataloader
    21→
    22→
    23→def train(args):
    24→    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    25→    print(f"Device: {device}")
    26→
    27→    # Load pretrained checkpoint for projections
    28→    ckpt = torch.load(args.pretrained, map_location=device, weights_only=False)
    29→    config = ckpt["config"]
    30→    config["pred_weight"] = args.pred_weight
    31→    config["dur_weight"] = args.dur_weight
    32→    config["input_noise"] = 0.0
    33→
    34→    model = LeWMTTSv5(config).to(device)
    35→
    36→    # Load what we can from pretrained (projections, etc.)
    37→    pretrained_state = ckpt["model"]
    38→    model_state = model.state_dict()
    39→    loaded_keys = []
    40→    skipped_keys = []
    41→    for k, v in pretrained_state.items():
    42→        if k in model_state and model_state[k].shape == v.shape:
    43→            model_state[k] = v
    44→            loaded_keys.append(k)
    45→        else:
    46→            skipped_keys.append(k)
    47→    model.load_state_dict(model_state)
    48→    print(f"Loaded {len(loaded_keys)} params from {args.pretrained} (skipped {len(skipped_keys)})")
    49→    if skipped_keys:
    50→        print(f"  Skipped: {skipped_keys[:10]}{'...' if len(skipped_keys) > 10 else ''}")

"""
Train predictor + text encoder + duration predictor with frozen projections.
Loads proj_in/proj_out from a v5 checkpoint (already good at roundtrip).
Text is expanded to frame level via duration predictor — can't be ignored.
"""

import os
import json
import time
import argparse
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from pathlib import Path

from model_v5 import (
    LeWMTTSv5, build_model, JEPAPredictor, TextEncoder,
    DurationPredictor, compute_uniform_durations, length_regulate,
)
from dataset_v5 import build_dataloader

def train(args):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(f"Device: {device}")

    # Load pretrained checkpoint for projections
    ckpt = torch.load(args.pretrained, map_location=device, weights_only=False)
    config = ckpt["config"]
    config["pred_weight"] = args.pred_weight
    config["dur_weight"] = args.dur_weight
    config["input_noise"] = 0.0

    model = LeWMTTSv5(config).to(device)

    # Load what we can from pretrained (projections, etc.)
    pretrained_state = ckpt["model"]
    model_state = model.state_dict()
    loaded_keys = []
    skipped_keys = []
    for k, v in pretrained_state.items():
        if k in model_state and model_state[k].shape == v.shape:
            model_state[k] = v
            loaded_keys.append(k)
        else:
            skipped_keys.append(k)
    model.load_state_dict(model_state)
    print(f"Loaded {len(loaded_keys)} params from {args.pretrained} (skipped {len(skipped_keys)})")
    if skipped_keys:
        print(f"  Skipped: {skipped_keys[:10]}{'...' if len(skipped_keys) > 10 else ''}")


mkdir -p samples_v5_pred && python3 -c "
import torch, torchaudio, numpy as np, sys

# Use the inference_v5.py but with pred_dur checkpoint
from inference_v5 import LeWMTTSv5Inference

ckpt_path = 'output_v5_pred_dur/checkpoints/best.pt'
tts = LeWMTTSv5Inference(ckpt_path)

# Test 1: AR synthesis with Hindi text
texts = [
    'नमस्कार, मैं आपका स्वागत करता हूं।',
    'भारत एक महान देश है और हम सब भारतवासी हैं।',
    'मन की बात में आपका स्वागत है।',
]

for i, text in enumerate(texts):
    print(f'\\nGenerating sample {i}: {text[:50]}...')
    try:
        waveform, sr = tts.synthesize(text, duration_scale=1.0, temperature=0.0)
        tts.save_audio(waveform, sr, f'samples_v5_pred/synth_{i}.wav')
    except Exception as e:
        print(f'  ERROR: {e}')

# Test 2: Reconstruction with prediction (teacher-forced)
audio_path = 'processed_data_100mel/audio/000000.wav'
text = 'मेरे प्यारे देशवासियों, नमस्कार। मन की बात में आपका स्वागत है, अभिन'
print(f'\\nReconstruct with prediction: {audio_path}')
try:
    waveform, sr = tts.reconstruct_with_prediction(audio_path, text)
    tts.save_audio(waveform, sr, 'samples_v5_pred/predict_tf_0.wav')
except Exception as e:
    print(f'  ERROR: {e}')

# Test 3: Pure reconstruction (roundtrip)
print(f'\\nRoundtrip reconstruct: {audio_path}')
try:
    waveform, sr = tts.reconstruct(audio_path)
    tts.save_audio(waveform, sr, 'samples_v5_pred/roundtrip_0.wav')
except Exception as e:
    print(f'  ERROR: {e}')

print('\\nDone!')
" 2>&1

Generate test samples from best pred_dur checkpoint


/home/ubuntu/.local/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
  WeightNorm.apply(module, name, dim)
v5 model loaded from output_v5_pred_dur/checkpoints/best.pt

Generating sample 0: नमस्कार, मैं आपका स्वागत करता हूं।...
Text: 34 chars → 358 frames (4.77s)
Saved: samples_v5_pred/synth_0.wav (4.79s)

Generating sample 1: भारत एक महान देश है और हम सब भारतवासी हैं।...
Text: 42 chars → 431 frames (5.75s)
Saved: samples_v5_pred/synth_1.wav (5.76s)

Generating sample 2: मन की बात में आपका स्वागत है।...
Text: 29 chars → 286 frames (3.81s)
Saved: samples_v5_pred/synth_2.wav (3.83s)

Reconstruct with prediction: processed_data_100mel/audio/000000.wav
Saved: samples_v5_pred/predict_tf_0.wav (9.52s)

Roundtrip reconstruct: processed_data_100mel/audio/000000.wav
Saved: samples_v5_pred/roundtrip_0.wav (9.52s)

Done!


Generated 5 samples in `samples_v5_pred/`:

| File | Mode | Description |
|------|------|-------------|
| `synth_0.wav` | AR synthesis | "नमस्कार, मैं आपका स्वागत करता हूं।" (4.8s) |
| `synth_1.wav` | AR synthesis | "भारत एक महान देश है..." (5.8s) |
| `synth_2.wav` | AR synthesis | "मन की बात में आपका स्वागत है।" (3.8s) |
| `predict_tf_0.wav` | Teacher-forced prediction | Reconstruction via predictor with text |
| `roundtrip_0.wav` | Roundtrip | proj_in → proj_out → decode (no predictor) |

Duration predictor is working — producing reasonable frame counts. Have a listen and let me know how they sound!


roundtrip_0.wav  clear .. predict_tf_0.wav robotic ..synth fuckk.. i dont know y u dng only with text .. provide soem audio for refernc and give text ..do u dng same


The user is saying:
1. roundtrip_0.wav sounds clear (good - proj_in/proj_out works)
2. predict_tf_0.wav sounds robotic (predictor is degrading quality)
3. synth samples are terrible
4. They want me to do synthesis with a reference audio prompt, not just text-only AR generation

Let me look at how the inference works - the user wants voice-prompted synthesis where you provide a reference audio clip and text, and the model uses the audio as a prompt/prefix for AR generation. Let me check if there's such a mode in inference_v5.py or if I need to create one.

Looking at the inference code, the `synthesize` method is pure text→AR with no audio prompt.

I need to check if the inference engine already supports voice prompting or if I'll need to add that capability myself.


You're right — pure text AR with no audio reference is pointless. The model needs a voice prompt to anchor the generation. Let me check if there's a voice-prompted synthesis mode.


     1→"""
     2→LeWM TTS v5 — JEPA with EnCodec backend.
     3→
     4→Architecture:
     5→  - EnCodec encoder/decoder (frozen): audio ↔ 128d embeddings @ 75Hz
     6→  - Simple linear projections: 128d ↔ d_model (no lossy compression)
     7→  - TextEncoder: byte-level transformer for Hindi text
     8→  - DurationPredictor: predicts per-character duration
     9→  - JEPAPredictor: causal transformer with frame-level text conditioning
    10→"""
    11→
    12→import math
    13→import copy
    14→import torch
    15→import torch.nn as nn
    16→import torch.nn.functional as F
    17→
    18→
    19→# ─── Duration Predictor ─────────────────────────────────────────────────────
    20→
    21→class DurationPredictor(nn.Module):
    22→    """Predicts log-duration per text token. Similar to FastSpeech."""
    23→    def __init__(self, d_model=256, kernel_size=3, num_layers=2, dropout=0.1):
    24→        super().__init__()
    25→        layers = []
    26→        for _ in range(num_layers):
    27→            layers.extend([
    28→                nn.Conv1d(d_model, d_model, kernel_size, padding=kernel_size // 2),
    29→                nn.ReLU(),
    30→                nn.LayerNorm(d_model),

"""
LeWM TTS v5 — JEPA with EnCodec backend.

Architecture:
  - EnCodec encoder/decoder (frozen): audio ↔ 128d embeddings @ 75Hz
  - Simple linear projections: 128d ↔ d_model (no lossy compression)
  - TextEncoder: byte-level transformer for Hindi text
  - DurationPredictor: predicts per-character duration
  - JEPAPredictor: causal transformer with frame-level text conditioning
"""

import math
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F

# ─── Duration Predictor ─────────────────────────────────────────────────────

class DurationPredictor(nn.Module):
    """Predicts log-duration per text token. Similar to FastSpeech."""
    def __init__(self, d_model=256, kernel_size=3, num_layers=2, dropout=0.1):
        super().__init__()
        layers = []
        for _ in range(num_layers):
            layers.extend([
                nn.Conv1d(d_model, d_model, kernel_size, padding=kernel_size // 2),
                nn.ReLU(),
                nn.LayerNorm(d_model),


     1→"""
     2→Production inference engine for LeWM TTS.
     3→Features:
     4→  - Batched inference (many concurrent requests in parallel)
     5→  - KV-cache (O(1) per step instead of O(n))
     6→  - FP16
     7→  - Audio prompt support (for multi-speaker / voice cloning)
     8→  - Continuous batching (add/remove requests dynamically)
     9→  - Griffin-Lim + Vocos vocoder pipeline
    10→"""
    11→
    12→import torch
    13→import torch.nn.functional as F
    14→import torchaudio
    15→import numpy as np
    16→import time
    17→from dataclasses import dataclass, field
    18→from typing import Optional
    19→from pathlib import Path
    20→
    21→from model import LeWMTTS
    22→
    23→
    24→# ─── Vocoder ────────────────────────────────────────────────────────────────
    25→
    26→class Vocoder:
    27→    """Vocos vocoder for 100-mel → waveform (direct decode, no Griffin-Lim)."""
    28→
    29→    def __init__(self):
    30→        from vocos import Vocos
    31→        self.vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
    32→        self.vocos.eval()
    33→
    34→    def __call__(self, mel):
    35→        """mel: [B, 100, T] → waveforms list of [T_audio] numpy arrays."""
    36→        with torch.no_grad():
    37→            # Vocos decode handles batched input
    38→            waveforms = []
    39→            for i in range(mel.shape[0]):
    40→                wav = self.vocos.decode(mel[i:i+1])  # [1, T_audio]
    41→                waveforms.append(wav.squeeze(0).numpy())
    42→        return waveforms
    43→
    44→
    45→# ─── Request / Result ───────────────────────────────────────────────────────
    46→
    47→@dataclass
    48→class TTSRequest:
    49→    text: str
    50→    max_steps: int = 300
    51→    temperature: float = 0.0
    52→    prompt_mel: Optional[torch.Tensor] = None  # [1, 80, T] for voice cloning
    53→
    54→
    55→@dataclass
    56→class TTSResult:
    57→    waveform: np.ndarray
    58→    sample_rate: int = 24000
    59→    steps_taken: int = 0
    60→    generation_time: float = 0.0

"""
Production inference engine for LeWM TTS.
Features:
  - Batched inference (many concurrent requests in parallel)
  - KV-cache (O(1) per step instead of O(n))
  - FP16
  - Audio prompt support (for multi-speaker / voice cloning)
  - Continuous batching (add/remove requests dynamically)
  - Griffin-Lim + Vocos vocoder pipeline
"""

import torch
import torch.nn.functional as F
import torchaudio
import numpy as np
import time
from dataclasses import dataclass, field
from typing import Optional
from pathlib import Path

from model import LeWMTTS

# ─── Vocoder ────────────────────────────────────────────────────────────────

class Vocoder:
    """Vocos vocoder for 100-mel → waveform (direct decode, no Griffin-Lim)."""

    def __init__(self):
        from vocos import Vocos
        self.vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
        self.vocos.eval()

    def __call__(self, mel):
        """mel: [B, 100, T] → waveforms list of [T_audio] numpy arrays."""
        with torch.no_grad():
            # Vocos decode handles batched input
            waveforms = []
            for i in range(mel.shape[0]):
                wav = self.vocos.decode(mel[i:i+1])  # [1, T_audio]
                waveforms.append(wav.squeeze(0).numpy())
        return waveforms

# ─── Request / Result ───────────────────────────────────────────────────────

@dataclass
class TTSRequest:
    text: str
    max_steps: int = 300
    temperature: float = 0.0
    prompt_mel: Optional[torch.Tensor] = None  # [1, 80, T] for voice cloning

@dataclass
class TTSResult:
    waveform: np.ndarray
    sample_rate: int = 24000
    steps_taken: int = 0
    generation_time: float = 0.0


grep -n "def.*synth\|def.*prompt\|def.*voice\|def.*ar\|def.*generate\|def predict_next\|def init_ar" /home/ubuntu/lewm-tts/model_v5.py

Find AR/generation methods in model_v5


36:    def forward(self, text_emb, text_mask=None):
126:    def forward(self, audio_emb, text_emb_expanded, audio_mask=None):
140:    def forward_cross_attn(self, audio_emb, text_emb, audio_mask=None, text_mask=None):
207:    def forward(self, x):
227:    def forward(self, text_tokens, text_mask=None):
311:    def forward(self, codec_emb, text_tokens, codec_mask=None, text_mask=None):
435:    def init_ar_cache(self):
439:    def predict_next_cached(self, new_emb, text_emb_frame, step_idx, cache):
447:def count_parameters(model):


   300→        # Input noise for train/inference gap
   301→        self.input_noise = config.get("input_noise", 0.0)
   302→
   303→        self.config = config
   304→
   305→    @torch.no_grad()
   306→    def update_ema(self):
   307→        for p_online, p_ema in zip(self.proj_in.parameters(),
   308→                                   self.ema_proj_in.parameters()):
   309→            p_ema.data.mul_(self.ema_decay).add_(p_online.data, alpha=1 - self.ema_decay)
   310→
   311→    def forward(self, codec_emb, text_tokens, codec_mask=None, text_mask=None):
   312→        """
   313→        Args:
   314→            codec_emb: [B, 128, T] — continuous EnCodec embeddings
   315→            text_tokens: [B, T_text]
   316→            codec_mask: [B, T] bool, True = padding
   317→            text_mask: [B, T_text] bool
   318→        """
   319→        B = codec_emb.shape[0]
   320→        T = codec_emb.shape[2]
   321→
   322→        # codec_emb is [B, 128, T] → transpose to [B, T, 128]
   323→        codec_seq = codec_emb.transpose(1, 2)
   324→
   325→        # Text encoding
   326→        text_emb = self.text_encoder(text_tokens, text_mask)
   327→
   328→        # Duration prediction + ground truth durations (uniform alignment)
   329→        log_dur_pred = self.duration_predictor(text_emb, text_mask)
   330→
   331→        # Compute actual text/audio lengths from masks
   332→        if text_mask is not None:
   333→            text_lengths = (~text_mask).sum(dim=1)  # [B]
   334→        else:
   335→            text_lengths = torch.full((B,), text_tokens.shape[1], device=text_tokens.device)
   336→        if codec_mask is not None:
   337→            audio_lengths = (~codec_mask).sum(dim=1)  # [B]
   338→        else:
   339→            audio_lengths = torch.full((B,), T, device=codec_emb.device)
   340→
   341→        # Ground truth durations (uniform distribution)
   342→        gt_durations = compute_uniform_durations(text_lengths, audio_lengths)
   343→        log_dur_gt = torch.log(gt_durations.float().clamp(min=1))
   344→
   345→        # Duration loss (MSE on log-durations)
   346→        if text_mask is not None:
   347→            valid_text = (~text_mask).float()
   348→            dur_loss = F.mse_loss(
   349→                log_dur_pred * valid_text, log_dur_gt * valid_text, reduction="sum"
   350→            ) / (valid_text.sum() + 1e-8)
   351→        else:
   352→            dur_loss = F.mse_loss(log_dur_pred, log_dur_gt)
   353→
   354→        # Expand text to frame level using GT durations (training uses GT)
   355→        text_emb_expanded = length_regulate(text_emb, gt_durations, text_mask)
   356→        # Trim/pad to match audio length T
   357→        if text_emb_expanded.shape[1] > T:
   358→            text_emb_expanded = text_emb_expanded[:, :T]
   359→        elif text_emb_expanded.shape[1] < T:
   360→            pad = torch.zeros(B, T - text_emb_expanded.shape[1], text_emb.shape[-1],
   361→                            device=text_emb.device)
   362→            text_emb_expanded = torch.cat([text_emb_expanded, pad], dim=1)
   363→
   364→        # Project codec embeddings to predictor space
   365→        z = self.proj_in(codec_seq)  # [B, T, d_model]
   366→
   367→        # EMA targets (deterministic)
   368→        with torch.no_grad():
   369→            self.ema_proj_in.eval()
   370→            target_emb = self.ema_proj_in(codec_seq)  # [B, T, d_model]
   371→            self.ema_proj_in.train()
   372→
   373→        # Prepend start embedding, shift right
   374→        start = self.start_emb.expand(B, -1, -1)
   375→        input_emb = torch.cat([start, z[:, :-1]], dim=1)  # [B, T, d_model]
   376→
   377→        # Input noise
   378→        if self.training and self.input_noise > 0:
   379→            noise = torch.zeros_like(input_emb)
   380→            noise[:, 1:] = torch.randn(B, T - 1, input_emb.shape[-1],
   381→                                        device=input_emb.device) * self.input_noise
   382→            input_emb = input_emb + noise
   383→
   384→        # Masks
   385→        if codec_mask is not None:
   386→            start_mask = torch.zeros(B, 1, dtype=torch.bool, device=codec_emb.device)
   387→            pred_mask = torch.cat([start_mask, codec_mask[:, :-1]], dim=1)
   388→            loss_mask = codec_mask
   389→        else:
   390→            pred_mask = None
   391→            loss_mask = None
   392→
   393→        # JEPA prediction with frame-level text
   394→        predicted = self.predictor(input_emb, text_emb_expanded, pred_mask)
   395→
   396→        # ─── Loss 1: Prediction (MSE vs EMA targets) ───
   397→        if loss_mask is not None:
   398→            valid = (~loss_mask).unsqueeze(-1)
   399→            prediction_loss = F.mse_loss(
   400→                predicted * valid, target_emb * valid, reduction="sum"
   401→            ) / (valid.sum() * predicted.shape[-1] + 1e-8)
   402→        else:
   403→            prediction_loss = F.mse_loss(predicted, target_emb)
   404→
   405→        # ─── Loss 2: Roundtrip (proj_in → proj_out → match original codec_emb) ───
   406→        codec_recon = self.proj_out(z)  # [B, T, 128]
   407→
   408→        if codec_mask is not None:
   409→            valid_rt = (~codec_mask).unsqueeze(-1).float()
   410→            roundtrip_loss = F.l1_loss(
   411→                codec_recon * valid_rt, codec_seq * valid_rt, reduction="sum"
   412→            ) / (valid_rt.sum() * self.codec_dim + 1e-8)
   413→        else:
   414→            roundtrip_loss = F.l1_loss(codec_recon, codec_seq)
   415→
   416→        total_loss = (self.pred_weight * prediction_loss
   417→                     + self.roundtrip_weight * roundtrip_loss
   418→                     + self.dur_weight * dur_loss)
   419→
   420→        return {
   421→            "total_loss": total_loss,
   422→            "prediction_loss": prediction_loss,
   423→            "roundtrip_loss": roundtrip_loss,
   424→            "dur_loss": dur_loss,
   425→        }
   426→
   427→    def predict_durations(self, text_emb, text_mask=None):
   428→        """Predict durations from text embeddings (for inference)."""
   429→        log_dur = self.duration_predictor(text_emb, text_mask)
   430→        durations = torch.exp(log_dur).round().long().clamp(min=1)
   431→        if text_mask is not None:
   432→            durations = durations.masked_fill(text_mask, 0)
   433→        return durations
   434→
   435→    def init_ar_cache(self):
   436→        num_layers = len(self.predictor.transformer.layers)
   437→        return self.predictor.init_cache(num_layers)
   438→
   439→    def predict_next_cached(self, new_emb, text_emb_frame, step_idx, cache):
   440→        """
   441→        new_emb: [B, 1, d_model]
   442→        text_emb_frame: [B, 1, d_model] — expanded text for this frame
   443→        """
   444→        return self.predictor.inference_step(new_emb, text_emb_frame, step_idx, cache)
   445→
   446→
   447→def count_parameters(model):
   448→    return sum(p.numel() for p in model.parameters() if p.requires_grad)
   449→
   450→
   451→def build_model(config=None):
   452→    if config is None:
   453→        config = {
   454→            "d_model": 256, "nhead": 4, "codec_dim": 128,
   455→            "text_vocab_size": 256, "text_encoder_layers": 4,
   456→            "predictor_layers": 6, "dropout": 0.1,
   457→            "pred_weight": 10.0, "roundtrip_weight": 1.0,
   458→            "ema_decay": 0.998, "input_noise": 0.0,
   459→        }

        # Input noise for train/inference gap
        self.input_noise = config.get("input_noise", 0.0)

        self.config = config

    @torch.no_grad()
    def update_ema(self):
        for p_online, p_ema in zip(self.proj_in.parameters(),
                                   self.ema_proj_in.parameters()):
            p_ema.data.mul_(self.ema_decay).add_(p_online.data, alpha=1 - self.ema_decay)

    def forward(self, codec_emb, text_tokens, codec_mask=None, text_mask=None):
        """
        Args:
            codec_emb: [B, 128, T] — continuous EnCodec embeddings
            text_tokens: [B, T_text]
            codec_mask: [B, T] bool, True = padding
            text_mask: [B, T_text] bool
        """
        B = codec_emb.shape[0]
        T = codec_emb.shape[2]

        # codec_emb is [B, 128, T] → transpose to [B, T, 128]
        codec_seq = codec_emb.transpose(1, 2)

        # Text encoding
        text_emb = self.text_encoder(text_tokens, text_mask)

        # Duration prediction + ground truth durations (uniform alignment)
        log_dur_pred = self.duration_predictor(text_emb, text_mask)

        # Compute actual text/audio lengths from masks
        if text_mask is not None:
            text_lengths = (~text_mask).sum(dim=1)  # [B]
        else:
            text_lengths = torch.full((B,), text_tokens.shape[1], device=text_tokens.device)
        if codec_mask is not None:
            audio_lengths = (~codec_mask).sum(dim=1)  # [B]
        else:
            audio_lengths = torch.full((B,), T, device=codec_emb.device)

        # Ground truth durations (uniform distribution)
        gt_durations = compute_uniform_durations(text_lengths, audio_lengths)
        log_dur_gt = torch.log(gt_durations.float().clamp(min=1))

        # Duration loss (MSE on log-durations)
        if text_mask is not None:
            valid_text = (~text_mask).float()
            dur_loss = F.mse_loss(
                log_dur_pred * valid_text, log_dur_gt * valid_text, reduction="sum"
            ) / (valid_text.sum() + 1e-8)
        else:
            dur_loss = F.mse_loss(log_dur_pred, log_dur_gt)

        # Expand text to frame level using GT durations (training uses GT)
        text_emb_expanded = length_regulate(text_emb, gt_durations, text_mask)
        # Trim/pad to match audio length T
        if text_emb_expanded.shape[1] > T:
            text_emb_expanded = text_emb_expanded[:, :T]
        elif text_emb_expanded.shape[1] < T:
            pad = torch.zeros(B, T - text_emb_expanded.shape[1], text_emb.shape[-1],
                            device=text_emb.device)
            text_emb_expanded = torch.cat([text_emb_expanded, pad], dim=1)

        # Project codec embeddings to predictor space
        z = self.proj_in(codec_seq)  # [B, T, d_model]

        # EMA targets (deterministic)
        with torch.no_grad():
            self.ema_proj_in.eval()
            target_emb = self.ema_proj_in(codec_seq)  # [B, T, d_model]
            self.ema_proj_in.train()

        # Prepend start embedding, shift right
        start = self.start_emb.expand(B, -1, -1)
        input_emb = torch.cat([start, z[:, :-1]], dim=1)  # [B, T, d_model]

        # Input noise
        if self.training and self.input_noise > 0:
            noise = torch.zeros_like(input_emb)
            noise[:, 1:] = torch.randn(B, T - 1, input_emb.shape[-1],
                                        device=input_emb.device) * self.input_noise
            input_emb = input_emb + noise

        # Masks
        if codec_mask is not None:
            start_mask = torch.zeros(B, 1, dtype=torch.bool, device=codec_emb.device)
            pred_mask = torch.cat([start_mask, codec_mask[:, :-1]], dim=1)
            loss_mask = codec_mask
        else:
            pred_mask = None
            loss_mask = None

        # JEPA prediction with frame-level text
        predicted = self.predictor(input_emb, text_emb_expanded, pred_mask)

        # ─── Loss 1: Prediction (MSE vs EMA targets) ───
        if loss_mask is not None:
            valid = (~loss_mask).unsqueeze(-1)
            prediction_loss = F.mse_loss(
                predicted * valid, target_emb * valid, reduction="sum"
            ) / (valid.sum() * predicted.shape[-1] + 1e-8)
        else:
            prediction_loss = F.mse_loss(predicted, target_emb)

        # ─── Loss 2: Roundtrip (proj_in → proj_out → match original codec_emb) ───
        codec_recon = self.proj_out(z)  # [B, T, 128]

        if codec_mask is not None:
            valid_rt = (~codec_mask).unsqueeze(-1).float()
            roundtrip_loss = F.l1_loss(
                codec_recon * valid_rt, codec_seq * valid_rt, reduction="sum"
            ) / (valid_rt.sum() * self.codec_dim + 1e-8)
        else:
            roundtrip_loss = F.l1_loss(codec_recon, codec_seq)

        total_loss = (self.pred_weight * prediction_loss
                     + self.roundtrip_weight * roundtrip_loss
                     + self.dur_weight * dur_loss)

        return {
            "total_loss": total_loss,
            "prediction_loss": prediction_loss,
            "roundtrip_loss": roundtrip_loss,
            "dur_loss": dur_loss,
        }

    def predict_durations(self, text_emb, text_mask=None):
        """Predict durations from text embeddings (for inference)."""
        log_dur = self.duration_predictor(text_emb, text_mask)
        durations = torch.exp(log_dur).round().long().clamp(min=1)
        if text_mask is not None:
            durations = durations.masked_fill(text_mask, 0)
        return durations

    def init_ar_cache(self):
        num_layers = len(self.predictor.transformer.layers)
        return self.predictor.init_cache(num_layers)

    def predict_next_cached(self, new_emb, text_emb_frame, step_idx, cache):
        """
        new_emb: [B, 1, d_model]
        text_emb_frame: [B, 1, d_model] — expanded text for this frame
        """
        return self.predictor.inference_step(new_emb, text_emb_frame, step_idx, cache)

def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

def build_model(config=None):
    if config is None:
        config = {
            "d_model": 256, "nhead": 4, "codec_dim": 128,
            "text_vocab_size": 256, "text_encoder_layers": 4,
            "predictor_layers": 6, "dropout": 0.1,
            "pred_weight": 10.0, "roundtrip_weight": 1.0,
            "ema_decay": 0.998, "input_noise": 0.0,
        }


Now I understand the architecture. The `synthesize` method does pure AR from text with no audio prompt. I need to add a voice-prompted mode: encode reference audio as the AR prefix, then continue generating. Let me add that.


    39→    @torch.no_grad()
    40→    def synthesize(self, text, duration_scale=1.0, temperature=0.0):
    41→        """AR synthesis with duration predictor for text alignment."""
    42→        text_tokens = self.text_to_tokens(text)
    43→        text_emb = self.model.text_encoder(text_tokens)
    44→
    45→        # Predict durations and expand text
    46→        durations = self.model.predict_durations(text_emb)
    47→        durations = (durations.float() * duration_scale).round().long().clamp(min=1)
    48→        total_frames = durations.sum().item()
    49→        print(f"Text: {len(text)} chars → {total_frames} frames ({total_frames/75:.2f}s)")
    50→
    51→        # Expand text to frame level
    52→        text_emb_expanded = length_regulate(text_emb, durations)  # [1, T_total, d_model]
    53→
    54→        # AR generation
    55→        start_emb = self.model.start_emb.to(self.device)
    56→        cache = self.model.init_ar_cache()
    57→        all_embeddings = [start_emb]
    58→
    59→        # First step
    60→        text_frame = text_emb_expanded[:, 0:1]  # [1, 1, d_model]
    61→        next_emb, cache = self.model.predict_next_cached(start_emb, text_frame, 0, cache)
    62→        if temperature > 0:
    63→            next_emb = next_emb + torch.randn_like(next_emb) * temperature
    64→        all_embeddings.append(next_emb)
    65→
    66→        for step in range(1, total_frames):
    67→            # Get text embedding for this frame
    68→            if step < text_emb_expanded.shape[1]:
    69→                text_frame = text_emb_expanded[:, step:step+1]
    70→            else:
    71→                text_frame = torch.zeros(1, 1, text_emb.shape[-1], device=self.device)
    72→
    73→            next_emb, cache = self.model.predict_next_cached(next_emb, text_frame, step, cache)
    74→            if temperature > 0:
    75→                next_emb = next_emb + torch.randn_like(next_emb) * temperature
    76→            all_embeddings.append(next_emb)
    77→
    78→        # Concat predicted embeddings and project to codec space
    79→        pred_embs = torch.cat(all_embeddings, dim=1)  # [1, T, d_model]
    80→        codec_emb = self.model.proj_out(pred_embs)     # [1, T, 128]
    81→        codec_emb = codec_emb.transpose(1, 2)          # [1, 128, T]
    82→
    83→        # EnCodec decode
    84→        waveform = self.codec.decoder(codec_emb)
    85→        waveform = waveform.squeeze().cpu().numpy()
    86→
    87→        return waveform, 24000
    88→

    @torch.no_grad()
    def synthesize(self, text, duration_scale=1.0, temperature=0.0):
        """AR synthesis with duration predictor for text alignment."""
        text_tokens = self.text_to_tokens(text)
        text_emb = self.model.text_encoder(text_tokens)

        # Predict durations and expand text
        durations = self.model.predict_durations(text_emb)
        durations = (durations.float() * duration_scale).round().long().clamp(min=1)
        total_frames = durations.sum().item()
        print(f"Text: {len(text)} chars → {total_frames} frames ({total_frames/75:.2f}s)")

        # Expand text to frame level
        text_emb_expanded = length_regulate(text_emb, durations)  # [1, T_total, d_model]

        # AR generation
        start_emb = self.model.start_emb.to(self.device)
        cache = self.model.init_ar_cache()
        all_embeddings = [start_emb]

        # First step
        text_frame = text_emb_expanded[:, 0:1]  # [1, 1, d_model]
        next_emb, cache = self.model.predict_next_cached(start_emb, text_frame, 0, cache)
        if temperature > 0:
            next_emb = next_emb + torch.randn_like(next_emb) * temperature
        all_embeddings.append(next_emb)

        for step in range(1, total_frames):
            # Get text embedding for this frame
            if step < text_emb_expanded.shape[1]:
                text_frame = text_emb_expanded[:, step:step+1]
            else:
                text_frame = torch.zeros(1, 1, text_emb.shape[-1], device=self.device)

            next_emb, cache = self.model.predict_next_cached(next_emb, text_frame, step, cache)
            if temperature > 0:
                next_emb = next_emb + torch.randn_like(next_emb) * temperature
            all_embeddings.append(next_emb)

        # Concat predicted embeddings and project to codec space
        pred_embs = torch.cat(all_embeddings, dim=1)  # [1, T, d_model]
        codec_emb = self.model.proj_out(pred_embs)     # [1, T, 128]
        codec_emb = codec_emb.transpose(1, 2)          # [1, 128, T]

        # EnCodec decode
        waveform = self.codec.decoder(codec_emb)
        waveform = waveform.squeeze().cpu().numpy()

        return waveform, 24000


ToolSearch

Question: select:Edit


Question: select:Edit


Edit

        return waveform, 24000

    @torch.no_grad()
    def reconstruct(self, audio_path):

        return waveform, 24000

    @torch.no_grad()
    def synthesize_prompted(self, text, prompt_audio_path, prompt_text=None,
                            duration_scale=1.0, temperature=0.0):
        """AR synthesis with audio prompt as prefix for voice/style reference."""
        from encodec.utils import convert_audio

        # Encode prompt audio
        wav, sr = torchaudio.load(prompt_audio_path)
        wav = convert_audio(wav, sr, 24000, 1).to(self.device)
        prompt_codec = self.codec.encoder(wav.unsqueeze(0))  # [1, 128, T_prompt]
        T_prompt = prompt_codec.shape[2]
        prompt_z = self.model.proj_in(prompt_codec.transpose(1, 2))  # [1, T_prompt, d_model]

        # Text encoding (full text = prompt_text + synthesis text)
        if prompt_text:
            full_text = prompt_text + text
        else:
            full_text = text
        text_tokens = self.text_to_tokens(full_text)
        text_emb = self.model.text_encoder(text_tokens)

        # Predict durations and expand text to frame level
        durations = self.model.predict_durations(text_emb)
        durations = (durations.float() * duration_scale).round().long().clamp(min=1)
        total_frames = durations.sum().item()
        text_emb_expanded = length_regulate(text_emb, durations)

        # How many new frames to generate beyond the prompt
        gen_frames = max(total_frames - T_prompt, total_frames // 2)
        print(f"Prompt: {T_prompt} frames ({T_prompt/75:.2f}s) | "
              f"Generate: {gen_frames} frames ({gen_frames/75:.2f}s) | "
              f"Total text frames: {total_frames}")

        # Feed prompt through predictor to build KV cache
        cache = self.model.init_ar_cache()

        # Process prompt: start_emb + prompt_z[:-1] as input
        start_emb = self.model.start_emb.to(self.device)
        prompt_input = torch.cat([start_emb, prompt_z[:, :-1]], dim=1)  # [1, T_prompt, d_model]

        # Feed prompt frames one by one to build cache
        for step in range(T_prompt):
            frame_emb = prompt_input[:, step:step+1]
            if step < text_emb_expanded.shape[1]:
                text_frame = text_emb_expanded[:, step:step+1]
            else:
                text_frame = torch.zeros(1, 1, text_emb.shape[-1], device=self.device)
            next_emb, cache = self.model.predict_next_cached(frame_emb, text_frame, step, cache)

        # AR generation continuing from prompt
        all_embeddings = [prompt_z]  # keep prompt embeddings
        cur_emb = prompt_z[:, -1:]   # last prompt frame as input

        for step in range(gen_frames):
            abs_step = T_prompt + step
            if abs_step < text_emb_expanded.shape[1]:
                text_frame = text_emb_expanded[:, abs_step:abs_step+1]
            else:
                text_frame = torch.zeros(1, 1, text_emb.shape[-1], device=self.device)

            next_emb, cache = self.model.predict_next_cached(cur_emb, text_frame, abs_step, cache)
            if temperature > 0:
                next_emb = next_emb + torch.randn_like(next_emb) * temperature
            all_embeddings.append(next_emb)
            cur_emb = next_emb

        # Decode
        pred_embs = torch.cat(all_embeddings, dim=1)
        codec_emb = self.model.proj_out(pred_embs).transpose(1, 2)
        waveform = self.codec.decoder(codec_emb).squeeze().cpu().numpy()

        return waveform, 24000

    @torch.no_grad()
    def reconstruct(self, audio_path):