python3 -c "
import torch, glob, torchaudio
from model import LeWMTTS
from vocos import Vocos

ckpt = torch.load('output_v3b/checkpoints/best.pt', map_location='cuda', weights_only=False)
model = LeWMTTS(ckpt['config']).cuda()
model.load_state_dict(ckpt['model'])
model.eval()
print(f'v3b epoch {ckpt[\"epoch\"]}')

vocos = Vocos.from_pretrained('charactr/vocos-mel-24khz').cuda()
vocos.eval()

mel_files = sorted(glob.glob('processed_data_100mel/mels/*.pt'))

with torch.no_grad():
    # Reconstructions
    for idx in [0, 100, 500]:
        mel = torch.load(mel_files[idx], weights_only=True).unsqueeze(0).cuda()
        z, mu, logvar = model.audio_encoder(mel)
        mel_recon = model.mel_decoder(mu)
        wav = vocos.decode(mel_recon)
        torchaudio.save(f'sample_recon_v3b_{idx}.wav', wav.cpu(), 24000)
        T = min(mel.shape[2], mel_recon.shape[2])
        l1 = (mel[:,:,:T] - mel_recon[:,:,:T]).abs().mean().item()
        print(f'Recon {idx}: L1={l1:.4f}')

    # AR synthesis
    text = 'नमस्ते भारत'
    text_tokens = torch.tensor(list(text.encode('utf-8')), dtype=torch.long).unsqueeze(0).cuda()
    text_emb = model.text_encoder(text_tokens)
    d = ckpt['config']['d_model']
    start_emb = torch.randn(1, 1, d, device='cuda') * 5.0
    all_embs = [start_emb]
    cache = model.init_ar_cache()
    emb = start_emb
    for step in range(200):
        emb, cache = model.predict_next_cached(emb, text_emb, step, cache)
        all_embs.append(emb)
    mel_synth = model.mel_decoder(torch.cat(all_embs, dim=1))
    wav_synth = vocos.decode(mel_synth)
    torchaudio.save('sample_synth_v3b.wav', wav_synth.cpu(), 24000)
    print('Synth saved')

    # GT references
    for idx in [0, 100, 500]:
        mel = torch.load(mel_files[idx], weights_only=True).unsqueeze(0).cuda()
        wav = vocos.decode(mel)
        torchaudio.save(f'sample_gt_{idx}.wav', wav.cpu(), 24000)

print('Done!')
" 2>&1

Generate v3b reconstruction and synthesis samples


v3b epoch 36
Recon 0: L1=0.6487
Recon 100: L1=0.6902
Recon 500: L1=0.6766
Synth saved
Done!


Samples saved in `/home/ubuntu/lewm-tts/`:

**Reconstructions** (v3b, epoch 36):
- `sample_recon_v3b_0.wav` (L1=0.65)
- `sample_recon_v3b_100.wav` (L1=0.69)
- `sample_recon_v3b_500.wav` (L1=0.68)

**AR synthesis**: `sample_synth_v3b.wav`

**GT references**: `sample_gt_0.wav`, `sample_gt_100.wav`, `sample_gt_500.wav`

Compare recon vs GT — if voice is still noisy/blurry, the decoder architecture needs upgrading.


voice is cmng but not audio noise cmng .. what u think do u think we can wait for smetime or what?


The user is saying voice is coming through now (in the reconstructions), but there's audio noise on top of it. This is actually progress compared to before! The formant structure is partially there but the noise is from the remaining reconstruction error.

The question is: will this improve with more training, or is it plateauing?

Let me check the recon loss trajectory to see if it's still dropping meaningfully.


That's progress — voice is audible means the formants are getting through. The noise is from the remaining L1=0.65 error. Let me check if recon loss is still dropping or plateauing.


Bash

grep "avg_loss" /home/ubuntu/lewm-tts/train_v3b.log | awk '{print $2, $6}' | sed 's/avg_loss=//;s/recon=//' | tail -20

Extract recon loss trend from v3b log


22/200 kl=1.5580
23/200 kl=1.6588
24/200 kl=1.7860
25/200 kl=1.8836
26/200 kl=1.9471
27/200 kl=2.0381
28/200 kl=2.1059
29/200 kl=2.1214
30/200 kl=2.1512
31/200 kl=2.2180
32/200 kl=2.2636
33/200 kl=2.2914
34/200 kl=2.3163
35/200 kl=2.3394
36/200 kl=2.3379
37/200 kl=2.3513
38/200 kl=2.3500
39/200 kl=2.3480
40/200 kl=2.3584
41/200 kl=2.3644


grep "avg_loss" /home/ubuntu/lewm-tts/train_v3b.log | sed 's/.*recon=//;s/ spec.*//' | paste - - - - - | tail -10

Extract recon loss values


2.3551	2.3552	2.3546	2.3558	2.2691
1.6966	1.4078	1.2074	1.0379	0.9624
0.9338	0.9131	0.8986	0.9788	0.9350
0.9295	0.8765	0.8774	0.8744	0.8822
0.8644	0.8416	0.8278	0.8170	0.8275
0.8247	0.8212	0.8323	0.8583	0.8065
0.7923	0.7842	0.7802	0.7752	0.7791
0.7666	0.7473	0.7348	0.7280	0.7266
0.7251


Recon loss trend (every 5 epochs):

| Epoch | Recon L1 |
|-------|----------|
| 10 | 0.96 |
| 15 | 0.94 |
| 20 | 0.88 |
| 25 | 0.83 |
| 30 | 0.81 |
| 35 | 0.77 |
| 40 | 0.73 |
| 41 | 0.73 |

**Still dropping but slowing down.** At this rate it might reach ~0.5-0.55 by epoch 200. Based on your noise simulation where L1=0.3 sounded clean, we need to get below ~0.4 for good quality.

**My take**: Let it run — the voice is already coming through at epoch 36, so by epoch 100-200 it'll likely be decent. The training is cheap (~27s/epoch, ~75 min left). No reason to kill it. We can always do the decoder upgrade after if it plateaus too high.

Let it cook.


what is param size now


Same 24M — we only changed loss weights, not architecture.


python3 -c "
from model import LeWMTTS, count_parameters
import torch
ckpt = torch.load('output_v3b/checkpoints/best.pt', map_location='cpu', weights_only=False)
model = LeWMTTS(ckpt['config'])
total = count_parameters(model)

# Per-component breakdown
for name, mod in [('TextEncoder', model.text_encoder), ('AudioEncoder', model.audio_encoder),
                   ('Predictor', model.predictor), ('MelDecoder', model.mel_decoder)]:
    p = sum(p.numel() for p in mod.parameters() if p.requires_grad)
    print(f'  {name}: {p/1e6:.2f}M ({100*p/total:.1f}%)')
"

Show parameter breakdown by component


  TextEncoder: 3.29M (13.7%)
  AudioEncoder: 3.99M (16.6%)
  Predictor: 6.52M (27.2%)
  MelDecoder: 10.19M (42.5%)


| Component | Params | % |
|-----------|--------|---|
| MelDecoder | 10.19M | 42.5% |
| Predictor | 6.52M | 27.2% |
| AudioEncoder | 3.99M | 16.6% |
| TextEncoder | 3.29M | 13.7% |
| **Total** | **24.00M** | |

The mel decoder already has the most params (42.5%). The issue isn't capacity — it's the 4x downsample/upsample bottleneck forcing it to hallucinate fine detail.


we using jepa ryt?


The user is asking about the architecture - yes, it's JEPA-based. But I think they might be leading somewhere with this question. Let me confirm and see what they're thinking.


Yes — JEPA (Joint Embedding Predictive Architecture). The flow is:

**Training**: text + mel → AudioEncoder encodes mel to latent `z` → Predictor predicts `z[t+1]` from `z[t]` + text → MelDecoder reconstructs mel from `z`

**Inference**: text → Predictor autoregressively generates latent embeddings → MelDecoder → Vocos → audio

The key JEPA idea is that prediction happens in **latent space** (predicting embeddings, not raw mels directly). The predictor loss (0.03) converged fast — it's good at predicting next embeddings. The bottleneck is the MelDecoder converting those embeddings back to sharp mels.

What are you thinking?


it's the 4x downsample/upsample bottleneck forcing it to hallucinate fine de expalin clealry


The user wants a clear explanation of the 4x downsample/upsample bottleneck issue.


OK, let me break it down simply.

**The AudioEncoder compresses mel 4x in time:**
```
Input mel:  [100 mel bins × 800 frames]  ← full detail
                    ↓ CNN stride 2
            [256 dim × 400 frames]
                    ↓ CNN stride 2
Latent z:   [256 dim × 200 frames]       ← 4x fewer frames
```

So 4 mel frames get squeezed into 1 latent vector. Each latent vector (256 numbers) has to represent what was originally 4 × 100 = **400 numbers** of mel data.

**The MelDecoder has to reverse this:**
```
Latent z:   [256 dim × 200 frames]       ← this is all it gets
                    ↓ ConvTranspose ×2
Output mel: [100 mel bins × 800 frames]   ← must recreate 400 numbers per latent
```

The decoder has to **invent** the fine detail between those 4x-spaced anchor points — the exact shape of each harmonic peak, the sharp transitions between formants. With L1/MSE loss, the safest bet is to output the **average** across those 4 frames → blurry/smooth mels → Vocos hears mush → noise.

**Why random noise at same L1 sounds fine but model error doesn't:**
- Random noise: every formant peak is still there, just slightly jittered
- Model blur: formant peaks are **flattened out entirely** — the spectral structure Vocos needs is gone

**Fix = reduce to 2x downsample**: each latent represents only 2 frames (200 numbers) instead of 4 (400 numbers). Much easier for the decoder to reconstruct sharp detail.


how much the param size increase?


python3 -c "
import torch.nn as nn

# Current 4x: two stride-2 convs in encoder, two ConvTranspose in decoder
# New 2x: one stride-2 conv in encoder, one ConvTranspose in decoder

# Encoder CNN change
# Current: Conv1d(100,256,7) + Conv1d(256,256,4,s=2) + Conv1d(256,256,4,s=2)
# New:     Conv1d(100,256,7) + Conv1d(256,256,4,s=2)
removed_enc = 256*256*4 + 256  # one less stride-2 conv
print(f'Encoder: remove {removed_enc/1e3:.1f}K params')

# Decoder upsample change
# Current: 2x ConvTranspose(512,512,4,s=2) + 2x ResConvBlock(512)
# New:     1x ConvTranspose(512,512,4,s=2) + 1x ResConvBlock(512)
removed_dec_ct = 512*512*4 + 512  # one less ConvTranspose
removed_dec_res = 2 * (512*512*3 + 512)  # one less ResConvBlock (2 convs)
removed_dec = removed_dec_ct + removed_dec_res
print(f'Decoder: remove {removed_dec/1e6:.2f}M params')

print(f'Total reduction: {(removed_enc + removed_dec)/1e6:.2f}M')
print(f'New model size: ~{24.0 - (removed_enc + removed_dec)/1e6:.1f}M')
print()
print('BUT: predictor sequence length doubles (200→400 for same audio)')
print('Predictor params stay same, just 4x more compute (attention is O(n²))')
"

Calculate param change for 2x vs 4x downsample


Encoder: remove 262.4K params
Decoder: remove 2.62M params
Total reduction: 2.89M
New model size: ~21.1M

BUT: predictor sequence length doubles (200→400 for same audio)
Predictor params stay same, just 4x more compute (attention is O(n²))


Params actually **decrease** by ~3M (24M → 21M) since we're removing conv layers.

The tradeoff:
- **Params**: 21M (smaller)
- **Compute**: predictor sees 2x longer sequences → **~4x slower** attention (O(n²))
- **Training time**: ~27s/epoch → ~50-60s/epoch roughly

Quality should be much better since the decoder only needs to fill in 2 frames instead of 4. Want me to implement it?


what is effect on inferncre?


**Inference impact:**

- **KV-cache AR generation**: each audio step now covers 2 mel frames instead of 4. So for same audio length, **2x more AR steps** (e.g., 200 steps → 400 steps)
- **Single request (9x realtime)**: drops to ~4-5x realtime
- **Batch=64 throughput (359x)**: drops to ~180x

Still well above realtime. The quality gain should be worth it.

Alternatively, we could keep 4x downsample and fix the decoder differently — add **self-attention layers** in the decoder so it can model cross-frequency patterns (formants) instead of just local convolutions. That keeps inference speed the same.

Which approach do you prefer?
1. **2x downsample** — simpler, guaranteed to help, but slower inference
2. **Better decoder** (attention + more layers) — keeps speed, but less certain to fix the blur


let it compelte and test and see


Makes sense. Let's let v3b finish all 200 epochs and evaluate properly. Training is at ~40/200 with ~70 min left.


Check current epoch


Epoch 52/200 | avg_loss=2.5127 pred=0.0260 kl=2.3622 recon=0.6897 spec=0.7224 | 26.6s
Epoch 53/200 | avg_loss=2.4939 pred=0.0257 kl=2.3670 recon=0.6829 spec=0.7192 | 26.7s
  New best model (loss=2.4939)


Epoch 53, recon=0.68 — still dropping. I'll check back when it's done.


kl is 2.3 is it ok?


With kl_weight=0.01, the actual KL contribution to the loss is only **0.01 × 2.37 = 0.024** — tiny compared to recon (0.68) and spectral (0.72).

High KL value (2.37) is actually **good for us right now** — it means the encoder is NOT collapsing to N(0,I). It's encoding rich information in the latents, giving the mel decoder more to work with. That's exactly what we wanted by lowering kl_weight from 0.1 → 0.01.

Compare:
- **v3** (kl_weight=0.1): KL=1.3, encoder was squished → less info → blurry mels
- **v3b** (kl_weight=0.01): KL=2.37, encoder preserves detail → decoder has more to reconstruct from

The tradeoff: high KL means the latent space is less "smooth", which could hurt AR synthesis (predictor has to hit exact latent positions). But we fix that later — first priority is getting the mel decoder to produce clean audio.


can u test sampels once again


python3 -c "
import torch, glob, torchaudio
from model import LeWMTTS
from vocos import Vocos

ckpt = torch.load('output_v3b/checkpoints/best.pt', map_location='cuda', weights_only=False)
model = LeWMTTS(ckpt['config']).cuda()
model.load_state_dict(ckpt['model'])
model.eval()
print(f'v3b epoch {ckpt[\"epoch\"]}')

vocos = Vocos.from_pretrained('charactr/vocos-mel-24khz').cuda()
vocos.eval()

mel_files = sorted(glob.glob('processed_data_100mel/mels/*.pt'))

with torch.no_grad():
    for idx in [0, 100, 500]:
        mel = torch.load(mel_files[idx], weights_only=True).unsqueeze(0).cuda()
        z, mu, logvar = model.audio_encoder(mel)
        mel_recon = model.mel_decoder(mu)
        wav = vocos.decode(mel_recon)
        torchaudio.save(f'sample_recon_v3b_{idx}.wav', wav.cpu(), 24000)
        wav_gt = vocos.decode(mel)
        torchaudio.save(f'sample_gt_{idx}.wav', wav_gt.cpu(), 24000)
        T = min(mel.shape[2], mel_recon.shape[2])
        l1 = (mel[:,:,:T] - mel_recon[:,:,:T]).abs().mean().item()
        print(f'Recon {idx}: L1={l1:.4f}')

    # AR synthesis
    text = 'नमस्ते भारत'
    text_tokens = torch.tensor(list(text.encode('utf-8')), dtype=torch.long).unsqueeze(0).cuda()
    text_emb = model.text_encoder(text_tokens)
    d = ckpt['config']['d_model']
    start_emb = torch.randn(1, 1, d, device='cuda') * 5.0
    all_embs = [start_emb]
    cache = model.init_ar_cache()
    emb = start_emb
    for step in range(200):
        emb, cache = model.predict_next_cached(emb, text_emb, step, cache)
        all_embs.append(emb)
    mel_synth = model.mel_decoder(torch.cat(all_embs, dim=1))
    wav_synth = vocos.decode(mel_synth)
    torchaudio.save('sample_synth_v3b.wav', wav_synth.cpu(), 24000)
    print('Synth saved')
print('Done!')
" 2>&1

Generate samples from latest v3b checkpoint


v3b epoch 57
Recon 0: L1=0.6297
Recon 100: L1=0.6475
Recon 500: L1=0.6409
Synth saved
Done!


Epoch 57 samples ready. L1 dropped from 0.65→0.63 since last test (epoch 36).

**Reconstructions**: `sample_recon_v3b_0/100/500.wav`
**GT reference**: `sample_gt_0/100/500.wav`
**AR synthesis**: `sample_synth_v3b.wav`

How do they sound compared to last time?


sample_synth_v3b.wav - it is silence only .. and reconn cmng noise + voice ( not so great) ... what mean be this 0,100,500


The 0, 100, 500 are just **indices into the mel file list** — picking sample #0, #100, and #500 from the dataset so we test on different utterances instead of just one.

**On the results:**

- **Synth = silence**: The predictor is outputting flat/constant embeddings. AR generation is completely broken — it has no grounding for what a "start" sounds like (random noise start token doesn't work, as we noted before)
- **Recon = noise + voice**: Mel decoder is slowly improving but still blurry

The synth silence is actually a separate problem from recon quality. Even if the decoder was perfect, AR synthesis would fail because:
1. Random start embedding gives the predictor nothing meaningful
2. Without scheduled sampling during training, the predictor never learned to recover from its own errors

Let's focus on getting **recon quality right first** (that's the decoder bottleneck). AR synthesis is a second problem we solve after.

Want to wait for 200 epochs to finish, or should I start working on the 2x downsample decoder upgrade in parallel so it's ready to train as soon as v3b finishes?


i think u dng something wrong with synth becuase before atlresat it came.. try tf


The user is saying that synthesis was at least producing some sound before (even if it was noise), but now it's silence. They want me to try teacher-forced (tf) prediction to test the predictor properly.

Let me do teacher-forced prediction: encode a real mel → feed through predictor with GT embeddings → decode → vocos. This tests the predictor without the AR error accumulation issue.


python3 -c "
import torch, glob, torchaudio, json
from model import LeWMTTS
from vocos import Vocos

ckpt = torch.load('output_v3b/checkpoints/best.pt', map_location='cuda', weights_only=False)
model = LeWMTTS(ckpt['config']).cuda()
model.load_state_dict(ckpt['model'])
model.eval()
print(f'v3b epoch {ckpt[\"epoch\"]}')

vocos = Vocos.from_pretrained('charactr/vocos-mel-24khz').cuda()
vocos.eval()

mel_files = sorted(glob.glob('processed_data_100mel/mels/*.pt'))

# Load manifest for matching text
with open('processed_data_100mel/manifest.json') as f:
    manifest = json.load(f)

with torch.no_grad():
    for idx in [0, 100, 500]:
        mel = torch.load(mel_files[idx], weights_only=True).unsqueeze(0).cuda()
        text = manifest[idx]['text']
        text_tokens = torch.tensor(list(text.encode('utf-8')), dtype=torch.long).unsqueeze(0).cuda()

        # Encode audio
        z, mu, logvar = model.audio_encoder(mel)

        # Encode text
        text_emb = model.text_encoder(text_tokens)

        # Teacher-forced prediction: feed GT embeddings, predict next
        predicted = model.predictor(mu[:, :-1], text_emb)
        combined = torch.cat([mu[:, :1], predicted], dim=1)

        # Decode predicted embeddings
        mel_pred = model.mel_decoder(combined)
        wav_pred = vocos.decode(mel_pred)
        torchaudio.save(f'sample_tf_{idx}.wav', wav_pred.cpu(), 24000)

        # Also pure recon for comparison
        mel_recon = model.mel_decoder(mu)
        wav_recon = vocos.decode(mel_recon)
        torchaudio.save(f'sample_recon_v3b_{idx}.wav', wav_recon.cpu(), 24000)

        T = min(mu.shape[1], predicted.shape[1])
        pred_l1 = (mu[:, 1:1+T] - predicted[:, :T]).abs().mean().item()
        T2 = min(mel.shape[2], mel_recon.shape[2])
        recon_l1 = (mel[:,:,:T2] - mel_recon[:,:,:T2]).abs().mean().item()
        print(f'Sample {idx}: recon_L1={recon_l1:.4f}, pred_embedding_L1={pred_l1:.4f}, text=\"{text[:50]}...\"')

    # AR with voice prompt (use first 20 frames of real mel as prompt)
    mel = torch.load(mel_files[0], weights_only=True).unsqueeze(0).cuda()
    text = manifest[0]['text']
    text_tokens = torch.tensor(list(text.encode('utf-8')), dtype=torch.long).unsqueeze(0).cuda()
    text_emb = model.text_encoder(text_tokens)

    z, mu, logvar = model.audio_encoder(mel)
    prompt_len = 20  # use first 20 latent frames as prompt

    all_embs = [mu[:, :prompt_len]]
    cache = model.init_ar_cache()

    # Prime cache with prompt
    for i in range(prompt_len):
        emb = mu[:, i:i+1]
        _, cache = model.predict_next_cached(emb, text_emb, i, cache)

    # Continue AR from prompt
    emb = mu[:, prompt_len-1:prompt_len]
    for step in range(prompt_len, 300):
        emb, cache = model.predict_next_cached(emb, text_emb, step, cache)
        all_embs.append(emb)

    audio_embs = torch.cat(all_embs, dim=1)
    mel_ar = model.mel_decoder(audio_embs)
    wav_ar = vocos.decode(mel_ar)
    torchaudio.save('sample_ar_prompted.wav', wav_ar.cpu(), 24000)
    print(f'AR prompted: {audio_embs.shape[1]} frames')

print('Done!')
" 2>&1

Generate teacher-forced and voice-prompted AR samples


v3b epoch 66
Sample 0: recon_L1=0.6364, pred_embedding_L1=0.0610, text="मेरे प्यारे देशवासियों, नमस्कार। मन की बात में आपक..."
Sample 100: recon_L1=0.6378, pred_embedding_L1=0.0580, text="लिए Chennai आमंत्रित किया था। स्नेह से भरा उनका वो..."
Sample 500: recon_L1=0.6424, pred_embedding_L1=0.0668, text="भावेश संस्कृत श्लोकों, आध्यात्मिक दर्शन और सिद्धां..."
AR prompted: 300 frames
Done!


Epoch 66 samples:

| File | What it tests |
|------|--------------|
| `sample_tf_0/100/500.wav` | Teacher-forced prediction (GT embeddings → predictor → decoder) |
| `sample_recon_v3b_0/100/500.wav` | Pure reconstruction (encoder → decoder, no predictor) |
| `sample_ar_prompted.wav` | AR with 20-frame voice prompt (not random start) |

Predictor embedding L1 is only **0.06** — it's predicting next embeddings very accurately. So if TF sounds similar to recon, the predictor is fine and the bottleneck is purely the mel decoder.

How do the TF and prompted AR samples sound?


sample_ar_prompted.wav   starting it cam esome voice and silednce came next


That makes sense — the first 20 frames are real embeddings (voice), then AR generation kicks in and collapses to silence. The predictor converges to a fixed point instead of continuing to generate.

This is the classic AR collapse problem — pred_loss=0.026 looks great on paper but the model learned to predict the **mean** of the next embedding, which during AR just converges to a constant → silence.

The TF samples — did they sound similar to the recon samples? That would confirm the predictor is fine when given GT context but can't sustain generation on its own.

Anyway, the two problems are now clear:
1. **Mel decoder** — blurry output (recon has noise + voice)
2. **AR collapse** — predictor outputs converge to constant during free-running generation

Both are fixable but separate issues. Let's let v3b finish first — where is it now?


Check current training epoch


Epoch 69/200 | avg_loss=2.3685 pred=0.0217 kl=2.4027 recon=0.6373 spec=0.6988 | 26.6s
  New best model (loss=2.3685)


Epoch 69/200, recon=0.637. About 60 min left.


sample_recon_v3b_0/100/500.wav │ Pure reconstruction (encoder → decoder, no predictor)         - this is not so good .. it is robotic vocie with noise


The user is saying the reconstruction sounds robotic with noise. At L1=0.64, it's producing voice but it's robotic and noisy. The mel decoder is the bottleneck - it can't produce natural-sounding mels at this compression ratio.

Let me think about what to do. The 4x downsample is the fundamental issue. We've been discussing the 2x downsample fix. But let me think if there's something else we can do.

Actually, maybe instead of waiting for v3b to finish (it's only going to improve marginally), I should start implementing the 2x downsample version now so it's ready to train.

Or another thought: what if we skip the encoder/decoder bottleneck entirely for reconstruction and just use Vocos directly on the GT mels? We know that works. The issue is the mel decoder quality.

I think the best move is to start building out the 2x downsample implementation right now while v3b keeps training in the background - that way we're not sitting idle waiting for marginal improvements.


That confirms it — the mel decoder with 4x downsample can't produce clean mels. Robotic + noise = spectral blur destroying natural voice quality. More epochs won't fix this fundamentally, it'll just reduce the noise slightly.

Let me start implementing the **2x downsample version** now while v3b keeps running. When v3b finishes we can compare, but I'm confident the 2x version will be much better since the decoder only needs to reconstruct 2 frames per latent instead of 4.


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Question: select:Edit


Question: select:Edit


Let me implement the 2x downsample version. I need to modify the AudioEncoder and MelDecoder in model.py. But I shouldn't modify the current model.py since v3b is still training and using it. Let me create a separate model file or... actually, I can add a config option for downsample_factor and make it configurable. But the current training is importing from model.py, so I need to be careful.

Actually, the training process already loaded the model into memory. Changing model.py won't affect the running process. So I can modify it safely.

But wait — when v3b saves checkpoints, it saves model state dict and config. When we load those later, we'd need the old model architecture.

If I change model.py, we won't be able to load v3b checkpoints. The better approach is to make downsample_factor configurable in the config dict with a default of 4 for backward compatibility, while new training uses 2. This way both versions can coexist. I'll need to update AudioEncoder and MelDecoder to accept this as a parameter instead of hardcoding it.

Looking at the AudioEncoder more closely, it already has a downsample_factor parameter but it's hardcoded to 2. I need to make the convolution stride conditional—if downsample is 2, I'll use one stride-2 convolution; if it's 4, I'll use two stride-2 convolutions.

Implementing this now...


I'll make `downsample_factor` configurable so v3b checkpoints still load fine (default=4), and new training can use 2.


Edit

    def __init__(self, n_mels=100, d_model=256, nhead=4, num_layers=4,
                 downsample_factor=4, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.downsample_factor = downsample_factor

        # CNN downsampling: reduces temporal resolution by downsample_factor
        self.conv_pre = nn.Sequential(
            nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),
            nn.GELU(),
            nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
            nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
        )  # Total stride = 4

    def __init__(self, n_mels=100, d_model=256, nhead=4, num_layers=4,
                 downsample_factor=4, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.downsample_factor = downsample_factor

        # CNN downsampling: reduces temporal resolution by downsample_factor
        if downsample_factor == 2:
            self.conv_pre = nn.Sequential(
                nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),
                nn.GELU(),
                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
            )  # Total stride = 2
        else:
            self.conv_pre = nn.Sequential(
                nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),
                nn.GELU(),
                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
            )  # Total stride = 4


The file /home/ubuntu/lewm-tts/model.py has been updated successfully.

    def __init__(self, n_mels=100, d_model=256, nhead=4, num_layers=4,
                 downsample_factor=4, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.downsample_factor = downsample_factor

        # CNN downsampling: reduces temporal resolution by downsample_factor
        if downsample_factor == 2:
            self.conv_pre = nn.Sequential(
                nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),
                nn.GELU(),
                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
            )  # Total stride = 2
        else:
            self.conv_pre = nn.Sequential(
                nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),
                nn.GELU(),
                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
            )  # Total stride = 4

"""
LeWM TTS Model — JEPA-based Text-to-Speech

Architecture:
  - TextEncoder: Character-level transformer for Hindi/Devanagari text
  - AudioEncoder: 1D CNN + Transformer that encodes mel spectrograms to embeddings
  - JEPAPredictor: Transformer that predicts next audio embedding given text + audio context
  - Two losses: next-embedding prediction (MSE) + Gaussian regularizer (KL to N(0,I))
"""

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

# ─── Text Encoder ───────────────────────────────────────────────────────────

class TextEncoder(nn.Module):
    """Character-level transformer encoder for Hindi text."""

    def __init__(self, vocab_size=256, d_model=256, nhead=4, num_layers=4, dropout=0.1):
        super().__init__()
        self.d_model = d_model

        # Character embedding (covers all Unicode Hindi chars via byte-level encoding)
        self.char_embed = nn.Embedding(vocab_size, d_model)
        self.pos_embed = SinusoidalPositionalEncoding(d_model, max_len=2048)
        self.dropout = nn.Dropout(dropout)

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead,
            dim_feedforward=d_model * 4,
            dropout=dropout, batch_first=True,
            activation="gelu",
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.proj = nn.Linear(d_model, d_model)

    def forward(self, text_tokens, text_mask=None):
        """
        Args:
            text_tokens: [B, T_text] long tensor of byte-level token IDs
            text_mask: [B, T_text] bool, True = padding
        Returns:
            text_emb: [B, T_text, d_model]
        """
        x = self.char_embed(text_tokens) * math.sqrt(self.d_model)
        x = self.pos_embed(x)
        x = self.dropout(x)
        x = self.transformer(x, src_key_padding_mask=text_mask)
        x = self.proj(x)
        return x

# ─── Audio Encoder ──────────────────────────────────────────────────────────

class AudioEncoder(nn.Module):
    """
    Encodes mel spectrograms into a sequence of audio embeddings.
    Uses 1D CNN for downsampling + Transformer for contextualization.
    """

    def __init__(self, n_mels=100, d_model=256, nhead=4, num_layers=4,
                 downsample_factor=4, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.downsample_factor = downsample_factor

        # CNN downsampling: reduces temporal resolution by downsample_factor
        self.conv_pre = nn.Sequential(
            nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),
            nn.GELU(),
            nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
            nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
        )  # Total stride = 4

        self.pos_embed = SinusoidalPositionalEncoding(d_model, max_len=4096)
        self.dropout = nn.Dropout(dropout)

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead,
            dim_feedforward=d_model * 4,
            dropout=dropout, batch_first=True,
            activation="gelu",
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

        # Project to embedding with mu and logvar for Gaussian regularizer
        self.proj_mu = nn.Linear(d_model, d_model)
        self.proj_logvar = nn.Linear(d_model, d_model)

    def forward(self, mel, mel_mask=None):
        """
        Args:
            mel: [B, n_mels, T_mel] — log mel spectrogram
            mel_mask: [B, T_mel] bool, True = padding (before downsampling)
        Returns:
            z: [B, T_down, d_model] — sampled latent embeddings
            mu: [B, T_down, d_model]
            logvar: [B, T_down, d_model]
        """
        # CNN encoding: [B, n_mels, T] -> [B, d_model, T//4]
        x = self.conv_pre(mel)
        x = x.transpose(1, 2)  # [B, T_down, d_model]

        # Downsample mask if provided
        if mel_mask is not None:
            # Approximate downsampled mask
            T_down = x.shape[1]
            mel_mask = mel_mask[:, ::self.downsample_factor][:, :T_down]

        x = self.pos_embed(x)
        x = self.dropout(x)
        x = self.transformer(x, src_key_padding_mask=mel_mask)

        mu = self.proj_mu(x)
        logvar = self.proj_logvar(x)

        # Reparameterization trick
        if self.training:
            std = torch.exp(0.5 * logvar)
            eps = torch.randn_like(std)
            z = mu + eps * std
        else:
            z = mu

        return z, mu, logvar

# ─── JEPA Predictor ─────────────────────────────────────────────────────────

class JEPAPredictor(nn.Module):
    """
    Predicts next audio embedding given:
    - Text context (from TextEncoder)
    - Previous audio embeddings (from AudioEncoder)

    Uses cross-attention to condition on text, and causal self-attention
    over the audio embedding sequence.
    """

    def __init__(self, d_model=256, nhead=4, num_layers=6, dropout=0.1):
        super().__init__()
        self.d_model = d_model

        self.audio_input_proj = nn.Linear(d_model, d_model)
        self.pos_embed = SinusoidalPositionalEncoding(d_model, max_len=4096)
        self.dropout = nn.Dropout(dropout)

        # Transformer decoder with causal self-attention + cross-attention to text
        decoder_layer = nn.TransformerDecoderLayer(
            d_model=d_model, nhead=nhead,
            dim_feedforward=d_model * 4,
            dropout=dropout, batch_first=True,
            activation="gelu",
        )
        self.transformer = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)

        # Output projection to predict next embedding
        self.output_proj = nn.Sequential(
            nn.Linear(d_model, d_model),
            nn.GELU(),
            nn.Linear(d_model, d_model),
        )

    def forward(self, audio_emb, text_emb, audio_mask=None, text_mask=None):
        """
        Args:
            audio_emb: [B, T_audio, d_model] — audio embeddings (from encoder)
            text_emb: [B, T_text, d_model] — text embeddings
            audio_mask: [B, T_audio] bool, True = padding
            text_mask: [B, T_text] bool, True = padding
        Returns:
            predicted: [B, T_audio, d_model] — predicted next embeddings
        """
        x = self.audio_input_proj(audio_emb)
        x = self.pos_embed(x)
        x = self.dropout(x)

        # Causal mask: each position can only attend to previous positions
        T = x.shape[1]
        causal_mask = torch.triu(
            torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1
        )

        predicted = self.transformer(
            x, text_emb,
            tgt_mask=causal_mask,
            tgt_key_padding_mask=audio_mask,
            memory_key_padding_mask=text_mask,
        )

        predicted = self.output_proj(predicted)
        return predicted

    def _cached_mha(self, mha, q_input, kv_input, cache_k, cache_v):
        """Run multi-head attention with KV cache.
        q_input: [B, 1, d], kv_input: [B, 1, d] (new token for K,V)
        cache_k, cache_v: [B, T_prev, d] projected keys/values
        Returns: output [B, 1, d], updated cache_k, updated cache_v
        """
        d = mha.embed_dim
        nhead = mha.num_heads
        head_dim = d // nhead
        B = q_input.shape[0]

        # Project Q from q_input, K/V from kv_input
        # in_proj_weight is [3d, d] = [Wq; Wk; Wv]
        Wq, Wk, Wv = mha.in_proj_weight.chunk(3, dim=0)
        bq, bk, bv = mha.in_proj_bias.chunk(3, dim=0)

        q = F.linear(q_input, Wq, bq)        # [B, 1, d]
        k_new = F.linear(kv_input, Wk, bk)    # [B, 1, d]
        v_new = F.linear(kv_input, Wv, bv)    # [B, 1, d]

        # Append to cache
        if cache_k is not None and cache_k.shape[1] > 0:
            k = torch.cat([cache_k, k_new], dim=1)
            v = torch.cat([cache_v, v_new], dim=1)
        else:
            k = k_new
            v = v_new

        # Multi-head attention
        q = q.view(B, 1, nhead, head_dim).transpose(1, 2)      # [B, nhead, 1, head_dim]
        k_mh = k.view(B, -1, nhead, head_dim).transpose(1, 2)  # [B, nhead, T, head_dim]
        v_mh = v.view(B, -1, nhead, head_dim).transpose(1, 2)

        attn = torch.matmul(q, k_mh.transpose(-2, -1)) / (head_dim ** 0.5)
        attn = F.softmax(attn, dim=-1)
        out = torch.matmul(attn, v_mh)  # [B, nhead, 1, head_dim]

        out = out.transpose(1, 2).contiguous().view(B, 1, d)
        out = F.linear(out, mha.out_proj.weight, mha.out_proj.bias)

        return out, k, v

    def _cached_cross_attn(self, mha, q_input, memory, cache_k, cache_v, memory_mask=None):
        """Cross-attention with cached memory K,V (computed once).
        memory_mask: [B, T_text] bool, True = padding (applied as -inf)."""
        d = mha.embed_dim
        nhead = mha.num_heads
        head_dim = d // nhead
        B = q_input.shape[0]

        Wq, Wk, Wv = mha.in_proj_weight.chunk(3, dim=0)
        bq, bk, bv = mha.in_proj_bias.chunk(3, dim=0)

        q = F.linear(q_input, Wq, bq)  # [B, 1, d]

        if cache_k is None:
            k = F.linear(memory, Wk, bk)  # [B, T_text, d]
            v = F.linear(memory, Wv, bv)
        else:
            k, v = cache_k, cache_v

        q = q.view(B, 1, nhead, head_dim).transpose(1, 2)
        k_mh = k.view(B, -1, nhead, head_dim).transpose(1, 2)
        v_mh = v.view(B, -1, nhead, head_dim).transpose(1, 2)

        attn = torch.matmul(q, k_mh.transpose(-2, -1)) / (head_dim ** 0.5)
        if memory_mask is not None:
            # memory_mask: [B, T_text] → [B, 1, 1, T_text]
            attn = attn.masked_fill(memory_mask.unsqueeze(1).unsqueeze(2), float('-inf'))
        attn = F.softmax(attn, dim=-1)
        out = torch.matmul(attn, v_mh)

        out = out.transpose(1, 2).contiguous().view(B, 1, d)
        out = F.linear(out, mha.out_proj.weight, mha.out_proj.bias)

        return out, k, v

    def init_cache(self, num_layers):
        """Initialize empty KV cache."""
        return {
            'self_k': [None] * num_layers,
            'self_v': [None] * num_layers,
            'cross_k': [None] * num_layers,
            'cross_v': [None] * num_layers,
        }

    def inference_step(self, new_emb, text_emb, step_idx, cache, text_mask=None):
        """Process one token with KV cache.
        new_emb: [B, 1, d_model] — raw audio embedding (not projected)
        text_mask: [B, T_text] bool, True = padding
        Returns: predicted [B, 1, d_model], updated cache
        """
        x = self.audio_input_proj(new_emb)
        # Add positional encoding for this specific position
        x = x + self.pos_embed.pe[:, step_idx:step_idx + 1]

        for i, layer in enumerate(self.transformer.layers):
            # Self-attention with cache (post-norm)
            sa_out, cache['self_k'][i], cache['self_v'][i] = self._cached_mha(
                layer.self_attn, x, x, cache['self_k'][i], cache['self_v'][i]
            )
            x = layer.norm1(x + layer.dropout1(sa_out))

            # Cross-attention with cached text KV
            ca_out, cache['cross_k'][i], cache['cross_v'][i] = self._cached_cross_attn(
                layer.multihead_attn, x, text_emb, cache['cross_k'][i], cache['cross_v'][i],
                memory_mask=text_mask,
            )
            x = layer.norm2(x + layer.dropout2(ca_out))

            # FFN
            ff_out = layer.linear2(layer.dropout(layer.activation(layer.linear1(x))))
            x = layer.norm3(x + layer.dropout3(ff_out))

        return self.output_proj(x), cache

# ─── Positional Encoding ────────────────────────────────────────────────────

class SinusoidalPositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=8192):
        super().__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        self.register_buffer("pe", pe.unsqueeze(0))  # [1, max_len, d_model]

    def forward(self, x):
        return x + self.pe[:, :x.shape[1]]

# ─── Mel Decoder ────────────────────────────────────────────────────────────

class ResConvBlock(nn.Module):
    """Residual conv block for mel decoder."""

    def __init__(self, channels, kernel_size=3):
        super().__init__()
        self.net = nn.Sequential(
            nn.Conv1d(channels, channels, kernel_size, padding=kernel_size // 2),
            nn.GELU(),
            nn.Conv1d(channels, channels, kernel_size, padding=kernel_size // 2),
        )

    def forward(self, x):
        return x + self.net(x)

class MelDecoder(nn.Module):
    """Decodes latent embeddings back to mel spectrograms.
    Improved architecture with residual blocks for sharper mel output."""

    def __init__(self, d_model=256, n_mels=100):
        super().__init__()
        hidden = d_model * 2  # 512 — more capacity

        self.proj = nn.Sequential(
            nn.Linear(d_model, hidden),
            nn.GELU(),
        )
        self.upsample = nn.Sequential(
            # 4x upsample (2x + 2x)
            nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=3),
            nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=3),
        )
        # Refinement + projection to mel bins
        self.out = nn.Sequential(
            nn.Conv1d(hidden, hidden, kernel_size=7, padding=3),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=5),
            nn.Conv1d(hidden, n_mels, kernel_size=7, padding=3),
        )

    def forward(self, z):
        """z: [B, T_down, d_model] → mel: [B, n_mels, T_up]"""
        x = self.proj(z)
        x = x.transpose(1, 2)  # [B, hidden, T_down]
        x = self.upsample(x)   # [B, hidden, T_up]
        return self.out(x)      # [B, n_mels, T_up]

# ─── Multi-Resolution Spectral Loss ─────────────────────────────────────────

class MultiResolutionSpectralLoss(nn.Module):
    """Computes spectral convergence + log magnitude loss at multiple STFT resolutions.
    This forces the mel decoder to produce sharp, well-defined spectral structure
    rather than blurry averages that Vocos can't decode properly."""

    def __init__(self, resolutions=((64, 16, 64), (128, 32, 128), (256, 64, 256))):
        super().__init__()
        # Each resolution is (n_fft, hop_length, win_length)
        self.resolutions = resolutions

    def _stft_loss(self, pred, target, n_fft, hop_length, win_length):
        """Compute spectral convergence + log magnitude loss for one resolution."""
        window = torch.hann_window(win_length, device=pred.device)

        # pred/target: [B, n_mels, T] — treat as 1D signal per mel bin
        # Flatten mel bins into batch: [B*n_mels, T]
        B, M, T = pred.shape
        pred_flat = pred.reshape(B * M, T)
        target_flat = target.reshape(B * M, T)

        pred_stft = torch.stft(pred_flat, n_fft, hop_length, win_length,
                               window=window, return_complex=True)
        target_stft = torch.stft(target_flat, n_fft, hop_length, win_length,
                                 window=window, return_complex=True)

        pred_mag = pred_stft.abs()
        target_mag = target_stft.abs()

        # Spectral convergence: Frobenius norm of difference / Frobenius norm of target
        sc_loss = torch.norm(target_mag - pred_mag, p="fro") / (torch.norm(target_mag, p="fro") + 1e-8)

        # Log magnitude loss
        log_mag_loss = F.l1_loss(
            torch.log(pred_mag + 1e-7),
            torch.log(target_mag + 1e-7),
        )

        return sc_loss + log_mag_loss

    def forward(self, pred, target):
        """pred, target: [B, n_mels, T]"""
        loss = torch.tensor(0.0, device=pred.device)
        count = 0
        for n_fft, hop, win in self.resolutions:
            # Skip if T is too short for this resolution
            if pred.shape[2] >= n_fft:
                loss = loss + self._stft_loss(pred, target, n_fft, hop, win)
                count += 1
        if count > 0:
            loss = loss / count
        return loss

# ─── Full LeWM TTS Model ────────────────────────────────────────────────────

class LeWMTTS(nn.Module):
    """
    Complete LeWM TTS model.

    Training: text + mel → audio embeddings → predict next embedding
    Two losses:
        1. Next-embedding prediction: MSE(predicted[t], target[t+1])
        2. Gaussian regularizer: KL(q(z|x) || N(0, I))
    """

    def __init__(self, config):
        super().__init__()
        d_model = config.get("d_model", 256)
        nhead = config.get("nhead", 4)
        n_mels = config.get("n_mels", 100)
        text_vocab = config.get("text_vocab_size", 256)
        text_layers = config.get("text_encoder_layers", 4)
        audio_layers = config.get("audio_encoder_layers", 4)
        predictor_layers = config.get("predictor_layers", 6)
        dropout = config.get("dropout", 0.1)
        self.kl_weight = config.get("kl_weight", 0.01)
        n_speakers = config.get("n_speakers", 1)

        self.text_encoder = TextEncoder(
            vocab_size=text_vocab, d_model=d_model, nhead=nhead,
            num_layers=text_layers, dropout=dropout,
        )
        self.audio_encoder = AudioEncoder(
            n_mels=n_mels, d_model=d_model, nhead=nhead,
            num_layers=audio_layers, dropout=dropout,
        )
        self.predictor = JEPAPredictor(
            d_model=d_model, nhead=nhead,
            num_layers=predictor_layers, dropout=dropout,
        )

        # Mel decoder: reconstructs mel from latent embeddings
        # Needed so the latent space is grounded to actual audio
        self.mel_decoder = MelDecoder(d_model=d_model, n_mels=n_mels)
        self.recon_weight = config.get("recon_weight", 1.0)

        # Speaker embedding (for multi-speaker)
        self.n_speakers = n_speakers
        if n_speakers > 1:
            self.speaker_embed = nn.Embedding(n_speakers, d_model)
        else:
            self.speaker_embed = None

        # Multi-resolution spectral loss for sharper mel output
        self.spectral_loss = MultiResolutionSpectralLoss()
        self.spectral_weight = config.get("spectral_weight", 0.5)

        self.config = config

    def _add_speaker(self, text_emb, speaker_id):
        """Add speaker embedding to text embeddings."""
        if self.speaker_embed is not None and speaker_id is not None:
            spk = self.speaker_embed(speaker_id).unsqueeze(1)  # [B, 1, d]
            text_emb = text_emb + spk
        return text_emb

    def forward(self, mel, text_tokens, mel_mask=None, text_mask=None, speaker_id=None):
        """
        Args:
            mel: [B, n_mels, T_mel]
            text_tokens: [B, T_text]
            mel_mask: [B, T_mel] bool
            text_mask: [B, T_text] bool
        Returns:
            loss_dict with 'prediction_loss', 'kl_loss', 'total_loss'
        """
        # Encode text + speaker conditioning
        text_emb = self.text_encoder(text_tokens, text_mask)
        text_emb = self._add_speaker(text_emb, speaker_id)

        # Encode audio → latent embeddings
        z, mu, logvar = self.audio_encoder(mel, mel_mask)

        # Predict next embedding: input z[:-1], target z[1:]
        input_emb = z[:, :-1]     # [B, T-1, d]
        target_emb = z[:, 1:]     # [B, T-1, d]

        # Adjust mask for downsampled sequence
        if mel_mask is not None:
            T_down = z.shape[1]
            ds_mask = mel_mask[:, ::self.audio_encoder.downsample_factor][:, :T_down]
            pred_mask = ds_mask[:, :-1]
        else:
            pred_mask = None

        predicted = self.predictor(input_emb, text_emb, pred_mask, text_mask)

        # ─── Loss 1: Next-embedding prediction (MSE) ───
        # Mask out padding positions
        if pred_mask is not None:
            valid = ~pred_mask  # True = valid
            valid = valid.unsqueeze(-1)  # [B, T-1, 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: Gaussian regularizer — KL(N(mu, sigma²) || N(0, I)) ───
        # KL = -0.5 * sum(1 + logvar - mu² - exp(logvar))
        if mel_mask is not None:
            T_down = mu.shape[1]
            ds_mask = mel_mask[:, ::self.audio_encoder.downsample_factor][:, :T_down]
            valid = (~ds_mask).unsqueeze(-1).float()
            kl = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp())
            kl_loss = (kl * valid).sum() / (valid.sum() * mu.shape[-1] + 1e-8)
        else:
            kl_loss = -0.5 * torch.mean(1 + logvar - mu.pow(2) - logvar.exp())

        # ─── Loss 3: Mel reconstruction ───
        # Decode latents back to mel and compare with input
        mel_recon = self.mel_decoder(z)  # [B, n_mels, T_up]
        # Trim/pad to match input mel length
        T_mel = mel.shape[2]
        T_recon = mel_recon.shape[2]
        if T_recon > T_mel:
            mel_recon = mel_recon[:, :, :T_mel]
        elif T_recon < T_mel:
            mel = mel[:, :, :T_recon]
            if mel_mask is not None:
                mel_mask = mel_mask[:, :T_recon]

        if mel_mask is not None:
            valid_mel = (~mel_mask).unsqueeze(1).float()  # [B, 1, T]
            recon_loss = F.l1_loss(
                mel_recon * valid_mel, mel * valid_mel, reduction="sum"
            ) / (valid_mel.sum() * mel.shape[1] + 1e-8)
        else:
            recon_loss = F.l1_loss(mel_recon, mel)

        # ─── Loss 4: Multi-resolution spectral loss ───
        # Apply on valid (non-padded) regions only
        if mel_mask is not None:
            spectral_loss = self.spectral_loss(mel_recon * valid_mel, mel * valid_mel)
        else:
            spectral_loss = self.spectral_loss(mel_recon, mel)

        total_loss = (prediction_loss
                      + self.kl_weight * kl_loss
                      + self.recon_weight * recon_loss
                      + self.spectral_weight * spectral_loss)

        return {
            "total_loss": total_loss,
            "prediction_loss": prediction_loss,
            "kl_loss": kl_loss,
            "recon_loss": recon_loss,
            "spectral_loss": spectral_loss,
        }

    def encode_audio(self, mel):
        """Encode mel to latent embeddings (for inference)."""
        z, mu, _ = self.audio_encoder(mel)
        return mu  # Use mean at inference

    def predict_next(self, audio_emb, text_emb, text_mask=None):
        """Predict next audio embedding autoregressively."""
        predicted = self.predictor(audio_emb, text_emb, text_mask=text_mask)
        return predicted[:, -1:]  # Only the last prediction

    def init_ar_cache(self):
        """Initialize KV cache for autoregressive inference."""
        num_layers = len(self.predictor.transformer.layers)
        return self.predictor.init_cache(num_layers)

    def predict_next_cached(self, new_emb, text_emb, step_idx, cache, text_mask=None):
        """Predict next embedding using KV cache. O(1) per step instead of O(n)."""
        return self.predictor.inference_step(new_emb, text_emb, step_idx, cache, text_mask=text_mask)

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,
            "n_mels": 100,
            "text_vocab_size": 256,
            "text_encoder_layers": 4,
            "audio_encoder_layers": 4,
            "predictor_layers": 6,
            "dropout": 0.1,
            "kl_weight": 0.01,
        }
    model = LeWMTTS(config)
    print(f"LeWM TTS model: {count_parameters(model)/1e6:.2f}M parameters")
    return model, config

-            nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),

-            nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),

+            self.conv_pre = nn.Sequential(

+                nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),

+                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),


Now update the MelDecoder to support 2x upsample:


class MelDecoder(nn.Module):
    """Decodes latent embeddings back to mel spectrograms.
    Improved architecture with residual blocks for sharper mel output."""

    def __init__(self, d_model=256, n_mels=100):
        super().__init__()
        hidden = d_model * 2  # 512 — more capacity

        self.proj = nn.Sequential(
            nn.Linear(d_model, hidden),
            nn.GELU(),
        )
        self.upsample = nn.Sequential(
            # 4x upsample (2x + 2x)
            nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=3),
            nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=3),
        )
        # Refinement + projection to mel bins
        self.out = nn.Sequential(
            nn.Conv1d(hidden, hidden, kernel_size=7, padding=3),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=5),
            nn.Conv1d(hidden, n_mels, kernel_size=7, padding=3),
        )

class MelDecoder(nn.Module):
    """Decodes latent embeddings back to mel spectrograms.
    Improved architecture with residual blocks for sharper mel output."""

    def __init__(self, d_model=256, n_mels=100, upsample_factor=4):
        super().__init__()
        hidden = d_model * 2  # 512 — more capacity

        self.proj = nn.Sequential(
            nn.Linear(d_model, hidden),
            nn.GELU(),
        )
        if upsample_factor == 2:
            self.upsample = nn.Sequential(
                nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
                ResConvBlock(hidden, kernel_size=3),
            )
        else:
            self.upsample = nn.Sequential(
                # 4x upsample (2x + 2x)
                nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
                ResConvBlock(hidden, kernel_size=3),
                nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
                ResConvBlock(hidden, kernel_size=3),
            )
        # Refinement + projection to mel bins
        self.out = nn.Sequential(
            nn.Conv1d(hidden, hidden, kernel_size=7, padding=3),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=5),
            nn.Conv1d(hidden, n_mels, kernel_size=7, padding=3),
        )


class MelDecoder(nn.Module):
    """Decodes latent embeddings back to mel spectrograms.
    Improved architecture with residual blocks for sharper mel output."""

    def __init__(self, d_model=256, n_mels=100, upsample_factor=4):
        super().__init__()
        hidden = d_model * 2  # 512 — more capacity

        self.proj = nn.Sequential(
            nn.Linear(d_model, hidden),
            nn.GELU(),
        )
        if upsample_factor == 2:
            self.upsample = nn.Sequential(
                nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
                ResConvBlock(hidden, kernel_size=3),
            )
        else:
            self.upsample = nn.Sequential(
                # 4x upsample (2x + 2x)
                nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
                ResConvBlock(hidden, kernel_size=3),
                nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
                ResConvBlock(hidden, kernel_size=3),
            )
        # Refinement + projection to mel bins
        self.out = nn.Sequential(
            nn.Conv1d(hidden, hidden, kernel_size=7, padding=3),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=5),
            nn.Conv1d(hidden, n_mels, kernel_size=7, padding=3),
        )

"""
LeWM TTS Model — JEPA-based Text-to-Speech

Architecture:
  - TextEncoder: Character-level transformer for Hindi/Devanagari text
  - AudioEncoder: 1D CNN + Transformer that encodes mel spectrograms to embeddings
  - JEPAPredictor: Transformer that predicts next audio embedding given text + audio context
  - Two losses: next-embedding prediction (MSE) + Gaussian regularizer (KL to N(0,I))
"""

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

# ─── Text Encoder ───────────────────────────────────────────────────────────

class TextEncoder(nn.Module):
    """Character-level transformer encoder for Hindi text."""

    def __init__(self, vocab_size=256, d_model=256, nhead=4, num_layers=4, dropout=0.1):
        super().__init__()
        self.d_model = d_model

        # Character embedding (covers all Unicode Hindi chars via byte-level encoding)
        self.char_embed = nn.Embedding(vocab_size, d_model)
        self.pos_embed = SinusoidalPositionalEncoding(d_model, max_len=2048)
        self.dropout = nn.Dropout(dropout)

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead,
            dim_feedforward=d_model * 4,
            dropout=dropout, batch_first=True,
            activation="gelu",
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.proj = nn.Linear(d_model, d_model)

    def forward(self, text_tokens, text_mask=None):
        """
        Args:
            text_tokens: [B, T_text] long tensor of byte-level token IDs
            text_mask: [B, T_text] bool, True = padding
        Returns:
            text_emb: [B, T_text, d_model]
        """
        x = self.char_embed(text_tokens) * math.sqrt(self.d_model)
        x = self.pos_embed(x)
        x = self.dropout(x)
        x = self.transformer(x, src_key_padding_mask=text_mask)
        x = self.proj(x)
        return x

# ─── Audio Encoder ──────────────────────────────────────────────────────────

class AudioEncoder(nn.Module):
    """
    Encodes mel spectrograms into a sequence of audio embeddings.
    Uses 1D CNN for downsampling + Transformer for contextualization.
    """

    def __init__(self, n_mels=100, d_model=256, nhead=4, num_layers=4,
                 downsample_factor=4, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.downsample_factor = downsample_factor

        # CNN downsampling: reduces temporal resolution by downsample_factor
        if downsample_factor == 2:
            self.conv_pre = nn.Sequential(
                nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),
                nn.GELU(),
                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
            )  # Total stride = 2
        else:
            self.conv_pre = nn.Sequential(
                nn.Conv1d(n_mels, d_model, kernel_size=7, padding=3),
                nn.GELU(),
                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
                nn.Conv1d(d_model, d_model, kernel_size=4, stride=2, padding=1),
                nn.GELU(),
            )  # Total stride = 4

        self.pos_embed = SinusoidalPositionalEncoding(d_model, max_len=4096)
        self.dropout = nn.Dropout(dropout)

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead,
            dim_feedforward=d_model * 4,
            dropout=dropout, batch_first=True,
            activation="gelu",
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

        # Project to embedding with mu and logvar for Gaussian regularizer
        self.proj_mu = nn.Linear(d_model, d_model)
        self.proj_logvar = nn.Linear(d_model, d_model)

    def forward(self, mel, mel_mask=None):
        """
        Args:
            mel: [B, n_mels, T_mel] — log mel spectrogram
            mel_mask: [B, T_mel] bool, True = padding (before downsampling)
        Returns:
            z: [B, T_down, d_model] — sampled latent embeddings
            mu: [B, T_down, d_model]
            logvar: [B, T_down, d_model]
        """
        # CNN encoding: [B, n_mels, T] -> [B, d_model, T//4]
        x = self.conv_pre(mel)
        x = x.transpose(1, 2)  # [B, T_down, d_model]

        # Downsample mask if provided
        if mel_mask is not None:
            # Approximate downsampled mask
            T_down = x.shape[1]
            mel_mask = mel_mask[:, ::self.downsample_factor][:, :T_down]

        x = self.pos_embed(x)
        x = self.dropout(x)
        x = self.transformer(x, src_key_padding_mask=mel_mask)

        mu = self.proj_mu(x)
        logvar = self.proj_logvar(x)

        # Reparameterization trick
        if self.training:
            std = torch.exp(0.5 * logvar)
            eps = torch.randn_like(std)
            z = mu + eps * std
        else:
            z = mu

        return z, mu, logvar

# ─── JEPA Predictor ─────────────────────────────────────────────────────────

class JEPAPredictor(nn.Module):
    """
    Predicts next audio embedding given:
    - Text context (from TextEncoder)
    - Previous audio embeddings (from AudioEncoder)

    Uses cross-attention to condition on text, and causal self-attention
    over the audio embedding sequence.
    """

    def __init__(self, d_model=256, nhead=4, num_layers=6, dropout=0.1):
        super().__init__()
        self.d_model = d_model

        self.audio_input_proj = nn.Linear(d_model, d_model)
        self.pos_embed = SinusoidalPositionalEncoding(d_model, max_len=4096)
        self.dropout = nn.Dropout(dropout)

        # Transformer decoder with causal self-attention + cross-attention to text
        decoder_layer = nn.TransformerDecoderLayer(
            d_model=d_model, nhead=nhead,
            dim_feedforward=d_model * 4,
            dropout=dropout, batch_first=True,
            activation="gelu",
        )
        self.transformer = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)

        # Output projection to predict next embedding
        self.output_proj = nn.Sequential(
            nn.Linear(d_model, d_model),
            nn.GELU(),
            nn.Linear(d_model, d_model),
        )

    def forward(self, audio_emb, text_emb, audio_mask=None, text_mask=None):
        """
        Args:
            audio_emb: [B, T_audio, d_model] — audio embeddings (from encoder)
            text_emb: [B, T_text, d_model] — text embeddings
            audio_mask: [B, T_audio] bool, True = padding
            text_mask: [B, T_text] bool, True = padding
        Returns:
            predicted: [B, T_audio, d_model] — predicted next embeddings
        """
        x = self.audio_input_proj(audio_emb)
        x = self.pos_embed(x)
        x = self.dropout(x)

        # Causal mask: each position can only attend to previous positions
        T = x.shape[1]
        causal_mask = torch.triu(
            torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1
        )

        predicted = self.transformer(
            x, text_emb,
            tgt_mask=causal_mask,
            tgt_key_padding_mask=audio_mask,
            memory_key_padding_mask=text_mask,
        )

        predicted = self.output_proj(predicted)
        return predicted

    def _cached_mha(self, mha, q_input, kv_input, cache_k, cache_v):
        """Run multi-head attention with KV cache.
        q_input: [B, 1, d], kv_input: [B, 1, d] (new token for K,V)
        cache_k, cache_v: [B, T_prev, d] projected keys/values
        Returns: output [B, 1, d], updated cache_k, updated cache_v
        """
        d = mha.embed_dim
        nhead = mha.num_heads
        head_dim = d // nhead
        B = q_input.shape[0]

        # Project Q from q_input, K/V from kv_input
        # in_proj_weight is [3d, d] = [Wq; Wk; Wv]
        Wq, Wk, Wv = mha.in_proj_weight.chunk(3, dim=0)
        bq, bk, bv = mha.in_proj_bias.chunk(3, dim=0)

        q = F.linear(q_input, Wq, bq)        # [B, 1, d]
        k_new = F.linear(kv_input, Wk, bk)    # [B, 1, d]
        v_new = F.linear(kv_input, Wv, bv)    # [B, 1, d]

        # Append to cache
        if cache_k is not None and cache_k.shape[1] > 0:
            k = torch.cat([cache_k, k_new], dim=1)
            v = torch.cat([cache_v, v_new], dim=1)
        else:
            k = k_new
            v = v_new

        # Multi-head attention
        q = q.view(B, 1, nhead, head_dim).transpose(1, 2)      # [B, nhead, 1, head_dim]
        k_mh = k.view(B, -1, nhead, head_dim).transpose(1, 2)  # [B, nhead, T, head_dim]
        v_mh = v.view(B, -1, nhead, head_dim).transpose(1, 2)

        attn = torch.matmul(q, k_mh.transpose(-2, -1)) / (head_dim ** 0.5)
        attn = F.softmax(attn, dim=-1)
        out = torch.matmul(attn, v_mh)  # [B, nhead, 1, head_dim]

        out = out.transpose(1, 2).contiguous().view(B, 1, d)
        out = F.linear(out, mha.out_proj.weight, mha.out_proj.bias)

        return out, k, v

    def _cached_cross_attn(self, mha, q_input, memory, cache_k, cache_v, memory_mask=None):
        """Cross-attention with cached memory K,V (computed once).
        memory_mask: [B, T_text] bool, True = padding (applied as -inf)."""
        d = mha.embed_dim
        nhead = mha.num_heads
        head_dim = d // nhead
        B = q_input.shape[0]

        Wq, Wk, Wv = mha.in_proj_weight.chunk(3, dim=0)
        bq, bk, bv = mha.in_proj_bias.chunk(3, dim=0)

        q = F.linear(q_input, Wq, bq)  # [B, 1, d]

        if cache_k is None:
            k = F.linear(memory, Wk, bk)  # [B, T_text, d]
            v = F.linear(memory, Wv, bv)
        else:
            k, v = cache_k, cache_v

        q = q.view(B, 1, nhead, head_dim).transpose(1, 2)
        k_mh = k.view(B, -1, nhead, head_dim).transpose(1, 2)
        v_mh = v.view(B, -1, nhead, head_dim).transpose(1, 2)

        attn = torch.matmul(q, k_mh.transpose(-2, -1)) / (head_dim ** 0.5)
        if memory_mask is not None:
            # memory_mask: [B, T_text] → [B, 1, 1, T_text]
            attn = attn.masked_fill(memory_mask.unsqueeze(1).unsqueeze(2), float('-inf'))
        attn = F.softmax(attn, dim=-1)
        out = torch.matmul(attn, v_mh)

        out = out.transpose(1, 2).contiguous().view(B, 1, d)
        out = F.linear(out, mha.out_proj.weight, mha.out_proj.bias)

        return out, k, v

    def init_cache(self, num_layers):
        """Initialize empty KV cache."""
        return {
            'self_k': [None] * num_layers,
            'self_v': [None] * num_layers,
            'cross_k': [None] * num_layers,
            'cross_v': [None] * num_layers,
        }

    def inference_step(self, new_emb, text_emb, step_idx, cache, text_mask=None):
        """Process one token with KV cache.
        new_emb: [B, 1, d_model] — raw audio embedding (not projected)
        text_mask: [B, T_text] bool, True = padding
        Returns: predicted [B, 1, d_model], updated cache
        """
        x = self.audio_input_proj(new_emb)
        # Add positional encoding for this specific position
        x = x + self.pos_embed.pe[:, step_idx:step_idx + 1]

        for i, layer in enumerate(self.transformer.layers):
            # Self-attention with cache (post-norm)
            sa_out, cache['self_k'][i], cache['self_v'][i] = self._cached_mha(
                layer.self_attn, x, x, cache['self_k'][i], cache['self_v'][i]
            )
            x = layer.norm1(x + layer.dropout1(sa_out))

            # Cross-attention with cached text KV
            ca_out, cache['cross_k'][i], cache['cross_v'][i] = self._cached_cross_attn(
                layer.multihead_attn, x, text_emb, cache['cross_k'][i], cache['cross_v'][i],
                memory_mask=text_mask,
            )
            x = layer.norm2(x + layer.dropout2(ca_out))

            # FFN
            ff_out = layer.linear2(layer.dropout(layer.activation(layer.linear1(x))))
            x = layer.norm3(x + layer.dropout3(ff_out))

        return self.output_proj(x), cache

# ─── Positional Encoding ────────────────────────────────────────────────────

class SinusoidalPositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=8192):
        super().__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        self.register_buffer("pe", pe.unsqueeze(0))  # [1, max_len, d_model]

    def forward(self, x):
        return x + self.pe[:, :x.shape[1]]

# ─── Mel Decoder ────────────────────────────────────────────────────────────

class ResConvBlock(nn.Module):
    """Residual conv block for mel decoder."""

    def __init__(self, channels, kernel_size=3):
        super().__init__()
        self.net = nn.Sequential(
            nn.Conv1d(channels, channels, kernel_size, padding=kernel_size // 2),
            nn.GELU(),
            nn.Conv1d(channels, channels, kernel_size, padding=kernel_size // 2),
        )

    def forward(self, x):
        return x + self.net(x)

class MelDecoder(nn.Module):
    """Decodes latent embeddings back to mel spectrograms.
    Improved architecture with residual blocks for sharper mel output."""

    def __init__(self, d_model=256, n_mels=100):
        super().__init__()
        hidden = d_model * 2  # 512 — more capacity

        self.proj = nn.Sequential(
            nn.Linear(d_model, hidden),
            nn.GELU(),
        )
        self.upsample = nn.Sequential(
            # 4x upsample (2x + 2x)
            nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=3),
            nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=3),
        )
        # Refinement + projection to mel bins
        self.out = nn.Sequential(
            nn.Conv1d(hidden, hidden, kernel_size=7, padding=3),
            nn.GELU(),
            ResConvBlock(hidden, kernel_size=5),
            nn.Conv1d(hidden, n_mels, kernel_size=7, padding=3),
        )

    def forward(self, z):
        """z: [B, T_down, d_model] → mel: [B, n_mels, T_up]"""
        x = self.proj(z)
        x = x.transpose(1, 2)  # [B, hidden, T_down]
        x = self.upsample(x)   # [B, hidden, T_up]
        return self.out(x)      # [B, n_mels, T_up]

# ─── Multi-Resolution Spectral Loss ─────────────────────────────────────────

class MultiResolutionSpectralLoss(nn.Module):
    """Computes spectral convergence + log magnitude loss at multiple STFT resolutions.
    This forces the mel decoder to produce sharp, well-defined spectral structure
    rather than blurry averages that Vocos can't decode properly."""

    def __init__(self, resolutions=((64, 16, 64), (128, 32, 128), (256, 64, 256))):
        super().__init__()
        # Each resolution is (n_fft, hop_length, win_length)
        self.resolutions = resolutions

    def _stft_loss(self, pred, target, n_fft, hop_length, win_length):
        """Compute spectral convergence + log magnitude loss for one resolution."""
        window = torch.hann_window(win_length, device=pred.device)

        # pred/target: [B, n_mels, T] — treat as 1D signal per mel bin
        # Flatten mel bins into batch: [B*n_mels, T]
        B, M, T = pred.shape
        pred_flat = pred.reshape(B * M, T)
        target_flat = target.reshape(B * M, T)

        pred_stft = torch.stft(pred_flat, n_fft, hop_length, win_length,
                               window=window, return_complex=True)
        target_stft = torch.stft(target_flat, n_fft, hop_length, win_length,
                                 window=window, return_complex=True)

        pred_mag = pred_stft.abs()
        target_mag = target_stft.abs()

        # Spectral convergence: Frobenius norm of difference / Frobenius norm of target
        sc_loss = torch.norm(target_mag - pred_mag, p="fro") / (torch.norm(target_mag, p="fro") + 1e-8)

        # Log magnitude loss
        log_mag_loss = F.l1_loss(
            torch.log(pred_mag + 1e-7),
            torch.log(target_mag + 1e-7),
        )

        return sc_loss + log_mag_loss

    def forward(self, pred, target):
        """pred, target: [B, n_mels, T]"""
        loss = torch.tensor(0.0, device=pred.device)
        count = 0
        for n_fft, hop, win in self.resolutions:
            # Skip if T is too short for this resolution
            if pred.shape[2] >= n_fft:
                loss = loss + self._stft_loss(pred, target, n_fft, hop, win)
                count += 1
        if count > 0:
            loss = loss / count
        return loss

# ─── Full LeWM TTS Model ────────────────────────────────────────────────────

class LeWMTTS(nn.Module):
    """
    Complete LeWM TTS model.

    Training: text + mel → audio embeddings → predict next embedding
    Two losses:
        1. Next-embedding prediction: MSE(predicted[t], target[t+1])
        2. Gaussian regularizer: KL(q(z|x) || N(0, I))
    """

    def __init__(self, config):
        super().__init__()
        d_model = config.get("d_model", 256)
        nhead = config.get("nhead", 4)
        n_mels = config.get("n_mels", 100)
        text_vocab = config.get("text_vocab_size", 256)
        text_layers = config.get("text_encoder_layers", 4)
        audio_layers = config.get("audio_encoder_layers", 4)
        predictor_layers = config.get("predictor_layers", 6)
        dropout = config.get("dropout", 0.1)
        self.kl_weight = config.get("kl_weight", 0.01)
        n_speakers = config.get("n_speakers", 1)

        self.text_encoder = TextEncoder(
            vocab_size=text_vocab, d_model=d_model, nhead=nhead,
            num_layers=text_layers, dropout=dropout,
        )
        self.audio_encoder = AudioEncoder(
            n_mels=n_mels, d_model=d_model, nhead=nhead,
            num_layers=audio_layers, dropout=dropout,
        )
        self.predictor = JEPAPredictor(
            d_model=d_model, nhead=nhead,
            num_layers=predictor_layers, dropout=dropout,
        )

        # Mel decoder: reconstructs mel from latent embeddings
        # Needed so the latent space is grounded to actual audio
        self.mel_decoder = MelDecoder(d_model=d_model, n_mels=n_mels)
        self.recon_weight = config.get("recon_weight", 1.0)

        # Speaker embedding (for multi-speaker)
        self.n_speakers = n_speakers
        if n_speakers > 1:
            self.speaker_embed = nn.Embedding(n_speakers, d_model)
        else:
            self.speaker_embed = None

        # Multi-resolution spectral loss for sharper mel output
        self.spectral_loss = MultiResolutionSpectralLoss()
        self.spectral_weight = config.get("spectral_weight", 0.5)

        self.config = config

    def _add_speaker(self, text_emb, speaker_id):
        """Add speaker embedding to text embeddings."""
        if self.speaker_embed is not None and speaker_id is not None:
            spk = self.speaker_embed(speaker_id).unsqueeze(1)  # [B, 1, d]
            text_emb = text_emb + spk
        return text_emb

    def forward(self, mel, text_tokens, mel_mask=None, text_mask=None, speaker_id=None):
        """
        Args:
            mel: [B, n_mels, T_mel]
            text_tokens: [B, T_text]
            mel_mask: [B, T_mel] bool
            text_mask: [B, T_text] bool
        Returns:
            loss_dict with 'prediction_loss', 'kl_loss', 'total_loss'
        """
        # Encode text + speaker conditioning
        text_emb = self.text_encoder(text_tokens, text_mask)
        text_emb = self._add_speaker(text_emb, speaker_id)

        # Encode audio → latent embeddings
        z, mu, logvar = self.audio_encoder(mel, mel_mask)

        # Predict next embedding: input z[:-1], target z[1:]
        input_emb = z[:, :-1]     # [B, T-1, d]
        target_emb = z[:, 1:]     # [B, T-1, d]

        # Adjust mask for downsampled sequence
        if mel_mask is not None:
            T_down = z.shape[1]
            ds_mask = mel_mask[:, ::self.audio_encoder.downsample_factor][:, :T_down]
            pred_mask = ds_mask[:, :-1]
        else:
            pred_mask = None

        predicted = self.predictor(input_emb, text_emb, pred_mask, text_mask)

        # ─── Loss 1: Next-embedding prediction (MSE) ───
        # Mask out padding positions
        if pred_mask is not None:
            valid = ~pred_mask  # True = valid
            valid = valid.unsqueeze(-1)  # [B, T-1, 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: Gaussian regularizer — KL(N(mu, sigma²) || N(0, I)) ───
        # KL = -0.5 * sum(1 + logvar - mu² - exp(logvar))
        if mel_mask is not None:
            T_down = mu.shape[1]
            ds_mask = mel_mask[:, ::self.audio_encoder.downsample_factor][:, :T_down]
            valid = (~ds_mask).unsqueeze(-1).float()
            kl = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp())
            kl_loss = (kl * valid).sum() / (valid.sum() * mu.shape[-1] + 1e-8)
        else:
            kl_loss = -0.5 * torch.mean(1 + logvar - mu.pow(2) - logvar.exp())

        # ─── Loss 3: Mel reconstruction ───
        # Decode latents back to mel and compare with input
        mel_recon = self.mel_decoder(z)  # [B, n_mels, T_up]
        # Trim/pad to match input mel length
        T_mel = mel.shape[2]
        T_recon = mel_recon.shape[2]
        if T_recon > T_mel:
            mel_recon = mel_recon[:, :, :T_mel]
        elif T_recon < T_mel:
            mel = mel[:, :, :T_recon]
            if mel_mask is not None:
                mel_mask = mel_mask[:, :T_recon]

        if mel_mask is not None:
            valid_mel = (~mel_mask).unsqueeze(1).float()  # [B, 1, T]
            recon_loss = F.l1_loss(
                mel_recon * valid_mel, mel * valid_mel, reduction="sum"
            ) / (valid_mel.sum() * mel.shape[1] + 1e-8)
        else:
            recon_loss = F.l1_loss(mel_recon, mel)

        # ─── Loss 4: Multi-resolution spectral loss ───
        # Apply on valid (non-padded) regions only
        if mel_mask is not None:
            spectral_loss = self.spectral_loss(mel_recon * valid_mel, mel * valid_mel)
        else:
            spectral_loss = self.spectral_loss(mel_recon, mel)

        total_loss = (prediction_loss
                      + self.kl_weight * kl_loss
                      + self.recon_weight * recon_loss
                      + self.spectral_weight * spectral_loss)

        return {
            "total_loss": total_loss,
            "prediction_loss": prediction_loss,
            "kl_loss": kl_loss,
            "recon_loss": recon_loss,
            "spectral_loss": spectral_loss,
        }

    def encode_audio(self, mel):
        """Encode mel to latent embeddings (for inference)."""
        z, mu, _ = self.audio_encoder(mel)
        return mu  # Use mean at inference

    def predict_next(self, audio_emb, text_emb, text_mask=None):
        """Predict next audio embedding autoregressively."""
        predicted = self.predictor(audio_emb, text_emb, text_mask=text_mask)
        return predicted[:, -1:]  # Only the last prediction

    def init_ar_cache(self):
        """Initialize KV cache for autoregressive inference."""
        num_layers = len(self.predictor.transformer.layers)
        return self.predictor.init_cache(num_layers)

    def predict_next_cached(self, new_emb, text_emb, step_idx, cache, text_mask=None):
        """Predict next embedding using KV cache. O(1) per step instead of O(n)."""
        return self.predictor.inference_step(new_emb, text_emb, step_idx, cache, text_mask=text_mask)

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,
            "n_mels": 100,
            "text_vocab_size": 256,
            "text_encoder_layers": 4,
            "audio_encoder_layers": 4,
            "predictor_layers": 6,
            "dropout": 0.1,
            "kl_weight": 0.01,
        }
    model = LeWMTTS(config)
    print(f"LeWM TTS model: {count_parameters(model)/1e6:.2f}M parameters")
    return model, config

-    def __init__(self, d_model=256, n_mels=100):

+    def __init__(self, d_model=256, n_mels=100, upsample_factor=4):

-            nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),

-            ResConvBlock(hidden, kernel_size=3),

+            self.upsample = nn.Sequential(

+                nn.ConvTranspose1d(hidden, hidden, kernel_size=4, stride=2, padding=1),

+                ResConvBlock(hidden, kernel_size=3),

+                # 4x upsample (2x + 2x)


Now update LeWMTTS to pass downsample_factor from config:


        self.audio_encoder = AudioEncoder(
            n_mels=n_mels, d_model=d_model, nhead=nhead,
            num_layers=audio_layers, dropout=dropout,
        )

        downsample_factor = config.get("downsample_factor", 4)
        self.audio_encoder = AudioEncoder(
            n_mels=n_mels, d_model=d_model, nhead=nhead,
            num_layers=audio_layers, downsample_factor=downsample_factor,
            dropout=dropout,
        )