type: user

role: user

content: hi

permissionMode: bypassPermissions

userType: external

entrypoint: cli

gitBranch: HEAD


type: message

role: assistant

type: text

text: Hi! How can I help you today?

service_tier: standard

speed: standard

type: assistant


content: what is tatsus of traing


type: thinking

thinking: The user is asking about the status of training, likely referring to the LeWM TTS project mentioned in my memory. Let me check my memory files for details.


text: Let me check my notes on the LeWM TTS training status.


ToolSearch

query: select:Read


tool_name: Read

query: select:Read


content:
     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 (v3 — Current)
    10→- **TextEncoder**: Byte-level char transformer (4 layers, 256d, 4 heads)
    11→- **AudioEncoder**: 1D CNN (stride 4 downsample) + Transformer (4 layers) → mu/logvar → reparameterization
    12→- **JEPAPredictor**: Transformer decoder (6 layers) with causal self-attn + cross-attn to text + **KV-cache**
    13→- **MelDecoder**: Upgraded — Linear→512 + 2x ConvTranspose1d + ResConvBlock residual blocks + refinement conv
    14→- **Vocoder**: Direct Vocos decode (model outputs 100 mels matching `charactr/vocos-mel-24khz`)
    15→- **4 losses**: prediction (MSE), KL (Gaussian reg), reconstruction (L1 mel), **multi-resolution spectral loss**
    16→- **Speaker embedding**: support for n_speakers > 1 (not yet trained)
    17→- **Model size**: 24.00M parameters
    18→- **KL annealing**: ramps up over first 20% of training
    19→
    20→## Multi-Resolution Spectral Loss
    21→- STFT at 3 resolutions: (64,16,64), (128,32,128), (256,64,256)
    22→- Applied per mel-bin (flattens [B, n_mels, T] → [B*n_mels, T])
    23→- Spectral convergence + log magnitude L1
    24→- Weight: 0.5 (configurable via --spectral_weight)
    25→
    26→## Files
    27→- `preprocess.py` — segments audio, extracts mels, saves manifest
    28→- `model.py` — TextEncoder, AudioEncoder, JEPAPredictor (with KV-cache), MelDecoder (upgraded), MultiResolutionSpectralLoss, LeWMTTS
    29→- `dataset.py` — TTSDataset, collate_fn, build_dataloader
    30→- `train.py` — training loop with logging, checkpointing, KL annealing, spectral loss
    31→- `inference.py` — AR generation with Vocos vocoder (single request)
    32→- `inference_engine.py` — Production engine: batched inference, KV-cache, FP16, voice prompts
    33→
    34→## Dataset
    35→- **Raw**: `/home/ubuntu/modi_dataset` (2,330 clips, 18.25h, Hindi, 24kHz)
    36→- **100-mel processed**: `/home/ubuntu/lewm-tts/processed_data_100mel` (7,009 segments, 17.88h)
    37→- Mel config: n_fft=1024, hop=256, n_mels=100, sr=24000, f_max=None, power=1.0, clip_val=1e-7
    38→
    39→## Training History
    40→- **v1**: 80 mels, simple decoder, GL→Vocos pipeline. Rustic sound.
    41→- **v2**: DAC-based, checkpoints preserved at `/home/ubuntu/lewm-tts/v2/output/checkpoints/`
    42→- **v3** (current): 100 mels, upgraded MelDecoder + spectral loss, direct Vocos. Training at output_v3/.
    43→
    44→## Inference Findings
    45→- **Predict (teacher-forced)**: works well, good quality
    46→- **AR synthesis**: broken with random start token — needs audio prompt (voice cloning)
    47→- **Best early checkpoints**: epoch 70-100 (later epochs collapse for AR)
    48→- Noise tolerance test: GT mel + noise at L1=0.3 sounds natural; model's structured error sounds worse
    49→
    50→## Inference Performance (A100 80GB)
    51→- KV-cache: 9x real-time single request
    52→- Batch=64: 359x throughput
    53→
    54→## Next Steps
    55→- Wait for v3 training to converge, test quality
    56→- If mel quality good with Vocos, proceed to multi-speaker training
    57→- Scheduled sampling during training (robustify AR)
    58→

content:
# 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 (v3 — Current)
- **TextEncoder**: Byte-level char transformer (4 layers, 256d, 4 heads)
- **AudioEncoder**: 1D CNN (stride 4 downsample) + Transformer (4 layers) → mu/logvar → reparameterization
- **JEPAPredictor**: Transformer decoder (6 layers) with causal self-attn + cross-attn to text + **KV-cache**
- **MelDecoder**: Upgraded — Linear→512 + 2x ConvTranspose1d + ResConvBlock residual blocks + refinement conv
- **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)
- **Model size**: 24.00M parameters
- **KL annealing**: ramps up over first 20% of training

## Multi-Resolution Spectral Loss
- STFT at 3 resolutions: (64,16,64), (128,32,128), (256,64,256)
- Applied per mel-bin (flattens [B, n_mels, T] → [B*n_mels, T])
- Spectral convergence + log magnitude L1
- Weight: 0.5 (configurable via --spectral_weight)

## Files
- `preprocess.py` — segments audio, extracts mels, saves manifest
- `model.py` — TextEncoder, AudioEncoder, JEPAPredictor (with KV-cache), MelDecoder (upgraded), MultiResolutionSpectralLoss, LeWMTTS
- `dataset.py` — TTSDataset, collate_fn, build_dataloader
- `train.py` — training loop with logging, checkpointing, KL annealing, spectral loss
- `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

## Training History
- **v1**: 80 mels, simple decoder, GL→Vocos pipeline. Rustic sound.
- **v2**: DAC-based, checkpoints preserved at `/home/ubuntu/lewm-tts/v2/output/checkpoints/`
- **v3** (current): 100 mels, upgraded MelDecoder + spectral loss, direct Vocos. Training at output_v3/.

## Inference Findings
- **Predict (teacher-forced)**: works well, good quality
- **AR synthesis**: broken with random start token — needs audio prompt (voice cloning)
- **Best early checkpoints**: epoch 70-100 (later epochs collapse for AR)
- Noise tolerance test: GT mel + noise at L1=0.3 sounds natural; model's structured error sounds worse

## Inference Performance (A100 80GB)
- KV-cache: 9x real-time single request
- Batch=64: 359x throughput

## Next Steps
- Wait for v3 training to converge, test quality
- If mel quality good with Vocos, proceed to multi-speaker training
- Scheduled sampling during training (robustify AR)

slug: quirky-bouncing-wirth


type: progress

hookEvent: PostToolUse

command: callback


text:
Based on my notes, here's the status of **LeWM TTS v3 training**:

- **Stage**: v3 is the current training run, outputting to `output_v3/`
- **What's new in v3**: Upgraded MelDecoder with residual conv blocks, multi-resolution spectral loss, 100 mels with direct Vocos vocoding
- **Status**: Training was in progress — waiting for convergence

Want me to check the actual training output directory for the latest checkpoint and logs to see how far it's gotten?


content: can u genarte last checkpoint sample


thinking: The user wants me to generate a sample from the latest v3 checkpoint. Let me check what's available.


text: Let me check the latest checkpoint and the inference script.


query: select:Bash,Glob


tool_name: Bash

tool_name: Glob

matches: Bash Glob

query: select:Bash,Glob


Bash

command: ls -lt /home/ubuntu/lewm-tts/output_v3/checkpoints/ 2>/dev/null | head -10

description: List latest v3 checkpoints


content:
     1→"""
     2→Inference pipeline for LeWM TTS.
     3→Text → JEPA predictor → mel reconstruction → Vocos vocoder → 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 import LeWMTTS
    13→
    14→
    15→class LeWMTTSInference:
    16→    """Full inference pipeline."""
    17→
    18→    def __init__(self, checkpoint_path, device="cuda"):
    19→        self.device = torch.device(device if torch.cuda.is_available() else "cpu")
    20→
    21→        # Load checkpoint
    22→        ckpt = torch.load(checkpoint_path, map_location=self.device, weights_only=False)
    23→        config = ckpt["config"]
    24→
    25→        # Build model
    26→        self.model = LeWMTTS(config).to(self.device)
    27→        self.model.load_state_dict(ckpt["model"])
    28→        self.model.eval()
    29→
    30→        # Vocos vocoder (directly decodes 100-mel)
    31→        from vocos import Vocos
    32→        self.vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
    33→        self.vocos.eval()
    34→
    35→        self.config = config
    36→        self._start_emb_scale = 5.0
    37→        print(f"Model loaded from {checkpoint_path}")
    38→
    39→    def vocos_decode(self, mel):
    40→        """Decode mel [B, 100, T] → waveform via Vocos."""
    41→        with torch.no_grad():
    42→            waveform = self.vocos.decode(mel)  # [B, T_audio]
    43→        return waveform
    44→
    45→    def text_to_tokens(self, text):
    46→        tokens = list(text.encode("utf-8"))
    47→        return torch.tensor(tokens, dtype=torch.long).unsqueeze(0).to(self.device)
    48→
    49→    @torch.no_grad()
    50→    def synthesize(self, text, max_steps=300, temperature=0.0):
    51→        """Generate speech from text using AR prediction with KV-cache."""
    52→        text_tokens = self.text_to_tokens(text)
    53→        text_emb = self.model.text_encoder(text_tokens)
    54→
    55→        d = self.config["d_model"]
    56→        start_emb = torch.randn(1, 1, d, device=self.device) * self._start_emb_scale
    57→        all_embeddings = [start_emb]
    58→
    59→        cache = self.model.init_ar_cache()
    60→        next_emb, cache = self.model.predict_next_cached(start_emb, text_emb, 0, cache)
    61→        if temperature > 0:
    62→            next_emb = next_emb + torch.randn_like(next_emb) * temperature
    63→        all_embeddings.append(next_emb)
    64→
    65→        prev_norms = [next_emb.norm().item()]
    66→
    67→        for step in range(1, max_steps):
    68→            next_emb, cache = self.model.predict_next_cached(next_emb, text_emb, step, cache)
    69→            if temperature > 0:
    70→                next_emb = next_emb + torch.randn_like(next_emb) * temperature
    71→            all_embeddings.append(next_emb)
    72→
    73→            norm = next_emb.norm().item()
    74→            prev_norms.append(norm)
    75→            if len(prev_norms) > 20:
    76→                recent = prev_norms[-20:]
    77→                if np.std(recent) < 0.01 * np.mean(recent) and step > 30:
    78→                    break
    79→
    80→        audio_embs = torch.cat(all_embeddings, dim=1)
    81→        mel = self.model.mel_decoder(audio_embs)
    82→        waveform = self.vocos_decode(mel.cpu())
    83→        return waveform.squeeze().numpy(), 24000
    84→
    85→    @torch.no_grad()
    86→    def reconstruct(self, mel_path):
    87→        """Teacher-forced reconstruction: encode real mel → decode back."""
    88→        mel = torch.load(mel_path, weights_only=True).unsqueeze(0).to(self.device)
    89→
    90→        z, mu, logvar = self.model.audio_encoder(mel)
    91→        mel_recon = self.model.mel_decoder(mu)
    92→
    93→        waveform = self.vocos_decode(mel_recon.cpu())
    94→        return waveform.squeeze().numpy(), 24000
    95→
    96→    @torch.no_grad()
    97→    def reconstruct_with_prediction(self, mel_path):
    98→        """Hybrid: encode real mel → predictor → decode predicted embeddings."""
    99→        mel = torch.load(mel_path, weights_only=True).unsqueeze(0).to(self.device)
   100→
   101→        z, mu, logvar = self.model.audio_encoder(mel)
   102→
   103→        text_tokens = torch.zeros(1, 1, dtype=torch.long, device=self.device)
   104→        text_emb = self.model.text_encoder(text_tokens)
   105→
   106→        predicted = self.model.predictor(mu[:, :-1], text_emb)
   107→        combined = torch.cat([mu[:, :1], predicted], dim=1)
   108→
   109→        mel_recon = self.model.mel_decoder(combined)
   110→        waveform = self.vocos_decode(mel_recon.cpu())
   111→        return waveform.squeeze().numpy(), 24000
   112→
   113→    def save_audio(self, waveform, sr, output_path):
   114→        # Normalize
   115→        mx = np.abs(waveform).max()
   116→        if mx > 0:
   117→            waveform = waveform / mx * 0.95
   118→        wav_tensor = torch.from_numpy(waveform).unsqueeze(0)
   119→        torchaudio.save(output_path, wav_tensor, sr)
   120→        print(f"Saved: {output_path} ({len(waveform)/sr:.2f}s)")
   121→
   122→
   123→def main():
   124→    parser = argparse.ArgumentParser()
   125→    parser.add_argument("--checkpoint", required=True)
   126→    parser.add_argument("--text", default=None, help="Text to synthesize")
   127→    parser.add_argument("--mel", default=None, help="Mel path for reconstruction test")
   128→    parser.add_argument("--mode", choices=["synthesize", "reconstruct", "predict"],
   129→                        default="synthesize")
   130→    parser.add_argument("--output", default="output.wav")
   131→    parser.add_argument("--max_steps", type=int, default=300)
   132→    parser.add_argument("--temperature", type=float, default=0.0)
   133→    args = parser.parse_args()
   134→
   135→    tts = LeWMTTSInference(args.checkpoint)
   136→
   137→    if args.mode == "synthesize":
   138→        assert args.text, "Need --text for synthesis"
   139→        waveform, sr = tts.synthesize(args.text, args.max_steps, args.temperature)
   140→    elif args.mode == "reconstruct":
   141→        assert args.mel, "Need --mel for reconstruction"
   142→        waveform, sr = tts.reconstruct(args.mel)
   143→    elif args.mode == "predict":
   144→        assert args.mel, "Need --mel for prediction test"
   145→        waveform, sr = tts.reconstruct_with_prediction(args.mel)
   146→
   147→    tts.save_audio(waveform, sr, args.output)
   148→
   149→
   150→if __name__ == "__main__":
   151→    main()
   152→

content:
"""
Inference pipeline for LeWM TTS.
Text → JEPA predictor → mel reconstruction → Vocos vocoder → waveform
"""

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

from model import LeWMTTS

class LeWMTTSInference:
    """Full inference pipeline."""

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

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

        # Build model
        self.model = LeWMTTS(config).to(self.device)
        self.model.load_state_dict(ckpt["model"])
        self.model.eval()

        # Vocos vocoder (directly decodes 100-mel)
        from vocos import Vocos
        self.vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
        self.vocos.eval()

        self.config = config
        self._start_emb_scale = 5.0
        print(f"Model loaded from {checkpoint_path}")

    def vocos_decode(self, mel):
        """Decode mel [B, 100, T] → waveform via Vocos."""
        with torch.no_grad():
            waveform = self.vocos.decode(mel)  # [B, T_audio]
        return waveform

    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, max_steps=300, temperature=0.0):
        """Generate speech from text using AR prediction with KV-cache."""
        text_tokens = self.text_to_tokens(text)
        text_emb = self.model.text_encoder(text_tokens)

        d = self.config["d_model"]
        start_emb = torch.randn(1, 1, d, device=self.device) * self._start_emb_scale
        all_embeddings = [start_emb]

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

        prev_norms = [next_emb.norm().item()]

        for step in range(1, max_steps):
            next_emb, cache = self.model.predict_next_cached(next_emb, text_emb, step, cache)
            if temperature > 0:
                next_emb = next_emb + torch.randn_like(next_emb) * temperature
            all_embeddings.append(next_emb)

            norm = next_emb.norm().item()
            prev_norms.append(norm)
            if len(prev_norms) > 20:
                recent = prev_norms[-20:]
                if np.std(recent) < 0.01 * np.mean(recent) and step > 30:
                    break

        audio_embs = torch.cat(all_embeddings, dim=1)
        mel = self.model.mel_decoder(audio_embs)
        waveform = self.vocos_decode(mel.cpu())
        return waveform.squeeze().numpy(), 24000

    @torch.no_grad()
    def reconstruct(self, mel_path):
        """Teacher-forced reconstruction: encode real mel → decode back."""
        mel = torch.load(mel_path, weights_only=True).unsqueeze(0).to(self.device)

        z, mu, logvar = self.model.audio_encoder(mel)
        mel_recon = self.model.mel_decoder(mu)

        waveform = self.vocos_decode(mel_recon.cpu())
        return waveform.squeeze().numpy(), 24000

    @torch.no_grad()
    def reconstruct_with_prediction(self, mel_path):
        """Hybrid: encode real mel → predictor → decode predicted embeddings."""
        mel = torch.load(mel_path, weights_only=True).unsqueeze(0).to(self.device)

        z, mu, logvar = self.model.audio_encoder(mel)

        text_tokens = torch.zeros(1, 1, dtype=torch.long, device=self.device)
        text_emb = self.model.text_encoder(text_tokens)

        predicted = self.model.predictor(mu[:, :-1], text_emb)
        combined = torch.cat([mu[:, :1], predicted], dim=1)

        mel_recon = self.model.mel_decoder(combined)
        waveform = self.vocos_decode(mel_recon.cpu())
        return waveform.squeeze().numpy(), 24000

    def save_audio(self, waveform, sr, output_path):
        # Normalize
        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, help="Text to synthesize")
    parser.add_argument("--mel", default=None, help="Mel path for reconstruction test")
    parser.add_argument("--mode", choices=["synthesize", "reconstruct", "predict"],
                        default="synthesize")
    parser.add_argument("--output", default="output.wav")
    parser.add_argument("--max_steps", type=int, default=300)
    parser.add_argument("--temperature", type=float, default=0.0)
    args = parser.parse_args()

    tts = LeWMTTSInference(args.checkpoint)

    if args.mode == "synthesize":
        assert args.text, "Need --text for synthesis"
        waveform, sr = tts.synthesize(args.text, args.max_steps, args.temperature)
    elif args.mode == "reconstruct":
        assert args.mel, "Need --mel for reconstruction"
        waveform, sr = tts.reconstruct(args.mel)
    elif args.mode == "predict":
        assert args.mel, "Need --mel for prediction test"
        waveform, sr = tts.reconstruct_with_prediction(args.mel)

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

if __name__ == "__main__":
    main()


content:
total 687584
-rw-rw-r-- 1 ubuntu ubuntu 106556117 Mar 24 06:06 best.pt
-rw-rw-r-- 1 ubuntu ubuntu 298760488 Mar 24 06:06 epoch_0020.pt
-rw-rw-r-- 1 ubuntu ubuntu 298760488 Mar 24 06:02 epoch_0010.pt


text: Latest checkpoint is `best.pt` (epoch 20). Let me grab a sample mel for reconstruction and generate both a reconstruction and a synthesis sample.


command: ls /home/ubuntu/lewm-tts/processed_data_100mel/mels/ | head -5

description: List available mel files for reconstruction


content:
000000.pt
000001.pt
000002.pt
000003.pt
000004.pt


command: cd /home/ubuntu/lewm-tts && python inference.py --checkpoint output_v3/checkpoints/best.pt --mel processed_data_100mel/mels/000000.pt --mode reconstruct --output sample_reconstruct_v3.wav 2>&1

description: Generate reconstruction sample from best v3 checkpoint


toolUseResult:
Error: Exit code 127
/bin/bash: line 1: python: command not found


command: which python3 && cd /home/ubuntu/lewm-tts && python3 inference.py --checkpoint output_v3/checkpoints/best.pt --mel processed_data_100mel/mels/000000.pt --mode reconstruct --output sample_reconstruct_v3.wav 2>&1

description: Generate reconstruction sample using python3


content:
/usr/bin/python3
Model loaded from output_v3/checkpoints/best.pt
Saved: sample_reconstruct_v3.wav (9.50s)


text: Reconstruction sample is saved. Now let me also generate a synthesis (AR) sample with some text.


command: python3 inference.py --checkpoint output_v3/checkpoints/best.pt --text "नमस्ते, यह एक परीक्षण है" --mode synthesize --output sample_synth_v3.wav --max_steps 300 2>&1

description: Generate AR synthesis sample from best v3 checkpoint


content:
Model loaded from output_v3/checkpoints/best.pt
Saved: sample_synth_v3.wav (3.36s)


text:
Both samples are generated from the **best.pt** checkpoint (epoch 20):

- **`sample_reconstruct_v3.wav`** (9.50s) — teacher-forced reconstruction (encodes a real mel, decodes it back). This shows the mel decoder quality.
- **`sample_synth_v3.wav`** (3.36s) — AR synthesis from text "नमस्ते, यह एक परीक्षण है". This shows end-to-end generation quality.

Training is still early (only 20 epochs so far). The reconstruction should sound reasonable, but AR synthesis may still be rough at this stage. You can listen to them in `/home/ubuntu/lewm-tts/`.


type: system


content: bo


thinking: The user said "bo" - they probably mean "both" sound bad, or they want to hear both. Let me check the training log to see the current training status since they opened it.


text:
Not sure what you mean — could you clarify? Did you mean:

- **"both"** sound bad?
- Something else?

Also, want me to check the training log to see how the losses are looking?