     1→"""
     2→Data preprocessing for LeWM TTS.
     3→- Loads Modi dataset from HuggingFace Arrow format
     4→- Segments long clips into 3-15 second chunks using silence detection
     5→- Extracts mel spectrograms
     6→- Saves processed dataset
     7→"""
     8→
     9→import os
    10→import json
    11→import numpy as np
    12→import torch
    13→import torchaudio
    14→from datasets import load_from_disk
    15→from pathlib import Path
    16→import argparse
    17→
    18→
    19→# ─── Mel spectrogram config ───
    20→MEL_CONFIG = {
    21→    "sample_rate": 24000,
    22→    "n_fft": 1024,
    23→    "hop_length": 256,
    24→    "n_mels": 100,
    25→    "f_min": 0,
    26→    "f_max": None,      # full bandwidth (matches Vocos mel-24khz)
    27→    "power": 1.0,
    28→    "clip_val": 1e-7,   # log clamp value (matches Vocos safe_log)
    29→}
    30→
    31→# ─── Segmentation config ───
    32→SEG_CONFIG = {
    33→    "min_duration": 3.0,   # seconds
    34→    "max_duration": 12.0,  # seconds
    35→    "target_duration": 8.0,  # preferred chunk size
    36→    "silence_threshold_db": -40,
    37→    "min_silence_len": 0.3,  # seconds
    38→}
    39→
    40→
    41→def detect_silence_points(waveform: np.ndarray, sr: int,
    42→                          threshold_db: float = -40,
    43→                          min_silence_len: float = 0.3) -> list:
    44→    """Find silence points in audio for splitting."""
    45→    # Convert to dB
    46→    frame_length = int(0.025 * sr)  # 25ms frames
    47→    hop = int(0.010 * sr)           # 10ms hop
    48→
    49→    # RMS energy per frame
    50→    n_frames = 1 + (len(waveform) - frame_length) // hop
    51→    rms_db = []
    52→    for i in range(n_frames):
    53→        start = i * hop
    54→        frame = waveform[start:start + frame_length]
    55→        rms = np.sqrt(np.mean(frame ** 2) + 1e-10)
    56→        rms_db.append(20 * np.log10(rms + 1e-10))
    57→
    58→    rms_db = np.array(rms_db)
    59→    is_silence = rms_db < threshold_db
    60→
    61→    # Find contiguous silence regions
    62→    silence_points = []
    63→    min_frames = int(min_silence_len / 0.010)
    64→
    65→    in_silence = False
    66→    silence_start = 0
    67→    for i, s in enumerate(is_silence):
    68→        if s and not in_silence:
    69→            silence_start = i
    70→            in_silence = True
    71→        elif not s and in_silence:
    72→            if i - silence_start >= min_frames:
    73→                # Midpoint of silence region in samples
    74→                mid_frame = (silence_start + i) // 2
    75→                mid_sample = mid_frame * hop
    76→                silence_points.append(mid_sample)
    77→            in_silence = False
    78→
    79→    return silence_points
    80→
    81→
    82→def segment_audio(waveform: np.ndarray, sr: int, text: str, config: dict) -> list:
    83→    """Segment a long audio clip into shorter chunks at silence boundaries."""
    84→    total_duration = len(waveform) / sr
    85→
    86→    # If already short enough, return as-is
    87→    if total_duration <= config["max_duration"]:
    88→        if total_duration >= config["min_duration"]:
    89→            return [(waveform, text)]
    90→        else:
    91→            return []  # Too short
    92→
    93→    # Find silence points for natural splitting
    94→    silence_points = detect_silence_points(
    95→        waveform, sr,
    96→        threshold_db=config["silence_threshold_db"],
    97→        min_silence_len=config["min_silence_len"]
    98→    )
    99→
   100→    # Add start and end
   101→    split_points = [0] + silence_points + [len(waveform)]
   102→
   103→    # Greedily merge segments to hit target duration
   104→    segments = []
   105→    current_start = 0
   106→
   107→    for i, point in enumerate(split_points[1:], 1):
   108→        current_duration = (point - current_start) / sr
   109→
   110→        if current_duration >= config["target_duration"]:
   111→            # Take this segment
   112→            chunk = waveform[current_start:point]
   113→            chunk_duration = len(chunk) / sr
   114→            if config["min_duration"] <= chunk_duration <= config["max_duration"] * 1.2:
   115→                segments.append(chunk)
   116→            elif chunk_duration > config["max_duration"] * 1.2:
   117→                # Force split at max_duration
   118→                max_samples = int(config["max_duration"] * sr)
   119→                for j in range(0, len(chunk), max_samples):
   120→                    sub = chunk[j:j + max_samples]
   121→                    if len(sub) / sr >= config["min_duration"]:
   122→                        segments.append(sub)
   123→            current_start = point
   124→
   125→    # Handle remaining audio
   126→    if current_start < len(waveform):
   127→        remaining = waveform[current_start:]
   128→        if len(remaining) / sr >= config["min_duration"]:
   129→            segments.append(remaining)
   130→        elif segments:
   131→            # Append to last segment if it won't make it too long
   132→            combined = np.concatenate([segments[-1], remaining])
   133→            if len(combined) / sr <= config["max_duration"] * 1.2:
   134→                segments[-1] = combined
   135→
   136→    # For text: we can't perfectly align without forced alignment,
   137→    # so we use the full text for all segments (will be refined later with forced alignment)
   138→    # For now, distribute text roughly proportionally
   139→    results = []
   140→    total_samples = sum(len(s) for s in segments)
   141→    text_chars = list(text)
   142→    char_idx = 0
   143→
   144→    for seg in segments:
   145→        proportion = len(seg) / total_samples
   146→        n_chars = max(1, int(len(text) * proportion))
   147→        seg_text = text[char_idx:char_idx + n_chars]
   148→        char_idx += n_chars
   149→        results.append((seg, seg_text.strip()))
   150→
   151→    return results
   152→
   153→
   154→def extract_mel(waveform: torch.Tensor, config: dict) -> torch.Tensor:
   155→    """Extract log-mel spectrogram (matches Vocos mel-24khz config)."""
   156→    mel_kwargs = dict(
   157→        sample_rate=config["sample_rate"],
   158→        n_fft=config["n_fft"],
   159→        hop_length=config["hop_length"],
   160→        n_mels=config["n_mels"],
   161→        power=config["power"],
   162→    )
   163→    if config.get("f_min") is not None:
   164→        mel_kwargs["f_min"] = config["f_min"]
   165→    if config.get("f_max") is not None:
   166→        mel_kwargs["f_max"] = config["f_max"]
   167→
   168→    mel_transform = torchaudio.transforms.MelSpectrogram(**mel_kwargs)
   169→    mel = mel_transform(waveform)
   170→    clip_val = config.get("clip_val", 1e-7)
   171→    log_mel = torch.log(torch.clamp(mel, min=clip_val))
   172→    return log_mel
   173→
   174→
   175→def trim_silence(waveform: np.ndarray, sr: int, threshold_db: float = -45) -> np.ndarray:
   176→    """Trim leading and trailing silence."""
   177→    # Convert to amplitude threshold
   178→    threshold = 10 ** (threshold_db / 20)
   179→
   180→    # Find non-silent regions
   181→    abs_wav = np.abs(waveform)
   182→
   183→    # Use a sliding window
   184→    window = int(0.01 * sr)  # 10ms
   185→    for start in range(0, len(abs_wav) - window, window):
   186→        if np.max(abs_wav[start:start + window]) > threshold:
   187→            break
   188→
   189→    for end in range(len(abs_wav), window, -window):
   190→        if np.max(abs_wav[end - window:end]) > threshold:
   191→            break
   192→
   193→    # Add small padding
   194→    pad = int(0.05 * sr)  # 50ms padding
   195→    start = max(0, start - pad)
   196→    end = min(len(waveform), end + pad)
   197→
   198→    return waveform[start:end]
   199→
   200→
   201→def preprocess_dataset(input_path: str, output_path: str):
   202→    """Main preprocessing pipeline."""
   203→    print(f"Loading dataset from {input_path}...")
   204→    ds = load_from_disk(input_path)
   205→
   206→    output_dir = Path(output_path)
   207→    audio_dir = output_dir / "audio"
   208→    mel_dir = output_dir / "mels"
   209→    audio_dir.mkdir(parents=True, exist_ok=True)
   210→    mel_dir.mkdir(parents=True, exist_ok=True)
   211→
   212→    manifest = []
   213→    total_duration = 0.0
   214→    idx = 0
   215→
   216→    for i in range(len(ds)):
   217→        sample = ds[i]
   218→        audio_data = sample["audio"]
   219→        waveform = np.array(audio_data["array"], dtype=np.float32)
   220→        sr = audio_data["sampling_rate"]
   221→        text = sample["text"]
   222→
   223→        # Trim silence
   224→        waveform = trim_silence(waveform, sr)
   225→
   226→        # Segment into chunks
   227→        segments = segment_audio(waveform, sr, text, SEG_CONFIG)
   228→
   229→        for seg_wav, seg_text in segments:
   230→            if not seg_text:
   231→                continue
   232→
   233→            duration = len(seg_wav) / sr
   234→            total_duration += duration
   235→
   236→            # Save audio
   237→            audio_path = audio_dir / f"{idx:06d}.wav"
   238→            wav_tensor = torch.from_numpy(seg_wav).unsqueeze(0)
   239→            torchaudio.save(str(audio_path), wav_tensor, sr)
   240→
   241→            # Extract and save mel
   242→            mel = extract_mel(wav_tensor, MEL_CONFIG)
   243→            mel_path = mel_dir / f"{idx:06d}.pt"
   244→            torch.save(mel.squeeze(0), str(mel_path))  # [n_mels, T]
   245→
   246→            manifest.append({
   247→                "id": idx,
   248→                "audio_path": str(audio_path),
   249→                "mel_path": str(mel_path),
   250→                "text": seg_text,
   251→                "duration": round(duration, 3),
   252→                "mel_frames": mel.shape[-1],
   253→            })
   254→
   255→            idx += 1
   256→
   257→        if (i + 1) % 100 == 0:
   258→            print(f"  Processed {i+1}/{len(ds)} samples, {idx} segments, {total_duration/3600:.2f}h total")
   259→
   260→    # Save manifest
   261→    manifest_path = output_dir / "manifest.json"
   262→    with open(manifest_path, "w", encoding="utf-8") as f:
   263→        json.dump(manifest, f, ensure_ascii=False, indent=2)
   264→
   265→    # Save config
   266→    config = {
   267→        "mel": MEL_CONFIG,
   268→        "segmentation": SEG_CONFIG,
   269→        "total_segments": idx,
   270→        "total_duration_hours": round(total_duration / 3600, 2),
   271→        "original_samples": len(ds),
   272→    }
   273→    config_path = output_dir / "config.json"
   274→    with open(config_path, "w") as f:
   275→        json.dump(config, f, indent=2)
   276→
   277→    print(f"\nDone! {idx} segments, {total_duration/3600:.2f} hours")
   278→    print(f"Manifest: {manifest_path}")
   279→    print(f"Config: {config_path}")
   280→
   281→
   282→if __name__ == "__main__":
   283→    parser = argparse.ArgumentParser()
   284→    parser.add_argument("--input", default="/home/ubuntu/modi_dataset")
   285→    parser.add_argument("--output", default="/home/ubuntu/lewm-tts/processed_data")
   286→    args = parser.parse_args()
   287→    preprocess_dataset(args.input, args.output)
   288→

"""
Data preprocessing for LeWM TTS.
- Loads Modi dataset from HuggingFace Arrow format
- Segments long clips into 3-15 second chunks using silence detection
- Extracts mel spectrograms
- Saves processed dataset
"""

import os
import json
import numpy as np
import torch
import torchaudio
from datasets import load_from_disk
from pathlib import Path
import argparse

# ─── Mel spectrogram config ───
MEL_CONFIG = {
    "sample_rate": 24000,
    "n_fft": 1024,
    "hop_length": 256,
    "n_mels": 100,
    "f_min": 0,
    "f_max": None,      # full bandwidth (matches Vocos mel-24khz)
    "power": 1.0,
    "clip_val": 1e-7,   # log clamp value (matches Vocos safe_log)
}

# ─── Segmentation config ───
SEG_CONFIG = {
    "min_duration": 3.0,   # seconds
    "max_duration": 12.0,  # seconds
    "target_duration": 8.0,  # preferred chunk size
    "silence_threshold_db": -40,
    "min_silence_len": 0.3,  # seconds
}

def detect_silence_points(waveform: np.ndarray, sr: int,
                          threshold_db: float = -40,
                          min_silence_len: float = 0.3) -> list:
    """Find silence points in audio for splitting."""
    # Convert to dB
    frame_length = int(0.025 * sr)  # 25ms frames
    hop = int(0.010 * sr)           # 10ms hop

    # RMS energy per frame
    n_frames = 1 + (len(waveform) - frame_length) // hop
    rms_db = []
    for i in range(n_frames):
        start = i * hop
        frame = waveform[start:start + frame_length]
        rms = np.sqrt(np.mean(frame ** 2) + 1e-10)
        rms_db.append(20 * np.log10(rms + 1e-10))

    rms_db = np.array(rms_db)
    is_silence = rms_db < threshold_db

    # Find contiguous silence regions
    silence_points = []
    min_frames = int(min_silence_len / 0.010)

    in_silence = False
    silence_start = 0
    for i, s in enumerate(is_silence):
        if s and not in_silence:
            silence_start = i
            in_silence = True
        elif not s and in_silence:
            if i - silence_start >= min_frames:
                # Midpoint of silence region in samples
                mid_frame = (silence_start + i) // 2
                mid_sample = mid_frame * hop
                silence_points.append(mid_sample)
            in_silence = False

    return silence_points

def segment_audio(waveform: np.ndarray, sr: int, text: str, config: dict) -> list:
    """Segment a long audio clip into shorter chunks at silence boundaries."""
    total_duration = len(waveform) / sr

    # If already short enough, return as-is
    if total_duration <= config["max_duration"]:
        if total_duration >= config["min_duration"]:
            return [(waveform, text)]
        else:
            return []  # Too short

    # Find silence points for natural splitting
    silence_points = detect_silence_points(
        waveform, sr,
        threshold_db=config["silence_threshold_db"],
        min_silence_len=config["min_silence_len"]
    )

    # Add start and end
    split_points = [0] + silence_points + [len(waveform)]

    # Greedily merge segments to hit target duration
    segments = []
    current_start = 0

    for i, point in enumerate(split_points[1:], 1):
        current_duration = (point - current_start) / sr

        if current_duration >= config["target_duration"]:
            # Take this segment
            chunk = waveform[current_start:point]
            chunk_duration = len(chunk) / sr
            if config["min_duration"] <= chunk_duration <= config["max_duration"] * 1.2:
                segments.append(chunk)
            elif chunk_duration > config["max_duration"] * 1.2:
                # Force split at max_duration
                max_samples = int(config["max_duration"] * sr)
                for j in range(0, len(chunk), max_samples):
                    sub = chunk[j:j + max_samples]
                    if len(sub) / sr >= config["min_duration"]:
                        segments.append(sub)
            current_start = point

    # Handle remaining audio
    if current_start < len(waveform):
        remaining = waveform[current_start:]
        if len(remaining) / sr >= config["min_duration"]:
            segments.append(remaining)
        elif segments:
            # Append to last segment if it won't make it too long
            combined = np.concatenate([segments[-1], remaining])
            if len(combined) / sr <= config["max_duration"] * 1.2:
                segments[-1] = combined

    # For text: we can't perfectly align without forced alignment,
    # so we use the full text for all segments (will be refined later with forced alignment)
    # For now, distribute text roughly proportionally
    results = []
    total_samples = sum(len(s) for s in segments)
    text_chars = list(text)
    char_idx = 0

    for seg in segments:
        proportion = len(seg) / total_samples
        n_chars = max(1, int(len(text) * proportion))
        seg_text = text[char_idx:char_idx + n_chars]
        char_idx += n_chars
        results.append((seg, seg_text.strip()))

    return results

def extract_mel(waveform: torch.Tensor, config: dict) -> torch.Tensor:
    """Extract log-mel spectrogram (matches Vocos mel-24khz config)."""
    mel_kwargs = dict(
        sample_rate=config["sample_rate"],
        n_fft=config["n_fft"],
        hop_length=config["hop_length"],
        n_mels=config["n_mels"],
        power=config["power"],
    )
    if config.get("f_min") is not None:
        mel_kwargs["f_min"] = config["f_min"]
    if config.get("f_max") is not None:
        mel_kwargs["f_max"] = config["f_max"]

    mel_transform = torchaudio.transforms.MelSpectrogram(**mel_kwargs)
    mel = mel_transform(waveform)
    clip_val = config.get("clip_val", 1e-7)
    log_mel = torch.log(torch.clamp(mel, min=clip_val))
    return log_mel

def trim_silence(waveform: np.ndarray, sr: int, threshold_db: float = -45) -> np.ndarray:
    """Trim leading and trailing silence."""
    # Convert to amplitude threshold
    threshold = 10 ** (threshold_db / 20)

    # Find non-silent regions
    abs_wav = np.abs(waveform)

    # Use a sliding window
    window = int(0.01 * sr)  # 10ms
    for start in range(0, len(abs_wav) - window, window):
        if np.max(abs_wav[start:start + window]) > threshold:
            break

    for end in range(len(abs_wav), window, -window):
        if np.max(abs_wav[end - window:end]) > threshold:
            break

    # Add small padding
    pad = int(0.05 * sr)  # 50ms padding
    start = max(0, start - pad)
    end = min(len(waveform), end + pad)

    return waveform[start:end]

def preprocess_dataset(input_path: str, output_path: str):
    """Main preprocessing pipeline."""
    print(f"Loading dataset from {input_path}...")
    ds = load_from_disk(input_path)

    output_dir = Path(output_path)
    audio_dir = output_dir / "audio"
    mel_dir = output_dir / "mels"
    audio_dir.mkdir(parents=True, exist_ok=True)
    mel_dir.mkdir(parents=True, exist_ok=True)

    manifest = []
    total_duration = 0.0
    idx = 0

    for i in range(len(ds)):
        sample = ds[i]
        audio_data = sample["audio"]
        waveform = np.array(audio_data["array"], dtype=np.float32)
        sr = audio_data["sampling_rate"]
        text = sample["text"]

        # Trim silence
        waveform = trim_silence(waveform, sr)

        # Segment into chunks
        segments = segment_audio(waveform, sr, text, SEG_CONFIG)

        for seg_wav, seg_text in segments:
            if not seg_text:
                continue

            duration = len(seg_wav) / sr
            total_duration += duration

            # Save audio
            audio_path = audio_dir / f"{idx:06d}.wav"
            wav_tensor = torch.from_numpy(seg_wav).unsqueeze(0)
            torchaudio.save(str(audio_path), wav_tensor, sr)

            # Extract and save mel
            mel = extract_mel(wav_tensor, MEL_CONFIG)
            mel_path = mel_dir / f"{idx:06d}.pt"
            torch.save(mel.squeeze(0), str(mel_path))  # [n_mels, T]

            manifest.append({
                "id": idx,
                "audio_path": str(audio_path),
                "mel_path": str(mel_path),
                "text": seg_text,
                "duration": round(duration, 3),
                "mel_frames": mel.shape[-1],
            })

            idx += 1

        if (i + 1) % 100 == 0:
            print(f"  Processed {i+1}/{len(ds)} samples, {idx} segments, {total_duration/3600:.2f}h total")

    # Save manifest
    manifest_path = output_dir / "manifest.json"
    with open(manifest_path, "w", encoding="utf-8") as f:
        json.dump(manifest, f, ensure_ascii=False, indent=2)

    # Save config
    config = {
        "mel": MEL_CONFIG,
        "segmentation": SEG_CONFIG,
        "total_segments": idx,
        "total_duration_hours": round(total_duration / 3600, 2),
        "original_samples": len(ds),
    }
    config_path = output_dir / "config.json"
    with open(config_path, "w") as f:
        json.dump(config, f, indent=2)

    print(f"\nDone! {idx} segments, {total_duration/3600:.2f} hours")
    print(f"Manifest: {manifest_path}")
    print(f"Config: {config_path}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--input", default="/home/ubuntu/modi_dataset")
    parser.add_argument("--output", default="/home/ubuntu/lewm-tts/processed_data")
    args = parser.parse_args()
    preprocess_dataset(args.input, args.output)


Let me check the key issue. The preprocessing uses `torchaudio.transforms.MelSpectrogram` without `center=True` explicitly, and Vocos uses `center=True` (padding="center"). Let me check...

Actually, torchaudio's MelSpectrogram has `center=True` by default, so that should match.

The preprocessing uses `f_min=0` while Vocos doesn't set f_min (defaults to 0 in torchaudio). And f_max=None in both cases.

The main difference I see: preprocessing doesn't pass `center` explicitly (defaults to True in torchaudio), and Vocos uses `center=padding == "center"` which is True.

So the mel specs should be compatible.

The L1 reconstruction error of 0.73 is about 5.6% relative error on the mel range, which isn't terrible for epoch 28, but the real question is whether the mel decoder is producing good enough spectrograms yet—if both synthesis and reconstruction are coming out as noise, that's the bottleneck.

Looking at the mel stats, the reconstructed range [-7.17, 3.07] versus the real range [-8.69, 4.46] seems reasonable, but the L1 error of 0.73 is more than double what earlier experiments showed sounding acceptable at 0.3. The model's only been training for about 28 epochs, so the mel decoder likely just hasn't converged yet. I should also verify that the Vocos decoder is being called correctly—it expects raw mel features, not the processed output from its own forward pass.


Bash

python3 -c "
# Check Vocos decode method signature
from vocos import Vocos
import inspect
src = inspect.getsource(Vocos.decode)
print(src)
" 2>&1

Check Vocos decode method


    @torch.inference_mode()
    def decode(self, features_input: torch.Tensor, **kwargs: Any) -> torch.Tensor:
        """
        Method to decode audio waveform from already calculated features. The features input is passed through
        the backbone and the head to reconstruct the audio output.

        Args:
            features_input (Tensor): The input tensor of features of shape (B, C, L), where B is the batch size,
                                     C denotes the feature dimension, and L is the sequence length.

        Returns:
            Tensor: The output tensor representing the reconstructed audio waveform of shape (B, T).
        """
        x = self.backbone(features_input, **kwargs)
        audio_output = self.head(x)
        return audio_output


python3 -c "
# Check training log - how many epochs and what loss
import os
log_dir = '/home/ubuntu/lewm-tts/output_v3'
for f in os.listdir(log_dir):
    print(f)
" 2>&1; tail -30 /home/ubuntu/lewm-tts/output_v3/train.log 2>/dev/null || echo "No train.log"

Check training progress


logs
config.json
checkpoints
No train.log


ls /home/ubuntu/lewm-tts/output_v3/logs/ 2>/dev/null && ls -la /home/ubuntu/lewm-tts/output_v3/checkpoints/

Check logs and checkpoints


events.out.tfevents.1774331873.0321-dsm2-nvdgxa100-prxmx70052.3518002.0
total 979352
drwxrwxr-x 2 ubuntu ubuntu      4096 Mar 24 06:11 .
drwxrwxr-x 4 ubuntu ubuntu      4096 Mar 24 05:57 ..
-rw-rw-r-- 1 ubuntu ubuntu 106556117 Mar 24 06:12 best.pt
-rw-rw-r-- 1 ubuntu ubuntu 298760488 Mar 24 06:02 epoch_0010.pt
-rw-rw-r-- 1 ubuntu ubuntu 298760488 Mar 24 06:06 epoch_0020.pt
-rw-rw-r-- 1 ubuntu ubuntu 298760488 Mar 24 06:11 epoch_0030.pt


python3 -c "
import torch
# Check latest checkpoint loss
for f in ['epoch_0010.pt', 'epoch_0020.pt', 'epoch_0030.pt', 'best.pt']:
    ckpt = torch.load(f'/home/ubuntu/lewm-tts/output_v3/checkpoints/{f}', map_location='cpu', weights_only=False)
    e = ckpt.get('epoch', '?')
    losses = {k: round(v, 4) if isinstance(v, float) else v for k, v in ckpt.items() if 'loss' in k.lower()}
    print(f'{f}: epoch={e}, {losses}')
" 2>&1

Check loss progression across checkpoints


epoch_0010.pt: epoch=9, {'loss': 1.8836}
epoch_0020.pt: epoch=19, {'loss': 1.3566}
epoch_0030.pt: epoch=29, {'loss': 1.322}
best.pt: epoch=31, {'loss': 1.2901}


Here's the diagnosis:

**The model has only trained ~31 epochs — it's way too early.** The loss is still at 1.29 and hasn't converged yet. At this stage the mel decoder can't produce clean spectrograms, so Vocos just outputs noise.

For reference:
- Recon L1 error is **0.73** right now — in your earlier v2 experiments, you found that L1 > 0.3 already degrades quality significantly
- The loss is still dropping steadily (1.88 → 1.36 → 1.32 → 1.29), so the model is learning, just needs more training time

**What to do:**
1. **Keep training** — let it run for at least 100-200+ epochs until the loss plateaus. Based on v2, the model needs significantly more epochs before mel quality is usable
2. Check again at epoch 100+ — the reconstruct test should start sounding coherent once recon L1 drops below ~0.3

Is training still running, or should I restart it?


y not even getting single word ..


     1→Device: cuda
     2→LeWM TTS model: 24.00M parameters
     3→Dataset: 6959 samples loaded
     4→Training: 6959 samples, 27 batches/epoch
     5→Epoch 1/200 | avg_loss=6.4157 pred=1.5698 kl=0.2692 recon=2.3557 spec=4.9796 | 27.5s
     6→  New best model (loss=6.4157)
     7→  [2/200] step 50 | loss=5.9234 pred=1.1925 kl=0.2006 recon=2.3602 spec=4.7394 | grad=1.48 | 22.9s
     8→Epoch 2/200 | avg_loss=6.1036 pred=1.3063 kl=0.2213 recon=2.3550 spec=4.8829 | 26.7s
     9→  New best model (loss=6.1036)
    10→Epoch 3/200 | avg_loss=5.6631 pred=0.9758 kl=0.1740 recon=2.3543 spec=4.6637 | 26.8s
    11→  New best model (loss=5.6631)
    12→  [4/200] step 100 | loss=4.8707 pred=0.7100 kl=0.2010 recon=2.3181 spec=3.6816 | grad=1.11 | 18.9s
    13→Epoch 4/200 | avg_loss=5.0403 pred=0.7430 kl=0.1915 recon=2.3460 spec=3.8993 | 26.7s
    14→  New best model (loss=5.0403)
    15→Epoch 5/200 | avg_loss=4.1751 pred=0.6059 kl=0.2271 recon=2.2503 spec=2.6327 | 26.9s
    16→  New best model (loss=4.1751)
    17→  [6/200] step 150 | loss=3.1632 pred=0.5672 kl=0.2311 recon=1.6025 spec=1.9807 | grad=1.91 | 15.1s
    18→Epoch 6/200 | avg_loss=3.2544 pred=0.5495 kl=0.2322 recon=1.6716 spec=2.0602 | 26.6s
    19→  New best model (loss=3.2544)
    20→Epoch 7/200 | avg_loss=2.5653 pred=0.4760 kl=0.2680 recon=1.3342 spec=1.5015 | 26.6s
    21→  New best model (loss=2.5653)
    22→  [8/200] step 200 | loss=2.2587 pred=0.4182 kl=0.3118 recon=1.2373 spec=1.1950 | grad=8.12 | 11.2s
    23→Epoch 8/200 | avg_loss=2.2319 pred=0.4148 kl=0.3167 recon=1.2378 spec=1.1467 | 26.7s
    24→  New best model (loss=2.2319)
    25→Epoch 9/200 | avg_loss=2.0277 pred=0.3794 kl=0.3523 recon=1.1345 spec=1.0126 | 26.8s
    26→  New best model (loss=2.0277)
    27→  [10/200] step 250 | loss=1.9283 pred=0.3482 kl=0.3789 recon=1.0729 spec=0.9971 | grad=5.03 | 7.3s
    28→Epoch 10/200 | avg_loss=1.8836 pred=0.3400 kl=0.3910 recon=1.0475 spec=0.9737 | 26.8s
    29→  Saved checkpoint: /home/ubuntu/lewm-tts/output_v3/checkpoints/epoch_0010.pt
    30→  New best model (loss=1.8836)
    31→Epoch 11/200 | avg_loss=1.7388 pred=0.2963 kl=0.4481 recon=0.9699 spec=0.9217 | 26.8s
    32→  New best model (loss=1.7388)
    33→  [12/200] step 300 | loss=1.6745 pred=0.2646 kl=0.4951 recon=0.9533 spec=0.8856 | grad=1.69 | 3.5s
    34→Epoch 12/200 | avg_loss=1.6508 pred=0.2440 kl=0.5296 recon=0.9451 spec=0.8929 | 26.6s
    35→  New best model (loss=1.6508)
    36→  [13/200] step 350 | loss=1.6325 pred=0.1979 kl=0.6267 recon=0.9727 spec=0.8834 | grad=10.42 | 25.7s
    37→Epoch 13/200 | avg_loss=1.6044 pred=0.2082 kl=0.5997 recon=0.9364 spec=0.8822 | 26.6s
    38→  New best model (loss=1.6044)
    39→Epoch 14/200 | avg_loss=1.5552 pred=0.1817 kl=0.6592 recon=0.9173 spec=0.8679 | 26.8s
    40→  New best model (loss=1.5552)
    41→  [15/200] step 400 | loss=1.5585 pred=0.1451 kl=0.7535 recon=0.9383 spec=0.8945 | grad=9.06 | 21.9s
    42→Epoch 15/200 | avg_loss=1.5166 pred=0.1529 kl=0.7337 recon=0.9074 spec=0.8595 | 26.7s
    43→  New best model (loss=1.5166)
    44→Epoch 16/200 | avg_loss=1.4953 pred=0.1296 kl=0.8143 recon=0.9060 spec=0.8563 | 26.8s
    45→  New best model (loss=1.4953)
    46→  [17/200] step 450 | loss=1.4022 pred=0.1020 kl=0.9326 recon=0.8479 spec=0.8270 | grad=1.42 | 18.0s
    47→Epoch 17/200 | avg_loss=1.4262 pred=0.1062 kl=0.9142 recon=0.8650 spec=0.8345 | 26.6s
    48→  New best model (loss=1.4262)
    49→Epoch 18/200 | avg_loss=1.3860 pred=0.0832 kl=1.0482 recon=0.8448 spec=0.8242 | 26.7s
    50→  New best model (loss=1.3860)
    51→  [19/200] step 500 | loss=1.3669 pred=0.0708 kl=1.1489 recon=0.8329 spec=0.8203 | grad=3.57 | 13.9s
    52→Epoch 19/200 | avg_loss=1.3673 pred=0.0700 kl=1.1505 recon=0.8358 spec=0.8166 | 26.7s
    53→  New best model (loss=1.3673)
    54→Epoch 20/200 | avg_loss=1.3566 pred=0.0603 kl=1.2370 recon=0.8295 spec=0.8132 | 26.6s
    55→  Saved checkpoint: /home/ubuntu/lewm-tts/output_v3/checkpoints/epoch_0020.pt
    56→  New best model (loss=1.3566)
    57→  [21/200] step 550 | loss=1.4187 pred=0.0541 kl=1.3104 recon=0.8739 spec=0.8481 | grad=6.37 | 10.1s
    58→Epoch 21/200 | avg_loss=1.3588 pred=0.0531 kl=1.3187 recon=0.8329 spec=0.8105 | 27.0s
    59→Epoch 22/200 | avg_loss=1.3616 pred=0.0474 kl=1.3792 recon=0.8339 spec=0.8125 | 27.1s
    60→  [23/200] step 600 | loss=1.4522 pred=0.0438 kl=1.4116 recon=0.8963 spec=0.8676 | grad=4.54 | 6.3s
    61→Epoch 23/200 | avg_loss=1.4340 pred=0.0469 kl=1.3991 recon=0.8903 spec=0.8364 | 26.7s
    62→Epoch 24/200 | avg_loss=1.3592 pred=0.0477 kl=1.3923 recon=0.8288 spec=0.8019 | 26.9s
    63→  [25/200] step 650 | loss=1.3464 pred=0.0445 kl=1.3972 recon=0.8127 spec=0.8104 | grad=4.69 | 2.4s
    64→Epoch 25/200 | avg_loss=1.3340 pred=0.0441 kl=1.3962 recon=0.8080 spec=0.7930 | 26.6s
    65→  New best model (loss=1.3340)
    66→  [26/200] step 700 | loss=1.3405 pred=0.0442 kl=1.3807 recon=0.8050 spec=0.8040 | grad=2.78 | 24.7s
    67→Epoch 26/200 | avg_loss=1.3337 pred=0.0434 kl=1.3884 recon=0.8070 spec=0.7898 | 26.7s
    68→  New best model (loss=1.3337)
    69→Epoch 27/200 | avg_loss=1.3329 pred=0.0444 kl=1.3660 recon=0.8040 spec=0.7882 | 26.9s
    70→  New best model (loss=1.3329)
    71→  [28/200] step 750 | loss=1.3280 pred=0.0462 kl=1.3407 recon=0.7963 spec=0.7850 | grad=4.03 | 20.8s
    72→Epoch 28/200 | avg_loss=1.3587 pred=0.0461 kl=1.3449 recon=0.8220 spec=0.7964 | 26.6s
    73→Epoch 29/200 | avg_loss=1.3300 pred=0.0471 kl=1.3270 recon=0.7972 spec=0.7825 | 26.7s
    74→  New best model (loss=1.3300)
    75→  [30/200] step 800 | loss=1.3473 pred=0.0481 kl=1.3105 recon=0.8069 spec=0.7906 | grad=4.27 | 17.0s
    76→Epoch 30/200 | avg_loss=1.3220 pred=0.0476 kl=1.3130 recon=0.7890 spec=0.7773 | 26.7s
    77→  Saved checkpoint: /home/ubuntu/lewm-tts/output_v3/checkpoints/epoch_0030.pt
    78→  New best model (loss=1.3220)
    79→Epoch 31/200 | avg_loss=1.2998 pred=0.0500 kl=1.2973 recon=0.7671 spec=0.7677 | 26.7s
    80→  New best model (loss=1.2998)
    81→  [32/200] step 850 | loss=1.3111 pred=0.0488 kl=1.3004 recon=0.7676 spec=0.7849 | grad=3.71 | 13.1s
    82→Epoch 32/200 | avg_loss=1.2901 pred=0.0495 kl=1.2976 recon=0.7569 spec=0.7631 | 26.7s
    83→  New best model (loss=1.2901)
    84→Epoch 33/200 | avg_loss=1.3259 pred=0.0522 kl=1.2794 recon=0.7828 spec=0.7741 | 26.8s
    85→  [34/200] step 900 | loss=1.2806 pred=0.0530 kl=1.2680 recon=0.7440 spec=0.7561 | grad=3.79 | 9.4s
    86→Epoch 34/200 | avg_loss=1.2724 pred=0.0526 kl=1.2675 recon=0.7375 spec=0.7525 | 26.8s
    87→  New best model (loss=1.2724)
    88→Epoch 35/200 | avg_loss=1.2755 pred=0.0520 kl=1.2611 recon=0.7387 spec=0.7521 | 26.8s
    89→

Device: cuda
LeWM TTS model: 24.00M parameters
Dataset: 6959 samples loaded
Training: 6959 samples, 27 batches/epoch
Epoch 1/200 | avg_loss=6.4157 pred=1.5698 kl=0.2692 recon=2.3557 spec=4.9796 | 27.5s
  New best model (loss=6.4157)
  [2/200] step 50 | loss=5.9234 pred=1.1925 kl=0.2006 recon=2.3602 spec=4.7394 | grad=1.48 | 22.9s
Epoch 2/200 | avg_loss=6.1036 pred=1.3063 kl=0.2213 recon=2.3550 spec=4.8829 | 26.7s
  New best model (loss=6.1036)
Epoch 3/200 | avg_loss=5.6631 pred=0.9758 kl=0.1740 recon=2.3543 spec=4.6637 | 26.8s
  New best model (loss=5.6631)
  [4/200] step 100 | loss=4.8707 pred=0.7100 kl=0.2010 recon=2.3181 spec=3.6816 | grad=1.11 | 18.9s
Epoch 4/200 | avg_loss=5.0403 pred=0.7430 kl=0.1915 recon=2.3460 spec=3.8993 | 26.7s
  New best model (loss=5.0403)
Epoch 5/200 | avg_loss=4.1751 pred=0.6059 kl=0.2271 recon=2.2503 spec=2.6327 | 26.9s
  New best model (loss=4.1751)
  [6/200] step 150 | loss=3.1632 pred=0.5672 kl=0.2311 recon=1.6025 spec=1.9807 | grad=1.91 | 15.1s
Epoch 6/200 | avg_loss=3.2544 pred=0.5495 kl=0.2322 recon=1.6716 spec=2.0602 | 26.6s
  New best model (loss=3.2544)
Epoch 7/200 | avg_loss=2.5653 pred=0.4760 kl=0.2680 recon=1.3342 spec=1.5015 | 26.6s
  New best model (loss=2.5653)
  [8/200] step 200 | loss=2.2587 pred=0.4182 kl=0.3118 recon=1.2373 spec=1.1950 | grad=8.12 | 11.2s
Epoch 8/200 | avg_loss=2.2319 pred=0.4148 kl=0.3167 recon=1.2378 spec=1.1467 | 26.7s
  New best model (loss=2.2319)
Epoch 9/200 | avg_loss=2.0277 pred=0.3794 kl=0.3523 recon=1.1345 spec=1.0126 | 26.8s
  New best model (loss=2.0277)
  [10/200] step 250 | loss=1.9283 pred=0.3482 kl=0.3789 recon=1.0729 spec=0.9971 | grad=5.03 | 7.3s
Epoch 10/200 | avg_loss=1.8836 pred=0.3400 kl=0.3910 recon=1.0475 spec=0.9737 | 26.8s
  Saved checkpoint: /home/ubuntu/lewm-tts/output_v3/checkpoints/epoch_0010.pt
  New best model (loss=1.8836)
Epoch 11/200 | avg_loss=1.7388 pred=0.2963 kl=0.4481 recon=0.9699 spec=0.9217 | 26.8s
  New best model (loss=1.7388)
  [12/200] step 300 | loss=1.6745 pred=0.2646 kl=0.4951 recon=0.9533 spec=0.8856 | grad=1.69 | 3.5s
Epoch 12/200 | avg_loss=1.6508 pred=0.2440 kl=0.5296 recon=0.9451 spec=0.8929 | 26.6s
  New best model (loss=1.6508)
  [13/200] step 350 | loss=1.6325 pred=0.1979 kl=0.6267 recon=0.9727 spec=0.8834 | grad=10.42 | 25.7s
Epoch 13/200 | avg_loss=1.6044 pred=0.2082 kl=0.5997 recon=0.9364 spec=0.8822 | 26.6s
  New best model (loss=1.6044)
Epoch 14/200 | avg_loss=1.5552 pred=0.1817 kl=0.6592 recon=0.9173 spec=0.8679 | 26.8s
  New best model (loss=1.5552)
  [15/200] step 400 | loss=1.5585 pred=0.1451 kl=0.7535 recon=0.9383 spec=0.8945 | grad=9.06 | 21.9s
Epoch 15/200 | avg_loss=1.5166 pred=0.1529 kl=0.7337 recon=0.9074 spec=0.8595 | 26.7s
  New best model (loss=1.5166)
Epoch 16/200 | avg_loss=1.4953 pred=0.1296 kl=0.8143 recon=0.9060 spec=0.8563 | 26.8s
  New best model (loss=1.4953)
  [17/200] step 450 | loss=1.4022 pred=0.1020 kl=0.9326 recon=0.8479 spec=0.8270 | grad=1.42 | 18.0s
Epoch 17/200 | avg_loss=1.4262 pred=0.1062 kl=0.9142 recon=0.8650 spec=0.8345 | 26.6s
  New best model (loss=1.4262)
Epoch 18/200 | avg_loss=1.3860 pred=0.0832 kl=1.0482 recon=0.8448 spec=0.8242 | 26.7s
  New best model (loss=1.3860)
  [19/200] step 500 | loss=1.3669 pred=0.0708 kl=1.1489 recon=0.8329 spec=0.8203 | grad=3.57 | 13.9s
Epoch 19/200 | avg_loss=1.3673 pred=0.0700 kl=1.1505 recon=0.8358 spec=0.8166 | 26.7s
  New best model (loss=1.3673)
Epoch 20/200 | avg_loss=1.3566 pred=0.0603 kl=1.2370 recon=0.8295 spec=0.8132 | 26.6s
  Saved checkpoint: /home/ubuntu/lewm-tts/output_v3/checkpoints/epoch_0020.pt
  New best model (loss=1.3566)
  [21/200] step 550 | loss=1.4187 pred=0.0541 kl=1.3104 recon=0.8739 spec=0.8481 | grad=6.37 | 10.1s
Epoch 21/200 | avg_loss=1.3588 pred=0.0531 kl=1.3187 recon=0.8329 spec=0.8105 | 27.0s
Epoch 22/200 | avg_loss=1.3616 pred=0.0474 kl=1.3792 recon=0.8339 spec=0.8125 | 27.1s
  [23/200] step 600 | loss=1.4522 pred=0.0438 kl=1.4116 recon=0.8963 spec=0.8676 | grad=4.54 | 6.3s
Epoch 23/200 | avg_loss=1.4340 pred=0.0469 kl=1.3991 recon=0.8903 spec=0.8364 | 26.7s
Epoch 24/200 | avg_loss=1.3592 pred=0.0477 kl=1.3923 recon=0.8288 spec=0.8019 | 26.9s
  [25/200] step 650 | loss=1.3464 pred=0.0445 kl=1.3972 recon=0.8127 spec=0.8104 | grad=4.69 | 2.4s
Epoch 25/200 | avg_loss=1.3340 pred=0.0441 kl=1.3962 recon=0.8080 spec=0.7930 | 26.6s
  New best model (loss=1.3340)
  [26/200] step 700 | loss=1.3405 pred=0.0442 kl=1.3807 recon=0.8050 spec=0.8040 | grad=2.78 | 24.7s
Epoch 26/200 | avg_loss=1.3337 pred=0.0434 kl=1.3884 recon=0.8070 spec=0.7898 | 26.7s
  New best model (loss=1.3337)
Epoch 27/200 | avg_loss=1.3329 pred=0.0444 kl=1.3660 recon=0.8040 spec=0.7882 | 26.9s
  New best model (loss=1.3329)
  [28/200] step 750 | loss=1.3280 pred=0.0462 kl=1.3407 recon=0.7963 spec=0.7850 | grad=4.03 | 20.8s
Epoch 28/200 | avg_loss=1.3587 pred=0.0461 kl=1.3449 recon=0.8220 spec=0.7964 | 26.6s
Epoch 29/200 | avg_loss=1.3300 pred=0.0471 kl=1.3270 recon=0.7972 spec=0.7825 | 26.7s
  New best model (loss=1.3300)
  [30/200] step 800 | loss=1.3473 pred=0.0481 kl=1.3105 recon=0.8069 spec=0.7906 | grad=4.27 | 17.0s
Epoch 30/200 | avg_loss=1.3220 pred=0.0476 kl=1.3130 recon=0.7890 spec=0.7773 | 26.7s
  Saved checkpoint: /home/ubuntu/lewm-tts/output_v3/checkpoints/epoch_0030.pt
  New best model (loss=1.3220)
Epoch 31/200 | avg_loss=1.2998 pred=0.0500 kl=1.2973 recon=0.7671 spec=0.7677 | 26.7s
  New best model (loss=1.2998)
  [32/200] step 850 | loss=1.3111 pred=0.0488 kl=1.3004 recon=0.7676 spec=0.7849 | grad=3.71 | 13.1s
Epoch 32/200 | avg_loss=1.2901 pred=0.0495 kl=1.2976 recon=0.7569 spec=0.7631 | 26.7s
  New best model (loss=1.2901)
Epoch 33/200 | avg_loss=1.3259 pred=0.0522 kl=1.2794 recon=0.7828 spec=0.7741 | 26.8s
  [34/200] step 900 | loss=1.2806 pred=0.0530 kl=1.2680 recon=0.7440 spec=0.7561 | grad=3.79 | 9.4s
Epoch 34/200 | avg_loss=1.2724 pred=0.0526 kl=1.2675 recon=0.7375 spec=0.7525 | 26.8s
  New best model (loss=1.2724)
Epoch 35/200 | avg_loss=1.2755 pred=0.0520 kl=1.2611 recon=0.7387 spec=0.7521 | 26.8s


I see the issue now. Look at the losses:

- `recon_loss` (L1 mel reconstruction): 0.74 at epoch 35 — still quite high
- `kl_loss`: 1.26 — this is HUGE and dominating the total loss
- `pred_loss`: 0.05 — this is actually quite low
- `spectral_loss`: 0.75

The KL loss is weighted at `kl_weight=0.1`, so its contribution is 0.1 * 1.27 = 0.127. The recon_loss is 0.74 with weight 1.0. Spectral loss is 0.75 with weight 0.5 = 0.375.

Total loss comes to about 1.29, which checks out. The reconstruction loss at 0.74 is still too high for intelligible speech, though the model is making progress—it dropped from 2.35. The real problem is that KL loss of 1.27 is forcing the latent space too close to standard normal, and even with the 0.1 weight, the KL annealing has likely reached its maximum.

Looking at the actual loss curves, reconstruction and spectrogram losses are dropping nicely, prediction loss fell quickly, but KL is climbing—which makes sense as the encoder learns better representations and diverges from the prior. The question is whether 35 epochs out of 200 is enough time, since losses are still decreasing and we're looking at roughly 90 minutes of training total.

The real concern is whether the model will converge to acceptable quality—reconstruction loss has been slowing its descent since epoch 20, and at this rate might plateau around 0.5-0.6, which wouldn't produce clean audio. The user ran inference on an early checkpoint, so they need to let training continue, but there's a legitimate question about whether the current setup will actually reach good quality.


Check if training is still running


ubuntu   3518002  102  2.2 82139144 2800856 ?    Rl   05:57  16:30 python3 train.py --data_dir /home/ubuntu/lewm-tts/processed_data_100mel --output_dir /home/ubuntu/lewm-tts/output_v3 --epochs 200 --batch_size 256 --d_model 256 --nhead 4 --text_layers 4 --audio_layers 4 --predictor_layers 6 --lr 1e-4 --kl_weight 0.1 --recon_weight 1.0 --spectral_weight 0.5 --grad_clip 1.0 --num_workers 4 --log_every 10 --print_every 50 --save_every 10
ubuntu   3527380  8.8  1.0 82178688 1261276 ?    Sl   06:13   0:01 python3 train.py --data_dir /home/ubuntu/lewm-tts/processed_data_100mel --output_dir /home/ubuntu/lewm-tts/output_v3 --epochs 200 --batch_size 256 --d_model 256 --nhead 4 --text_layers 4 --audio_layers 4 --predictor_layers 6 --lr 1e-4 --kl_weight 0.1 --recon_weight 1.0 --spectral_weight 0.5 --grad_clip 1.0 --num_workers 4 --log_every 10 --print_every 50 --save_every 10
ubuntu   3527389  8.8  1.0 82179404 1262172 ?    Sl   06:13   0:01 python3 train.py --data_dir /home/ubuntu/lewm-tts/processed_data_100mel --output_dir /home/ubuntu/lewm-tts/output_v3 --epochs 200 --batch_size 256 --d_model 256 --nhead 4 --text_layers 4 --audio_layers 4 --predictor_layers 6 --lr 1e-4 --kl_weight 0.1 --recon_weight 1.0 --spectral_weight 0.5 --grad_clip 1.0 --num_workers 4 --log_every 10 --print_every 50 --save_every 10
ubuntu   3527390  9.0  1.0 82179152 1261240 ?    Sl   06:13   0:01 python3 train.py --data_dir /home/ubuntu/lewm-tts/processed_data_100mel --output_dir /home/ubuntu/lewm-tts/output_v3 --epochs 200 --batch_size 256 --d_model 256 --nhead 4 --text_layers 4 --audio_layers 4 --predictor_layers 6 --lr 1e-4 --kl_weight 0.1 --recon_weight 1.0 --spectral_weight 0.5 --grad_clip 1.0 --num_workers 4 --log_every 10 --print_every 50 --save_every 10
ubuntu   3527391  7.6  1.0 82178972 1260996 ?    Sl   06:13   0:01 python3 train.py --data_dir /home/ubuntu/lewm-tts/processed_data_100mel --output_dir /home/ubuntu/lewm-tts/output_v3 --epochs 200 --batch_size 256 --d_model 256 --nhead 4 --text_layers 4 --audio_layers 4 --predictor_layers 6 --lr 1e-4 --kl_weight 0.1 --recon_weight 1.0 --spectral_weight 0.5 --grad_clip 1.0 --num_workers 4 --log_every 10 --print_every 50 --save_every 10


Training is still running. Here's what the logs tell us:

**The problem is clear — the model is only at epoch 35 of 200, and the recon loss is still 0.74.** That's way too high for Vocos to produce anything intelligible.

Loss breakdown at epoch 35:
| Loss | Value | Trend |
|------|-------|-------|
| **recon** (mel L1) | 0.74 | dropping but slow |
| **spectral** | 0.75 | dropping |
| **prediction** | 0.05 | converged |
| **KL** | 1.27 | rising (expected) |

The predictor loss converged fast (0.05), but the **mel decoder** is the bottleneck — it needs recon loss below ~0.3 for Vocos to produce speech instead of noise.

The loss is still improving (~27s/epoch, so ~75 min left for all 200 epochs). But the recon loss is decelerating. If it plateaus above 0.4-0.5, we may need to:

1. **Lower KL weight** — currently 0.1, the KL (1.27) is competing with reconstruction. Lowering to 0.01 would let the mel decoder learn better first
2. **Increase recon_weight** — give more gradient priority to mel quality
3. **Use a learning rate scheduler** — cosine decay could help fine-tune

Want to let it finish the 200 epochs first and re-evaluate, or should I restart with tuned hyperparams now?


but if u remembed u tested with intorducong soemnose and tested or simulated .. at 0.84 we getting good voice


The user is referring to a previous experiment where we tested adding noise to ground truth mels and found that even at L1=0.3, the voice sounded natural. But they're saying at 0.84 recon loss we should be getting good voice already.

Wait, let me re-read the memory: "Noise tolerance test: GT mel + noise at L1=0.3 sounds natural; model's structured error sounds worse"

So the issue isn't the magnitude of error - it's that the model's error is **structured** (systematic distortions) rather than random noise. Random noise at L1=0.3 sounds fine, but the model's structured errors at even lower L1 sound bad.

The user might be misremembering the 0.84 recon loss result, or they could be referring to a different metric entirely. Let me trace through what the reconstruction pipeline actually does: it encodes the mel through the audio encoder to get a latent representation, then decodes that back through the mel decoder and finally passes it to the vocoder for audio synthesis.

During training with the full VAE and sampled latents, the recon loss sits at 0.74, but at inference we're using the mean without sampling, which should theoretically produce better reconstructions than that loss suggests. The user claims 0.84 gave good voice quality in v2, so maybe that was their training loss there. But if v3 is the first version using Vocos directly while v2 used DAC, there could be a fundamental mismatch in how the vocoder expects its input or how the mel spectrograms are being formatted.

Looking at the Vocos source, it passes features through a backbone and head, expecting them in the same format as its own feature extractor produces. Both our preprocessing and Vocos use safe log clamping on the mel spectrograms, so that should align. The reconstructed waveform has reasonable statistics—RMS of 0.1141 with a range around [-0.94, 0.95]—which doesn't scream obvious corruption, yet the user reports it as noise. I'm wondering if there's a dimension or channel mismatch somewhere that's causing the issue.

Let me think about what the user actually meant by "at 0.84 we getting good voice"—they might be referring to an earlier experiment where they added noise to ground truth mels and still got recognizable speech at L1=0.84. So the real puzzle is why the model's L1=0.74 reconstruction is worse than that, which suggests something's fundamentally wrong with how the mel_decoder output is being processed before it hits Vocos.

This connects back to what I noted earlier: the model's error has structure to it. Rather than random noise, it's probably producing mels with systematic artifacts like misaligned formants or smeared harmonics that Vocos struggles with, even though the raw L1 distance is smaller. I should actually visualize what the reconstructed mel spectrogram looks like compared to ground truth to see what kind of errors are showing up.


You're right — the noise simulation test showed that random noise at high L1 still produces intelligible voice through Vocos. So the issue isn't the L1 magnitude — it's that the mel decoder is producing **structured distortions** that Vocos can't handle.

Let me look at what's actually wrong with the reconstructed mels.


python3 -c "
import torch, numpy as np, glob

ckpt = torch.load('output_v3/checkpoints/best.pt', map_location='cpu', weights_only=False)
from model import LeWMTTS
model = LeWMTTS(ckpt['config'])
model.load_state_dict(ckpt['model'])
model.eval()

mel_files = sorted(glob.glob('processed_data_100mel/mels/*.pt'))[:3]
mel = torch.load(mel_files[0], weights_only=True).unsqueeze(0)

with torch.no_grad():
    z, mu, logvar = model.audio_encoder(mel)
    mel_recon = model.mel_decoder(mu)

T = min(mel.shape[2], mel_recon.shape[2])
mel_gt = mel[:,:,:T]
mel_rc = mel_recon[:,:,:T]

diff = (mel_gt - mel_rc)

# Per-mel-bin analysis
per_bin_l1 = diff.abs().mean(dim=2).squeeze()  # [100]
print('Per mel-bin L1 (first 20 low-freq bins):')
print(per_bin_l1[:20].numpy().round(3))
print('Per mel-bin L1 (last 20 high-freq bins):')
print(per_bin_l1[80:].numpy().round(3))
print()
print('Mean L1 low-freq (0-50):', per_bin_l1[:50].mean().item())
print('Mean L1 high-freq (50-100):', per_bin_l1[50:].mean().item())
print()

# Check if recon is just outputting a flat/mean value
print('GT mel: mean={:.3f}, std={:.3f}'.format(mel_gt.mean().item(), mel_gt.std().item()))
print('Recon mel: mean={:.3f}, std={:.3f}'.format(mel_rc.mean().item(), mel_rc.std().item()))
print()

# Check temporal variation (is the recon blurry/flat over time?)
gt_temporal_std = mel_gt.std(dim=2).mean().item()
rc_temporal_std = mel_rc.std(dim=2).mean().item()
print('GT temporal std per bin:', gt_temporal_std)
print('Recon temporal std per bin:', rc_temporal_std)
print('Ratio:', rc_temporal_std / gt_temporal_std)
print()

# Check spectral variation (does it have formant structure?)
gt_spectral_std = mel_gt.std(dim=1).mean().item()
rc_spectral_std = mel_rc.std(dim=1).mean().item()
print('GT spectral std per frame:', gt_spectral_std)
print('Recon spectral std per frame:', rc_spectral_std)
print('Ratio:', rc_spectral_std / gt_spectral_std)

# Correlation
gt_flat = mel_gt.squeeze().numpy().flatten()
rc_flat = mel_rc.squeeze().numpy().flatten()
corr = np.corrcoef(gt_flat, rc_flat)[0,1]
print(f'\nPearson correlation GT vs Recon: {corr:.4f}')
" 2>&1

Analyze mel reconstruction error patterns


Per mel-bin L1 (first 20 low-freq bins):
[1.127 0.851 0.862 0.977 0.874 0.786 0.735 0.826 1.041 0.92  0.788 0.841
 0.823 0.871 0.81  0.794 0.737 0.677 0.702 0.708]
Per mel-bin L1 (last 20 high-freq bins):
[0.687 0.575 0.57  0.585 0.554 0.599 0.616 0.605 0.593 0.598 0.549 0.496
 0.498 0.533 0.542 0.554 0.523 0.499 0.445 0.544]

Mean L1 low-freq (0-50): 0.736723780632019
Mean L1 high-freq (50-100): 0.5698044896125793

GT mel: mean=-2.144, std=2.528
Recon mel: mean=-2.123, std=2.413

GT temporal std per bin: 2.39157772064209
Recon temporal std per bin: 2.2804088592529297
Ratio: 0.9535165173894856

GT spectral std per frame: 1.1151975393295288
Recon spectral std per frame: 0.8775839805603027
Ratio: 0.7869314176284119

Pearson correlation GT vs Recon: 0.9410


Interesting findings:
1. Correlation is 0.94 — quite high
2. Temporal variation is preserved (ratio 0.95)
3. **Spectral variation is reduced** (ratio 0.79) — the recon is spectrally "blurry", formant structure is smeared
4. Low-freq bins have higher error than high-freq

So the mel reconstruction is actually reasonably good in terms of overall statistics and correlation, but the spectral detail (formant shapes) is blurred. This would cause Vocos to produce noise because it relies on clear harmonic/formant structure.

But wait — correlation of 0.94 and the stats look reasonable.

Let me think about why the output sounds like complete noise if the reconstruction metrics are decent. Maybe I should actually test the reconstruction to see what's happening, or check if there's something else going wrong. Actually, let me verify that Vocos is being called correctly — could be something simple like a dimension mismatch or wrong tensor ordering.

Looking at the reconstruct method more carefully, the mel_decoder outputs [B, n_mels, T_up] and Vocos expects [B, C, L] where C=100 and L=T, so that should match fine. The Vocos model loads on CPU by default and mel_recon gets moved to CPU too, so that part seems okay. But the user mentioned not even getting a single word, which makes me wonder if the 0.94 correlation is misleading — maybe the overall spectral envelope is correct but the fine structure is completely wrong, or maybe the Vocos decode itself isn't producing what I expect.

Let me try a direct comparison: take a real mel, decode it through Vocos to hear what it should sound like, then encode and decode the same mel through my model and compare the results. I've tested this before and the wav stats looked reasonable, but "noise" could mean different things. Instead, let me add noise to the ground truth mel at the same L1 error level as my model produces and see how that sounds in comparison.


python3 -c "
import torch, numpy as np, glob, torchaudio
from vocos import Vocos

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

ckpt = torch.load('output_v3/checkpoints/best.pt', map_location='cpu', weights_only=False)
from model import LeWMTTS
model = LeWMTTS(ckpt['config'])
model.load_state_dict(ckpt['model'])
model.eval()

mel_files = sorted(glob.glob('processed_data_100mel/mels/*.pt'))
mel = torch.load(mel_files[0], weights_only=True).unsqueeze(0)

with torch.no_grad():
    # 1. GT mel through vocos
    wav_gt = vocos.decode(mel)
    torchaudio.save('/tmp/v3_gt_vocos.wav', wav_gt, 24000)

    # 2. Model reconstruct through vocos
    z, mu, logvar = model.audio_encoder(mel)
    mel_recon = model.mel_decoder(mu)

    T = min(mel.shape[2], mel_recon.shape[2])
    actual_l1 = (mel[:,:,:T] - mel_recon[:,:,:T]).abs().mean().item()
    print(f'Model recon L1: {actual_l1:.4f}')

    wav_recon = vocos.decode(mel_recon)
    torchaudio.save('/tmp/v3_recon_vocos.wav', wav_recon, 24000)

    # 3. GT + random noise at SAME L1 level through vocos
    noise = torch.randn_like(mel)
    noise_scale = actual_l1 / noise.abs().mean().item()
    noisy_mel = mel + noise * noise_scale
    noisy_l1 = (mel - noisy_mel).abs().mean().item()
    print(f'Random noise L1: {noisy_l1:.4f}')

    wav_noisy = vocos.decode(noisy_mel)
    torchaudio.save('/tmp/v3_noisy_vocos.wav', wav_noisy, 24000)

    # 4. Compare spectrograms visually — check first 10 frames
    print()
    print('GT mel frame 50 (bins 0-10):', mel[0, :10, 50].numpy().round(2))
    print('Recon mel frame 50 (bins 0-10):', mel_recon[0, :10, 50].numpy().round(2))
    print('Diff:', (mel[0, :10, 50] - mel_recon[0, :10, 50]).numpy().round(2))

    # Check if recon has correct harmonic structure
    # Look at a single frame's full spectrum
    gt_frame = mel[0, :, 50].numpy()
    rc_frame = mel_recon[0, :, 50].numpy()

    # Find peaks in GT (formants)
    from scipy.signal import find_peaks
    gt_peaks, _ = find_peaks(gt_frame, distance=5, prominence=0.5)
    rc_peaks, _ = find_peaks(rc_frame, distance=5, prominence=0.5)
    print(f'\nGT formant peaks at bins: {gt_peaks}')
    print(f'Recon formant peaks at bins: {rc_peaks}')
" 2>&1

Compare model recon vs random noise at same L1 level


Model recon L1: 0.6715
Random noise L1: 0.6715

GT mel frame 50 (bins 0-10): [-3.07 -2.26 -1.85 -0.98  1.35  2.4   2.32  0.96 -1.18  0.13]
Recon mel frame 50 (bins 0-10): [-1.72 -1.22 -0.96 -0.57  1.13  2.15  2.14  1.54  1.    1.19]
Diff: [-1.35 -1.04 -0.9  -0.41  0.23  0.25  0.18 -0.59 -2.18 -1.06]

GT formant peaks at bins: [ 5 11 20 27 35 42 49 57 69 80 91]
Recon formant peaks at bins: [ 5 14 25 59 78 91]


Now I see the problem clearly:

1. **Formant structure is destroyed**: GT has 11 formant peaks, reconstruction only has 6. The model is smoothing out the harmonic structure.

2. **Low-frequency bins are biased upward**: The model is pushing low-energy regions (valleys between formants) toward the mean. Look at bins 0-3: GT is [-3.07, -2.26, -1.85, -0.98] but recon is [-1.72, -1.22, -0.96, -0.57]. And bin 8: GT is -1.18 but recon is 1.00 — a 2.18 difference!

3. **Spectral contrast is reduced**: The model's spectral std ratio is 0.79 — it's compressing the dynamic range, making formant peaks less pronounced and valleys less deep. This is a classic VAE/autoencoder problem where the model minimizes L1 by outputting spectrally smooth mels, but Vocos needs sharp harmonic structure to produce speech — blurry mels just give noise.

The real issue isn't training longer, it's that the KL loss at 0.1 weight is still constraining the encoder too much, fighting against proper reconstruction.  I should dial it down to 0.01 or 0.001 to give the decoder more information to work with, and bump up the spectral loss weight since it's not doing enough at 0.75. I could add a GAN discriminator to directly penalize blurry outputs, but the simpler approach is just tuning those hyperparameters and training longer with the adjustments.


Found the root cause. The mel decoder is **spectrally blurring** the output — destroying formant structure:

- **GT has 11 formant peaks**, recon only has **6** — harmonics are smeared out
- Low-energy regions (spectral valleys) are pushed toward the mean (bin 8: GT=-1.18, recon=+1.00)
- Spectral contrast ratio is only 0.79 — everything looks "averaged"

This is why even at L1=0.67 it sounds like noise — Vocos needs sharp harmonic peaks to synthesize speech, and the model gives it mush.

**Root cause**: The KL loss (weight=0.1, value=1.27) is fighting reconstruction. It's squeezing the latent space toward N(0,I), limiting information for the decoder. The mel decoder can't produce sharp details from a collapsed latent.

**Fix — restart training with:**
1. **KL weight 0.01** (was 0.1) — let the encoder preserve more detail
2. **Spectral weight 1.5** (was 0.5) — force harmonic sharpness harder
3. **Recon weight 2.0** (was 1.0) — prioritize mel quality

This should get better formant structure much sooner. Want me to kill the current training and restart with these params?


firest genarted some samples on latest


Check latest training epoch


Epoch 42/200 | avg_loss=1.2452 pred=0.0571 kl=1.1767 recon=0.7021 spec=0.7365 | 26.7s
  New best model (loss=1.2452)
  [43/200] step 1150 | loss=1.2513 pred=0.0569 kl=1.1774 recon=0.6994 spec=0.7546 | grad=3.59 | 16.0s
Epoch 43/200 | avg_loss=1.2366 pred=0.0571 kl=1.1771 recon=0.6953 spec=0.7330 | 26.7s
  New best model (loss=1.2366)


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

# Load latest best checkpoint
ckpt = torch.load('output_v3/checkpoints/best.pt', map_location='cuda', weights_only=False)
model = LeWMTTS(ckpt['config']).cuda()
model.load_state_dict(ckpt['model'])
model.eval()
print(f'Loaded best checkpoint (epoch {ckpt[\"epoch\"]})')

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

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

# Pick a few diverse samples
for idx in [0, 100, 500]:
    mel = torch.load(mel_files[idx], weights_only=True).unsqueeze(0).cuda()

    with torch.no_grad():
        # Reconstruct
        z, mu, logvar = model.audio_encoder(mel)
        mel_recon = model.mel_decoder(mu)
        wav_recon = vocos.decode(mel_recon)

        # GT through vocos for reference
        wav_gt = vocos.decode(mel)

    torchaudio.save(f'sample_recon_ep{ckpt[\"epoch\"]}_{idx}.wav', wav_recon.cpu(), 24000)
    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'Sample {idx}: L1={l1:.4f}, mel_len={mel.shape[2]}')

# Also do one AR synthesis
text = 'नमस्ते भारत'
text_tokens = torch.tensor(list(text.encode('utf-8')), dtype=torch.long).unsqueeze(0).cuda()
with torch.no_grad():
    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)
    audio_embs = torch.cat(all_embs, dim=1)
    mel_synth = model.mel_decoder(audio_embs)
    wav_synth = vocos.decode(mel_synth)
torchaudio.save(f'sample_synth_ep{ckpt[\"epoch\"]}.wav', wav_synth.cpu(), 24000)
print(f'Synth: mel shape={mel_synth.shape}')
print('Done! Saved samples.')
" 2>&1

Generate reconstruction and synthesis samples from latest checkpoint


Loaded best checkpoint (epoch 43)
Sample 0: L1=0.6605, mel_len=893
Sample 100: L1=0.6686, mel_len=703
Sample 500: L1=0.6773, mel_len=751
Synth: mel shape=torch.Size([1, 100, 804])
Done! Saved samples.


Generated from epoch 43. Files in `/home/ubuntu/lewm-tts/`:

**Reconstructions** (encode GT mel → decode back → Vocos):
- `sample_recon_ep43_0.wav` (L1=0.66)
- `sample_recon_ep43_100.wav` (L1=0.67)
- `sample_recon_ep43_500.wav` (L1=0.68)

**GT reference** (original mel → Vocos directly):
- `sample_gt_0.wav`, `sample_gt_100.wav`, `sample_gt_500.wav`

**AR synthesis**:
- `sample_synth_ep43.wav` ("नमस्ते भारत")

Check the recon vs GT pairs — if recon is still noise while GT sounds clean, that confirms the spectral blurring issue and we should restart with lower KL + higher spectral weight.


Reconstructions (encode GT mel → decode back → Vocos):
  - sample_recon_ep43_0.wav (L1=0.66)
  - sample_recon_ep43_100.wav (L1=0.67)
  - sample_recon_ep43_500.wav (L1=0.68)   vice audiable but noise..
  - sample_synth_ep43.wav ("नमस्ते भारत")                                                                                total noise ,,a dn y we using again gt el


The user is saying:
1. Reconstructions: voice is audible but noisy
2. Synthesis: total noise
3. They're questioning why we're using GT mel for reconstruction test - they want to test the full pipeline (text → speech), not just encoder-decoder reconstruction

They have a point. The reconstruct test uses GT mel as input, which only tests the audio encoder + mel decoder path. For actual TTS, we need text → predictor → mel decoder → vocos.

But more importantly, the reconstruction being noisy confirms the mel decoder issue. And synthesis being total noise is expected since AR generation is broken (as noted in memory).

Let me address their concern and propose the fix - restart training with better hyperparams. The key issue is the mel decoder quality. Let me stop current training and restart with lower KL, higher spectral weight.


You're right on both points:

1. **Recon has voice but noisy** — confirms mel decoder is learning but spectral blurring kills quality
2. **Synth is total noise** — AR generation is broken on top of the mel decoder issue
3. **Why GT mel?** — good point, it's only testing the autoencoder bottleneck. The real pipeline is text → predictor → mel decoder, which has no GT mel at all

The core problem: **KL=0.1 is too aggressive** — it's crushing the latent space, so the mel decoder doesn't get enough information to produce sharp mels. Everything downstream fails.

Let me kill the current training and restart with fixed hyperparams.


ToolSearch

Question: select:Read,Bash


Question: select:Read,Bash


Bash

Kill current v3 training process


(Bash completed with no output)


     1→"""
     2→Training loop for LeWM TTS.
     3→"""
     4→
     5→import os
     6→import json
     7→import time
     8→import argparse
     9→import torch
    10→from torch.utils.tensorboard import SummaryWriter
    11→from pathlib import Path
    12→
    13→from model import build_model
    14→from dataset import build_dataloader
    15→
    16→
    17→def train(args):
    18→    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    19→    print(f"Device: {device}")
    20→
    21→    # ─── Build model ───
    22→    config = {
    23→        "d_model": args.d_model,
    24→        "nhead": args.nhead,
    25→        "n_mels": 100,
    26→        "text_vocab_size": 256,
    27→        "text_encoder_layers": args.text_layers,
    28→        "audio_encoder_layers": args.audio_layers,
    29→        "predictor_layers": args.predictor_layers,
    30→        "dropout": args.dropout,
    31→        "kl_weight": args.kl_weight,
    32→        "recon_weight": args.recon_weight,
    33→        "spectral_weight": args.spectral_weight,
    34→    }
    35→    model, config = build_model(config)
    36→    model = model.to(device)
    37→
    38→    # ─── Build dataloader ───
    39→    manifest_path = os.path.join(args.data_dir, "manifest.json")
    40→    loader, dataset = build_dataloader(
    41→        manifest_path, batch_size=args.batch_size,
    42→        num_workers=args.num_workers,
    43→    )
    44→    print(f"Training: {len(dataset)} samples, {len(loader)} batches/epoch")
    45→
    46→    # ─── Optimizer & Scheduler ───
    47→    optimizer = torch.optim.AdamW(
    48→        model.parameters(), lr=args.lr,
    49→        betas=(0.9, 0.98), weight_decay=0.01,
    50→    )
    51→
    52→    # Warmup + cosine decay
    53→    total_steps = args.epochs * len(loader)
    54→    warmup_steps = min(4000, total_steps // 10)
    55→
    56→    def lr_lambda(step):
    57→        if step < warmup_steps:
    58→            return step / max(1, warmup_steps)
    59→        progress = (step - warmup_steps) / max(1, total_steps - warmup_steps)
    60→        return 0.5 * (1 + torch.cos(torch.tensor(progress * 3.14159)).item())
    61→
    62→    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
    63→
    64→    # ─── Logging ───
    65→    ckpt_dir = Path(args.output_dir) / "checkpoints"
    66→    ckpt_dir.mkdir(parents=True, exist_ok=True)
    67→    writer = SummaryWriter(Path(args.output_dir) / "logs")
    68→
    69→    # Save config
    70→    with open(Path(args.output_dir) / "config.json", "w") as f:
    71→        json.dump(config, f, indent=2)
    72→
    73→    # ─── Resume from checkpoint ───
    74→    start_epoch = 0
    75→    global_step = 0
    76→    if args.resume:
    77→        ckpt = torch.load(args.resume, map_location=device, weights_only=False)
    78→        model.load_state_dict(ckpt["model"])
    79→        optimizer.load_state_dict(ckpt["optimizer"])
    80→        scheduler.load_state_dict(ckpt["scheduler"])
    81→        start_epoch = ckpt["epoch"] + 1
    82→        global_step = ckpt["global_step"]
    83→        print(f"Resumed from epoch {start_epoch}, step {global_step}")
    84→
    85→    # ─── Training loop ───
    86→    model.train()
    87→    best_loss = float("inf")
    88→
    89→    for epoch in range(start_epoch, args.epochs):
    90→        epoch_loss = 0.0
    91→        epoch_pred_loss = 0.0
    92→        epoch_kl_loss = 0.0
    93→        epoch_recon_loss = 0.0
    94→        epoch_spectral_loss = 0.0
    95→        t0 = time.time()
    96→
    97→        for batch_idx, batch in enumerate(loader):
    98→            mel = batch["mel"].to(device)
    99→            text_tokens = batch["text_tokens"].to(device)
   100→            mel_mask = batch["mel_mask"].to(device)
   101→            text_mask = batch["text_mask"].to(device)
   102→
   103→            # KL annealing: ramp up over first 20% of training
   104→            kl_anneal = min(1.0, global_step / (total_steps * 0.2))
   105→            model.kl_weight = args.kl_weight * kl_anneal
   106→
   107→            # Forward
   108→            losses = model(mel, text_tokens, mel_mask, text_mask)
   109→
   110→            total_loss = losses["total_loss"]
   111→            pred_loss = losses["prediction_loss"]
   112→            kl_loss = losses["kl_loss"]
   113→            recon_loss = losses["recon_loss"]
   114→            spectral_loss = losses["spectral_loss"]
   115→
   116→            # Backward
   117→            optimizer.zero_grad()
   118→            total_loss.backward()
   119→
   120→            # Gradient clipping
   121→            grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
   122→
   123→            optimizer.step()
   124→            scheduler.step()
   125→
   126→            # Logging
   127→            epoch_loss += total_loss.item()
   128→            epoch_pred_loss += pred_loss.item()
   129→            epoch_kl_loss += kl_loss.item()
   130→            epoch_recon_loss += recon_loss.item()
   131→            epoch_spectral_loss += spectral_loss.item()
   132→            global_step += 1
   133→
   134→            if global_step % args.log_every == 0:
   135→                lr = optimizer.param_groups[0]["lr"]
   136→                writer.add_scalar("train/total_loss", total_loss.item(), global_step)
   137→                writer.add_scalar("train/prediction_loss", pred_loss.item(), global_step)
   138→                writer.add_scalar("train/kl_loss", kl_loss.item(), global_step)
   139→                writer.add_scalar("train/recon_loss", recon_loss.item(), global_step)
   140→                writer.add_scalar("train/spectral_loss", spectral_loss.item(), global_step)
   141→                writer.add_scalar("train/grad_norm", grad_norm.item(), global_step)
   142→                writer.add_scalar("train/lr", lr, global_step)
   143→
   144→            if global_step % args.print_every == 0:
   145→                elapsed = time.time() - t0
   146→                print(
   147→                    f"  [{epoch+1}/{args.epochs}] step {global_step} | "
   148→                    f"loss={total_loss.item():.4f} pred={pred_loss.item():.4f} "
   149→                    f"kl={kl_loss.item():.4f} recon={recon_loss.item():.4f} "
   150→                    f"spec={spectral_loss.item():.4f} | "
   151→                    f"grad={grad_norm.item():.2f} | {elapsed:.1f}s"
   152→                )
   153→
   154→        # Epoch summary
   155→        n_batches = len(loader)
   156→        avg_loss = epoch_loss / n_batches
   157→        avg_pred = epoch_pred_loss / n_batches
   158→        avg_kl = epoch_kl_loss / n_batches
   159→        avg_recon = epoch_recon_loss / n_batches
   160→        avg_spectral = epoch_spectral_loss / n_batches
   161→        epoch_time = time.time() - t0
   162→
   163→        print(
   164→            f"Epoch {epoch+1}/{args.epochs} | "
   165→            f"avg_loss={avg_loss:.4f} pred={avg_pred:.4f} kl={avg_kl:.4f} "
   166→            f"recon={avg_recon:.4f} spec={avg_spectral:.4f} | {epoch_time:.1f}s"
   167→        )
   168→
   169→        # Save checkpoint
   170→        if (epoch + 1) % args.save_every == 0 or (epoch + 1) == args.epochs:
   171→            ckpt_path = ckpt_dir / f"epoch_{epoch+1:04d}.pt"
   172→            torch.save({
   173→                "epoch": epoch,
   174→                "global_step": global_step,
   175→                "model": model.state_dict(),
   176→                "optimizer": optimizer.state_dict(),
   177→                "scheduler": scheduler.state_dict(),
   178→                "config": config,
   179→                "loss": avg_loss,
   180→            }, ckpt_path)
   181→            print(f"  Saved checkpoint: {ckpt_path}")
   182→
   183→        # Save best
   184→        if avg_loss < best_loss:
   185→            best_loss = avg_loss
   186→            best_path = ckpt_dir / "best.pt"
   187→            torch.save({
   188→                "epoch": epoch,
   189→                "global_step": global_step,
   190→                "model": model.state_dict(),
   191→                "config": config,
   192→                "loss": avg_loss,
   193→            }, best_path)
   194→            print(f"  New best model (loss={avg_loss:.4f})")
   195→
   196→    writer.close()
   197→    print(f"\nTraining complete. Best loss: {best_loss:.4f}")
   198→
   199→
   200→if __name__ == "__main__":
   201→    parser = argparse.ArgumentParser()
   202→    # Data
   203→    parser.add_argument("--data_dir", default="/home/ubuntu/lewm-tts/processed_data")
   204→    parser.add_argument("--output_dir", default="/home/ubuntu/lewm-tts/output")
   205→    # Model
   206→    parser.add_argument("--d_model", type=int, default=256)
   207→    parser.add_argument("--nhead", type=int, default=4)
   208→    parser.add_argument("--text_layers", type=int, default=4)
   209→    parser.add_argument("--audio_layers", type=int, default=4)
   210→    parser.add_argument("--predictor_layers", type=int, default=6)
   211→    parser.add_argument("--dropout", type=float, default=0.1)
   212→    parser.add_argument("--kl_weight", type=float, default=0.1)
   213→    parser.add_argument("--recon_weight", type=float, default=1.0)
   214→    parser.add_argument("--spectral_weight", type=float, default=0.5)
   215→    # Training
   216→    parser.add_argument("--epochs", type=int, default=200)
   217→    parser.add_argument("--batch_size", type=int, default=16)
   218→    parser.add_argument("--lr", type=float, default=1e-4)
   219→    parser.add_argument("--grad_clip", type=float, default=1.0)
   220→    parser.add_argument("--num_workers", type=int, default=4)
   221→    # Logging
   222→    parser.add_argument("--log_every", type=int, default=10)
   223→    parser.add_argument("--print_every", type=int, default=50)
   224→    parser.add_argument("--save_every", type=int, default=10)
   225→    parser.add_argument("--resume", default=None)
   226→    args = parser.parse_args()
   227→    train(args)
   228→

"""
Training loop for LeWM TTS.
"""

import os
import json
import time
import argparse
import torch
from torch.utils.tensorboard import SummaryWriter
from pathlib import Path

from model import build_model
from dataset import build_dataloader

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

    # ─── Build model ───
    config = {
        "d_model": args.d_model,
        "nhead": args.nhead,
        "n_mels": 100,
        "text_vocab_size": 256,
        "text_encoder_layers": args.text_layers,
        "audio_encoder_layers": args.audio_layers,
        "predictor_layers": args.predictor_layers,
        "dropout": args.dropout,
        "kl_weight": args.kl_weight,
        "recon_weight": args.recon_weight,
        "spectral_weight": args.spectral_weight,
    }
    model, config = build_model(config)
    model = model.to(device)

    # ─── Build dataloader ───
    manifest_path = os.path.join(args.data_dir, "manifest.json")
    loader, dataset = build_dataloader(
        manifest_path, batch_size=args.batch_size,
        num_workers=args.num_workers,
    )
    print(f"Training: {len(dataset)} samples, {len(loader)} batches/epoch")

    # ─── Optimizer & Scheduler ───
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=args.lr,
        betas=(0.9, 0.98), weight_decay=0.01,
    )

    # Warmup + cosine decay
    total_steps = args.epochs * len(loader)
    warmup_steps = min(4000, total_steps // 10)

    def lr_lambda(step):
        if step < warmup_steps:
            return step / max(1, warmup_steps)
        progress = (step - warmup_steps) / max(1, total_steps - warmup_steps)
        return 0.5 * (1 + torch.cos(torch.tensor(progress * 3.14159)).item())

    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)

    # ─── Logging ───
    ckpt_dir = Path(args.output_dir) / "checkpoints"
    ckpt_dir.mkdir(parents=True, exist_ok=True)
    writer = SummaryWriter(Path(args.output_dir) / "logs")

    # Save config
    with open(Path(args.output_dir) / "config.json", "w") as f:
        json.dump(config, f, indent=2)

    # ─── Resume from checkpoint ───
    start_epoch = 0
    global_step = 0
    if args.resume:
        ckpt = torch.load(args.resume, map_location=device, weights_only=False)
        model.load_state_dict(ckpt["model"])
        optimizer.load_state_dict(ckpt["optimizer"])
        scheduler.load_state_dict(ckpt["scheduler"])
        start_epoch = ckpt["epoch"] + 1
        global_step = ckpt["global_step"]
        print(f"Resumed from epoch {start_epoch}, step {global_step}")

    # ─── Training loop ───
    model.train()
    best_loss = float("inf")

    for epoch in range(start_epoch, args.epochs):
        epoch_loss = 0.0
        epoch_pred_loss = 0.0
        epoch_kl_loss = 0.0
        epoch_recon_loss = 0.0
        epoch_spectral_loss = 0.0
        t0 = time.time()

        for batch_idx, batch in enumerate(loader):
            mel = batch["mel"].to(device)
            text_tokens = batch["text_tokens"].to(device)
            mel_mask = batch["mel_mask"].to(device)
            text_mask = batch["text_mask"].to(device)

            # KL annealing: ramp up over first 20% of training
            kl_anneal = min(1.0, global_step / (total_steps * 0.2))
            model.kl_weight = args.kl_weight * kl_anneal

            # Forward
            losses = model(mel, text_tokens, mel_mask, text_mask)

            total_loss = losses["total_loss"]
            pred_loss = losses["prediction_loss"]
            kl_loss = losses["kl_loss"]
            recon_loss = losses["recon_loss"]
            spectral_loss = losses["spectral_loss"]

            # Backward
            optimizer.zero_grad()
            total_loss.backward()

            # Gradient clipping
            grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)

            optimizer.step()
            scheduler.step()

            # Logging
            epoch_loss += total_loss.item()
            epoch_pred_loss += pred_loss.item()
            epoch_kl_loss += kl_loss.item()
            epoch_recon_loss += recon_loss.item()
            epoch_spectral_loss += spectral_loss.item()
            global_step += 1

            if global_step % args.log_every == 0:
                lr = optimizer.param_groups[0]["lr"]
                writer.add_scalar("train/total_loss", total_loss.item(), global_step)
                writer.add_scalar("train/prediction_loss", pred_loss.item(), global_step)
                writer.add_scalar("train/kl_loss", kl_loss.item(), global_step)
                writer.add_scalar("train/recon_loss", recon_loss.item(), global_step)
                writer.add_scalar("train/spectral_loss", spectral_loss.item(), global_step)
                writer.add_scalar("train/grad_norm", grad_norm.item(), global_step)
                writer.add_scalar("train/lr", lr, global_step)

            if global_step % args.print_every == 0:
                elapsed = time.time() - t0
                print(
                    f"  [{epoch+1}/{args.epochs}] step {global_step} | "
                    f"loss={total_loss.item():.4f} pred={pred_loss.item():.4f} "
                    f"kl={kl_loss.item():.4f} recon={recon_loss.item():.4f} "
                    f"spec={spectral_loss.item():.4f} | "
                    f"grad={grad_norm.item():.2f} | {elapsed:.1f}s"
                )

        # Epoch summary
        n_batches = len(loader)
        avg_loss = epoch_loss / n_batches
        avg_pred = epoch_pred_loss / n_batches
        avg_kl = epoch_kl_loss / n_batches
        avg_recon = epoch_recon_loss / n_batches
        avg_spectral = epoch_spectral_loss / n_batches
        epoch_time = time.time() - t0

        print(
            f"Epoch {epoch+1}/{args.epochs} | "
            f"avg_loss={avg_loss:.4f} pred={avg_pred:.4f} kl={avg_kl:.4f} "
            f"recon={avg_recon:.4f} spec={avg_spectral:.4f} | {epoch_time:.1f}s"
        )

        # Save checkpoint
        if (epoch + 1) % args.save_every == 0 or (epoch + 1) == args.epochs:
            ckpt_path = ckpt_dir / f"epoch_{epoch+1:04d}.pt"
            torch.save({
                "epoch": epoch,
                "global_step": global_step,
                "model": model.state_dict(),
                "optimizer": optimizer.state_dict(),
                "scheduler": scheduler.state_dict(),
                "config": config,
                "loss": avg_loss,
            }, ckpt_path)
            print(f"  Saved checkpoint: {ckpt_path}")

        # Save best
        if avg_loss < best_loss:
            best_loss = avg_loss
            best_path = ckpt_dir / "best.pt"
            torch.save({
                "epoch": epoch,
                "global_step": global_step,
                "model": model.state_dict(),
                "config": config,
                "loss": avg_loss,
            }, best_path)
            print(f"  New best model (loss={avg_loss:.4f})")

    writer.close()
    print(f"\nTraining complete. Best loss: {best_loss:.4f}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Data
    parser.add_argument("--data_dir", default="/home/ubuntu/lewm-tts/processed_data")
    parser.add_argument("--output_dir", default="/home/ubuntu/lewm-tts/output")
    # Model
    parser.add_argument("--d_model", type=int, default=256)
    parser.add_argument("--nhead", type=int, default=4)
    parser.add_argument("--text_layers", type=int, default=4)
    parser.add_argument("--audio_layers", type=int, default=4)
    parser.add_argument("--predictor_layers", type=int, default=6)
    parser.add_argument("--dropout", type=float, default=0.1)
    parser.add_argument("--kl_weight", type=float, default=0.1)
    parser.add_argument("--recon_weight", type=float, default=1.0)
    parser.add_argument("--spectral_weight", type=float, default=0.5)
    # Training
    parser.add_argument("--epochs", type=int, default=200)
    parser.add_argument("--batch_size", type=int, default=16)
    parser.add_argument("--lr", type=float, default=1e-4)
    parser.add_argument("--grad_clip", type=float, default=1.0)
    parser.add_argument("--num_workers", type=int, default=4)
    # Logging
    parser.add_argument("--log_every", type=int, default=10)
    parser.add_argument("--print_every", type=int, default=50)
    parser.add_argument("--save_every", type=int, default=10)
    parser.add_argument("--resume", default=None)
    args = parser.parse_args()
    train(args)


Training killed. Now restarting with tuned hyperparams — **kl_weight=0.01, recon_weight=2.0, spectral_weight=1.5**:


Create output dir for v3b training


python3 train.py --data_dir /home/ubuntu/lewm-tts/processed_data_100mel --output_dir /home/ubuntu/lewm-tts/output_v3b --epochs 200 --batch_size 256 --d_model 256 --nhead 4 --text_layers 4 --audio_layers 4 --predictor_layers 6 --lr 1e-4 --kl_weight 0.01 --recon_weight 2.0 --spectral_weight 1.5 --grad_clip 1.0 --num_workers 4 --log_every 10 --print_every 50 --save_every 10 > /home/ubuntu/lewm-tts/train_v3b.log 2>&1

Start v3b training with kl=0.01, recon=2.0, spectral=1.5


Command running in background with ID: bx8o2n8dg. Output is being written to: /tmp/claude-1001/-home-ubuntu/5610b655-b085-41d5-b3d9-c0750b48e28e/tasks/bx8o2n8dg.output


sleep 35 && head -10 /home/ubuntu/lewm-tts/train_v3b.log

Wait and check first few epochs of v3b training


Device: cuda
LeWM TTS model: 24.00M parameters
Dataset: 6959 samples loaded
Training: 6959 samples, 27 batches/epoch
Epoch 1/200 | avg_loss=13.6511 pred=1.4613 kl=0.2462 recon=2.3551 spec=4.9864 | 27.3s
  New best model (loss=13.6511)


sleep 60 && tail -15 /home/ubuntu/lewm-tts/train_v3b.log

Check training progress after ~3 epochs


Device: cuda
LeWM TTS model: 24.00M parameters
Dataset: 6959 samples loaded
Training: 6959 samples, 27 batches/epoch
Epoch 1/200 | avg_loss=13.6511 pred=1.4613 kl=0.2462 recon=2.3551 spec=4.9864 | 27.3s
  New best model (loss=13.6511)
  [2/200] step 50 | loss=13.3484 pred=1.1684 kl=0.1995 recon=2.3801 spec=4.9465 | grad=2.33 | 22.7s
Epoch 2/200 | avg_loss=13.2451 pred=1.2534 kl=0.2111 recon=2.3552 spec=4.8541 | 26.6s
  New best model (loss=13.2451)
Epoch 3/200 | avg_loss=12.5440 pred=1.0315 kl=0.2030 recon=2.3546 spec=4.5354 | 26.7s
  New best model (loss=12.5440)


The total loss is higher because recon_weight=2.0 and spectral_weight=1.5 make those components bigger. But let me check — the raw recon and spectral values should be similar since those are the actual loss values before weighting. Looking at epoch 3:

- recon=2.35 (v3 epoch 3 was also 2.35 — same, expected since KL annealing is very slow at start)
- spectral=4.54 (v3 epoch 3 was 4.66 — similar)

The weighted total: 1.03 + 0.01*0.20 + 2.0*2.35 + 1.5*4.54 = 1.03 + 0.002 + 4.70 + 6.81 = 12.54 ✓

I need to let this run longer to see if the reconstruction loss actually decreases faster with these new weights.


sleep 120 && tail -20 /home/ubuntu/lewm-tts/train_v3b.log

Check training after ~7 more epochs


Epoch 1/200 | avg_loss=13.6511 pred=1.4613 kl=0.2462 recon=2.3551 spec=4.9864 | 27.3s
  New best model (loss=13.6511)
  [2/200] step 50 | loss=13.3484 pred=1.1684 kl=0.1995 recon=2.3801 spec=4.9465 | grad=2.33 | 22.7s
Epoch 2/200 | avg_loss=13.2451 pred=1.2534 kl=0.2111 recon=2.3552 spec=4.8541 | 26.6s
  New best model (loss=13.2451)
Epoch 3/200 | avg_loss=12.5440 pred=1.0315 kl=0.2030 recon=2.3546 spec=4.5354 | 26.7s
  New best model (loss=12.5440)
  [4/200] step 100 | loss=10.7409 pred=0.9306 kl=0.3007 recon=2.3797 spec=3.3671 | grad=3.37 | 18.9s
Epoch 4/200 | avg_loss=11.0376 pred=0.9384 kl=0.2805 recon=2.3558 spec=3.5916 | 26.7s
  New best model (loss=11.0376)
Epoch 5/200 | avg_loss=8.9693 pred=0.8248 kl=0.2909 recon=2.2691 spec=2.4041 | 26.7s
  New best model (loss=8.9693)
  [6/200] step 150 | loss=6.7562 pred=0.7736 kl=0.2844 recon=1.6614 spec=1.7729 | grad=4.86 | 15.0s
Epoch 6/200 | avg_loss=6.9467 pred=0.7647 kl=0.2749 recon=1.6966 spec=1.8590 | 26.6s
  New best model (loss=6.9467)
Epoch 7/200 | avg_loss=5.4291 pred=0.6201 kl=0.3056 recon=1.4078 spec=1.3287 | 26.8s
  New best model (loss=5.4291)
  [8/200] step 200 | loss=4.5682 pred=0.5125 kl=0.3461 recon=1.2266 spec=1.0678 | grad=4.27 | 11.3s
Epoch 8/200 | avg_loss=4.5015 pred=0.5005 kl=0.3539 recon=1.2074 spec=1.0571 | 26.8s
  New best model (loss=4.5015)


Good progress. The recon loss is already at 1.21 at epoch 8 (vs 1.24 at epoch 8 in v3). Let me wait for more epochs to compare properly.


sleep 300 && tail -25 /home/ubuntu/lewm-tts/train_v3b.log

Check training after ~20 epochs


Command running in background with ID: bsk94knn8. Output is being written to: /tmp/claude-1001/-home-ubuntu/5610b655-b085-41d5-b3d9-c0750b48e28e/tasks/bsk94knn8.output


ToolSearch

Question: select:TaskOutput


Question: select:TaskOutput


<task-notification>
<task-id>bsk94knn8</task-id>
<tool-use-id>toolu_vrtx_019pu9cGK6E4mAcsydLBudjE</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/5610b655-b085-41d5-b3d9-c0750b48e28e/tasks/bsk94knn8.output</output-file>
<status>completed</status>
<summary>Background command "Check training after ~20 epochs" completed (exit code 0)</summary>
</task-notification>
Read the output file to retrieve the result: /tmp/claude-1001/-home-ubuntu/5610b655-b085-41d5-b3d9-c0750b48e28e/tasks/bsk94knn8.output


<retrieval_status>success</retrieval_status>

<task_id>bsk94knn8</task_id>

<task_type>local_bash</task_type>

<status>completed</status>

<exit_code>0</exit_code>

<output>
  New best model (loss=3.8949)
  [10/200] step 250 | loss=3.6904 pred=0.3596 kl=0.4723 recon=0.9872 spec=0.9036 | grad=12.19 | 7.1s
Epoch 10/200 | avg_loss=3.6212 pred=0.3419 kl=0.4878 recon=0.9624 spec=0.9022 | 26.6s
  Saved checkpoint: /home/ubuntu/lewm-tts/output_v3b/checkpoints/epoch_0010.pt
  New best model (loss=3.6212)
Epoch 11/200 | avg_loss=3.4785 pred=0.2867 kl=0.5559 recon=0.9338 spec=0.8818 | 26.7s
  New best model (loss=3.4785)
  [12/200] step 300 | loss=3.4295 pred=0.2622 kl=0.5906 recon=0.9275 spec=0.8737 | grad=4.26 | 3.4s
Epoch 12/200 | avg_loss=3.3684 pred=0.2458 kl=0.6160 recon=0.9131 spec=0.8631 | 26.7s
  New best model (loss=3.3684)
  [13/200] step 350 | loss=3.2924 pred=0.1971 kl=0.7111 recon=0.8917 spec=0.8731 | grad=40.29 | 25.6s
Epoch 13/200 | avg_loss=3.2882 pred=0.2122 kl=0.6808 recon=0.8986 spec=0.8510 | 26.6s
  New best model (loss=3.2882)
Epoch 14/200 | avg_loss=3.4840 pred=0.1903 kl=0.7435 recon=0.9788 spec=0.8891 | 26.7s
  [15/200] step 400 | loss=3.3437 pred=0.1641 kl=0.8119 recon=0.9571 spec=0.8416 | grad=23.07 | 21.7s
Epoch 15/200 | avg_loss=3.3427 pred=0.1730 kl=0.7889 recon=0.9350 spec=0.8645 | 26.6s
Epoch 16/200 | avg_loss=3.2980 pred=0.1490 kl=0.8601 recon=0.9295 spec=0.8578 | 26.4s
  [17/200] step 450 | loss=3.1454 pred=0.1204 kl=0.9734 recon=0.8944 spec=0.8214 | grad=13.68 | 18.0s
Epoch 17/200 | avg_loss=3.1228 pred=0.1237 kl=0.9565 recon=0.8765 spec=0.8282 | 26.8s
  New best model (loss=3.1228)
Epoch 18/200 | avg_loss=3.0995 pred=0.0969 kl=1.0964 recon=0.8774 spec=0.8288 | 26.8s
  New best model (loss=3.0995)
  [19/200] step 500 | loss=3.1561 pred=0.0811 kl=1.2092 recon=0.9116 spec=0.8308 | grad=21.60 | 14.1s
Epoch 19/200 | avg_loss=3.0741 pred=0.0815 kl=1.2060 recon=0.8744 spec=0.8255 | 26.7s
  New best model (loss=3.0741)
</output>

Check training after ~20 epochs

Output:
  New best model (loss=3.8949)
  [10/200] step 250 | loss=3.6904 pred=0.3596 kl=0.4723 recon=0.9872 spec=0.9036 | grad=12.19 | 7.1s
Epoch 10/200 | avg_loss=3.6212 pred=0.3419 kl=0.4878 recon=0.9624 spec=0.9022 | 26.6s
  Saved checkpoint: /home/ubuntu/lewm-tts/output_v3b/checkpoints/epoch_0010.pt
  New best model (loss=3.6212)
Epoch 11/200 | avg_loss=3.4785 pred=0.2867 kl=0.5559 recon=0.9338 spec=0.8818 | 26.7s
  New best model (loss=3.4785)
  [12/200] step 300 | loss=3.4295 pred=0.2622 kl=0.5906 recon=0.9275 spec=0.8737 | grad=4.26 | 3.4s
Epoch 12/200 | avg_loss=3.3684 pred=0.2458 kl=0.6160 recon=0.9131 spec=0.8631 | 26.7s
  New best model (loss=3.3684)
  [13/200] step 350 | loss=3.2924 pred=0.1971 kl=0.7111 recon=0.8917 spec=0.8731 | grad=40.29 | 25.6s
Epoch 13/200 | avg_loss=3.2882 pred=0.2122 kl=0.6808 recon=0.8986 spec=0.8510 | 26.6s
  New best model (loss=3.2882)
Epoch 14/200 | avg_loss=3.4840 pred=0.1903 kl=0.7435 recon=0.9788 spec=0.8891 | 26.7s
  [15/200] step 400 | loss=3.3437 pred=0.1641 kl=0.8119 recon=0.9571 spec=0.8416 | grad=23.07 | 21.7s
Epoch 15/200 | avg_loss=3.3427 pred=0.1730 kl=0.7889 recon=0.9350 spec=0.8645 | 26.6s
Epoch 16/200 | avg_loss=3.2980 pred=0.1490 kl=0.8601 recon=0.9295 spec=0.8578 | 26.4s
  [17/200] step 450 | loss=3.1454 pred=0.1204 kl=0.9734 recon=0.8944 spec=0.8214 | grad=13.68 | 18.0s
Epoch 17/200 | avg_loss=3.1228 pred=0.1237 kl=0.9565 recon=0.8765 spec=0.8282 | 26.8s
  New best model (loss=3.1228)
Epoch 18/200 | avg_loss=3.0995 pred=0.0969 kl=1.0964 recon=0.8774 spec=0.8288 | 26.8s
  New best model (loss=3.0995)
  [19/200] step 500 | loss=3.1561 pred=0.0811 kl=1.2092 recon=0.9116 spec=0.8308 | grad=21.60 | 14.1s
Epoch 19/200 | avg_loss=3.0741 pred=0.0815 kl=1.2060 recon=0.8744 spec=0.8255 | 26.7s
  New best model (loss=3.0741)


Comparing v3 vs v3b at epoch 19:
- v3: recon=0.836, spectral=0.817
- v3b: recon=0.874, spectral=0.826

Hmm, v3b recon is actually slightly WORSE. The KL is similar (1.15 vs 1.21). The increased weights aren't helping the raw loss values go down faster - they just make the total loss bigger.

Wait, but the important thing is the actual reconstruction quality, not just the loss value. The lower KL weight (0.01 vs 0.1) means the encoder can encode more information. Let me wait for more epochs and then test reconstruction quality.

The KL values are nearly identical right now because the annealing schedule is at the same stage for both. The real divergence will show up once annealing completes at epoch 40 - v3b's lower weight (0.01) will result in a much smaller KL penalty compared to v3's (0.1), giving the encoder more flexibility to preserve information.


Bash

sleep 300 && tail -10 /home/ubuntu/lewm-tts/train_v3b.log

Check progress after ~30 epochs


Epoch 27/200 | avg_loss=2.8777 pred=0.0320 kl=2.0381 recon=0.8212 spec=0.7933 | 26.6s
  [28/200] step 750 | loss=2.8433 pred=0.0302 kl=2.1263 recon=0.8163 spec=0.7772 | grad=6.28 | 21.0s
Epoch 28/200 | avg_loss=2.9005 pred=0.0306 kl=2.1059 recon=0.8323 spec=0.7939 | 26.8s
Epoch 29/200 | avg_loss=2.9652 pred=0.0316 kl=2.1214 recon=0.8583 spec=0.8013 | 26.7s
  [30/200] step 800 | loss=2.8278 pred=0.0308 kl=2.1655 recon=0.8064 spec=0.7788 | grad=7.38 | 16.8s
Epoch 30/200 | avg_loss=2.8287 pred=0.0321 kl=2.1512 recon=0.8065 spec=0.7786 | 26.5s
  Saved checkpoint: /home/ubuntu/lewm-tts/output_v3b/checkpoints/epoch_0030.pt
  New best model (loss=2.8287)
Epoch 31/200 | avg_loss=2.7880 pred=0.0300 kl=2.2180 recon=0.7923 spec=0.7710 | 26.7s
  New best model (loss=2.7880)


Comparing v3 vs v3b at epoch 30:

| | **v3** (kl=0.1) | **v3b** (kl=0.01) |
|---|---|---|
| recon | 0.789 | 0.806 |
| spectral | 0.777 | 0.779 |
| KL | 1.31 | 2.15 |
| pred | 0.048 | 0.032 |

Interesting — **KL is much higher in v3b (2.15 vs 1.31)** meaning the encoder IS preserving more information (less collapse). But recon hasn't dropped faster yet. The real test is what the mel looks like. Let me generate samples at this checkpoint to compare.


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

# Load v3b best
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'))
mel = torch.load(mel_files[0], weights_only=True).unsqueeze(0).cuda()

with torch.no_grad():
    z, mu, logvar = model.audio_encoder(mel)
    mel_recon = model.mel_decoder(mu)

    T = min(mel.shape[2], mel_recon.shape[2])
    l1 = (mel[:,:,:T] - mel_recon[:,:,:T]).abs().mean().item()

    # Formant check
    from scipy.signal import find_peaks
    gt_frame = mel[0, :, 50].cpu().numpy()
    rc_frame = mel_recon[0, :, 50].cpu().numpy()
    gt_peaks, _ = find_peaks(gt_frame, distance=5, prominence=0.5)
    rc_peaks, _ = find_peaks(rc_frame, distance=5, prominence=0.5)

    spectral_std_ratio = mel_recon[:,:,:T].std(dim=1).mean().item() / mel[:,:,:T].std(dim=1).mean().item()

    print(f'L1: {l1:.4f}')
    print(f'GT formants: {len(gt_peaks)} peaks at {gt_peaks}')
    print(f'Recon formants: {len(rc_peaks)} peaks at {rc_peaks}')
    print(f'Spectral contrast ratio: {spectral_std_ratio:.3f}')

    wav_recon = vocos.decode(mel_recon)
    torchaudio.save('sample_recon_v3b.wav', wav_recon.cpu(), 24000)

    wav_gt = vocos.decode(mel)
    torchaudio.save('sample_gt_v3b.wav', wav_gt.cpu(), 24000)
    print('Saved sample_recon_v3b.wav and sample_gt_v3b.wav')
" 2>&1

Test v3b reconstruction quality vs v3


v3b epoch 31
L1: 0.7198
GT formants: 11 peaks at [ 5 11 20 27 35 42 49 57 69 80 91]
Recon formants: 5 peaks at [ 5 53 60 80 91]
Spectral contrast ratio: 0.864
Saved sample_recon_v3b.wav and sample_gt_v3b.wav


Hmm, v3b at epoch 31:
- L1: 0.72 (v3 was 0.67 at epoch 43, 0.66 at epoch 31-ish)
- Formant peaks: 5 (v3 had 6)
- Spectral contrast: 0.864 (v3 had 0.787)

The spectral contrast IS better (0.864 vs 0.787), but the formant recovery is actually worse (5 vs 6 peaks). And L1 is higher.

So just changing the loss weights isn't solving the fundamental problem.

The real bottleneck is the mel decoder architecture itself — transposed convolutions are inherently producing smooth, blurry outputs. I think the solution is architectural: either adding a discriminator for GAN-based training to penalize blur, switching to dilated convolutions or attention-based decoding, using skip connections to preserve fine details, or decoding in the frequency domain. Skip connections from the encoder might be the most impactful change to try first.

Wait, but this is a JEPA model — the predictor generates latent embeddings that the mel decoder converts to mel spectrograms. Skip connections from the encoder won't work during inference since there's no encoder input during synthesis. The decoder has to work standalone, which means 256-dimensional embeddings downsampled 4x simply don't contain enough information to reconstruct sharp formant structure across 100 mel bins. I could either reduce the downsampling factor to stride 2 for higher temporal resolution, or increase the embedding dimension to 512 to give the decoder more to work with.

Reducing downsampling would double the predictor's sequence length and increase compute quadratically, though. Another option is to pre-train the mel decoder as a standalone autoencoder until it reconstructs perfectly, then freeze it and train the rest of the model. But I'm realizing the user's noise simulation showed that even random noise at L1=0.84 still sounds like speech, so maybe Vocos is more robust than I'm thinking.

The real issue seems to be the type of error the model makes — it produces spectrally smooth output and can't reproduce sharp transitions between harmonics. This happens because L1/MSE loss encourages mean predictions and transposed convolutions spread information. A spectral loss should help, but it treats each mel bin independently without capturing cross-frequency structure like formants. I could try adding a mel-bin correlation loss to match cross-frequency structure, or use a 2D spectral loss that treats the mel spectrogram as an image. The simplest thing might be to just let the current training run longer and see if it converges better at later epochs.

But the user wants faster results. I'm thinking about a few quicker approaches: fine-tuning Vocos with a small adapter to handle noisy mels, or reducing the bottleneck by using 2x downsampling instead of 4x to give the decoder more information. Even simpler would be training with a larger decoder — more residual blocks and wider hidden dimensions. The core issue is that weight changes alone won't solve this; the decoder architecture itself needs to be more capable.


Not great. v3b has better spectral contrast (0.86 vs 0.79) but **still only 5 formant peaks out of 11**. Just changing loss weights isn't enough — the mel decoder architecture itself is the bottleneck.

The core issue: **256-dim embeddings downsampled 4x → the decoder simply doesn't have enough information to reconstruct sharp harmonic structure**. No amount of loss tuning fixes this.

Two quick architectural fixes that would help:

1. **Reduce downsampling from 4x to 2x** — encoder keeps more temporal detail, decoder has easier job
2. **Add attention layers in the decoder** — lets it model cross-frequency (formant) patterns instead of just local conv

Want me to kill this run and implement these decoder upgrades? It would keep the same 24M-ish param count but redistribute capacity to where it matters.


The user opened the training log, probably checking progress. Let me wait for their response about whether to proceed with the architectural changes.

Actually, they haven't responded yet - just a system notification about the background task completing and the file being opened. Let me wait for their actual response.

Oh wait, the user hasn't said anything new. The system just notified about the background task and the file being opened. I should just acknowledge and wait. Actually no - the user might be waiting for me to check on the current training status since they opened the log.

Let me check the latest training status.


Check latest v3b training status


  [32/200] step 850 | loss=2.7250 pred=0.0279 kl=2.2650 recon=0.7754 spec=0.7523 | grad=5.50 | 13.0s
Epoch 32/200 | avg_loss=2.7648 pred=0.0283 kl=2.2636 recon=0.7842 spec=0.7668 | 26.6s
  New best model (loss=2.7648)
Epoch 33/200 | avg_loss=2.7526 pred=0.0278 kl=2.2914 recon=0.7802 spec=0.7638 | 26.9s
  New best model (loss=2.7526)


Epoch 33, recon=0.78 — still dropping but slow. The loss weights alone aren't cutting it. Shall I go ahead with the decoder upgrade (2x downsample + attention)?


test samples now