### Programmatic Distributed Training Setup

Source: https://context7.com/nvidia/waveglow/llms.txt

This snippet shows how to programmatically initialize distributed training utilities within your training script, including setting up ranks, GPUs, and backend.

```python
from distributed import init_distributed, apply_gradient_allreduce, reduce_tensor

init_distributed(rank=0, num_gpus=4, group_name='group_2024',
                 dist_backend='nccl', dist_url='tcp://localhost:54321')
model = apply_gradient_allreduce(model)  # wraps backward hook for allreduce
reduced_loss = reduce_tensor(loss.data, num_gpus=4).item()
```

--------------------------------

### Train WaveGlow Model (Single GPU, FP32)

Source: https://context7.com/nvidia/waveglow/llms.txt

Use this command to start training a WaveGlow model on a single GPU using FP32 precision. Ensure your configuration file is correctly set up.

```bash
python train.py -c config.json
```

--------------------------------

### Batch Audio Synthesis (FP16 Inference)

Source: https://context7.com/nvidia/waveglow/llms.txt

Perform audio synthesis using FP16 inference for increased speed, especially on GPUs like V100. This requires the Apex library to be installed.

```bash
python3 inference.py \
    -f mel_files.txt \
    -w waveglow_256channels.pt \
    -o synthesized/ \
    --is_fp16 \
    --sigma 0.6
```

--------------------------------

### Batch Audio Synthesis with Denoising

Source: https://context7.com/nvidia/waveglow/llms.txt

Synthesize audio with an optional denoising step. Adjust the --denoiser_strength parameter starting from 0.1 to control the level of denoising.

```bash
python3 inference.py \
    -f mel_files.txt \
    -w waveglow_256channels.pt \
    -o synthesized/ \
    --sigma 0.6 \
    --denoiser_strength 0.1 \
    --sampling_rate 22050
```

--------------------------------

### Prepare Training Files for WaveGlow

Source: https://github.com/nvidia/waveglow/blob/master/README.md

Create lists of .wav file names for training and testing datasets. This is a prerequisite for training your own WaveGlow model.

```bash
ls data/*.wav | tail -n+10 > train_files.txt
ls data/*.wav | head -n10 > test_files.txt
```

--------------------------------

### Clone WaveGlow Repository and Initialize Submodules

Source: https://github.com/nvidia/waveglow/blob/master/README.md

Use these commands to clone the WaveGlow repository and initialize its submodules, which is the first step in setting up the project.

```bash
git clone https://github.com/NVIDIA/waveglow.git
cd waveglow
git submodule init
git submodule update
```

--------------------------------

### Prepare Training and Test File Lists

Source: https://context7.com/nvidia/waveglow/llms.txt

Shell commands to create text files listing WAV files for training and testing. `train_files.txt` contains all WAVs except the first 10, while `test_files.txt` contains the first 10.

```bash
# Prepare file lists
ls data/*.wav | tail -n+10 > train_files.txt
ls data/*.wav | head -n10  > test_files.txt
```

--------------------------------

### WaveGlow Core Model: Training and Inference (`glow.py`)

Source: https://context7.com/nvidia/waveglow/llms.txt

Demonstrates instantiation of the WaveGlow model, a forward pass for training using mel-spectrograms and audio, and an inference pass to generate audio from mel-spectrograms. Ensure the model is moved to CUDA and weight normalization is removed for inference speed.

```python
import torch
from glow import WaveGlow, WaveGlowLoss

# --- Instantiate the model ---
waveglow_config = {
    "n_mel_channels": 80,
    "n_flows": 12,
    "n_group": 8,
    "n_early_every": 4,
    "n_early_size": 2,
    "WN_config": {
        "n_layers": 8,
        "n_channels": 256,
        "kernel_size": 3
    }
}
model = WaveGlow(**waveglow_config).cuda()

# --- Training forward pass ---
# mel:   (batch, n_mel_channels, mel_frames)
# audio: (batch, audio_samples)
mel   = torch.randn(2, 80, 62).cuda()
audio = torch.randn(2, 16000).cuda()

z, log_s_list, log_det_W_list = model((mel, audio))
# z shape: (batch, n_group, time_groups)

criterion = WaveGlowLoss(sigma=1.0)
loss = criterion((z, log_s_list, log_det_W_list))
loss.backward()

# --- Inference (mel → audio) ---
model.eval()
model = WaveGlow.remove_weightnorm(model)  # fuse weight norm for speed
with torch.no_grad():
    mel_input = torch.randn(1, 80, 62).cuda()
    audio_out = model.infer(mel_input, sigma=0.6)
    # audio_out shape: (1, audio_samples)  — values in [-1, 1]
    print(audio_out.shape)  # e.g., torch.Size([1, 15872])
```

--------------------------------

### CLI: Precompute Mel-Spectrograms with Mel2Samp

Source: https://context7.com/nvidia/waveglow/llms.txt

Command-line interface for precomputing mel-spectrograms for a list of WAV files. Specify input file list, output directory, and configuration file.

```bash
python mel2samp.py \
    -f test_files.txt \
    -o mel_spectrograms/ \
    -c config.json
```

--------------------------------

### Generate Audio with Pre-existing Model

Source: https://github.com/nvidia/waveglow/blob/master/README.md

Generate audio samples from mel-spectrograms using a published WaveGlow model. Ensure you have downloaded the model and mel-spectrograms beforehand.

```bash
python3 inference.py -f <(ls mel_spectrograms/*.pt) -w waveglow_256channels.pt -o . --is_fp16 -s 0.6
```

--------------------------------

### WaveGlow Configuration File (`config.json`)

Source: https://context7.com/nvidia/waveglow/llms.txt

This JSON file controls the entire WaveGlow system, including training parameters, data preprocessing settings, distributed training configuration, and model architecture details.

```json
{
    "train_config": {
        "fp16_run": true,
        "output_directory": "checkpoints",
        "epochs": 100000,
        "learning_rate": 1e-4,
        "sigma": 1.0,
        "iters_per_checkpoint": 2000,
        "batch_size": 12,
        "seed": 1234,
        "checkpoint_path": "",
        "with_tensorboard": false
    },
    "data_config": {
        "training_files": "train_files.txt",
        "segment_length": 16000,
        "sampling_rate": 22050,
        "filter_length": 1024,
        "hop_length": 256,
        "win_length": 1024,
        "mel_fmin": 0.0,
        "mel_fmax": 8000.0
    },
    "dist_config": {
        "dist_backend": "nccl",
        "dist_url": "tcp://localhost:54321"
    },
    "waveglow_config": {
        "n_mel_channels": 80,
        "n_flows": 12,
        "n_group": 8,
        "n_early_every": 4,
        "n_early_size": 2,
        "WN_config": {
            "n_layers": 8,
            "n_channels": 256,
            "kernel_size": 3
        }
    }
}
```

--------------------------------

### Train WaveGlow Network

Source: https://github.com/nvidia/waveglow/blob/master/README.md

Initiate the training process for WaveGlow networks using a configuration file. For multi-GPU training, use 'distributed.py'. Mixed precision training can be enabled by setting 'fp16_run' to true in the config file.

```bash
mkdir checkpoints
python train.py -c config.json
```

--------------------------------

### Post-Process Audio with Denoiser

Source: https://context7.com/nvidia/waveglow/llms.txt

Applies a denoiser to remove spectral bias introduced by WaveGlow, reducing background hiss. Load the WaveGlow model and then initialize the Denoiser with it. The `strength` parameter controls the aggressiveness of the bias removal.

```python
import torch
from glow import WaveGlow
from denoiser import Denoiser
from scipy.io.wavfile import write

# Load model
checkpoint = torch.load('waveglow_256channels.pt')
waveglow = WaveGlow.remove_weightnorm(checkpoint['model'])
waveglow.cuda().eval()

# Build denoiser from the same model (uses zeros mode by default)
denoiser = Denoiser(waveglow, filter_length=1024, n_overlap=4,
                    win_length=1024, mode='zeros').cuda()

mel = torch.load('mel_spectrograms/LJ001-0001.wav.pt').cuda().unsqueeze(0)

with torch.no_grad():
    audio = waveglow.infer(mel, sigma=0.6)            # raw synthesis
    audio_denoised = denoiser(audio, strength=0.1)    # remove bias
    audio_denoised = (audio_denoised * 32768.0).squeeze().cpu().numpy().astype('int16')

write('output_denoised.wav', 22050, audio_denoised)
```

--------------------------------

### WaveGlow Inference: Load Checkpoint and Synthesize Audio

Source: https://context7.com/nvidia/waveglow/llms.txt

Loads a pre-trained WaveGlow model from a checkpoint file and prepares it for inference. The `remove_weightnorm` function should be called to fuse weight normalization layers for faster inference.

```python
import torch
from glow import WaveGlow

# Load a pre-trained checkpoint
checkpoint = torch.load('waveglow_256channels.pt', map_location='cpu')
waveglow = checkpoint['model']
waveglow = WaveGlow.remove_weightnorm(waveglow)
waveglow.cuda().eval()

```

--------------------------------

### Launch Multi-GPU Training with distributed.py

Source: https://context7.com/nvidia/waveglow/llms.txt

Use distributed.py to launch data-parallel multi-GPU training. It automatically detects CUDA devices and spawns subprocesses for each GPU, synchronizing gradients using NCCL.

```bash
python distributed.py \
    -c config.json \
    -s stdout_logs/
```

--------------------------------

### WaveGlow Training Loss Calculation

Source: https://context7.com/nvidia/waveglow/llms.txt

Demonstrates how to use WaveGlowLoss for training WaveGlow. This loss computes the negative log-likelihood of the audio. Ensure the model and criterion are on CUDA and gradients are zeroed before backward pass.

```python
import torch
from glow import WaveGlow, WaveGlowLoss

model = WaveGlow(**waveglow_config).cuda()
criterion = WaveGlowLoss(sigma=1.0)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

mel   = torch.randn(4, 80, 62).cuda()
audio = torch.randn(4, 16000).cuda()

model.zero_grad()
outputs = model((mel, audio))          # returns (z, log_s_list, log_det_W_list)
loss = criterion(outputs)              # scalar negative log-likelihood
loss.backward()
optimizer.step()

print(f"Loss: {loss.item():.6f}")
```

--------------------------------

### Batch Audio Synthesis (FP32)

Source: https://context7.com/nvidia/waveglow/llms.txt

Generate audio files from pre-computed mel-spectrograms using FP32 precision. The output files are saved to the specified output directory.

```bash
python3 inference.py \
    -f <(ls mel_spectrograms/*.pt) \
    -w waveglow_256channels.pt \
    -o synthesized/ \
    --sigma 0.6
```

--------------------------------

### Convert Legacy WaveGlow Checkpoint

Source: https://context7.com/nvidia/waveglow/llms.txt

Use convert_model.py to migrate old WaveGlow checkpoints to the current fused architecture. This script handles the fusion of residual/skip connections.

```bash
python convert_model.py old_waveglow_checkpoint.pt new_waveglow_checkpoint.pt
```

--------------------------------

### Perform Inference with Trained WaveGlow Network

Source: https://github.com/nvidia/waveglow/blob/master/README.md

Generate audio using your custom-trained WaveGlow network. This command assumes you have trained a model and generated mel-spectrograms for inference.

```bash
ls *.pt > mel_files.txt
python3 inference.py -f mel_files.txt -w checkpoints/waveglow_10000 -o . --is_fp16 -s 0.6
```

--------------------------------

### Mel2Samp Dataset and Mel-Spectrogram Extraction

Source: https://context7.com/nvidia/waveglow/llms.txt

Shows how to use Mel2Samp for creating a dataset of audio segments and their corresponding mel-spectrograms. It can be used for training or standalone to precompute mel-spectrograms for inference.

```python
from mel2samp import Mel2Samp, load_wav_to_torch
from torch.utils.data import DataLoader

# Dataset for training
dataset = Mel2Samp(
    training_files="train_files.txt",  # text file with one WAV path per line
    segment_length=16000,
    filter_length=1024,
    hop_length=256,
    win_length=1024,
    sampling_rate=22050,
    mel_fmin=0.0,
    mel_fmax=8000.0
)

loader = DataLoader(dataset, batch_size=8, shuffle=True, num_workers=2, drop_last=True)
mel, audio = next(iter(loader))
# mel:   torch.Size([8, 80, 63])
# audio: torch.Size([8, 16000])  — normalized to [-1, 1]

# Standalone: compute and save mel-spectrogram for a single WAV
audio_tensor, sr = load_wav_to_torch('data/LJ001-0001.wav')
mel_spec = dataset.get_mel(audio_tensor)
# mel_spec shape: (80, T)  — ready to save with torch.save()
import torch
torch.save(mel_spec, 'LJ001-0001.wav.pt')
```

--------------------------------

### Programmatic Checkpoint Conversion

Source: https://context7.com/nvidia/waveglow/llms.txt

This snippet demonstrates how to programmatically update a WaveGlow model checkpoint using the `update_model` function from `convert_model.py`.

```python
import torch
from convert_model import update_model

checkpoint = torch.load('old_checkpoint.pt', map_location='cpu')
checkpoint['model'] = update_model(checkpoint['model'])
torch.save(checkpoint, 'new_checkpoint.pt')
print("Conversion complete.")
```

--------------------------------

### Generate Mel-Spectrograms for Test Set

Source: https://github.com/nvidia/waveglow/blob/master/README.md

Convert audio files from the test set into mel-spectrograms using the specified configuration. This step is necessary before performing inference on your trained model.

```bash
python mel2samp.py -f test_files.txt -o . -c config.json
```

--------------------------------

### Infer Audio with WaveGlow

Source: https://context7.com/nvidia/waveglow/llms.txt

Loads a pre-computed mel-spectrogram and uses WaveGlow to synthesize audio. Ensure the mel-spectrogram is loaded and moved to CUDA, then unsqueeze to add a batch dimension. The output audio is scaled to int16 range.

```python
import torch
mel = torch.load('mel_spectrograms/LJ001-0001.wav.pt')
mel = mel.cuda().unsqueeze(0)  # add batch dimension → (1, 80, T)

with torch.no_grad():
    audio = waveglow.infer(mel, sigma=0.6)  # sigma=0.6 recommended for quality
    # Scale to int16 range
    audio = audio * 32768.0
    audio = audio.squeeze().cpu().numpy().astype('int16')

from scipy.io.wavfile import write
write('output.wav', 22050, audio)
```

--------------------------------

### Optimize WaveGlow Model for Inference

Source: https://context7.com/nvidia/waveglow/llms.txt

The `remove_weightnorm` static method strips weight normalization layers, fusing them into the model weights for faster inference. This operation is irreversible and should only be called after training.

```python
import torch
from glow import WaveGlow

checkpoint = torch.load('waveglow_256channels.pt', map_location='cpu')
model = checkpoint['model']

# Remove weight norm for inference (irreversible — do NOT call during training)
model = WaveGlow.remove_weightnorm(model)
model.cuda().eval()

# Verify: WN layers no longer have weight_g / weight_v parameters
for name, param in model.WN[0].named_parameters():
    print(name, param.shape)
```

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