### Multi-GPU Training Setup with Accelerate

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Demonstrates how to configure and launch multi-GPU training for AudioLM using the 🤗 Accelerate library. First, run 'accelerate config' to set up the environment, then use 'accelerate launch train.py' to start the training script.

```bash
$ accelerate config
```

```bash
$ accelerate launch train.py
```

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

### Install AudioLM-Pytorch

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Install the package via pip.

```bash
$ pip install audiolm-pytorch
```

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

### Install audiolm-pytorch with Pip

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Installs the audiolm-pytorch library and its dependencies using pip. This command should be run in an environment with Python and pip available.

```python
!pip install audiolm-pytorch
```

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

### Load SoundStream from Checkpoint

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Initialize and load a SoundStream model directly from a saved checkpoint file using a class method. This simplifies setup by automatically applying saved configurations.

```python
from audiolm_pytorch import SoundStream

soundstream = SoundStream.init_and_load_from('./path/to/checkpoint.pt')
```

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

### Initialize Fine Transformer Trainer

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Configures the FineTransformer and its trainer. This setup requires a trained SoundStream codec. The trainer is initialized with the transformer, codec, dataset path, batch size, and training steps.

```python
soundstream = SoundStream.init_and_load_from('/path/to/trained/soundstream.pt')

fine_transformer = FineTransformer(
    num_coarse_quantizers = 3,
    num_fine_quantizers = 5,
    codebook_size = 1024,
    dim = 512,
    depth = 6,
    flash_attn = True
)

trainer = FineTransformerTrainer(
    transformer = fine_transformer,
    codec = soundstream,
    folder = '/path/to/audio/files',
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1_000_000
)

trainer.train()
```

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

### Configure and Launch Distributed Training

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Use HuggingFace Accelerate to manage multi-GPU training environments.

```bash
# Configure accelerate (run once)
accelerate config

# Launch distributed training
accelerate launch train.py
```

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

### Initialize SoundStream Trainer

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Sets up the SoundStream trainer for AudioLM. This requires the `SoundStream` and `SoundStreamTrainer` classes. The `dataset_folder` must be defined. Note the effective batch size calculation and the reduced `num_train_steps` for demonstration.

```python
soundstream = SoundStream(
    codebook_size = 1024,
    rq_num_quantizers = 8,
)

trainer = SoundStreamTrainer(
    soundstream,
    folder = dataset_folder,
    batch_size = 4,
    grad_accum_every = 8,         # effective batch size of 32
    data_max_length = 320 * 32,
    save_results_every = 2,
    save_model_every = 4,
    num_train_steps = 9
).cuda()
# NOTE: I changed num_train_steps to 9 (aka 8 + 1) from 10000 to make things go faster for demo purposes
```

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

### Initialize AudioLM and MusicLM SoundStream Presets

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Use these pre-configured classes to instantiate SoundStream models with hyperparameters optimized for speech or music tasks.

```python
from audiolm_pytorch import AudioLMSoundStream, MusicLMSoundStream

# AudioLM preset: 16kHz, 12 quantizers, optimized for speech
audiolm_soundstream = AudioLMSoundStream(
    codebook_size = 1024,
    # Default: strides = (2, 4, 5, 8), target_sample_hz = 16000, rq_num_quantizers = 12
)

# MusicLM preset: 24kHz, 12 quantizers, optimized for music
musiclm_soundstream = MusicLMSoundStream(
    codebook_size = 1024,
    # Default: strides = (3, 4, 5, 8), target_sample_hz = 24000, rq_num_quantizers = 12
)
```

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

### Initialize and Train SemanticTransformer

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Downloads required HuBERT checkpoints and configures the SemanticTransformer trainer.

```python
# hubert checkpoints can be downloaded at
# https://github.com/facebookresearch/fairseq/tree/main/examples/hubert
if not os.path.isdir("hubert"):
  os.makedirs("hubert")
if not os.path.isfile(hubert_ckpt):
  hubert_ckpt_download = f"https://dl.fbaipublicfiles.com/{hubert_ckpt}"
  urllib.request.urlretrieve(hubert_ckpt_download, f"./{hubert_ckpt}")
if not os.path.isfile(hubert_quantizer):
  hubert_quantizer_download = f"https://dl.fbaipublicfiles.com/{hubert_quantizer}"
  urllib.request.urlretrieve(hubert_quantizer_download, f"./{hubert_quantizer}")

wav2vec = HubertWithKmeans(
    checkpoint_path = f'./{hubert_ckpt}',
    kmeans_path = f'./{hubert_quantizer}'
)

semantic_transformer = SemanticTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    dim = 1024,
    depth = 6
).cuda()


trainer = SemanticTransformerTrainer(
    transformer = semantic_transformer,
    wav2vec = wav2vec,
    folder = dataset_folder,
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1
)

trainer.train()
```

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

### Initialize Coarse Transformer Trainer

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Sets up the CoarseTransformer and its trainer. This requires a pre-trained wav2vec checkpoint and a trained SoundStream codec. The trainer is configured with the dataset path, batch size, and number of training steps.

```python
wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)

soundstream = SoundStream.init_and_load_from('/path/to/trained/soundstream.pt')

coarse_transformer = CoarseTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    codebook_size = 1024,
    num_coarse_quantizers = 3,
    dim = 512,
    depth = 6,
    flash_attn = True
)

trainer = CoarseTransformerTrainer(
    transformer = coarse_transformer,
    codec = soundstream,
    wav2vec = wav2vec,
    folder = '/path/to/audio/files',
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1_000_000
)

trainer.train()
```

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

### Initialize Text-Conditioned Semantic Transformer Trainer

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Sets up the SemanticTransformer for text-conditioned audio generation. Requires a wav2vec model and a custom dataset that returns both audio and its text description. The trainer is configured with dataset, batch size, gradient accumulation, and training steps.

```python
import torch
from audiolm_pytorch import HubertWithKmeans, SemanticTransformer, SemanticTransformerTrainer
from torch.utils.data import Dataset

wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)

semantic_transformer = SemanticTransformer(
    num_semantic_tokens = 500,
    dim = 1024,
    depth = 6,
    has_condition = True,               # this will have to be set to True
    cond_as_self_attn_prefix = True     # whether to condition as prefix to self attention, instead of cross attention, as was done in 'VALL-E' paper
).cuda()

# mock text audio dataset (as an example)

# you will have to extend your own from `Dataset`, and return an audio tensor as well as a string (the audio description) in any order (the framework will autodetect and route it into the transformer)


class MockTextAudioDataset(Dataset):
    def __init__(self, length = 100, audio_length = 320 * 32):
        super().__init__()
        self.audio_length = audio_length
        self.len = length

    def __len__(self):
        return self.len

    def __getitem__(self, idx):
        mock_audio = torch.randn(self.audio_length)
        mock_caption = 'audio caption'
        return mock_caption, mock_audio

dataset = MockTextAudioDataset()

# instantiate semantic transformer trainer and train

trainer = SemanticTransformerTrainer(
    transformer = semantic_transformer,
    wav2vec = wav2vec,
    dataset = dataset,
    batch_size = 4,
    grad_accum_every = 8,
    data_max_length = 320 * 32,
    num_train_steps = 1_000_000
)

trainer.train()

# after much training above

sample = trainer.generate(text = ['sound of rain drops on the rooftops'], batch_size = 1, max_length = 2) # (1, < 128) - may terminate early if it detects [eos]
```

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

### Configure CoarseTransformer Trainer

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Sets up the CoarseTransformer with a pre-trained SoundStream codec and HuBERT quantizer.

```python
wav2vec = HubertWithKmeans(
    checkpoint_path = f'./{hubert_ckpt}',
    kmeans_path = f'./{hubert_quantizer}'
)

soundstream = SoundStream(
    codebook_size = 1024,
    rq_num_quantizers = 8,
)

soundstream.load(f"./{soundstream_ckpt}")

coarse_transformer = CoarseTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    codebook_size = 1024,
    num_coarse_quantizers = 3,
    dim = 512,
    depth = 6
)

trainer = CoarseTransformerTrainer(
    transformer = coarse_transformer,
    codec = soundstream,
    wav2vec = wav2vec,
    folder = dataset_folder,
    batch_size = 1,
    data_max_length = 320 * 32,
    save_results_every = 2,
    save_model_every = 4,
    num_train_steps = 9
)
# NOTE: I changed num_train_steps to 9 (aka 8 + 1) from 10000 to make things go faster for demo purposes
```

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

### Create Placeholder Dataset

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Generates a directory structure and populates it with placeholder WAV files. This function uses `get_sinewave` and `save_wav`. It will not create the dataset if the directory already exists.

```python
import os

def make_placeholder_dataset():
  # Make a placeholder dataset with a few .wav files that you can "train" on, just to verify things work e2e
  if os.path.isdir(dataset_folder):
    return
  os.makedirs(dataset_folder)
  save_wav(f"{dataset_folder}/example.wav", get_sinewave())
  save_wav(f"{dataset_folder}/example2.wav", get_sinewave(duration_ms=500))
  os.makedirs(f"{dataset_folder}/subdirectory")
  save_wav(f"{dataset_folder}/subdirectory/example.wav", get_sinewave(freq=330.0))

make_placeholder_dataset()
```

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

### Integrate SoundStreamTrainer with Weights & Biases

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Enable Weights & Biases (wandb) tracking for SoundStream training. Set `use_wandb_tracking` to True and use the `wandb_tracker` context manager to specify project and run names.

```python
trainer = SoundStreamTrainer(
    soundstream,
    ...,
    use_wandb_tracking = True
)

# wrap .train() with contextmanager, specifying project and run name

with trainer.wandb_tracker(project = 'soundstream', run = 'baseline'):
    trainer.train()
```

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

### Initialize and Use AudioLM

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Combines all trained components (wav2vec, codec, semantic, coarse, and fine transformers) into a single AudioLM model for audio generation. Supports generation from scratch, with priming audio, or with text conditioning.

```python
from audiolm_pytorch import AudioLM

# Assuming wav2vec, soundstream, semantic_transformer, coarse_transformer, fine_transformer are already initialized and trained

# Initialize AudioLM with trained components
# audiolm = AudioLM(
#     wav2vec = wav2vec,
#     codec = soundstream,
#     semantic_transformer = semantic_transformer,
#     coarse_transformer = coarse_transformer,
#     fine_transformer = fine_transformer
# )

# generated_wav = audiolm(batch_size = 1)

# or with priming

# generated_wav_with_prime = audiolm(prime_wave = torch.randn(1, 320 * 8))

# or with text condition, if given

# generated_wav_with_text_condition = audiolm(text = ['chirping of birds and the distant echos of bells'])
```

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

### Define dataset and checkpoint paths

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Sets up essential variables for dataset locations and pre-trained model checkpoints. Ensure these paths are updated to reflect your project structure and downloaded model locations.

```python
# define all dataset paths, checkpoints, etc
dataset_folder = "placeholder_dataset"
soundstream_ckpt = "results/soundstream.8.pt" # this can change depending on number of steps
hubert_ckpt = 'hubert/hubert_base_ls960.pt'
hubert_quantizer = f'hubert/hubert_base_ls960_L9_km500.bin' # listed in row "HuBERT Base (~95M params)", column Quantizer
```

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

### Assemble and Run AudioLM

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Combines pre-trained components into the AudioLM pipeline to generate audio.

```python
# Everything together
audiolm = AudioLM(
    wav2vec = wav2vec,
    codec = soundstream,
    semantic_transformer = semantic_transformer,
    coarse_transformer = coarse_transformer,
    fine_transformer = fine_transformer
)

generated_wav = audiolm(batch_size = 1)
```

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

### Initialize EncodecWrapper

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Create an instance of the EncodecWrapper to use the pretrained 24kHz Encodec model.

```python
from audiolm_pytorch import EncodecWrapper
encodec = EncodecWrapper()
```

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

### Initialize and Train Fine Transformer

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Initializes the FineTransformer for generating fine acoustic tokens from coarse tokens. Requires SoundStream for acoustic tokenization and a trainer for the training process.

```python
from audiolm_pytorch import SoundStream, FineTransformer, FineTransformerTrainer

soundstream = SoundStream.init_and_load_from('/path/to/trained/soundstream.pt')

fine_transformer = FineTransformer(
    num_coarse_quantizers = 3,
    num_fine_quantizers = 5,
    codebook_size = 1024,
    dim = 512,
    depth = 6,
    flash_attn = True
)

trainer = FineTransformerTrainer(
    transformer = fine_transformer,
    codec = soundstream,
    folder = '/path/to/audio/files',
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1_000_000
)

trainer.train()
```

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

### Initialize and Train SoundStream

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Configures the SoundStream neural codec with residual vector quantization and trains it using the SoundStreamTrainer.

```python
from audiolm_pytorch import SoundStream, SoundStreamTrainer

# Initialize SoundStream with residual vector quantization
soundstream = SoundStream(
    codebook_size = 4096,
    rq_num_quantizers = 8,
    rq_groups = 2,                       # multi-headed residual VQ
    use_lookup_free_quantizer = True,    # use lookup-free quantization
    use_finite_scalar_quantizer = False,
    attn_window_size = 128,              # local attention at bottleneck
    attn_depth = 2                       # transformer blocks in bottleneck
)

# Train SoundStream on audio files
trainer = SoundStreamTrainer(
    soundstream,
    folder = '/path/to/audio/files',
    batch_size = 4,
    grad_accum_every = 8,         # effective batch size of 32
    data_max_length_seconds = 2,  # train on 2 second audio
    num_train_steps = 1_000_000
).cuda()

trainer.train()

# After training, use for encoding/decoding
soundstream.eval()
audio = torch.randn(10080).cuda()
recons = soundstream(audio, return_recons_only = True)  # (1, 10080)
```

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

### Initialize and Use AudioLM for End-to-End Generation

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Combines SemanticTransformer, CoarseTransformer, and FineTransformer for end-to-end audio generation. Supports unconditional generation, audio priming, and text conditioning.

```python
from audiolm_pytorch import AudioLM, HubertWithKmeans, SoundStream
from audiolm_pytorch import SemanticTransformer, CoarseTransformer, FineTransformer

# Initialize components (assuming all are trained)
wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)
soundstream = SoundStream.init_and_load_from('./soundstream.pt')

semantic_transformer = SemanticTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    dim = 1024,
    depth = 6
)

coarse_transformer = CoarseTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    codebook_size = 1024,
    num_coarse_quantizers = 3,
    dim = 512,
    depth = 6
)

fine_transformer = FineTransformer(
    num_coarse_quantizers = 3,
    num_fine_quantizers = 5,
    codebook_size = 1024,
    dim = 512,
    depth = 6
)

# Create AudioLM with all components
audiolm = AudioLM(
    wav2vec = wav2vec,
    codec = soundstream,
    semantic_transformer = semantic_transformer,
    coarse_transformer = coarse_transformer,
    fine_transformer = fine_transformer
)
```

```python
# Generate audio unconditionally
generated_wav = audiolm(batch_size = 1)
```

```python
# Generate with audio priming
generated_wav_with_prime = audiolm(prime_wave = torch.randn(1, 320 * 8))
```

```python
# Generate with text conditioning (if transformers have has_condition=True)
generated_wav_with_text = audiolm(
    text = ['chirping of birds and the distant echos of bells']
)
```

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

### Initialize and Train SoundStream

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Instantiate and train the SoundStream model. Configure parameters like codebook size, quantizer groups, and attention settings. Training requires specifying an audio file folder, batch size, and training steps.

```python
from audiolm_pytorch import SoundStream, SoundStreamTrainer

soundstream = SoundStream(
    codebook_size = 4096,
    rq_num_quantizers = 8,
    rq_groups = 2,                       # this paper proposes using multi-headed residual vector quantization - https://arxiv.org/abs/2305.02765
    use_lookup_free_quantizer = True,    # whether to use residual lookup free quantization - there are now reports of successful usage of this unpublished technique
    use_finite_scalar_quantizer = False, # whether to use residual finite scalar quantization
    attn_window_size = 128,              # local attention receptive field at bottleneck
    attn_depth = 2                       # 2 local attention transformer blocks - the soundstream folks were not experts with attention, so i took the liberty to add some. encodec went with lstms, but attention should be better
)

trainer = SoundStreamTrainer(
    soundstream,
    folder = '/path/to/audio/files',
    batch_size = 4,
    grad_accum_every = 8,         # effective batch size of 32
    data_max_length_seconds = 2,  # train on 2 second audio
    num_train_steps = 1_000_000
).cuda()

trainer.train()
```

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

### Train FineTransformer

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Initializes the SoundStream codec and FineTransformer, then executes the training loop.

```python
soundstream = SoundStream(
    codebook_size = 1024,
    rq_num_quantizers = 8,
)

soundstream.load(f"./{soundstream_ckpt}")

fine_transformer = FineTransformer(
    num_coarse_quantizers = 3,
    num_fine_quantizers = 5,
    codebook_size = 1024,
    dim = 512,
    depth = 6
)

trainer = FineTransformerTrainer(
    transformer = fine_transformer,
    codec = soundstream,
    folder = dataset_folder,
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 9
)
# NOTE: I changed num_train_steps to 9 (aka 8 + 1) from 10000 to make things go faster for demo purposes
# adjusting save_*_every variables for the same reason

trainer.train()
```

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

### Implement Distributed Training Script

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

The trainer automatically handles distributed execution when launched via Accelerate.

```python
# train.py
from audiolm_pytorch import SoundStream, SoundStreamTrainer

soundstream = SoundStream(codebook_size = 4096, rq_num_quantizers = 8)

trainer = SoundStreamTrainer(
    soundstream,
    folder = '/path/to/audio/files',
    batch_size = 4,
    grad_accum_every = 8,
    data_max_length_seconds = 2,
    num_train_steps = 1_000_000
)

# Accelerate handles multi-GPU automatically
trainer.train()
```

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

### Enable Weights & Biases Tracking for Trainers

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Enable experiment tracking by setting use_wandb_tracking to True and wrapping the training loop in the wandb_tracker context manager.

```python
from audiolm_pytorch import SoundStream, SoundStreamTrainer

soundstream = SoundStream(
    codebook_size = 4096,
    rq_num_quantizers = 8
)

trainer = SoundStreamTrainer(
    soundstream,
    folder = '/path/to/audio/files',
    batch_size = 4,
    grad_accum_every = 8,
    data_max_length_seconds = 2,
    num_train_steps = 1_000_000,
    use_wandb_tracking = True  # Enable W&B tracking
)

# Wrap training with W&B context manager
with trainer.wandb_tracker(project = 'soundstream', run = 'baseline'):
    trainer.train()
```

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

### Initialize Semantic Transformer Trainer

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Initializes the SemanticTransformer and its trainer. Requires a pre-trained wav2vec checkpoint and kmeans model. The trainer is configured with dataset path, batch size, and training steps.

```python
wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)

semantic_transformer = SemanticTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    dim = 1024,
    depth = 6,
    flash_attn = True
).cuda()


trainer = SemanticTransformerTrainer(
    transformer = semantic_transformer,
    wav2vec = wav2vec,
    folder ='/path/to/audio/files',
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1
)

trainer.train()
```

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

### Train SoundStream Model

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Initiates the training process for a SoundStream model.

```python
trainer.train()
```

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

### Initialize and Train Coarse Transformer

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Initializes the CoarseTransformer for generating coarse acoustic tokens conditioned on semantic tokens. Requires HubertWithKmeans for semantic tokenization and SoundStream for acoustic tokenization, along with a trainer.

```python
from audiolm_pytorch import HubertWithKmeans, SoundStream, CoarseTransformer, CoarseTransformerTrainer

wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)

soundstream = SoundStream.init_and_load_from('/path/to/trained/soundstream.pt')

coarse_transformer = CoarseTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    codebook_size = 1024,
    num_coarse_quantizers = 3,
    dim = 512,
    depth = 6,
    flash_attn = True
)

trainer = CoarseTransformerTrainer(
    transformer = coarse_transformer,
    codec = soundstream,
    wav2vec = wav2vec,
    folder = '/path/to/audio/files',
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1_000_000
)

trainer.train()
```

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

### Import necessary libraries for audiolm-pytorch

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Imports all required modules from the audiolm-pytorch library, along with standard Python libraries for file operations, data handling, and deep learning.

```python
import math
import wave
import struct
import os
import urllib.request
import tarfile
from audiolm_pytorch import SoundStream, SoundStreamTrainer, HubertWithKmeans, SemanticTransformer, SemanticTransformerTrainer, HubertWithKmeans, CoarseTransformer, CoarseTransformerWrapper, CoarseTransformerTrainer, FineTransformer, FineTransformerWrapper, FineTransformerTrainer, AudioLM
from torch import nn
import torch
import torchaudio
```

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

### Check GPU Availability with NVIDIA-SMI

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

This command displays information about the NVIDIA GPU(s) available on the system. Ensure a GPU is detected and functioning correctly before proceeding.

```bash
Mon Jan 30 20:47:47 2023       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |
|-------------------------------+----------------------+----------------------|
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   73C    P0    32W /  70W |  10692MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      5896      C                                   10689MiB |
+-----------------------------------------------------------------------------+
```

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

### Import Specialized SoundStream Variants

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Import and use SoundStream variants specifically designed for AudioLM and MusicLM.

```python
from audiolm_pytorch import AudioLMSoundStream, MusicLMSoundStream

soundstream = AudioLMSoundStream(...) # say you want the hyperparameters as in Audio LM paper

# rest is the same as above
```

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

### Generate Placeholder Sine Wave Audio

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Creates a sine wave audio signal. Useful for generating synthetic data for testing or demonstration purposes. Requires `math` module.

```python
import math

def get_sinewave(freq=440.0, duration_ms=200, volume=1.0, sample_rate=44100.0):
  # code adapted from https://stackoverflow.com/a/33913403
  audio = []
  num_samples = duration_ms * (sample_rate / 1000.0)
  for x in range(int(num_samples)):
    audio.append(volume * math.sin(2 * math.pi * freq * (x / sample_rate)))
  return audio
```

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

### Initialize and Train Semantic Transformer

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Initializes the SemanticTransformer for predicting semantic tokens autoregressively, optionally conditioned on text embeddings. Requires HubertWithKmeans for tokenization and a trainer for the training process.

```python
from audiolm_pytorch import HubertWithKmeans, SemanticTransformer, SemanticTransformerTrainer

wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)

semantic_transformer = SemanticTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    dim = 1024,
    depth = 6,
    flash_attn = True,
    has_condition = True,               # enable text conditioning
    cond_as_self_attn_prefix = True     # VALL-E style conditioning
).cuda()

trainer = SemanticTransformerTrainer(
    transformer = semantic_transformer,
    wav2vec = wav2vec,
    folder = '/path/to/audio/files',
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1_000_000
)

trainer.train()
```

```python
# Generate semantic tokens from text
sample = trainer.generate(
    text = ['sound of rain drops on the rooftops'],
    batch_size = 1,
    max_length = 2048
)
```

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

### Extract Semantic Tokens with HubertWithKmeans

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Extracts high-level semantic tokens from audio using a pre-trained HuBERT model and k-means clustering.

```python
from audiolm_pytorch import HubertWithKmeans
import torch

# Load HuBERT with k-means clustering
# Checkpoints available at: https://github.com/facebookresearch/fairseq/tree/main/examples/hubert
wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin',
    target_sample_hz = 16000,
    output_layer = 9  # which transformer layer to extract features from
)

# Extract semantic tokens from audio
audio = torch.randn(2, 16000)  # batch of 2, 1 second each
semantic_tokens = wav2vec(audio, flatten = False)  # [batch, seq_len]
```

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

### Use EncodecWrapper for Audio Encoding

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Provides a drop-in replacement for SoundStream using Meta AI's pre-trained Encodec model.

```python
from audiolm_pytorch import EncodecWrapper
import torch

# Use pretrained 24kHz Encodec (no training required)
encodec = EncodecWrapper(
    target_sample_hz = 24000,
    bandwidth = 6.0  # 6kbps uses 8 codebooks
)

# Encode audio to get embeddings and codebook indices
audio = torch.randn(1, 24000)  # 1 second of audio at 24kHz
emb, codes, _ = encodec(audio, return_encoded = True)
# emb: embeddings, codes: [batch, timesteps, num_quantizers]

# Decode back to audio from codebook indices
reconstructed = encodec.decode_from_codebook_indices(codes)
# reconstructed: [batch, 1, num_samples]
```

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

### Access Codebook Properties

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Prints the codebook size and downsample factor of the wav2vec model. Useful for understanding model configuration.

```python
print(f"Codebook size: {wav2vec.codebook_size}")      # e.g., 500
print(f"Downsample factor: {wav2vec.downsample_factor}")  # 320
```

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

### Save Audio Data to WAV File

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Saves a list of audio samples to a WAV file. This function requires `wave` and `struct` modules. Ensure the audio data is within the range of a 16-bit signed integer (-32767 to 32767).

```python
import wave
import struct

def save_wav(file_name, audio, sample_rate=44100.0):
  # Open up a wav file
  wav_file=wave.open(file_name,"w")
  # wav params
  nchannels = 1
  sampwidth = 2
  # 44100 is the industry standard sample rate - CD quality.  If you need to
  # save on file size you can adjust it downwards. The stanard for low quality
  # is 8000 or 8kHz.
  nframes = len(audio)
  comptype = "NONE"
  compname = "not compressed"
  wav_file.setparams((nchannels, sampwidth, sample_rate, nframes, comptype, compname))
  # WAV files here are using short, 16 bit, signed integers for the 
  # sample size.  So we multiply the floating point data we have by 32767, the
  # maximum value for a short integer.  NOTE: It is theortically possible to
  # use the floating point -1.0 to 1.0 data directly in a WAV file but not
  # obvious how to do that using the wave module in python.
  for sample in audio:
      wav_file.writeframes(struct.pack('h', int( sample * 32767.0 )))
  wav_file.close()
  return
```

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

### Tokenize Audio with SoundStream

Source: https://context7.com/lucidrains/audiolm-pytorch/llms.txt

Converts raw audio waveforms into discrete codebook indices using a pre-trained SoundStream model.

```python
from audiolm_pytorch import SoundStream
import torch

# Load trained SoundStream from checkpoint
soundstream = SoundStream.init_and_load_from('./path/to/checkpoint.pt')
soundstream.eval()

# Tokenize audio into codebook indices
audio = torch.randn(1, 512 * 320)  # batch of 1, ~10 seconds at 16kHz
codes = soundstream.tokenize(audio)  # returns codebook indices

# Decode back from codebook indices to audio
recon_audio = soundstream.decode_from_codebook_indices(codes)

# Verify reconstruction matches forward pass
assert torch.allclose(
    recon_audio,
    soundstream(audio, return_recons_only = True)
)
```

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

### Download and Extract Dataset

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Downloads a specified dataset archive (e.g., from OpenSLR) and extracts its contents. This code requires `urllib.request` and `tarfile` modules. It checks if the files/directories already exist to avoid re-downloading or re-extracting.

```python
# Get actual dataset. Uncomment this if you want to try training on real data

# full dataset: https://www.openslr.org/12
# We'll use https://us.openslr.org/resources/12/dev-clean.tar.gz development set, "clean" speech.
# We *should* train on, well, training, but this is just to demo running things end-to-end at all so I just picked a small clean set.

# url = "https://us.openslr.org/resources/12/dev-clean.tar.gz"
# filename = "dev-clean"
# filename_targz = filename + ".tar.gz"
# if not os.path.isfile(filename_targz):
#   urllib.request.urlretrieve(url, filename_targz)
# if not os.path.isdir(filename):
#   # open file
#   with tarfile.open(filename_targz) as t:
#     t.extractall(filename)
```

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

### Save Generated Audio

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch_demo.ipynb

Saves the generated tensor to a WAV file using torchaudio.

```python
output_path = "out.wav"
sample_rate = 44100
torchaudio.save(output_path, generated_wav.cpu(), sample_rate)
```

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

### Test SoundStream Autoencoding

Source: https://github.com/lucidrains/audiolm-pytorch/blob/main/README.md

Evaluate the autoencoding capabilities of a trained SoundStream model. Ensure the model is in evaluation mode to disable residual dropout.

```python
# after a lot of training, you can test the autoencoding as so

soundstream.eval() # your soundstream must be in eval mode, to avoid having the residual dropout of the residual VQ necessary for training

audio = torch.randn(10080).cuda()
recons = soundstream(audio, return_recons_only = True) # (1, 10080) - 1 channel
```

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