ITTS, VALL-E,soundstorm

ITTS, VALL-E, and SoundStorm are all advanced technologies and models related to speech and audio processing.

ITTS (Interactive Text-to-Speech): ITTS usually refers to a Text-to-Speech (TTS) system that allows interactive control over the output. This could mean the user can modify pitch, speed, tone, and style dynamically. Advanced ITTS systems may integrate machine learning and neural network approaches to deliver more natural and flexible speech synthesis.

VALL-E: VALL-E is a neural codec language model developed by Microsoft for text-to-speech synthesis. It can generate high-quality speech audio from textual input and is notable for its ability to mimic the voice style and tone of a speaker given just a few seconds of reference audio. VALL-E uses a neural codec-based approach and is trained with a large amount of speech data to enable realistic and contextually accurate speech synthesis.

SoundStorm: SoundStorm is a model by Google that improves speech generation. It’s designed to generate high-quality and fast TTS audio by addressing issues like distortion and noise. SoundStorm enhances TTS models’ ability to generate longer, more coherent audio without the artifacts or errors present in previous models. It often uses transformer-based architectures for its predictions and generation of realistic sound.

These technologies are pushing the boundaries of natural language processing, voice synthesis, and real-time interaction.

To extract pre-encoded codebook IDs from a lot of raw audio using SoundStream, you will need to follow these general steps. SoundStream is a neural codec used for audio compression by learning efficient representations of raw audio. Here’s an overview of the process:

Steps to get pre-encoded codebook IDs:

1. Install Required Libraries: You need to have a SoundStream implementation ready. Google’s SoundStream may not have an official release, but several third-party implementations or similar models like Encodec are available.

pip install soundstream-pytorch
pip install torchaudio

pip install encodec

2. Load a Pre-trained Model: You should load a pre-trained SoundStream model if available or train it on a large corpus of audio data. Ensure that the model is trained on your specific audio domain to get high-quality compressed representations.

Example (using Encodec as an alternative):

from encodec import EncodecModel
from encodec.utils import convert_audio

model = EncodecModel.encodec_model_24khz()
model.set_target_bandwidth(6.0)  # Set the bitrate for the compression

3. Preprocess the Audio Data: Your raw audio needs to be preprocessed into the appropriate format (e.g., mono-channel, correct sampling rate, etc.).

from torchaudio.transforms import Resample
import torch

# Example of resampling to 24kHz mono
audio_data = torch.load("path_to_your_audio_file.pt")
resampler = Resample(orig_freq=44100, new_freq=24000)
audio_data = resampler(audio_data)

4. Convert Raw Audio into Codebook Representations: Pass your audio data through the encoder to get compressed tokens (codebook IDs). This involves running the audio through the neural codec model to obtain quantized vectors, which represent the compressed audio.

# Convert audio to the right format
audio_data = convert_audio(audio_data, model.sample_rate, model.channels)

# Pass the audio data through the model to get the codebook IDs
with torch.no_grad():
    encoded_frames = model.encode(audio_data)

# Extract codebook indices from the encoded frames
codebook_ids = encoded_frames["codebook_indices"]

Batch Processing for Large-Scale Data: For large datasets, you may want to process the audio in batches, especially if you have a lot of audio files.

from torch.utils.data import DataLoader

# Assuming you have a dataset of raw audio
audio_dataset = CustomAudioDataset('path_to_audio_files')
dataloader = DataLoader(audio_dataset, batch_size=16, shuffle=True)

for batch in dataloader:
    # Preprocess and encode each batch
    audio_data = preprocess_audio(batch)
    codebook_ids = model.encode(audio_data)
    # Save or use the codebook IDs

6. Save the Codebook IDs: Once you have the codebook IDs for your audio, you can save them to disk for later use or pass them into downstream models.

torch.save(codebook_ids, "path_to_save_codebook_ids.pt")

Summary
Load the pre-trained SoundStream or a similar neural codec model.
Preprocess your raw audio (resampling, converting to mono, etc.).
Encode the audio through the model to get the codebook IDs.
Process in batches for scalability.
Save the codebook IDs for future use.

To directly train on raw audio using a model like SoundStream (or any similar neural codec model), you’ll need to prepare your data and model in a way that can learn efficient audio compression through unsupervised or self-supervised training. Below is a step-by-step guide to training a neural codec on raw audio data:

Steps for Training on Raw Audio

1. Prepare Your Dataset
   Collect raw audio data, ensuring you have a diverse and representative dataset for the domain you’re targeting (speech, music, environmental sounds, etc.).
   Preprocess the data: Typically, this includes downsampling, converting stereo to mono, normalization, and possibly augmenting the data.
   Example:

Resampling to a specific frequency (e.g., 24kHz for SoundStream).
Normalizing the amplitude.
Converting to mono if required.
2. Define the SoundStream Model Architecture
SoundStream, like other neural codecs, consists of the following main components:

Encoder: Maps the raw audio into a latent representation.
Residual Vector Quantizer (RVQ): Quantizes the latent representation into a discrete set of codebook indices.
Decoder: Reconstructs the audio from the quantized latent representation.
For your training, you need a neural network that can handle these components, especially focusing on the Encoder, Quantizer, and Decoder.

import torch
import torch.nn as nn

class Encoder(nn.Module):
    def __init__(self):
        super(Encoder, self).__init__()
        # Define convolutional layers to encode raw audio into latent features
        self.conv1 = nn.Conv1d(in_channels=1, out_channels=64, kernel_size=4, stride=2, padding=1)
        # Additional layers to downsample and extract features

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        # Pass through additional layers
        return x

class Decoder(nn.Module):
    def __init__(self):
        super(Decoder, self).__init__()
        # Define transpose convolutional layers to reconstruct audio from latent features
        self.deconv1 = nn.ConvTranspose1d(in_channels=64, out_channels=1, kernel_size=4, stride=2, padding=1)

    def forward(self, x):
        x = torch.relu(self.deconv1(x))
        # Pass through additional layers
        return x

3. Quantization (Residual Vector Quantization - RVQ)
   You’ll need a vector quantizer to discretize the latent space. Residual Vector Quantization (RVQ) allows for efficient compression by reducing the bit rate.

class VectorQuantizer(nn.Module):
    def __init__(self, num_embeddings, embedding_dim):
        super(VectorQuantizer, self).__init__()
        self.embedding_dim = embedding_dim
        self.num_embeddings = num_embeddings
        self.embeddings = nn.Embedding(self.num_embeddings, self.embedding_dim)

    def forward(self, x):
        # Quantize latent vectors (discrete bottleneck)
        x_flattened = x.view(-1, self.embedding_dim)
        distances = (torch.sum(x_flattened**2, dim=1, keepdim=True) +
                     torch.sum(self.embeddings.weight**2, dim=1) -
                     2 * torch.matmul(x_flattened, self.embeddings.weight.t()))
        encoding_indices = torch.argmin(distances, dim=1)
        quantized = self.embeddings(encoding_indices).view(x.size())
        return quantized, encoding_indices

4. Loss Function
   Your training loss should balance the following:
   Reconstruction loss: Measure how well the audio is reconstructed (e.g., using Mean Squared Error (MSE)).
   Quantization loss: Encourage the model to use discrete representations effectively (e.g., using codebook commitment loss).

class SoundStream(nn.Module):
    def __init__(self, encoder, decoder, quantizer):
        super(SoundStream, self).__init__()
        self.encoder = encoder
        self.decoder = decoder
        self.quantizer = quantizer

    def forward(self, x):
        latent = self.encoder(x)
        quantized, _ = self.quantizer(latent)
        reconstructed = self.decoder(quantized)
        return reconstructed

5. Training Loop
   Now, create the training loop to optimize the model using backpropagation and your chosen optimizer (e.g., Adam).

import torch.optim as optim

# Define the model, optimizer, and loss function
encoder = Encoder()
decoder = Decoder()
quantizer = VectorQuantizer(num_embeddings=512, embedding_dim=64)
model = SoundStream(encoder, decoder, quantizer)

optimizer = optim.Adam(model.parameters(), lr=1e-4)
mse_loss = nn.MSELoss()

# Training loop
for epoch in range(num_epochs):
    for batch in train_loader:
        optimizer.zero_grad()
        
        # Forward pass
        raw_audio = batch['audio']  # Your raw audio batch
        reconstructed_audio = model(raw_audio)
        
        # Compute the reconstruction loss
        loss = mse_loss(reconstructed_audio, raw_audio)
        
        # Backward pass and optimization
        loss.backward()
        optimizer.step()

    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

6. Evaluation and Saving the Model
   Evaluate the model on the test set by computing reconstruction quality (e.g., signal-to-noise ratio or perceptual metrics like MOS). Once satisfied, save the model for future use.

torch.save(model.state_dict(), 'soundstream_model.pth')

Additional Considerations
Batch Size: Make sure your batch size fits your GPU/CPU memory requirements.
Augmentation: You can apply augmentations like time-stretching, noise addition, etc., to make the model more robust.
Compression: Depending on your requirements, fine-tune the number of quantization levels and embeddings to adjust the bitrate and audio quality trade-offs.
Summary of the Training Process:
Prepare your dataset by loading and preprocessing raw audio.
Build the Encoder, Quantizer, and Decoder components for a SoundStream-like model.
Train the model using a reconstruction loss (MSE) and codebook commitment loss.
Evaluate and save the trained model for inference.
By training directly on raw audio, you’ll create a neural codec that can compress audio data into efficient codebook representations. If you’d like, I can help you implement these steps with more specific code details or work through any of the steps in greater depth.

Create a Custom Dataset Class
You’ll need to create a PyTorch Dataset class to load raw audio files from a directory. This class will be responsible for loading, preprocessing (e.g., resampling), and returning the audio tensors.

Here’s an example AudioDataset:

import os
import torch
import torchaudio
from torch.utils.data import Dataset

class AudioDataset(Dataset):
    def __init__(self, data_dir, transform=None):
        """
        Args:
            data_dir (string): Directory with all the raw audio files.
            transform (callable, optional): Optional transform to be applied on a sample.
        """
        self.data_dir = data_dir
        self.audio_files = os.listdir(data_dir)  # List of all audio files
        self.transform = transform

    def __len__(self):
        return len(self.audio_files)

    def __getitem__(self, idx):
        audio_path = os.path.join(self.data_dir, self.audio_files[idx])
        waveform, sample_rate = torchaudio.load(audio_path)  # Load raw audio file
        
        # Resample if needed, e.g., to 24kHz
        if sample_rate != 24000:
            resample_transform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=24000)
            waveform = resample_transform(waveform)
        
        # Optionally, apply transformations (e.g., normalization)
        if self.transform:
            waveform = self.transform(waveform)
        
        return waveform

In this AudioDataset, you:

Load audio from files using torchaudio.load().
Optionally resample the audio to 24kHz or apply other transformations.
3. Create the DataLoader
Once the dataset is prepared, use PyTorch’s DataLoader to batch the data, shuffle it for training, and handle parallel loading with multiple workers.

from torch.utils.data import DataLoader

# Create a dataset instance
train_dataset = AudioDataset(data_dir='path_to_your_audio_files')

# Create the DataLoader with batching, shuffling, and multi-process loading
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=4)

Example Usage in Training Loop
Now, you can use the train_loader in your training loop as shown earlier:

for epoch in range(num_epochs):
    for batch in train_loader:
        optimizer.zero_grad()
        
        # Forward pass
        raw_audio = batch  # This will be your batch of raw audio from the train_loader
        reconstructed_audio = model(raw_audio)
        
        # Compute the reconstruction loss
        loss = mse_loss(reconstructed_audio, raw_audio)
        
        # Backward pass and optimization
        loss.backward()
        optimizer.step()

    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

With this dataset structure, the AudioDataset you create will loop through the folder, load .wav files using torchaudio.load(), and return batches for training. Here’s the key part that handles dataset loading:

import torchaudio
from torch.utils.data import Dataset, DataLoader

class AudioDataset(Dataset):
    def __init__(self, data_dir):
        self.data_dir = data_dir
        self.audio_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith('.wav')]

    def __len__(self):
        return len(self.audio_files)

    def __getitem__(self, idx):
        audio_path = self.audio_files[idx]
        waveform, sample_rate = torchaudio.load(audio_path)
        
        # Resample if needed (e.g., resample to 24kHz)
        if sample_rate != 24000:
            resample_transform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=24000)
            waveform = resample_transform(waveform)
        
        return waveform

# Create DataLoader
train_dataset = AudioDataset(data_dir='path_to_audio_files')
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=4)

For a text-to-semantic task, like the first stage in systems such as VALL-E or SoundStorm, where the goal is to map text into a sequence of semantic tokens (before generating audio), the dataset typically consists of paired text and audio data. Specifically, these datasets provide transcriptions (text) aligned with corresponding speech recordings (audio). However, since the task involves transforming text into semantic tokens (which represent the meaning of the speech), the key is to have audio data encoded into discrete semantic units.
Here’s a breakdown of what such a dataset looks like, and how you can prepare it for text-to-semantic tasks:
Components of a Text-to-Semantic Dataset
Text Data (Transcription):
The dataset will contain transcriptions of the speech audio.
The text can range from sentences to full paragraphs.
Example:
“Hello, how are you?”
“The quick brown fox jumps over the lazy dog.”
Audio Data (Paired Speech):
For each text transcription, there is a corresponding raw audio file (usually in .wav, .mp3, or .flac format).
The audio files represent spoken versions of the text.
These can be recordings of human speech or synthesized speech.
Semantic Tokens:
These are learned representations from the audio that encode the meaning or content of the speech in a compressed or discretized way.
For text-to-semantic tasks, a neural model like a SoundStream or VALL-E encoder is used to transform audio into a sequence of discrete semantic tokens (codebook indices).
Example Datasets for Text-to-Speech (TTS) and Text-to-Semantic:
LibriSpeech Dataset:
A widely used speech dataset for text-to-speech tasks.
Contains over 1,000 hours of 16kHz sampled speech derived from audiobooks.
Text: Transcripts from audiobooks.
Audio: Speech recordings of people reading books.
VCTK Dataset:
A dataset for TTS and speech synthesis.
Contains 44 hours of speech data from 109 speakers, recorded at 96kHz (can be downsampled).
Text: Sentences from different genres.
Audio: High-quality speech recordings from multiple speakers.

Common Voice:

A crowdsourced dataset for speech recognition and TTS tasks.
Contains hundreds of hours of speech from volunteers reading sentences.
Text: User-submitted sentences.
Audio: Speech recordings from a diverse range of voices and languages.
Dataset Structure
Here’s what a paired text-to-speech dataset would typically look like in terms of structure:

/dataset_root_directory/
├── audio/
│   ├── sentence1.wav
│   ├── sentence2.wav
│   ├── sentence3.wav
│   └── ...
│
├── transcriptions/
│   ├── sentence1.txt
│   ├── sentence2.txt
│   ├── sentence3.txt
│   └── ...
│
└── metadata.csv  # Optional metadata, like speaker ID, duration, etc.

Example Pipeline:
Input: Raw text: “The quick brown fox jumps over the lazy dog.”
Output: Sequence of semantic tokens that correspond to this sentence.

text = "The quick brown fox jumps over the lazy dog."
# Process the text into tokens
text_tokens = text_to_tokens(text)  # Tokenize the text
# Map the text tokens to semantic tokens (using a trained model)
semantic_tokens = text_model(text_tokens)

Available Text-to-Speech Datasets for Text-to-Semantic Tasks
LibriSpeech (audiobook transcriptions).
VCTK (multi-speaker dataset for speech synthesis).
Common Voice (crowdsourced multilingual dataset).
LJ Speech (read single-speaker speech dataset).
These datasets contain paired audio and text transcriptions, which are essential for training text-to-semantic models.

If you’re working on a text-to-semantic model, these datasets can be used as a base. From there, you would need to add the layer that encodes the audio into semantic tokens, which could be derived from models like SoundStream or VALL-E. Let me know if you’d like more specific guidance on dataset preprocessing or model training for this task!