### SpeechSpeechPretrainer Setup and Training

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

Initialize and run the SpeechSpeechPretrainer for the first pretraining stage. Supports distributed training, learning rate scheduling, and checkpointing.

```python
import torch
from torch.utils.data import Dataset
from spear_tts_pytorch import TextToSemantic, SpeechSpeechPretrainer

class AudioDataset(Dataset):
    def __init__(self, num_samples=1000):
        self.num_samples = num_samples

    def __len__(self):
        return self.num_samples

    def __getitem__(self, idx):
        return (torch.randn(16000),)  # 1 second of audio at 16kHz

model = TextToSemantic(
    dim=512,
    num_text_token_ids=256,
    num_semantic_token_ids=500,
    heads=8,
    source_depth=4,
    target_depth=4
)

trainer = SpeechSpeechPretrainer(
    model=model,
    wav2vec=None,  # Or pass HubertWithKmeans instance
    num_train_steps=10000,
    num_warmup_steps=500,
    batch_size=16,
    dataset=AudioDataset(1000),
    deletion_prob=0.6,
    reconstruct_seq=True,
    lr=3e-4,
    initial_lr=1e-5,
    grad_accum_every=2,
    max_grad_norm=0.5,
    valid_frac=0.05,
    log_every=10,
    save_results_every=100,
    save_model_every=1000,
    results_folder='./results_pretrain',
    force_clear_prev_results=True
)

# Run training
trainer.train()

# Save checkpoint
trainer.save('./checkpoints/pretrain_10000.pt')

# Load checkpoint
trainer.load('./checkpoints/pretrain_10000.pt')
```

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

### SemanticToTextTrainer Setup and Training

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

Initialize and run the SemanticToTextTrainer for the backtranslation stage. Encoder and speech embeddings are frozen during this stage.

```python
import torch
from torch.utils.data import Dataset
from spear_tts_pytorch import TextToSemantic, SemanticToTextTrainer

class SemanticTextDataset(Dataset):
    def __init__(self, num_samples=1000):
        self.num_samples = num_samples

    def __len__(self):
        return self.num_samples

    def __getitem__(self, idx):
        semantic_tokens = torch.randint(0, 500, (128,))
        text_tokens = torch.randint(0, 256, (64,))
        return semantic_tokens, text_tokens

model = TextToSemantic(
    dim=512,
    num_text_token_ids=256,
    num_semantic_token_ids=500,
    heads=8,
    source_depth=4,
    target_depth=4
)

trainer = SemanticToTextTrainer(
    model=model,
    num_train_steps=5000,
    num_warmup_steps=250,
    batch_size=16,
    dataset=SemanticTextDataset(1000),
    lr=3e-4,
    initial_lr=1e-5,
    grad_accum_every=2,
    max_grad_norm=0.5,
    valid_frac=0.05,
    log_every=10,
    save_results_every=100,
    save_model_every=1000,
    results_folder='./results_s2t',
    force_clear_prev_results=True
)

# Run backtranslation training
trainer.train()

# Save and load checkpoints
trainer.save('./checkpoints/s2t_5000.pt')
trainer.load('./checkpoints/s2t_5000.pt', restore_optimizer=True)
```

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

### Install spear-tts-pytorch

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

Install the spear-tts-pytorch package using pip. This is the first step to using the library.

```bash
pip install spear-tts-pytorch
```

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

### TextToSemanticTrainer Setup and Training

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

Initialize and run the TextToSemanticTrainer for the final stage. Supports combining real and pseudo-labeled data, and freezing encoder layers.

```python
import torch
from torch.utils.data import Dataset
from spear_tts_pytorch import TextToSemantic, TextToSemanticTrainer

class TextSemanticDataset(Dataset):
    def __init__(self, num_samples=500):
        self.num_samples = num_samples

    def __len__(self):
        return self.num_samples

    def __getitem__(self, idx):
        text_tokens = torch.randint(0, 256, (64,))
        semantic_tokens = torch.randint(0, 500, (128,))
        return text_tokens, semantic_tokens

model = TextToSemantic(
    dim=512,
    num_text_token_ids=256,
    num_semantic_token_ids=500,
    heads=8,
    source_depth=4,
    target_depth=4,
    target_early_exit_layer=2
)

trainer = TextToSemanticTrainer(
    model=model,
    num_train_steps=10000,
    num_warmup_steps=500,
    batch_size=16,
    dataset=TextSemanticDataset(500),                      # Small labeled dataset
    generated_audio_text_dataset_folder='./generated_audio_text_pairs',  # Pseudo-labeled data
    dataset_delimiter_id=-1,
    lr=3e-4,
    initial_lr=1e-5,
    grad_accum_every=2,
    max_grad_norm=0.5,
    valid_frac=0.05,
    freeze_encoder_layers_below=2,                         # Freeze lower encoder layers
    should_train_early_exit_layer_if_available=True,       # Train speculative decoding
    log_every=10,
    save_results_every=100,
    save_model_every=1000,
    results_folder='./results_t2s',
    force_clear_prev_results=True
)

# Run final training
trainer.train()

# Checkpointing
trainer.save('./checkpoints/t2s_final.pt')
```

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

### Semantic To Text Wrapper for Backtranslation

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

Utilize this wrapper for backtranslation training (speech-to-text). It trains the model to convert semantic audio tokens back to text, enabling pseudo-label dataset generation. The model must be initialized prior to instantiation.

```python
import torch
from spear_tts_pytorch import TextToSemantic, SemanticToTextWrapper

model = TextToSemantic(
    dim=512,
    num_text_token_ids=256,
    num_semantic_token_ids=500,
    heads=8,
    source_depth=4,
    target_depth=4
)

# Create wrapper for speech-to-text training
s2t_wrapper = SemanticToTextWrapper(model=model)

# Training data
semantic_token_ids = torch.randint(0, 500, (4, 128))  # Audio semantic tokens
grapheme_token_ids = torch.randint(0, 256, (4, 64))   # Text grapheme tokens

loss, logits = s2t_wrapper(
    semantic_token_ids=semantic_token_ids,
    grapheme_token_ids=grapheme_token_ids
)

print(f"Backtranslation loss: {loss.item():.4f}")
print(f"Output logits shape: {logits.shape}")
```

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

### MockDataset Usage

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

Demonstrates the usage of MockDataset for testing purposes, which generates random audio tensors.

```python
import torch
from spear_tts_pytorch import MockDataset, GeneratedAudioTextDataset

# MockDataset for testing - returns random audio tensors
mock_ds = MockDataset(length=100)
sample = mock_ds[0]
print(f"Mock sample shape: {sample.shape}")
# Output: Mock sample shape: torch.Size([1024])
```

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

### Initialize TextToSemantic Model

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

Initialize the core encoder-decoder transformer model for text-to-semantic conversion. Requires a pre-trained wav2vec model for audio tokenization. Configure model dimensions, attention heads, and transformer depths.

```python
import torch
from audiolm_pytorch import HubertWithKmeans
from spear_tts_pytorch import TextToSemantic

# Initialize wav2vec model for audio tokenization
wav2vec = HubertWithKmeans(
    checkpoint_path='./hubert_base_ls960.pt',
    kmeans_path='./hubert_base_ls960_L9_km500.bin'
)

# Create the TextToSemantic model
model = TextToSemantic(
    wav2vec=wav2vec,
    dim=512,
    num_text_token_ids=256,
    heads=8,
    target_kv_heads=2,           # Grouped query attention for efficient decoding
    source_depth=6,              # Encoder transformer depth
    target_depth=6,              # Decoder transformer depth
    attn_dropout=0.1,
    ff_dropout=0.1,
    attn_flash=True,             # Enable flash attention
    cond_drop_prob=0.2,          # Classifier-free guidance dropout
    target_early_exit_layer=3    # For speculative decoding
)

# Forward pass for training (text-to-speech)
text_tokens = torch.randint(0, 256, (2, 64))       # Batch of text tokens
semantic_tokens = torch.randint(0, 500, (2, 128))  # Batch of semantic tokens

loss = model(
    source=text_tokens,
    target=semantic_tokens,
    source_type='text',
    target_type='speech',
    return_loss=True
)

print(f"Training loss: {loss.item():.4f}")
# Output: Training loss: 6.2145
```

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

### Speech Speech Pretraining Wrapper

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

Use this wrapper for the initial self-supervised pretraining phase. It learns speech representations by reconstructing tokens with a specified deletion probability. Ensure the model is initialized before creating the wrapper.

```python
import torch
from spear_tts_pytorch import TextToSemantic, SpeechSpeechPretrainWrapper

model = TextToSemantic(
    dim=512,
    num_text_token_ids=256,
    num_semantic_token_ids=500,
    heads=8,
    source_depth=4,
    target_depth=4
)

# Create pretraining wrapper with 60% token deletion
pretrain_wrapper = SpeechSpeechPretrainWrapper(
    model=model,
    deletion_prob=0.6,
    reconstruct_seq=True,  # Reconstruct full sequence
    mask_id=499            # Token ID used for masking
)

# Training on raw audio or semantic tokens
semantic_tokens = torch.randint(0, 500, (4, 256))  # Batch of semantic tokens

loss, logits = pretrain_wrapper(semantic_tokens)

print(f"Pretraining loss: {loss.item():.4f}")
print(f"Output logits shape: {logits.shape}")
```

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

### Load Generated Audio-Text Dataset

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

Loads pseudo-labeled pairs from a directory and integrates with PyTorch DataLoader for batch processing.

```python
generated_ds = GeneratedAudioTextDataset(
    folder='./generated_audio_text_pairs',
    delimiter_id=-1
)

# Returns tuple of (semantic_tokens, text_tokens)
semantic_tokens, text_tokens = generated_ds[0]
print(f"Loaded pair - Semantic: {semantic_tokens.shape}, Text: {text_tokens.shape}")
# Output: Loaded pair - Semantic: torch.Size([128]), Text: torch.Size([64])

# Use with DataLoader
from torch.utils.data import DataLoader

dataloader = DataLoader(generated_ds, batch_size=4, shuffle=True)
for semantic_batch, text_batch in dataloader:
    print(f"Batch shapes - Semantic: {semantic_batch.shape}, Text: {text_batch.shape}")
    break
# Output: Batch shapes - Semantic: torch.Size([4, 128]), Text: torch.Size([4, 64])
```

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

### Execute Complete Training Pipeline

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

Performs end-to-end training including speech-speech pretraining, semantic-to-text backtranslation, pseudo-label generation, and final text-to-semantic training.

```python
import torch
from audiolm_pytorch import HubertWithKmeans
from spear_tts_pytorch import (
    TextToSemantic,
    SpeechSpeechPretrainer,
    SemanticToTextTrainer,
    TextToSemanticTrainer,
    SemanticToTextDatasetGenerator,
    GeneratedAudioTextDataset,
    MockDataset
)

# Initialize wav2vec for audio tokenization
wav2vec = HubertWithKmeans(
    checkpoint_path='./hubert_base_ls960.pt',
    kmeans_path='./hubert_base_ls960_L9_km500.bin'
)

# Create the TextToSemantic model
model = TextToSemantic(
    wav2vec=wav2vec,
    dim=512,
    num_text_token_ids=256,
    heads=8,
    target_kv_heads=2,
    source_depth=6,
    target_depth=6,
    attn_flash=True,
    cond_drop_prob=0.2,
    target_early_exit_layer=3
)

# Stage 1: Speech-Speech Pretraining
pretrain_dataset = MockDataset(10000)
pretrainer = SpeechSpeechPretrainer(
    model=model,
    wav2vec=wav2vec,
    dataset=pretrain_dataset,
    num_train_steps=50000,
    num_warmup_steps=1000,
    batch_size=32,
    deletion_prob=0.6,
    reconstruct_seq=True,
    results_folder='./results/stage1_pretrain'
)
pretrainer.train()

# Stage 2: Semantic-to-Text Backtranslation
# (requires paired semantic-text dataset)
s2t_trainer = SemanticToTextTrainer(
    model=model,
    dataset=your_paired_dataset,  # Your semantic-text pairs
    num_train_steps=20000,
    num_warmup_steps=500,
    batch_size=32,
    results_folder='./results/stage2_backtranslation'
)
s2t_trainer.train()

# Generate pseudo-labeled dataset
unlabeled_audio = MockDataset(100000)
generator = SemanticToTextDatasetGenerator(
    model=model,
    dataset=unlabeled_audio,
    folder='./pseudo_labeled_data',
    batch_size=16
)
generator(max_length=2048, beam_search_decode=True)

# Stage 3: Final Text-to-Semantic Training
t2s_trainer = TextToSemanticTrainer(
    model=model,
    dataset=your_small_labeled_dataset,  # Small labeled dataset
    generated_audio_text_dataset_folder='./pseudo_labeled_data',
    num_train_steps=100000,
    num_warmup_steps=2000,
    batch_size=32,
    freeze_encoder_layers_below=3,
    should_train_early_exit_layer_if_available=True,
    results_folder='./results/stage3_final'
)
t2s_trainer.train()

# Inference with trained model
text_input = torch.randint(0, 256, (1, 64))
semantic_output = model.generate(
    source=text_input,
    source_type='text',
    target_type='speech',
    max_length=1024,
    spec_decode=True,  # Fast speculative decoding
    cond_scale=2.0     # Classifier-free guidance
)
```

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

### Semantic To Text Dataset Generator

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

Use this generator to create pseudo-labeled audio-text datasets from unlabeled audio data. The generated dataset is suitable for the final text-to-semantic training stage. Configure folder paths, batch size, and padding IDs.

```python
import torch
from torch.utils.data import Dataset
from spear_tts_pytorch import (
    TextToSemantic,
    SemanticToTextDatasetGenerator,
    GeneratedAudioTextDataset,
    MockDataset
)

model = TextToSemantic(
    dim=512,
    num_text_token_ids=256,
    num_semantic_token_ids=500,
    heads=8,
    source_depth=4,
    target_depth=4
)

# Create dataset of audio samples (or use your own)
audio_dataset = MockDataset(100)

# Initialize dataset generator
dataset_generator = SemanticToTextDatasetGenerator(
    model=model,
    dataset=audio_dataset,
    folder='./generated_audio_text_pairs',
    batch_size=4,
    delimiter_id=-1,
    audio_pad_id=None,
    text_pad_id=0
)

# Generate pseudo-labeled dataset
dataset_generator(
    max_length=1024,
    beam_search_decode=True
)

# Load the generated dataset for training
generated_dataset = GeneratedAudioTextDataset(
    folder='./generated_audio_text_pairs',
    delimiter_id=-1
)

# Access a sample (returns semantic_tokens, text_tokens tuple)
semantic_tokens, text_tokens = generated_dataset[0]
print(f"Semantic tokens: {semantic_tokens.shape}, Text tokens: {text_tokens.shape}")
```

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

### Text To Semantic Wrapper for TTS Training

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

Employ this wrapper for the final text-to-speech training stage. It trains the model to convert text tokens to semantic audio tokens. Set `target_early_exit_layer` in the model to enable early exit loss for speculative decoding.

```python
import torch
from spear_tts_pytorch import TextToSemantic, TextToSemanticWrapper

model = TextToSemantic(
    dim=512,
    num_text_token_ids=256,
    num_semantic_token_ids=500,
    heads=8,
    source_depth=4,
    target_depth=4,
    target_early_exit_layer=2  # Enable early exit for speculative decoding
)

# Create wrapper for text-to-speech training
t2s_wrapper = TextToSemanticWrapper(model=model)

# Training data
grapheme_token_ids = torch.randint(0, 256, (4, 64))    # Text input
semantic_token_ids = torch.randint(0, 500, (4, 128))   # Audio target

loss, logits = t2s_wrapper(
    grapheme_token_ids=grapheme_token_ids,
    semantic_token_ids=semantic_token_ids,
    return_early_exit_loss=True  # Train early exit layer for speculative decoding
)

print(f"Text-to-semantic loss: {loss.item():.4f}")
print(f"Output logits shape: {logits.shape}")
```

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

### Spear-TTS Text-to-Semantic Model Usage

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

Demonstrates how to initialize and use the TextToSemantic model. Requires a pre-trained wav2vec model and dataset generator. The model is used for converting text into semantic representations.

```python
import torch

from audiolm_pytorch import HubertWithKmeans

from spear_tts_pytorch import (
    TextToSemantic,
    SemanticToTextDatasetGenerator,
    GeneratedAudioTextDataset,
    MockDataset
)

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

model = TextToSemantic(
    wav2vec = wav2vec,
    dim = 512,
    num_text_token_ids = 256,
    heads = 8,
    target_kv_heads = 2, # grouped query attention, for memory efficient decoding
    source_depth = 1,
    target_depth = 1
)

ds = MockDataset(10)

dataset_generator = SemanticToTextDatasetGenerator(
    model = model,
    dataset = ds,
    folder = './output_folder'
)

dataset_generator(max_length = 2)

generated_dataset = GeneratedAudioTextDataset(
    folder = './output_folder'
)

assert len(generated_dataset) == 10
```

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

### Generate Sequences with TextToSemantic

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

Generate target sequences from source sequences using autoregressive decoding. Supports standard sampling, beam search, and speculative decoding. Classifier-free guidance can be applied for improved quality. Use `return_target_mask=True` for speech-to-text tasks.

```python
import torch
from spear_tts_pytorch import TextToSemantic

model = TextToSemantic(
    dim=512,
    num_text_token_ids=256,
    num_semantic_token_ids=500,
    heads=8,
    target_kv_heads=2,
    source_depth=4,
    target_depth=4,
    cond_drop_prob=0.2,
    target_early_exit_layer=2
)

# Text-to-speech generation with standard sampling
text_tokens = torch.randint(0, 256, (1, 32))

semantic_output = model.generate(
    source=text_tokens,
    source_type='text',
    target_type='speech',
    max_length=512,
    temperature=0.9,
    filter_logits_fn=top_k,
    filter_fn_kwargs={'thres': 0.9}
)

print(f"Generated semantic tokens shape: {semantic_output.shape}")
# Output: Generated semantic tokens shape: torch.Size([1, 512])

# Text-to-speech with beam search decoding
semantic_output_beam = model.generate(
    source=text_tokens,
    source_type='text',
    target_type='speech',
    max_length=512,
    beam_search_decode=True,
    beam_size=4,
    temperature=0.7
)

# Text-to-speech with speculative decoding (faster inference)
semantic_output_spec = model.generate(
    source=text_tokens,
    source_type='text',
    target_type='speech',
    max_length=512,
    spec_decode=True,
    spec_decode_gamma=5,
    spec_decode_lenience=1.0
)

# Speech-to-text backtranslation with classifier-free guidance
semantic_input = torch.randint(0, 500, (1, 128))

text_output = model.generate(
    source=semantic_input,
    source_type='speech',
    target_type='text',
    max_length=256,
    cond_scale=2.0,  # Classifier-free guidance scale
    return_target_mask=True
)

text_tokens, text_mask = text_output
print(f"Generated text tokens: {text_tokens.shape}, mask: {text_mask.shape}")
# Output: Generated text tokens: torch.Size([1, 256]), mask: torch.Size([1, 256])
```

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