xtts2_config.py

from dataclasses import asdict, dataclass
from typing import Dict, Optional, List
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


@dataclass
class GPTAudioConfig:
    """Configuration for GPT audio processing parameters"""
    mel_channels: int = 80
    sample_rate: int = 22050
    output_sample_rate: int = 24000

@dataclass
class XTTSAudioConfig:
    """Configuration for audio processing parameters"""
    sample_rate: int = 22050
    output_sample_rate: int = 24000
    mel_channels: int = 80
    hop_length: int = 256
    win_length: int = 1024
    n_fft: int = 1024
    fmin: int = 0
    fmax: int = 8000
    power: float = 1.0
    mel_norms_file: Optional[str] = None


class XTTSGPTConfig(PretrainedConfig):
    """Configuration class for the GPT component of XTTS."""
    model_type = "xtts_gpt"

    def __init__(
            self,
            # Model architecture
            hidden_size: int = 1024,  # gpt_n_model_channels in original
            n_inner: int = 4096,
            num_hidden_layers: int = 30,  # gpt_layers in original
            num_attention_heads: int = 16,  # gpt_n_heads in original

            # Tokenizer settings
            vocab_size: int = 6681,  # gpt_number_text_tokens in original
            number_text_tokens: int = 6681,  # Explicit text token vocabulary size
            start_text_token: Optional[int] = None,
            stop_text_token: Optional[int] = None,

            # Audio token settings
            num_audio_tokens: int = 1026,  # gpt_num_audio_tokens in original
            start_audio_token: int = 1024,  # gpt_start_audio_token in original
            stop_audio_token: int = 1025,  # gpt_stop_audio_token in original

            # Sequence length settings
            max_audio_tokens: int = 605,  # gpt_max_audio_tokens in original
            max_text_tokens: int = 402,  # gpt_max_text_tokens in original
            max_prompt_tokens: int = 70,  # gpt_max_prompt_tokens in original
            gpt_max_audio_tokens: int = 605,  # Used for generation

            # Model behavior settings
            use_masking_gt_prompt_approach: bool = True,  # gpt_use_masking_gt_prompt_approach in original
            use_perceiver_resampler: bool = True,  # gpt_use_perceiver_resampler in original
            kv_cache: bool = True,
            enable_redaction: bool = False,

            # GPT batch settings
            gpt_batch_size: int = 1,

            # Audio processing
            audio_config: Optional[Dict] = None,

            # Architecture specifics
            layer_norm_epsilon: float = 1e-5,
            initializer_range: float = 0.02,
            add_cross_attention: bool = False,
            scale_attn_by_inverse_layer_idx: bool = False,
            reorder_and_upcast_attn: bool = False,

            # Size settings for the decoder
            decoder_input_dim: int = 1024,
            architectures=["XttsGPT"],
            auto_map={
                "AutoConfig": "AstraMindAI/xtts2-gpt--gpt_config.XTTSGPTConfig",
                "AutoModelForCausalLM": "AstraMindAI/xtts2-gpt--xtts2_gpt_modeling.XttsGPT",
            },
            activation_function: str = "gelu",
            attn_pdrop: float = 0.1,
            **kwargs
    ):
        super().__init__(**kwargs)
        self.architectures = architectures
        self.auto_map = auto_map
        self.audio_config = GPTAudioConfig(
            **audio_config if audio_config is not None else {}
        )
        self.activation_function = activation_function
        self.attn_pdrop = attn_pdrop
        self.hidden_size = hidden_size
        self.n_inner = n_inner
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        self.vocab_size = vocab_size
        self.number_text_tokens = number_text_tokens
        self.start_text_token = start_text_token
        self.stop_text_token = stop_text_token

        self.num_audio_tokens = num_audio_tokens
        self.start_audio_token = start_audio_token
        self.stop_audio_token = stop_audio_token

        self.max_audio_tokens = max_audio_tokens
        self.max_text_tokens = max_text_tokens
        self.max_prompt_tokens = max_prompt_tokens
        self.gpt_max_audio_tokens = gpt_max_audio_tokens

        self.use_masking_gt_prompt_approach = use_masking_gt_prompt_approach
        self.use_perceiver_resampler = use_perceiver_resampler
        self.kv_cache = kv_cache
        self.enable_redaction = enable_redaction

        self.gpt_batch_size = gpt_batch_size

        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range
        self.add_cross_attention = add_cross_attention
        self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
        self.reorder_and_upcast_attn = reorder_and_upcast_attn

        self.decoder_input_dim = decoder_input_dim

    def to_dict(self) -> Dict:
        """Convert the config to a dictionary."""
        output = super().to_dict()
        output["audio_config"] = asdict(self.audio_config)
        return output

    @classmethod
    def from_dict(cls, config_dict: Dict, *args, **kwargs) -> "XTTSGPTConfig":
        """Create a config from a dictionary."""
        return cls(**config_dict)


class XTTSConfig(PretrainedConfig):
    """Configuration class for XTTS model components except GPT."""
    model_type = "xtts"

    def __init__(
            self,
            # Audio settings
            audio_config: Optional[Dict] = None,
            input_sample_rate: int = 22050,
            output_sample_rate: int = 24000,
            output_hop_length: int = 256,

            # Model architecture
            decoder_input_dim: int = 1024,
            d_vector_dim: int = 512,
            cond_d_vector_in_each_upsampling_layer: bool = True,

            # Training settings
            gpt_code_stride_len: int = 1024,
            duration_const: int = 102400,

            # Tokenizer settings
            tokenizer_file: str = "",
            num_chars: int = 255,

            # Language support
            languages: Optional[List[str]] = None,

            # GPT configuration
            gpt_config: Optional[Dict] = None,
            architectures=["Xtts"],
            auto_map = {
                       "AutoConfig": "AstraMindAI/xtts2--xtts2_config.XTTSConfig",
                       "AutoModelForCausalLM": "AstraMindAI/xtts2--xtts2_modeling.Xtts",
                   },
            **kwargs
    ):
        super().__init__(**kwargs)
        self.architectures = architectures
        self.auto_map = auto_map
        # Initialize audio config
        self.audio_config = XTTSAudioConfig(
            **audio_config if audio_config is not None else {}
        )

        self.input_sample_rate = input_sample_rate
        self.output_sample_rate = output_sample_rate
        self.output_hop_length = output_hop_length

        self.decoder_input_dim = decoder_input_dim
        self.d_vector_dim = d_vector_dim
        self.cond_d_vector_in_each_upsampling_layer = cond_d_vector_in_each_upsampling_layer

        self.gpt_code_stride_len = gpt_code_stride_len
        self.duration_const = duration_const

        self.tokenizer_file = tokenizer_file
        self.num_chars = num_chars

        # Initialize GPT config
        self.gpt = XTTSGPTConfig(**gpt_config if gpt_config is not None else {})

        if languages is None:
            self.languages = [
                "en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru",
                "nl", "cs", "ar", "zh-cn", "hu", "ko", "ja", "hi"
            ]
        else:
            self.languages = languages

    def to_dict(self) -> Dict:
        """Convert the config to a dictionary."""
        output = super().to_dict()
        output["audio_config"] = asdict(self.audio_config)
        output["gpt_config"] = self.gpt.to_dict()
        return output

    @classmethod
    def from_dict(cls, config_dict: Dict, *args, **kwargs) -> "XTTSConfig":
        """Create a config from a dictionary."""
        if "gpt_config" in config_dict:
            gpt_config = config_dict["gpt_config"]
            config_dict = {k: v for k, v in config_dict.items() if k != "gpt_config"}
            return cls(gpt_config=gpt_config, **config_dict)
        return cls(**config_dict)