refactor(xtts): move configs into configs module

Avoids circular imports

TTS/__init__.py

@@ -13,8 +13,7 @@
     import torch
 
     from TTS.config.shared_configs import BaseDatasetConfig
-    from TTS.tts.configs.xtts_config import XttsConfig
-    from TTS.tts.models.xtts import XttsArgs, XttsAudioConfig
+    from TTS.tts.configs.xtts_config import XttsArgs, XttsAudioConfig, XttsConfig
     from TTS.utils.radam import RAdam
 
     torch.serialization.add_safe_globals([dict, defaultdict, RAdam])

TTS/demos/xtts_ft_demo/utils/gpt_train.py

@@ -4,8 +4,9 @@
 from trainer import Trainer, TrainerArgs
 
 from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.configs.xtts_config import XttsAudioConfig
 from TTS.tts.datasets import load_tts_samples
-from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
+from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig
 from TTS.utils.manage import ModelManager
 
 

TTS/tts/configs/xtts_config.py

@@ -1,8 +1,92 @@
 from dataclasses import dataclass, field
 from typing import List
 
+from coqpit import Coqpit
+
 from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.xtts import XttsArgs, XttsAudioConfig
+
+
+@dataclass
+class XttsAudioConfig(Coqpit):
+    """
+    Configuration class for audio-related parameters in the XTTS model.
+
+    Args:
+        sample_rate (int): The sample rate in which the GPT operates.
+        output_sample_rate (int): The sample rate of the output audio waveform.
+        dvae_sample_rate (int): The sample rate of the DVAE
+    """
+
+    sample_rate: int = 22050
+    output_sample_rate: int = 24000
+    dvae_sample_rate: int = 22050
+
+
+@dataclass
+class XttsArgs(Coqpit):
+    """A dataclass to represent XTTS model arguments that define the model structure.
+
+    Args:
+        gpt_batch_size (int): The size of the auto-regressive batch.
+        enable_redaction (bool, optional): Whether to enable redaction. Defaults to True.
+        kv_cache (bool, optional): Whether to use the kv_cache. Defaults to True.
+        gpt_checkpoint (str, optional): The checkpoint for the autoregressive model. Defaults to None.
+        clvp_checkpoint (str, optional): The checkpoint for the ConditionalLatentVariablePerseq model. Defaults to None.
+        decoder_checkpoint (str, optional): The checkpoint for the DiffTTS model. Defaults to None.
+        num_chars (int, optional): The maximum number of characters to generate. Defaults to 255.
+
+        For GPT model:
+        gpt_max_audio_tokens (int, optional): The maximum mel tokens for the autoregressive model. Defaults to 604.
+        gpt_max_text_tokens (int, optional): The maximum text tokens for the autoregressive model. Defaults to 402.
+        gpt_max_prompt_tokens (int, optional): The maximum prompt tokens or the autoregressive model. Defaults to 70.
+        gpt_layers (int, optional): The number of layers for the autoregressive model. Defaults to 30.
+        gpt_n_model_channels (int, optional): The model dimension for the autoregressive model. Defaults to 1024.
+        gpt_n_heads (int, optional): The number of heads for the autoregressive model. Defaults to 16.
+        gpt_number_text_tokens (int, optional): The number of text tokens for the autoregressive model. Defaults to 255.
+        gpt_start_text_token (int, optional): The start text token for the autoregressive model. Defaults to 255.
+        gpt_checkpointing (bool, optional): Whether to use checkpointing for the autoregressive model. Defaults to False.
+        gpt_train_solo_embeddings (bool, optional): Whether to train embeddings for the autoregressive model. Defaults to False.
+        gpt_code_stride_len (int, optional): The hop_size of dvae and consequently of the gpt output. Defaults to 1024.
+        gpt_use_masking_gt_prompt_approach (bool, optional):  If True, it will use ground truth as prompt and it will mask the loss to avoid repetition. Defaults to True.
+        gpt_use_perceiver_resampler (bool, optional):  If True, it will use perceiver resampler from flamingo paper - https://arxiv.org/abs/2204.14198. Defaults to False.
+    """
+
+    gpt_batch_size: int = 1
+    enable_redaction: bool = False
+    kv_cache: bool = True
+    gpt_checkpoint: str = None
+    clvp_checkpoint: str = None
+    decoder_checkpoint: str = None
+    num_chars: int = 255
+
+    # XTTS GPT Encoder params
+    tokenizer_file: str = ""
+    gpt_max_audio_tokens: int = 605
+    gpt_max_text_tokens: int = 402
+    gpt_max_prompt_tokens: int = 70
+    gpt_layers: int = 30
+    gpt_n_model_channels: int = 1024
+    gpt_n_heads: int = 16
+    gpt_number_text_tokens: int = None
+    gpt_start_text_token: int = None
+    gpt_stop_text_token: int = None
+    gpt_num_audio_tokens: int = 8194
+    gpt_start_audio_token: int = 8192
+    gpt_stop_audio_token: int = 8193
+    gpt_code_stride_len: int = 1024
+    gpt_use_masking_gt_prompt_approach: bool = True
+    gpt_use_perceiver_resampler: bool = False
+
+    # HifiGAN Decoder params
+    input_sample_rate: int = 22050
+    output_sample_rate: int = 24000
+    output_hop_length: int = 256
+    decoder_input_dim: int = 1024
+    d_vector_dim: int = 512
+    cond_d_vector_in_each_upsampling_layer: bool = True
+
+    # constants
+    duration_const: int = 102400
 
 
 @dataclass

TTS/tts/layers/xtts/trainer/gpt_trainer.py

@@ -11,14 +11,14 @@
 from trainer.torch import DistributedSampler
 from trainer.trainer_utils import get_optimizer, get_scheduler
 
-from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.configs.xtts_config import XttsArgs, XttsConfig
 from TTS.tts.datasets.dataset import TTSDataset
 from TTS.tts.layers.tortoise.arch_utils import TorchMelSpectrogram
 from TTS.tts.layers.xtts.dvae import DiscreteVAE
 from TTS.tts.layers.xtts.tokenizer import VoiceBpeTokenizer
 from TTS.tts.layers.xtts.trainer.dataset import XTTSDataset
 from TTS.tts.models.base_tts import BaseTTS
-from TTS.tts.models.xtts import Xtts, XttsArgs, XttsAudioConfig
+from TTS.tts.models.xtts import Xtts
 from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 logger = logging.getLogger(__name__)
@@ -34,11 +34,6 @@ class GPTTrainerConfig(XttsConfig):
     test_sentences: List[dict] = field(default_factory=lambda: [])
 
 
-@dataclass
-class XttsAudioConfig(XttsAudioConfig):
-    dvae_sample_rate: int = 22050
-
-
 @dataclass
 class GPTArgs(XttsArgs):
     min_conditioning_length: int = 66150

TTS/tts/models/xtts.py

@@ -1,6 +1,5 @@
 import logging
 import os
-from dataclasses import dataclass
 from pathlib import Path
 from typing import Optional
 
@@ -95,87 +94,6 @@ def load_audio(audiopath, sampling_rate):
     return audio
 
 
-@dataclass
-class XttsAudioConfig(Coqpit):
-    """
-    Configuration class for audio-related parameters in the XTTS model.
-
-    Args:
-        sample_rate (int): The sample rate in which the GPT operates.
-        output_sample_rate (int): The sample rate of the output audio waveform.
-    """
-
-    sample_rate: int = 22050
-    output_sample_rate: int = 24000
-
-
-@dataclass
-class XttsArgs(Coqpit):
-    """A dataclass to represent XTTS model arguments that define the model structure.
-
-    Args:
-        gpt_batch_size (int): The size of the auto-regressive batch.
-        enable_redaction (bool, optional): Whether to enable redaction. Defaults to True.
-        kv_cache (bool, optional): Whether to use the kv_cache. Defaults to True.
-        gpt_checkpoint (str, optional): The checkpoint for the autoregressive model. Defaults to None.
-        clvp_checkpoint (str, optional): The checkpoint for the ConditionalLatentVariablePerseq model. Defaults to None.
-        decoder_checkpoint (str, optional): The checkpoint for the DiffTTS model. Defaults to None.
-        num_chars (int, optional): The maximum number of characters to generate. Defaults to 255.
-
-        For GPT model:
-        gpt_max_audio_tokens (int, optional): The maximum mel tokens for the autoregressive model. Defaults to 604.
-        gpt_max_text_tokens (int, optional): The maximum text tokens for the autoregressive model. Defaults to 402.
-        gpt_max_prompt_tokens (int, optional): The maximum prompt tokens or the autoregressive model. Defaults to 70.
-        gpt_layers (int, optional): The number of layers for the autoregressive model. Defaults to 30.
-        gpt_n_model_channels (int, optional): The model dimension for the autoregressive model. Defaults to 1024.
-        gpt_n_heads (int, optional): The number of heads for the autoregressive model. Defaults to 16.
-        gpt_number_text_tokens (int, optional): The number of text tokens for the autoregressive model. Defaults to 255.
-        gpt_start_text_token (int, optional): The start text token for the autoregressive model. Defaults to 255.
-        gpt_checkpointing (bool, optional): Whether to use checkpointing for the autoregressive model. Defaults to False.
-        gpt_train_solo_embeddings (bool, optional): Whether to train embeddings for the autoregressive model. Defaults to False.
-        gpt_code_stride_len (int, optional): The hop_size of dvae and consequently of the gpt output. Defaults to 1024.
-        gpt_use_masking_gt_prompt_approach (bool, optional):  If True, it will use ground truth as prompt and it will mask the loss to avoid repetition. Defaults to True.
-        gpt_use_perceiver_resampler (bool, optional):  If True, it will use perceiver resampler from flamingo paper - https://arxiv.org/abs/2204.14198. Defaults to False.
-    """
-
-    gpt_batch_size: int = 1
-    enable_redaction: bool = False
-    kv_cache: bool = True
-    gpt_checkpoint: str = None
-    clvp_checkpoint: str = None
-    decoder_checkpoint: str = None
-    num_chars: int = 255
-
-    # XTTS GPT Encoder params
-    tokenizer_file: str = ""
-    gpt_max_audio_tokens: int = 605
-    gpt_max_text_tokens: int = 402
-    gpt_max_prompt_tokens: int = 70
-    gpt_layers: int = 30
-    gpt_n_model_channels: int = 1024
-    gpt_n_heads: int = 16
-    gpt_number_text_tokens: int = None
-    gpt_start_text_token: int = None
-    gpt_stop_text_token: int = None
-    gpt_num_audio_tokens: int = 8194
-    gpt_start_audio_token: int = 8192
-    gpt_stop_audio_token: int = 8193
-    gpt_code_stride_len: int = 1024
-    gpt_use_masking_gt_prompt_approach: bool = True
-    gpt_use_perceiver_resampler: bool = False
-
-    # HifiGAN Decoder params
-    input_sample_rate: int = 22050
-    output_sample_rate: int = 24000
-    output_hop_length: int = 256
-    decoder_input_dim: int = 1024
-    d_vector_dim: int = 512
-    cond_d_vector_in_each_upsampling_layer: bool = True
-
-    # constants
-    duration_const: int = 102400
-
-
 class Xtts(BaseTTS):
     """ⓍTTS model implementation.
 

docs/source/models/xtts.md

@@ -387,7 +387,7 @@ torchaudio.save(OUTPUT_WAV_PATH, torch.tensor(out["wav"]).unsqueeze(0), 24000)
 
 ## XttsArgs
 ```{eval-rst}
-.. autoclass:: TTS.tts.models.xtts.XttsArgs
+.. autoclass:: TTS.tts.configs.xtts_config.XttsArgs
     :members:
 ```
 

recipes/ljspeech/xtts_v1/train_gpt_xtts.py

@@ -3,8 +3,9 @@
 from trainer import Trainer, TrainerArgs
 
 from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.configs.xtts_config import XttsAudioConfig
 from TTS.tts.datasets import load_tts_samples
-from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
+from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig
 from TTS.utils.manage import ModelManager
 
 # Logging parameters

recipes/ljspeech/xtts_v2/train_gpt_xtts.py

@@ -3,8 +3,9 @@
 from trainer import Trainer, TrainerArgs
 
 from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.configs.xtts_config import XttsAudioConfig
 from TTS.tts.datasets import load_tts_samples
-from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
+from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig
 from TTS.utils.manage import ModelManager
 
 # Logging parameters

tests/xtts_tests/test_xtts_gpt_train.py

@@ -6,9 +6,10 @@
 
 from tests import get_tests_output_path
 from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.configs.xtts_config import XttsAudioConfig
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.layers.xtts.dvae import DiscreteVAE
-from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
+from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig
 
 config_dataset = BaseDatasetConfig(
     formatter="ljspeech",

tests/xtts_tests/test_xtts_v2-0_gpt_train.py

@@ -6,9 +6,10 @@
 
 from tests import get_tests_output_path
 from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.configs.xtts_config import XttsAudioConfig
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.layers.xtts.dvae import DiscreteVAE
-from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
+from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig
 
 config_dataset = BaseDatasetConfig(
     formatter="ljspeech",