fix: only enable load with weights_only in pytorch>=2.4

TTS/__init__.py

@@ -1,29 +1,33 @@
-import _codecs
 import importlib.metadata
-from collections import defaultdict
 
-import numpy as np
-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.utils.radam import RAdam
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 __version__ = importlib.metadata.version("coqui-tts")
 
 
-torch.serialization.add_safe_globals([dict, defaultdict, RAdam])
+if is_pytorch_at_least_2_4():
+    import _codecs
+    from collections import defaultdict
+
+    import numpy as np
+    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.utils.radam import RAdam
+
+    torch.serialization.add_safe_globals([dict, defaultdict, RAdam])
 
-# Bark
-torch.serialization.add_safe_globals(
-    [
-        np.core.multiarray.scalar,
-        np.dtype,
-        np.dtypes.Float64DType,
-        _codecs.encode,  # TODO: safe by default from Pytorch 2.5
-    ]
-)
+    # Bark
+    torch.serialization.add_safe_globals(
+        [
+            np.core.multiarray.scalar,
+            np.dtype,
+            np.dtypes.Float64DType,
+            _codecs.encode,  # TODO: safe by default from Pytorch 2.5
+        ]
+    )
 
-# XTTS
-torch.serialization.add_safe_globals([BaseDatasetConfig, XttsConfig, XttsAudioConfig, XttsArgs])
+    # XTTS
+    torch.serialization.add_safe_globals([BaseDatasetConfig, XttsConfig, XttsAudioConfig, XttsArgs])

TTS/tts/layers/bark/load_model.py

@@ -10,6 +10,7 @@
 
 from TTS.tts.layers.bark.model import GPT, GPTConfig
 from TTS.tts.layers.bark.model_fine import FineGPT, FineGPTConfig
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 if (
     torch.cuda.is_available()
@@ -118,7 +119,7 @@ def load_model(ckpt_path, device, config, model_type="text"):
         logger.info(f"{model_type} model not found, downloading...")
         _download(config.REMOTE_MODEL_PATHS[model_type]["path"], ckpt_path, config.CACHE_DIR)
 
-    checkpoint = torch.load(ckpt_path, map_location=device, weights_only=True)
+    checkpoint = torch.load(ckpt_path, map_location=device, weights_only=is_pytorch_at_least_2_4())
     # this is a hack
     model_args = checkpoint["model_args"]
     if "input_vocab_size" not in model_args:

TTS/tts/layers/tortoise/arch_utils.py

@@ -9,6 +9,7 @@
 from transformers import LogitsWarper
 
 from TTS.tts.layers.tortoise.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 
 def zero_module(module):
@@ -332,7 +333,7 @@ def __init__(
         self.mel_norm_file = mel_norm_file
         if self.mel_norm_file is not None:
             with fsspec.open(self.mel_norm_file) as f:
-                self.mel_norms = torch.load(f, weights_only=True)
+                self.mel_norms = torch.load(f, weights_only=is_pytorch_at_least_2_4())
         else:
             self.mel_norms = None
 

TTS/tts/layers/tortoise/audio_utils.py

@@ -10,6 +10,7 @@
 from scipy.io.wavfile import read
 
 from TTS.utils.audio.torch_transforms import TorchSTFT
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 logger = logging.getLogger(__name__)
 
@@ -124,7 +125,7 @@ def load_voice(voice: str, extra_voice_dirs: List[str] = []):
     voices = get_voices(extra_voice_dirs)
     paths = voices[voice]
     if len(paths) == 1 and paths[0].endswith(".pth"):
-        return None, torch.load(paths[0], weights_only=True)
+        return None, torch.load(paths[0], weights_only=is_pytorch_at_least_2_4())
     else:
         conds = []
         for cond_path in paths:

TTS/tts/layers/xtts/dvae.py

@@ -9,6 +9,8 @@
 import torchaudio
 from einops import rearrange
 
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
+
 logger = logging.getLogger(__name__)
 
 
@@ -46,7 +48,7 @@ def dvae_wav_to_mel(
     mel = mel_stft(wav)
     mel = torch.log(torch.clamp(mel, min=1e-5))
     if mel_norms is None:
-        mel_norms = torch.load(mel_norms_file, map_location=device, weights_only=True)
+        mel_norms = torch.load(mel_norms_file, map_location=device, weights_only=is_pytorch_at_least_2_4())
     mel = mel / mel_norms.unsqueeze(0).unsqueeze(-1)
     return mel
 

TTS/tts/layers/xtts/hifigan_decoder.py

@@ -9,6 +9,7 @@
 from torch.nn.utils.parametrize import remove_parametrizations
 from trainer.io import load_fsspec
 
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 from TTS.vocoder.models.hifigan_generator import get_padding
 
 logger = logging.getLogger(__name__)
@@ -328,7 +329,7 @@ def remove_weight_norm(self):
     def load_checkpoint(
         self, config, checkpoint_path, eval=False, cache=False
     ):  # pylint: disable=unused-argument, redefined-builtin
-        state = torch.load(checkpoint_path, map_location=torch.device("cpu"), weights_only=True)
+        state = torch.load(checkpoint_path, map_location=torch.device("cpu"), weights_only=is_pytorch_at_least_2_4())
         self.load_state_dict(state["model"])
         if eval:
             self.eval()

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

@@ -19,6 +19,7 @@
 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.utils.generic_utils import is_pytorch_at_least_2_4
 
 logger = logging.getLogger(__name__)
 
@@ -91,7 +92,9 @@ def __init__(self, config: Coqpit):
 
         # load GPT if available
         if self.args.gpt_checkpoint:
-            gpt_checkpoint = torch.load(self.args.gpt_checkpoint, map_location=torch.device("cpu"), weights_only=True)
+            gpt_checkpoint = torch.load(
+                self.args.gpt_checkpoint, map_location=torch.device("cpu"), weights_only=is_pytorch_at_least_2_4()
+            )
             # deal with coqui Trainer exported model
             if "model" in gpt_checkpoint.keys() and "config" in gpt_checkpoint.keys():
                 logger.info("Coqui Trainer checkpoint detected! Converting it!")
@@ -184,7 +187,9 @@ def __init__(self, config: Coqpit):
 
         self.dvae.eval()
         if self.args.dvae_checkpoint:
-            dvae_checkpoint = torch.load(self.args.dvae_checkpoint, map_location=torch.device("cpu"), weights_only=True)
+            dvae_checkpoint = torch.load(
+                self.args.dvae_checkpoint, map_location=torch.device("cpu"), weights_only=is_pytorch_at_least_2_4()
+            )
             self.dvae.load_state_dict(dvae_checkpoint, strict=False)
             logger.info("DVAE weights restored from: %s", self.args.dvae_checkpoint)
         else:

TTS/tts/layers/xtts/xtts_manager.py

@@ -1,9 +1,11 @@
 import torch
 
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
+
 
 class SpeakerManager:
     def __init__(self, speaker_file_path=None):
-        self.speakers = torch.load(speaker_file_path, weights_only=True)
+        self.speakers = torch.load(speaker_file_path, weights_only=is_pytorch_at_least_2_4())
 
     @property
     def name_to_id(self):

TTS/tts/models/neuralhmm_tts.py

@@ -18,7 +18,7 @@
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
-from TTS.utils.generic_utils import format_aux_input
+from TTS.utils.generic_utils import format_aux_input, is_pytorch_at_least_2_4
 
 logger = logging.getLogger(__name__)
 
@@ -107,7 +107,7 @@ def update_mean_std(self, statistics_dict: Dict):
 
     def preprocess_batch(self, text, text_len, mels, mel_len):
         if self.mean.item() == 0 or self.std.item() == 1:
-            statistics_dict = torch.load(self.mel_statistics_parameter_path, weights_only=True)
+            statistics_dict = torch.load(self.mel_statistics_parameter_path, weights_only=is_pytorch_at_least_2_4())
             self.update_mean_std(statistics_dict)
 
         mels = self.normalize(mels)
@@ -292,7 +292,9 @@ def on_init_start(self, trainer):
                 "Data parameters found for: %s. Loading mel normalization parameters...",
                 trainer.config.mel_statistics_parameter_path,
             )
-            statistics = torch.load(trainer.config.mel_statistics_parameter_path, weights_only=True)
+            statistics = torch.load(
+                trainer.config.mel_statistics_parameter_path, weights_only=is_pytorch_at_least_2_4()
+            )
             data_mean, data_std, init_transition_prob = (
                 statistics["mean"],
                 statistics["std"],

TTS/tts/models/overflow.py

@@ -19,7 +19,7 @@
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
-from TTS.utils.generic_utils import format_aux_input
+from TTS.utils.generic_utils import format_aux_input, is_pytorch_at_least_2_4
 
 logger = logging.getLogger(__name__)
 
@@ -120,7 +120,7 @@ def update_mean_std(self, statistics_dict: Dict):
 
     def preprocess_batch(self, text, text_len, mels, mel_len):
         if self.mean.item() == 0 or self.std.item() == 1:
-            statistics_dict = torch.load(self.mel_statistics_parameter_path, weights_only=True)
+            statistics_dict = torch.load(self.mel_statistics_parameter_path, weights_only=is_pytorch_at_least_2_4())
             self.update_mean_std(statistics_dict)
 
         mels = self.normalize(mels)
@@ -308,7 +308,9 @@ def on_init_start(self, trainer):
                 "Data parameters found for: %s. Loading mel normalization parameters...",
                 trainer.config.mel_statistics_parameter_path,
             )
-            statistics = torch.load(trainer.config.mel_statistics_parameter_path, weights_only=True)
+            statistics = torch.load(
+                trainer.config.mel_statistics_parameter_path, weights_only=is_pytorch_at_least_2_4()
+            )
             data_mean, data_std, init_transition_prob = (
                 statistics["mean"],
                 statistics["std"],

TTS/tts/models/tortoise.py

@@ -23,6 +23,7 @@
 from TTS.tts.layers.tortoise.vocoder import VocConf, VocType
 from TTS.tts.layers.tortoise.wav2vec_alignment import Wav2VecAlignment
 from TTS.tts.models.base_tts import BaseTTS
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 logger = logging.getLogger(__name__)
 
@@ -171,7 +172,11 @@ def classify_audio_clip(clip, model_dir):
         distribute_zero_label=False,
     )
     classifier.load_state_dict(
-        torch.load(os.path.join(model_dir, "classifier.pth"), map_location=torch.device("cpu"), weights_only=True)
+        torch.load(
+            os.path.join(model_dir, "classifier.pth"),
+            map_location=torch.device("cpu"),
+            weights_only=is_pytorch_at_least_2_4(),
+        )
     )
     clip = clip.cpu().unsqueeze(0)
     results = F.softmax(classifier(clip), dim=-1)
@@ -490,15 +495,15 @@ def get_random_conditioning_latents(self):
                 torch.load(
                     os.path.join(self.models_dir, "rlg_auto.pth"),
                     map_location=torch.device("cpu"),
-                    weights_only=True,
+                    weights_only=is_pytorch_at_least_2_4(),
                 )
             )
             self.rlg_diffusion = RandomLatentConverter(2048).eval()
             self.rlg_diffusion.load_state_dict(
                 torch.load(
                     os.path.join(self.models_dir, "rlg_diffuser.pth"),
                     map_location=torch.device("cpu"),
-                    weights_only=True,
+                    weights_only=is_pytorch_at_least_2_4(),
                 )
             )
         with torch.no_grad():
@@ -885,25 +890,25 @@ def load_checkpoint(
 
         if os.path.exists(ar_path):
             # remove keys from the checkpoint that are not in the model
-            checkpoint = torch.load(ar_path, map_location=torch.device("cpu"), weights_only=True)
+            checkpoint = torch.load(ar_path, map_location=torch.device("cpu"), weights_only=is_pytorch_at_least_2_4())
 
             # strict set False
             # due to removed `bias` and `masked_bias` changes in Transformers
             self.autoregressive.load_state_dict(checkpoint, strict=False)
 
         if os.path.exists(diff_path):
-            self.diffusion.load_state_dict(torch.load(diff_path, weights_only=True), strict=strict)
+            self.diffusion.load_state_dict(torch.load(diff_path, weights_only=is_pytorch_at_least_2_4()), strict=strict)
 
         if os.path.exists(clvp_path):
-            self.clvp.load_state_dict(torch.load(clvp_path, weights_only=True), strict=strict)
+            self.clvp.load_state_dict(torch.load(clvp_path, weights_only=is_pytorch_at_least_2_4()), strict=strict)
 
         if os.path.exists(vocoder_checkpoint_path):
             self.vocoder.load_state_dict(
                 config.model_args.vocoder.value.optionally_index(
                     torch.load(
                         vocoder_checkpoint_path,
                         map_location=torch.device("cpu"),
-                        weights_only=True,
+                        weights_only=is_pytorch_at_least_2_4(),
                     )
                 )
             )

TTS/tts/models/xtts.py

@@ -16,6 +16,7 @@
 from TTS.tts.layers.xtts.tokenizer import VoiceBpeTokenizer, split_sentence
 from TTS.tts.layers.xtts.xtts_manager import LanguageManager, SpeakerManager
 from TTS.tts.models.base_tts import BaseTTS
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 logger = logging.getLogger(__name__)
 
@@ -65,7 +66,7 @@ def wav_to_mel_cloning(
     mel = mel_stft(wav)
     mel = torch.log(torch.clamp(mel, min=1e-5))
     if mel_norms is None:
-        mel_norms = torch.load(mel_norms_file, map_location=device, weights_only=True)
+        mel_norms = torch.load(mel_norms_file, map_location=device, weights_only=is_pytorch_at_least_2_4())
     mel = mel / mel_norms.unsqueeze(0).unsqueeze(-1)
     return mel
 

TTS/tts/utils/fairseq.py

@@ -1,8 +1,10 @@
 import torch
 
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
+
 
 def rehash_fairseq_vits_checkpoint(checkpoint_file):
-    chk = torch.load(checkpoint_file, map_location=torch.device("cpu"), weights_only=True)["model"]
+    chk = torch.load(checkpoint_file, map_location=torch.device("cpu"), weights_only=is_pytorch_at_least_2_4())["model"]
     new_chk = {}
     for k, v in chk.items():
         if "enc_p." in k:

TTS/tts/utils/managers.py

@@ -9,6 +9,7 @@
 from TTS.config import load_config
 from TTS.encoder.utils.generic_utils import setup_encoder_model
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 
 def load_file(path: str):
@@ -17,7 +18,7 @@ def load_file(path: str):
             return json.load(f)
     elif path.endswith(".pth"):
         with fsspec.open(path, "rb") as f:
-            return torch.load(f, map_location="cpu", weights_only=True)
+            return torch.load(f, map_location="cpu", weights_only=is_pytorch_at_least_2_4())
     else:
         raise ValueError("Unsupported file type")
 

TTS/utils/generic_utils.py

@@ -6,6 +6,9 @@
 from pathlib import Path
 from typing import Dict, Optional
 
+import torch
+from packaging.version import Version
+
 logger = logging.getLogger(__name__)
 
 
@@ -131,3 +134,8 @@ def setup_logger(
         sh = logging.StreamHandler()
         sh.setFormatter(formatter)
         lg.addHandler(sh)
+
+
+def is_pytorch_at_least_2_4() -> bool:
+    """Check if the installed Pytorch version is 2.4 or higher."""
+    return Version(torch.__version__) >= Version("2.4")

TTS/vc/modules/freevc/wavlm/__init__.py

@@ -5,6 +5,7 @@
 import torch
 from trainer.io import get_user_data_dir
 
+from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 from TTS.vc.modules.freevc.wavlm.wavlm import WavLM, WavLMConfig
 
 logger = logging.getLogger(__name__)
@@ -26,7 +27,7 @@ def get_wavlm(device="cpu"):
         logger.info("Downloading WavLM model to %s ...", output_path)
         urllib.request.urlretrieve(model_uri, output_path)
 
-    checkpoint = torch.load(output_path, map_location=torch.device(device), weights_only=True)
+    checkpoint = torch.load(output_path, map_location=torch.device(device), weights_only=is_pytorch_at_least_2_4())
     cfg = WavLMConfig(checkpoint["cfg"])
     wavlm = WavLM(cfg).to(device)
     wavlm.load_state_dict(checkpoint["model"])