Merge pull request #210 from idiap/manager

feat: allow both Path and strings where possible and add type hints

TTS/api.py

@@ -157,7 +157,7 @@ def list_models() -> list[str]:
 
     def download_model_by_name(
         self, model_name: str, vocoder_name: Optional[str] = None
-    ) -> tuple[Optional[str], Optional[str], Optional[str]]:
+    ) -> tuple[Optional[Path], Optional[Path], Optional[Path]]:
         model_path, config_path, model_item = self.manager.download_model(model_name)
         if "fairseq" in model_name or (model_item is not None and isinstance(model_item["model_url"], list)):
             # return model directory if there are multiple files

TTS/bin/synthesize.py

@@ -34,7 +34,7 @@
   tts --model_info_by_name vocoder_models/en/ljspeech/hifigan_v2
   ```
 
-#### Single Speaker Models
+#### Single speaker models
 
 - Run TTS with the default model (`tts_models/en/ljspeech/tacotron2-DDC`):
 
@@ -102,7 +102,7 @@
       --vocoder_config_path path/to/vocoder_config.json
   ```
 
-#### Multi-speaker Models
+#### Multi-speaker models
 
 - List the available speakers and choose a `<speaker_id>` among them:
 
@@ -125,7 +125,7 @@
       --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
   ```
 
-#### Voice Conversion Models
+#### Voice conversion models
 
 ```sh
 tts --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" \\

TTS/config/__init__.py

@@ -1,7 +1,7 @@
 import json
 import os
 import re
-from typing import Dict
+from typing import Any, Dict, Union
 
 import fsspec
 import yaml
@@ -68,7 +68,7 @@ def _process_model_name(config_dict: Dict) -> str:
     return model_name
 
 
-def load_config(config_path: str) -> Coqpit:
+def load_config(config_path: Union[str, os.PathLike[Any]]) -> Coqpit:
     """Import `json` or `yaml` files as TTS configs. First, load the input file as a `dict` and check the model name
     to find the corresponding Config class. Then initialize the Config.
 
@@ -81,6 +81,7 @@ def load_config(config_path: str) -> Coqpit:
     Returns:
         Coqpit: TTS config object.
     """
+    config_path = str(config_path)
     config_dict = {}
     ext = os.path.splitext(config_path)[1]
     if ext in (".yml", ".yaml"):

TTS/tts/utils/languages.py

@@ -1,5 +1,5 @@
 import os
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 import fsspec
 import numpy as np
@@ -27,8 +27,8 @@ class LanguageManager(BaseIDManager):
 
     def __init__(
         self,
-        language_ids_file_path: str = "",
-        config: Coqpit = None,
+        language_ids_file_path: Union[str, os.PathLike[Any]] = "",
+        config: Optional[Coqpit] = None,
     ):
         super().__init__(id_file_path=language_ids_file_path)
 
@@ -76,7 +76,7 @@ def parse_ids_from_data(items: List, parse_key: str) -> Any:
     def set_ids_from_data(self, items: List, parse_key: str) -> Any:
         raise NotImplementedError
 
-    def save_ids_to_file(self, file_path: str) -> None:
+    def save_ids_to_file(self, file_path: Union[str, os.PathLike[Any]]) -> None:
         """Save language IDs to a json file.
 
         Args:

TTS/tts/utils/managers.py

@@ -1,4 +1,5 @@
 import json
+import os
 import random
 from typing import Any, Dict, List, Tuple, Union
 
@@ -12,7 +13,8 @@
 from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 
-def load_file(path: str):
+def load_file(path: Union[str, os.PathLike[Any]]):
+    path = str(path)
     if path.endswith(".json"):
         with fsspec.open(path, "r") as f:
             return json.load(f)
@@ -23,7 +25,8 @@ def load_file(path: str):
         raise ValueError("Unsupported file type")
 
 
-def save_file(obj: Any, path: str):
+def save_file(obj: Any, path: Union[str, os.PathLike[Any]]):
+    path = str(path)
     if path.endswith(".json"):
         with fsspec.open(path, "w") as f:
             json.dump(obj, f, indent=4)
@@ -39,20 +42,20 @@ class BaseIDManager:
     It defines common `ID` manager specific functions.
     """
 
-    def __init__(self, id_file_path: str = ""):
+    def __init__(self, id_file_path: Union[str, os.PathLike[Any]] = ""):
         self.name_to_id = {}
 
         if id_file_path:
             self.load_ids_from_file(id_file_path)
 
     @staticmethod
-    def _load_json(json_file_path: str) -> Dict:
-        with fsspec.open(json_file_path, "r") as f:
+    def _load_json(json_file_path: Union[str, os.PathLike[Any]]) -> Dict:
+        with fsspec.open(str(json_file_path), "r") as f:
             return json.load(f)
 
     @staticmethod
-    def _save_json(json_file_path: str, data: dict) -> None:
-        with fsspec.open(json_file_path, "w") as f:
+    def _save_json(json_file_path: Union[str, os.PathLike[Any]], data: dict) -> None:
+        with fsspec.open(str(json_file_path), "w") as f:
             json.dump(data, f, indent=4)
 
     def set_ids_from_data(self, items: List, parse_key: str) -> None:
@@ -63,15 +66,15 @@ def set_ids_from_data(self, items: List, parse_key: str) -> None:
         """
         self.name_to_id = self.parse_ids_from_data(items, parse_key=parse_key)
 
-    def load_ids_from_file(self, file_path: str) -> None:
+    def load_ids_from_file(self, file_path: Union[str, os.PathLike[Any]]) -> None:
         """Set IDs from a file.
 
         Args:
             file_path (str): Path to the file.
         """
         self.name_to_id = load_file(file_path)
 
-    def save_ids_to_file(self, file_path: str) -> None:
+    def save_ids_to_file(self, file_path: Union[str, os.PathLike[Any]]) -> None:
         """Save IDs to a json file.
 
         Args:
@@ -130,10 +133,10 @@ class EmbeddingManager(BaseIDManager):
 
     def __init__(
         self,
-        embedding_file_path: Union[str, List[str]] = "",
-        id_file_path: str = "",
-        encoder_model_path: str = "",
-        encoder_config_path: str = "",
+        embedding_file_path: Union[Union[str, os.PathLike[Any]], list[Union[str, os.PathLike[Any]]]] = "",
+        id_file_path: Union[str, os.PathLike[Any]] = "",
+        encoder_model_path: Union[str, os.PathLike[Any]] = "",
+        encoder_config_path: Union[str, os.PathLike[Any]] = "",
         use_cuda: bool = False,
     ):
         super().__init__(id_file_path=id_file_path)
@@ -176,7 +179,7 @@ def embedding_names(self):
         """Get embedding names."""
         return list(self.embeddings_by_names.keys())
 
-    def save_embeddings_to_file(self, file_path: str) -> None:
+    def save_embeddings_to_file(self, file_path: Union[str, os.PathLike[Any]]) -> None:
         """Save embeddings to a json file.
 
         Args:
@@ -185,7 +188,7 @@ def save_embeddings_to_file(self, file_path: str) -> None:
         save_file(self.embeddings, file_path)
 
     @staticmethod
-    def read_embeddings_from_file(file_path: str):
+    def read_embeddings_from_file(file_path: Union[str, os.PathLike[Any]]):
         """Load embeddings from a json file.
 
         Args:
@@ -204,7 +207,7 @@ def read_embeddings_from_file(file_path: str):
                 embeddings_by_names[x["name"]].append(x["embedding"])
         return name_to_id, clip_ids, embeddings, embeddings_by_names
 
-    def load_embeddings_from_file(self, file_path: str) -> None:
+    def load_embeddings_from_file(self, file_path: Union[str, os.PathLike[Any]]) -> None:
         """Load embeddings from a json file.
 
         Args:
@@ -214,7 +217,7 @@ def load_embeddings_from_file(self, file_path: str) -> None:
             file_path
         )
 
-    def load_embeddings_from_list_of_files(self, file_paths: List[str]) -> None:
+    def load_embeddings_from_list_of_files(self, file_paths: list[Union[str, os.PathLike[Any]]]) -> None:
         """Load embeddings from a list of json files and don't allow duplicate keys.
 
         Args:
@@ -313,7 +316,9 @@ def get_random_embedding(self) -> Any:
     def get_clips(self) -> List:
         return sorted(self.embeddings.keys())
 
-    def init_encoder(self, model_path: str, config_path: str, use_cuda=False) -> None:
+    def init_encoder(
+        self, model_path: Union[str, os.PathLike[Any]], config_path: Union[str, os.PathLike[Any]], use_cuda=False
+    ) -> None:
         """Initialize a speaker encoder model.
 
         Args:
@@ -325,11 +330,13 @@ def init_encoder(self, model_path: str, config_path: str, use_cuda=False) -> Non
         self.encoder_config = load_config(config_path)
         self.encoder = setup_encoder_model(self.encoder_config)
         self.encoder_criterion = self.encoder.load_checkpoint(
-            self.encoder_config, model_path, eval=True, use_cuda=use_cuda, cache=True
+            self.encoder_config, str(model_path), eval=True, use_cuda=use_cuda, cache=True
         )
         self.encoder_ap = AudioProcessor(**self.encoder_config.audio)
 
-    def compute_embedding_from_clip(self, wav_file: Union[str, List[str]]) -> list:
+    def compute_embedding_from_clip(
+        self, wav_file: Union[Union[str, os.PathLike[Any]], List[Union[str, os.PathLike[Any]]]]
+    ) -> list:
         """Compute a embedding from a given audio file.
 
         Args:

TTS/tts/utils/speakers.py

@@ -1,7 +1,7 @@
 import json
 import logging
 import os
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, List, Optional, Union
 
 import fsspec
 import numpy as np
@@ -56,11 +56,11 @@ class SpeakerManager(EmbeddingManager):
 
     def __init__(
         self,
-        data_items: List[List[Any]] = None,
+        data_items: Optional[list[list[Any]]] = None,
         d_vectors_file_path: str = "",
-        speaker_id_file_path: str = "",
-        encoder_model_path: str = "",
-        encoder_config_path: str = "",
+        speaker_id_file_path: Union[str, os.PathLike[Any]] = "",
+        encoder_model_path: Union[str, os.PathLike[Any]] = "",
+        encoder_config_path: Union[str, os.PathLike[Any]] = "",
         use_cuda: bool = False,
     ):
         super().__init__(

TTS/utils/audio/numpy_transforms.py

@@ -1,6 +1,7 @@
 import logging
+import os
 from io import BytesIO
-from typing import Optional
+from typing import Any, Optional, Union
 
 import librosa
 import numpy as np
@@ -406,7 +407,9 @@ def rms_volume_norm(*, x: np.ndarray, db_level: float = -27.0, **kwargs) -> np.n
     return rms_norm(wav=x, db_level=db_level)
 
 
-def load_wav(*, filename: str, sample_rate: Optional[int] = None, resample: bool = False, **kwargs) -> np.ndarray:
+def load_wav(
+    *, filename: Union[str, os.PathLike[Any]], sample_rate: Optional[int] = None, resample: bool = False, **kwargs
+) -> np.ndarray:
     """Read a wav file using Librosa and optionally resample, silence trim, volume normalize.
 
     Resampling slows down loading the file significantly. Therefore it is recommended to resample the file before.
@@ -434,7 +437,7 @@ def load_wav(*, filename: str, sample_rate: Optional[int] = None, resample: bool
 def save_wav(
     *,
     wav: np.ndarray,
-    path: str,
+    path: Union[str, os.PathLike[Any]],
     sample_rate: int,
     pipe_out=None,
     do_rms_norm: bool = False,

TTS/utils/audio/processor.py

@@ -1,5 +1,6 @@
 import logging
-from typing import Optional
+import os
+from typing import Any, Optional, Union
 
 import librosa
 import numpy as np
@@ -548,7 +549,7 @@ def sound_norm(x: np.ndarray) -> np.ndarray:
         return volume_norm(x=x)
 
     ### save and load ###
-    def load_wav(self, filename: str, sr: Optional[int] = None) -> np.ndarray:
+    def load_wav(self, filename: Union[str, os.PathLike[Any]], sr: Optional[int] = None) -> np.ndarray:
         """Read a wav file using Librosa and optionally resample, silence trim, volume normalize.
 
         Resampling slows down loading the file significantly. Therefore it is recommended to resample the file before.
@@ -575,7 +576,9 @@ def load_wav(self, filename: str, sr: Optional[int] = None) -> np.ndarray:
             x = rms_volume_norm(x=x, db_level=self.db_level)
         return x
 
-    def save_wav(self, wav: np.ndarray, path: str, sr: Optional[int] = None, pipe_out=None) -> None:
+    def save_wav(
+        self, wav: np.ndarray, path: Union[str, os.PathLike[Any]], sr: Optional[int] = None, pipe_out=None
+    ) -> None:
         """Save a waveform to a file using Scipy.
 
         Args:

TTS/utils/generic_utils.py

@@ -4,7 +4,7 @@
 import logging
 import re
 from pathlib import Path
-from typing import Callable, Dict, Optional, TypeVar, Union
+from typing import Any, Callable, Dict, Optional, TypeVar, Union
 
 import torch
 from packaging.version import Version
@@ -133,3 +133,8 @@ def setup_logger(
 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")
+
+
+def optional_to_str(x: Optional[Any]) -> str:
+    """Convert input to string, using empty string if input is None."""
+    return "" if x is None else str(x)