Proposal to integrate into 🤗 Hub

setup.py

@@ -25,6 +25,7 @@
         "tensorflow-gpu==2.3.1",
         "tensorflow-addons>=0.10.0",
         "setuptools>=38.5.1",
+        "huggingface_hub==0.0.8",
         "librosa>=0.7.0",
         "soundfile>=0.10.2",
         "matplotlib>=3.1.0",

tensorflow_tts/inference/auto_config.py

@@ -16,6 +16,7 @@
 
 import logging
 import yaml
+import os
 from collections import OrderedDict
 
 from tensorflow_tts.configs import (
@@ -28,6 +29,10 @@
     ParallelWaveGANGeneratorConfig,
 )
 
+from tensorflow_tts.utils import CACHE_DIRECTORY, CONFIG_FILE_NAME, LIBRARY_NAME
+from tensorflow_tts import __version__ as VERSION
+from huggingface_hub import hf_hub_url, cached_download
+
 CONFIG_MAPPING = OrderedDict(
     [
         ("fastspeech", FastSpeechConfig),
@@ -50,6 +55,20 @@ def __init__(self):
 
     @classmethod
     def from_pretrained(cls, pretrained_path, **kwargs):
+        # load weights from hf hub
+        if not os.path.isfile(pretrained_path):
+            # retrieve correct hub url
+            download_url = hf_hub_url(repo_id=pretrained_path, filename=CONFIG_FILE_NAME)
+
+            pretrained_path = str(
+                cached_download(
+                    url=download_url,
+                    library_name=LIBRARY_NAME,
+                    library_version=VERSION,
+                    cache_dir=CACHE_DIRECTORY,
+                )
+            )
+
         with open(pretrained_path) as f:
             config = yaml.load(f, Loader=yaml.SafeLoader)
 

tensorflow_tts/inference/auto_model.py

@@ -16,6 +16,8 @@
 
 import logging
 import warnings
+import os
+
 from collections import OrderedDict
 
 from tensorflow_tts.configs import (
@@ -40,6 +42,9 @@
     SavableTFFastSpeech2,
     SavableTFTacotron2
 )
+from tensorflow_tts.utils import CACHE_DIRECTORY, MODEL_FILE_NAME, LIBRARY_NAME
+from tensorflow_tts import __version__ as VERSION
+from huggingface_hub import hf_hub_url, cached_download
 
 
 TF_MODEL_MAPPING = OrderedDict(
@@ -62,8 +67,35 @@ def __init__(self):
         raise EnvironmentError("Cannot be instantiated using `__init__()`")
 
     @classmethod
-    def from_pretrained(cls, config, pretrained_path=None, **kwargs):
+    def from_pretrained(cls, config=None, pretrained_path=None, **kwargs):
         is_build = kwargs.pop("is_build", True)
+
+        # load weights from hf hub
+        if pretrained_path is not None:
+            if not os.path.isfile(pretrained_path):
+                # retrieve correct hub url
+                download_url = hf_hub_url(repo_id=pretrained_path, filename=MODEL_FILE_NAME)
+
+                downloaded_file = str(
+                    cached_download(
+                        url=download_url,
+                        library_name=LIBRARY_NAME,
+                        library_version=VERSION,
+                        cache_dir=CACHE_DIRECTORY,
+                    )
+                )
+
+                # load config from repo as well
+                if config is None:
+                    from tensorflow_tts.inference import AutoConfig
+
+                    config = AutoConfig.from_pretrained(pretrained_path)
+
+                pretraine_path = downloaded_file
+
+
+        assert config is not None, "Please make sure to pass a config along to load a model from a local file"
+
         for config_class, model_class in TF_MODEL_MAPPING.items():
             if isinstance(config, config_class) and str(config_class.__name__) in str(
                 config
@@ -79,6 +111,7 @@ def from_pretrained(cls, config, pretrained_path=None, **kwargs):
                             pretrained_path, by_name=True, skip_mismatch=True
                         )
                 return model
+
         raise ValueError(
             "Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
             "Model type should be one of {}.".format(

tensorflow_tts/inference/auto_processor.py

@@ -16,6 +16,7 @@
 
 import logging
 import json
+import os
 from collections import OrderedDict
 
 from tensorflow_tts.processor import (
@@ -26,6 +27,10 @@
     ThorstenProcessor,
 )
 
+from tensorflow_tts.utils import CACHE_DIRECTORY, PROCESSOR_FILE_NAME, LIBRARY_NAME
+from tensorflow_tts import __version__ as VERSION
+from huggingface_hub import hf_hub_url, cached_download
+
 CONFIG_MAPPING = OrderedDict(
     [
         ("LJSpeechProcessor", LJSpeechProcessor),
@@ -46,6 +51,19 @@ def __init__(self):
 
     @classmethod
     def from_pretrained(cls, pretrained_path, **kwargs):
+        # load weights from hf hub
+        if not os.path.isfile(pretrained_path):
+            # retrieve correct hub url
+            download_url = hf_hub_url(repo_id=pretrained_path, filename=PROCESSOR_FILE_NAME)
+
+            pretrained_path = str(
+                cached_download(
+                    url=download_url,
+                    library_name=LIBRARY_NAME,
+                    library_version=VERSION,
+                    cache_dir=CACHE_DIRECTORY,
+                )
+            )
         with open(pretrained_path, "r") as f:
             config = json.load(f)
 

tensorflow_tts/processor/baker.py

@@ -27,6 +27,7 @@
 from pypinyin.converter import DefaultConverter
 from pypinyin.core import Pinyin
 from tensorflow_tts.processor import BaseProcessor
+from tensorflow_tts.utils.utils import PROCESSOR_FILE_NAME
 
 _pad = ["pad"]
 _eos = ["eos"]
@@ -552,6 +553,13 @@ def __post_init__(self):
     def setup_eos_token(self):
         return _eos[0]
 
+    def save_pretrained(self, saved_path):
+        os.makedirs(saved_path, exist_ok=True)
+        self._save_mapper(
+            os.path.join(saved_path, PROCESSOR_FILE_NAME),
+            {"pinyin_dict": self.pinyin_dict},
+        )
+
     def create_items(self):
         items = []
         if self.data_dir:

tensorflow_tts/processor/base_processor.py

@@ -224,3 +224,8 @@ def _save_mapper(self, saved_path: str = None, extra_attrs_to_save: dict = None)
             if extra_attrs_to_save:
                 full_mapper = {**full_mapper, **extra_attrs_to_save}
             json.dump(full_mapper, f)
+
+    @abc.abstractmethod
+    def save_pretrained(self, saved_path):
+        """Save mappers to file"""
+        pass

tensorflow_tts/processor/kss.py

@@ -23,6 +23,7 @@
 from tensorflow_tts.processor import BaseProcessor
 from tensorflow_tts.utils import cleaners
 from tensorflow_tts.utils.korean import symbols as KSS_SYMBOLS
+from tensorflow_tts.utils.utils import PROCESSOR_FILE_NAME
 
 # Regular expression matching text enclosed in curly braces:
 _curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
@@ -57,6 +58,10 @@ def split_line(self, data_dir, line, split):
     def setup_eos_token(self):
         return "eos"
 
+    def save_pretrained(self, saved_path):
+        os.makedirs(saved_path, exist_ok=True)
+        self._save_mapper(os.path.join(saved_path, PROCESSOR_FILE_NAME), {})
+
     def get_one_sample(self, item):
         text, wav_path, speaker_name = item
 

tensorflow_tts/processor/libritts.py

@@ -24,6 +24,7 @@
 from g2p_en import g2p as grapheme_to_phonem
 
 from tensorflow_tts.processor.base_processor import BaseProcessor
+from tensorflow_tts.utils.utils import PROCESSOR_FILE_NAME
 
 g2p = grapheme_to_phonem.G2p()
 
@@ -84,7 +85,11 @@ def get_one_sample(self, item):
         return sample
 
     def setup_eos_token(self):
-        return None # because we do not use this 
+        return None # because we do not use this
+
+    def save_pretrained(self, saved_path):
+        os.makedirs(saved_path, exist_ok=True)
+        self._save_mapper(os.path.join(saved_path, PROCESSOR_FILE_NAME), {})
 
     def text_to_sequence(self, text):
         if (

tensorflow_tts/processor/ljspeech.py

@@ -22,6 +22,7 @@
 from dataclasses import dataclass
 from tensorflow_tts.processor import BaseProcessor
 from tensorflow_tts.utils import cleaners
+from tensorflow_tts.utils.utils import PROCESSOR_FILE_NAME
 
 valid_symbols = [
     "AA",
@@ -158,6 +159,10 @@ def split_line(self, data_dir, line, split):
     def setup_eos_token(self):
         return _eos
 
+    def save_pretrained(self, saved_path):
+        os.makedirs(saved_path, exist_ok=True)
+        self._save_mapper(os.path.join(saved_path, PROCESSOR_FILE_NAME), {})
+
     def get_one_sample(self, item):
         text, wav_path, speaker_name = item
 

tensorflow_tts/processor/thorsten.py

@@ -22,6 +22,7 @@
 from dataclasses import dataclass
 from tensorflow_tts.processor import BaseProcessor
 from tensorflow_tts.utils import cleaners
+from tensorflow_tts.utils.utils import PROCESSOR_FILE_NAME
 
 _pad = "pad"
 _eos = "eos"
@@ -67,6 +68,10 @@ def split_line(self, data_dir, line, split):
     def setup_eos_token(self):
         return _eos
 
+    def save_pretrained(self, saved_path):
+        os.makedirs(saved_path, exist_ok=True)
+        self._save_mapper(os.path.join(saved_path, PROCESSOR_FILE_NAME), {})
+
     def get_one_sample(self, item):
         text, wav_path, speaker_name = item
 

tensorflow_tts/utils/__init__.py

@@ -18,5 +18,5 @@
     calculate_3d_loss,
     return_strategy,
 )
-from tensorflow_tts.utils.utils import find_files
+from tensorflow_tts.utils.utils import find_files, MODEL_FILE_NAME, CONFIG_FILE_NAME, PROCESSOR_FILE_NAME, CACHE_DIRECTORY, LIBRARY_NAME
 from tensorflow_tts.utils.weight_norm import WeightNormalization

tensorflow_tts/utils/utils.py

@@ -8,9 +8,16 @@
 import os
 import re
 import tempfile
+from pathlib import Path
 
 import tensorflow as tf
 
+MODEL_FILE_NAME = "model.h5"
+CONFIG_FILE_NAME = "config.yml"
+PROCESSOR_FILE_NAME = "processor.json"
+LIBRARY_NAME = "tensorflow_tts"
+CACHE_DIRECTORY = os.path.join(Path.home(), ".cache", LIBRARY_NAME)
+
 
 def find_files(root_dir, query="*.wav", include_root_dir=True):
     """Find files recursively.

test/test_base_processor.py

@@ -24,6 +24,9 @@ def text_to_sequence(self, text):
     def setup_eos_token(self):
         return None
 
+    def save_pretrained(self, saved_path):
+        return super().save_pretrained(saved_path)
+
 
 @pytest.fixture
 def processor(tmpdir):