Implement FreeVC (#2451)

* Update .gitignore

* Draft FreeVC implementation

* Tests and relevant updates

* Update API tests

* Add missings

* Update requirements

* :(

* Lazy handle for vc

* Update docs for voice conversion

* Make style

.gitignore

@@ -137,7 +137,7 @@ VCTK-Corpus-removed-silence/*
 # ignore training logs
 trainer_*_log.txt
 
-# files used internally fro dev, test etc.
+# files used internally for dev, test etc.
 tests/outputs/*
 tests/train_outputs/*
 TODO.txt
@@ -168,3 +168,4 @@ internal/*
 wandb
 depot/*
 coqui_recipes/*
+local_scripts/*

TTS/.models.json

@@ -802,5 +802,18 @@
                 }
             }
         }
+    },
+    "voice_conversion_models":{
+        "multilingual":{
+            "vctk":{
+                "freevc24":{
+                    "github_rls_url": "https://coqui.gateway.scarf.sh/v0.13.0_models/voice_conversion_models--multilingual--vctk--freevc24.zip",
+                    "description": "FreeVC model trained on VCTK dataset from https://github.com/OlaWod/FreeVC",
+                    "author": "Jing-Yi Li @OlaWod",
+                    "license": "MIT",
+                    "commit": null
+                }
+            }
+        }
     }
 }

TTS/api.py

@@ -1,5 +1,7 @@
+import tempfile
 from pathlib import Path
 
+from TTS.utils.audio.numpy_transforms import save_wav
 from TTS.utils.manage import ModelManager
 from TTS.utils.synthesizer import Synthesizer
 
@@ -49,11 +51,14 @@ class TTS:
             gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
         """
         self.manager = ModelManager(models_file=self.get_models_file_path(), progress_bar=progress_bar, verbose=False)
+
         self.synthesizer = None
+        self.voice_converter = None
+
         if model_name:
-            self.load_model_by_name(model_name, gpu)
+            self.load_tts_model_by_name(model_name, gpu)
         if model_path:
-            self.load_model_by_path(
+            self.load_tts_model_by_path(
                 model_path, config_path, vocoder_path=vocoder_path, vocoder_config=vocoder_config_path, gpu=gpu
             )
 
@@ -96,12 +101,22 @@ class TTS:
 
     def download_model_by_name(self, model_name: str):
         model_path, config_path, model_item = self.manager.download_model(model_name)
-        if model_item["default_vocoder"] is None:
+        if model_item.get("default_vocoder") is None:
             return model_path, config_path, None, None
         vocoder_path, vocoder_config_path, _ = self.manager.download_model(model_item["default_vocoder"])
         return model_path, config_path, vocoder_path, vocoder_config_path
 
-    def load_model_by_name(self, model_name: str, gpu: bool = False):
+    def load_vc_model_by_name(self, model_name: str, gpu: bool = False):
+        """Load one of the voice conversion models by name.
+
+        Args:
+            model_name (str): Model name to load. You can list models by ```tts.models```.
+            gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
+        """
+        model_path, config_path, _, _ = self.download_model_by_name(model_name)
+        self.voice_converter = Synthesizer(vc_checkpoint=model_path, vc_config=config_path, use_cuda=gpu)
+
+    def load_tts_model_by_name(self, model_name: str, gpu: bool = False):
         """Load one of 🐸TTS models by name.
 
         Args:
@@ -127,7 +142,7 @@ class TTS:
             use_cuda=gpu,
         )
 
-    def load_model_by_path(
+    def load_tts_model_by_path(
         self, model_path: str, config_path: str, vocoder_path: str = None, vocoder_config: str = None, gpu: bool = False
     ):
         """Load a model from a path.
@@ -219,3 +234,67 @@ class TTS:
         """
         wav = self.tts(text=text, speaker=speaker, language=language, speaker_wav=speaker_wav)
         self.synthesizer.save_wav(wav=wav, path=file_path)
+
+    def voice_conversion(
+        self,
+        sourve_wav: str,
+        target_wav: str,
+    ):
+        """Voice conversion with FreeVC. Convert source wav to target speaker.
+
+        Args:
+            source_wav (str):
+                Path to the source wav file.
+            target_wav (str):
+                Path to the target wav file.
+        """
+        wav = self.synthesizer.voice_conversion(source_wav=sourve_wav, target_wav=target_wav)
+        return wav
+
+    def tts_with_vc(self, text: str, language: str = None, speaker_wav: str = None):
+        """Convert text to speech with voice conversion.
+
+        It combines tts with voice conversion to fake voice cloning.
+
+        - Convert text to speech with tts.
+        - Convert the output wav to target speaker with voice conversion.
+
+        Args:
+            text (str):
+                Input text to synthesize.
+            language (str, optional):
+                Language code for multi-lingual models. You can check whether loaded model is multi-lingual
+                `tts.is_multi_lingual` and list available languages by `tts.languages`. Defaults to None.
+            speaker_wav (str, optional):
+                Path to a reference wav file to use for voice cloning with supporting models like YourTTS.
+                Defaults to None.
+        """
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+            # Lazy code... save it to a temp file to resample it while reading it for VC
+            self.tts_to_file(text=text, speaker=None, language=language, file_path=fp.name)
+        if self.voice_converter is None:
+            self.load_vc_model_by_name("voice_conversion_models/multilingual/vctk/freevc24")
+        wav = self.voice_converter.voice_conversion(source_wav=fp.name, target_wav=speaker_wav)
+        return wav
+
+    def tts_with_vc_to_file(
+        self, text: str, language: str = None, speaker_wav: str = None, file_path: str = "output.wav"
+    ):
+        """Convert text to speech with voice conversion and save to file.
+
+        Check `tts_with_vc` for more details.
+
+        Args:
+            text (str):
+                Input text to synthesize.
+            language (str, optional):
+                Language code for multi-lingual models. You can check whether loaded model is multi-lingual
+                `tts.is_multi_lingual` and list available languages by `tts.languages`. Defaults to None.
+            speaker_wav (str, optional):
+                Path to a reference wav file to use for voice cloning with supporting models like YourTTS.
+                Defaults to None.
+            file_path (str, optional):
+                Output file path. Defaults to "output.wav".
+        """
+        wav = self.tts_with_vc(text=text, language=language, speaker_wav=speaker_wav)
+        save_wav(wav=wav, path=file_path, sample_rate=self.voice_converter.vc_config.audio.output_sample_rate)

TTS/bin/synthesize.py

@@ -100,6 +100,12 @@ If you don't specify any models, then it uses LJSpeech based English model.
     ```
     $ tts --text "Text for TTS" --out_path output/path/speech.wav --model_path path/to/config.json --config_path path/to/model.pth --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
     ```
+
+### Voice Conversion Models
+
+    ```
+    $ tts --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --source_wav <path/to/speaker/wav> --target_wav <path/to/reference/wav>
+    ```
     """
     # We remove Markdown code formatting programmatically here to allow us to copy-and-paste from main README to keep
     # documentation in sync more easily.
@@ -245,6 +251,20 @@ If you don't specify any models, then it uses LJSpeech based English model.
         default=True,
     )
 
+    # voice conversion args
+    parser.add_argument(
+        "--source_wav",
+        type=str,
+        default=None,
+        help="Original audio file to convert in the voice of the target_wav",
+    )
+    parser.add_argument(
+        "--target_wav",
+        type=str,
+        default=None,
+        help="Target audio file to convert in the voice of the source_wav",
+    )
+
     args = parser.parse_args()
 
     # print the description if either text or list_models is not set
@@ -256,6 +276,8 @@ If you don't specify any models, then it uses LJSpeech based English model.
         args.reference_wav,
         args.model_info_by_idx,
         args.model_info_by_name,
+        args.source_wav,
+        args.target_wav,
     ]
     if not any(check_args):
         parser.parse_args(["-h"])
@@ -264,21 +286,23 @@ If you don't specify any models, then it uses LJSpeech based English model.
     path = Path(__file__).parent / "../.models.json"
     manager = ModelManager(path, progress_bar=args.progress_bar)
 
-    model_path = None
-    config_path = None
+    tts_path = None
+    tts_config_path = None
     speakers_file_path = None
     language_ids_file_path = None
     vocoder_path = None
     vocoder_config_path = None
     encoder_path = None
     encoder_config_path = None
+    vc_path = None
+    vc_config_path = None
 
     # CASE1 #list : list pre-trained TTS models
     if args.list_models:
         manager.list_models()
         sys.exit()
 
-    # CASE2 #info : model info of pre-trained TTS models
+    # CASE2 #info : model info for pre-trained TTS models
     if args.model_info_by_idx:
         model_query = args.model_info_by_idx
         manager.model_info_by_idx(model_query)
@@ -292,15 +316,27 @@ If you don't specify any models, then it uses LJSpeech based English model.
     # CASE3: load pre-trained model paths
     if args.model_name is not None and not args.model_path:
         model_path, config_path, model_item = manager.download_model(args.model_name)
-        args.vocoder_name = model_item["default_vocoder"] if args.vocoder_name is None else args.vocoder_name
 
+        # tts model
+        if model_item["model_type"] == "tts_models":
+            tts_path = model_path
+            tts_config_path = config_path
+            if "default_vocoder" in model_item:
+                args.vocoder_name = model_item["default_vocoder"] if args.vocoder_name is None else args.vocoder_name
+
+        # voice conversion model
+        if model_item["model_type"] == "voice_conversion_models":
+            vc_path = model_path
+            vc_config_path = config_path
+
+    # load vocoder
     if args.vocoder_name is not None and not args.vocoder_path:
         vocoder_path, vocoder_config_path, _ = manager.download_model(args.vocoder_name)
 
     # CASE4: set custom model paths
     if args.model_path is not None:
-        model_path = args.model_path
-        config_path = args.config_path
+        tts_path = args.model_path
+        tts_config_path = args.config_path
         speakers_file_path = args.speakers_file_path
         language_ids_file_path = args.language_ids_file_path
 
@@ -314,14 +350,16 @@ If you don't specify any models, then it uses LJSpeech based English model.
 
     # load models
     synthesizer = Synthesizer(
-        model_path,
-        config_path,
+        tts_path,
+        tts_config_path,
         speakers_file_path,
         language_ids_file_path,
         vocoder_path,
         vocoder_config_path,
         encoder_path,
         encoder_config_path,
+        vc_path,
+        vc_config_path,
         args.use_cuda,
     )
 
@@ -354,16 +392,22 @@ If you don't specify any models, then it uses LJSpeech based English model.
         print(" > Text: {}".format(args.text))
 
     # kick it
-    wav = synthesizer.tts(
-        args.text,
-        args.speaker_idx,
-        args.language_idx,
-        args.speaker_wav,
-        reference_wav=args.reference_wav,
-        style_wav=args.capacitron_style_wav,
-        style_text=args.capacitron_style_text,
-        reference_speaker_name=args.reference_speaker_idx,
-    )
+    if tts_path is not None:
+        wav = synthesizer.tts(
+            args.text,
+            args.speaker_idx,
+            args.language_idx,
+            args.speaker_wav,
+            reference_wav=args.reference_wav,
+            style_wav=args.capacitron_style_wav,
+            style_text=args.capacitron_style_text,
+            reference_speaker_name=args.reference_speaker_idx,
+        )
+    elif vc_path is not None:
+        wav = synthesizer.voice_conversion(
+            source_wav=args.source_wav,
+            target_wav=args.target_wav,
+        )
 
     # save the results
     print(" > Saving output to {}".format(args.out_path))

TTS/config/__init__.py

@@ -37,7 +37,7 @@ def register_config(model_name: str) -> Coqpit:
     """
     config_class = None
     config_name = model_name + "_config"
-    paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.encoder.configs"]
+    paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.encoder.configs", "TTS.vc.configs"]
     for path in paths:
         try:
             config_class = find_module(path, config_name)

TTS/tts/models/base_tts.py

@@ -27,6 +27,8 @@ class BaseTTS(BaseTrainerModel):
     It defines common `tts` specific functions on top of `Model` implementation.
     """
 
+    MODEL_TYPE = "tts"
+
     def __init__(
         self,
         config: Coqpit,

TTS/utils/generic_utils.py

@@ -85,6 +85,7 @@ def to_camel(text):
     text = text.capitalize()
     text = re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text)
     text = text.replace("Tts", "TTS")
+    text = text.replace("vc", "VC")
     return text
 
 

TTS/utils/manage.py

@@ -185,6 +185,13 @@ class ModelManager(object):
         """
         return self._list_for_model_type("vocoder_models")
 
+    def list_vc_models(self):
+        """Print all the voice conversion models and return a list of model names
+
+        Format is `language/dataset/model`
+        """
+        return self._list_for_model_type("voice_conversion_models")
+
     def list_langs(self):
         """Print all the available languages"""
         print(" Name format: type/language")
@@ -234,6 +241,7 @@ class ModelManager(object):
         model_type, lang, dataset, model = model_name.split("/")
         model_full_name = f"{model_type}--{lang}--{dataset}--{model}"
         model_item = self.models_dict[model_type][lang][dataset][model]
+        model_item["model_type"] = model_type
         # set the model specific output path
         output_path = os.path.join(self.output_prefix, model_full_name)
         if os.path.exists(output_path):

TTS/utils/synthesizer.py

@@ -12,6 +12,8 @@ from TTS.tts.models import setup_model as setup_tts_model
 # pylint: disable=wildcard-import
 from TTS.tts.utils.synthesis import synthesis, transfer_voice, trim_silence
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.audio.numpy_transforms import save_wav
+from TTS.vc.models import setup_model as setup_vc_model
 from TTS.vocoder.models import setup_model as setup_vocoder_model
 from TTS.vocoder.utils.generic_utils import interpolate_vocoder_input
 
@@ -19,14 +21,16 @@ from TTS.vocoder.utils.generic_utils import interpolate_vocoder_input
 class Synthesizer(object):
     def __init__(
         self,
-        tts_checkpoint: str,
-        tts_config_path: str,
+        tts_checkpoint: str = "",
+        tts_config_path: str = "",
         tts_speakers_file: str = "",
         tts_languages_file: str = "",
         vocoder_checkpoint: str = "",
         vocoder_config: str = "",
         encoder_checkpoint: str = "",
         encoder_config: str = "",
+        vc_checkpoint: str = "",
+        vc_config: str = "",
         use_cuda: bool = False,
     ) -> None:
         """General 🐸 TTS interface for inference. It takes a tts and a vocoder
@@ -41,12 +45,14 @@ class Synthesizer(object):
         TODO: set the segmenter based on the source language
 
         Args:
-            tts_checkpoint (str): path to the tts model file.
-            tts_config_path (str): path to the tts config file.
+            tts_checkpoint (str, optional): path to the tts model file.
+            tts_config_path (str, optional): path to the tts config file.
             vocoder_checkpoint (str, optional): path to the vocoder model file. Defaults to None.
             vocoder_config (str, optional): path to the vocoder config file. Defaults to None.
             encoder_checkpoint (str, optional): path to the speaker encoder model file. Defaults to `""`,
             encoder_config (str, optional): path to the speaker encoder config file. Defaults to `""`,
+            vc_checkpoint (str, optional): path to the voice conversion model file. Defaults to `""`,
+            vc_config (str, optional): path to the voice conversion config file. Defaults to `""`,
             use_cuda (bool, optional): enable/disable cuda. Defaults to False.
         """
         self.tts_checkpoint = tts_checkpoint
@@ -57,10 +63,13 @@ class Synthesizer(object):
         self.vocoder_config = vocoder_config
         self.encoder_checkpoint = encoder_checkpoint
         self.encoder_config = encoder_config
+        self.vc_checkpoint = vc_checkpoint
+        self.vc_config = vc_config
         self.use_cuda = use_cuda
 
         self.tts_model = None
         self.vocoder_model = None
+        self.vc_model = None
         self.speaker_manager = None
         self.tts_speakers = {}
         self.language_manager = None
@@ -72,12 +81,19 @@ class Synthesizer(object):
 
         if self.use_cuda:
             assert torch.cuda.is_available(), "CUDA is not availabe on this machine."
-        self._load_tts(tts_checkpoint, tts_config_path, use_cuda)
-        self.output_sample_rate = self.tts_config.audio["sample_rate"]
+
+        if tts_checkpoint:
+            self._load_tts(tts_checkpoint, tts_config_path, use_cuda)
+            self.output_sample_rate = self.tts_config.audio["sample_rate"]
+
         if vocoder_checkpoint:
             self._load_vocoder(vocoder_checkpoint, vocoder_config, use_cuda)
             self.output_sample_rate = self.vocoder_config.audio["sample_rate"]
 
+        if vc_checkpoint:
+            self._load_vc(vc_checkpoint, vc_config, use_cuda)
+            self.output_sample_rate = self.vc_config.audio["output_sample_rate"]
+
     @staticmethod
     def _get_segmenter(lang: str):
         """get the sentence segmenter for the given language.
@@ -90,6 +106,26 @@ class Synthesizer(object):
         """
         return pysbd.Segmenter(language=lang, clean=True)
 
+    def _load_vc(self, vc_checkpoint: str, vc_config_path: str, use_cuda: bool) -> None:
+        """Load the voice conversion model.
+
+        1. Load the model config.
+        2. Init the model from the config.
+        3. Load the model weights.
+        4. Move the model to the GPU if CUDA is enabled.
+
+        Args:
+            vc_checkpoint (str): path to the model checkpoint.
+            tts_config_path (str): path to the model config file.
+            use_cuda (bool): enable/disable CUDA use.
+        """
+        # pylint: disable=global-statement
+        self.vc_config = load_config(vc_config_path)
+        self.vc_model = setup_vc_model(config=self.vc_config)
+        self.vc_model.load_checkpoint(self.vc_config, vc_checkpoint)
+        if use_cuda:
+            self.vc_model.cuda()
+
     def _load_tts(self, tts_checkpoint: str, tts_config_path: str, use_cuda: bool) -> None:
         """Load the TTS model.
 
@@ -168,7 +204,11 @@ class Synthesizer(object):
             path (str): output path to save the waveform.
         """
         wav = np.array(wav)
-        self.tts_model.ap.save_wav(wav, path, self.output_sample_rate)
+        save_wav(wav=wav, path=path, sample_rate=self.output_sample_rate)
+
+    def voice_conversion(self, source_wav: str, target_wav: str) -> List[int]:
+        output_wav = self.vc_model.voice_conversion(source_wav, target_wav)
+        return output_wav
 
     def tts(
         self,

TTS/vc/configs/freevc_config.py

@@ -0,0 +1,5 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.vc.configs.shared_configs import BaseVCConfig
+from TTS.vc.models.freevc import FreeVCArgs, FreeVCAudioConfig, FreeVCConfig

TTS/vc/configs/shared_configs.py

@@ -0,0 +1,155 @@
+from dataclasses import asdict, dataclass, field
+from typing import Dict, List
+
+from coqpit import Coqpit, check_argument
+
+from TTS.config import BaseAudioConfig, BaseDatasetConfig, BaseTrainingConfig
+
+
+@dataclass
+class BaseVCConfig(BaseTrainingConfig):
+    """Shared parameters among all the tts models.
+
+    Args:
+
+        audio (BaseAudioConfig):
+            Audio processor config object instance.
+
+        batch_group_size (int):
+            Size of the batch groups used for bucketing. By default, the dataloader orders samples by the sequence
+            length for a more efficient and stable training. If `batch_group_size > 1` then it performs bucketing to
+            prevent using the same batches for each epoch.
+
+        loss_masking (bool):
+            enable / disable masking loss values against padded segments of samples in a batch.
+
+        min_text_len (int):
+            Minimum length of input text to be used. All shorter samples will be ignored. Defaults to 0.
+
+        max_text_len (int):
+            Maximum length of input text to be used. All longer samples will be ignored. Defaults to float("inf").
+
+        min_audio_len (int):
+            Minimum length of input audio to be used. All shorter samples will be ignored. Defaults to 0.
+
+        max_audio_len (int):
+            Maximum length of input audio to be used. All longer samples will be ignored. The maximum length in the
+            dataset defines the VRAM used in the training. Hence, pay attention to this value if you encounter an
+            OOM error in training. Defaults to float("inf").
+
+        compute_f0 (int):
+            (Not in use yet).
+
+        compute_energy (int):
+            (Not in use yet).
+
+        compute_linear_spec (bool):
+            If True data loader computes and returns linear spectrograms alongside the other data.
+
+        precompute_num_workers (int):
+            Number of workers to precompute features. Defaults to 0.
+
+        use_noise_augment (bool):
+            Augment the input audio with random noise.
+
+        start_by_longest (bool):
+            If True, the data loader will start loading the longest batch first. It is useful for checking OOM issues.
+            Defaults to False.
+
+        shuffle (bool):
+            If True, the data loader will shuffle the dataset when there is not sampler defined. Defaults to True.
+
+        drop_last (bool):
+            If True, the data loader will drop the last batch if it is not complete. It helps to prevent
+            issues that emerge from the partial batch statistics. Defaults to True.
+
+        add_blank (bool):
+            Add blank characters between each other two characters. It improves performance for some models at expense
+            of slower run-time due to the longer input sequence.
+
+        datasets (List[BaseDatasetConfig]):
+            List of datasets used for training. If multiple datasets are provided, they are merged and used together
+            for training.
+
+        optimizer (str):
+            Optimizer used for the training. Set one from `torch.optim.Optimizer` or `TTS.utils.training`.
+            Defaults to ``.
+
+        optimizer_params (dict):
+            Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}`
+
+        lr_scheduler (str):
+            Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
+            `TTS.utils.training`. Defaults to ``.
+
+        lr_scheduler_params (dict):
+            Parameters for the generator learning rate scheduler. Defaults to `{"warmup": 4000}`.
+
+        test_sentences (List[str]):
+            List of sentences to be used at testing. Defaults to '[]'
+
+        eval_split_max_size (int):
+            Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
+
+        eval_split_size (float):
+            If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
+            If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
+
+        use_speaker_weighted_sampler (bool):
+            Enable / Disable the batch balancer by speaker. Defaults to ```False```.
+
+        speaker_weighted_sampler_alpha (float):
+            Number that control the influence of the speaker sampler weights. Defaults to ```1.0```.
+
+        use_language_weighted_sampler (bool):
+            Enable / Disable the batch balancer by language. Defaults to ```False```.
+
+        language_weighted_sampler_alpha (float):
+            Number that control the influence of the language sampler weights. Defaults to ```1.0```.
+
+        use_length_weighted_sampler (bool):
+            Enable / Disable the batch balancer by audio length. If enabled the dataset will be divided
+            into 10 buckets considering the min and max audio of the dataset. The sampler weights will be
+            computed forcing to have the same quantity of data for each bucket in each training batch. Defaults to ```False```.
+
+        length_weighted_sampler_alpha (float):
+            Number that control the influence of the length sampler weights. Defaults to ```1.0```.
+    """
+
+    audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
+    # training params
+    batch_group_size: int = 0
+    loss_masking: bool = None
+    # dataloading
+    min_audio_len: int = 1
+    max_audio_len: int = float("inf")
+    min_text_len: int = 1
+    max_text_len: int = float("inf")
+    compute_f0: bool = False
+    compute_energy: bool = False
+    compute_linear_spec: bool = False
+    precompute_num_workers: int = 0
+    use_noise_augment: bool = False
+    start_by_longest: bool = False
+    shuffle: bool = False
+    drop_last: bool = False
+    # dataset
+    datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
+    # optimizer
+    optimizer: str = "radam"
+    optimizer_params: dict = None
+    # scheduler
+    lr_scheduler: str = None
+    lr_scheduler_params: dict = field(default_factory=lambda: {})
+    # testing
+    test_sentences: List[str] = field(default_factory=lambda: [])
+    # evaluation
+    eval_split_max_size: int = None
+    eval_split_size: float = 0.01
+    # weighted samplers
+    use_speaker_weighted_sampler: bool = False
+    speaker_weighted_sampler_alpha: float = 1.0
+    use_language_weighted_sampler: bool = False
+    language_weighted_sampler_alpha: float = 1.0
+    use_length_weighted_sampler: bool = False
+    length_weighted_sampler_alpha: float = 1.0

TTS/vc/models/__init__.py

@@ -0,0 +1,17 @@
+import importlib
+import re
+from typing import Dict, List, Union
+
+
+def to_camel(text):
+    text = text.capitalize()
+    return re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text)
+
+
+def setup_model(config: "Coqpit", samples: Union[List[List], List[Dict]] = None) -> "BaseVC":
+    print(" > Using model: {}".format(config.model))
+    # fetch the right model implementation.
+    if "model" in config and config["model"].lower() == "freevc":
+        MyModel = importlib.import_module("TTS.vc.models.freevc").FreeVC
+        model = MyModel.init_from_config(config, samples)
+    return model

TTS/vc/models/base_vc.py

@@ -0,0 +1,429 @@
+import os
+import random
+from typing import Dict, List, Tuple, Union
+
+import torch
+import torch.distributed as dist
+from coqpit import Coqpit
+from torch import nn
+from torch.utils.data import DataLoader
+from torch.utils.data.sampler import WeightedRandomSampler
+from trainer.torch import DistributedSampler, DistributedSamplerWrapper
+
+from TTS.model import BaseTrainerModel
+from TTS.tts.datasets.dataset import TTSDataset
+from TTS.tts.utils.data import get_length_balancer_weights
+from TTS.tts.utils.languages import LanguageManager, get_language_balancer_weights
+from TTS.tts.utils.speakers import SpeakerManager, get_speaker_balancer_weights
+from TTS.tts.utils.synthesis import synthesis
+from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
+
+# pylint: skip-file
+
+
+class BaseVC(BaseTrainerModel):
+    """Base `vc` class. Every new `vc` model must inherit this.
+
+    It defines common `vc` specific functions on top of `Model` implementation.
+    """
+
+    MODEL_TYPE = "vc"
+
+    def __init__(
+        self,
+        config: Coqpit,
+        ap: "AudioProcessor",
+        speaker_manager: SpeakerManager = None,
+        language_manager: LanguageManager = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.ap = ap
+        self.speaker_manager = speaker_manager
+        self.language_manager = language_manager
+        self._set_model_args(config)
+
+    def _set_model_args(self, config: Coqpit):
+        """Setup model args based on the config type (`ModelConfig` or `ModelArgs`).
+
+        `ModelArgs` has all the fields reuqired to initialize the model architecture.
+
+        `ModelConfig` has all the fields required for training, inference and containes `ModelArgs`.
+
+        If the config is for training with a name like "*Config", then the model args are embeded in the
+        config.model_args
+
+        If the config is for the model with a name like "*Args", then we assign the directly.
+        """
+        # don't use isintance not to import recursively
+        if "Config" in config.__class__.__name__:
+            self.config = config
+            self.args = config.model_args
+        elif "Args" in config.__class__.__name__:
+            self.args = config
+        else:
+            raise ValueError("config must be either a *Config or *Args")
+
+    def init_multispeaker(self, config: Coqpit, data: List = None):
+        """Initialize a speaker embedding layer if needen and define expected embedding channel size for defining
+        `in_channels` size of the connected layers.
+
+        This implementation yields 3 possible outcomes:
+
+        1. If `config.use_speaker_embedding` and `config.use_d_vector_file are False, do nothing.
+        2. If `config.use_d_vector_file` is True, set expected embedding channel size to `config.d_vector_dim` or 512.
+        3. If `config.use_speaker_embedding`, initialize a speaker embedding layer with channel size of
+        `config.d_vector_dim` or 512.
+
+        You can override this function for new models.
+
+        Args:
+            config (Coqpit): Model configuration.
+        """
+        # set number of speakers
+        if self.speaker_manager is not None:
+            self.num_speakers = self.speaker_manager.num_speakers
+        elif hasattr(config, "num_speakers"):
+            self.num_speakers = config.num_speakers
+
+        # set ultimate speaker embedding size
+        if config.use_speaker_embedding or config.use_d_vector_file:
+            self.embedded_speaker_dim = (
+                config.d_vector_dim if "d_vector_dim" in config and config.d_vector_dim is not None else 512
+            )
+        # init speaker embedding layer
+        if config.use_speaker_embedding and not config.use_d_vector_file:
+            print(" > Init speaker_embedding layer.")
+            self.speaker_embedding = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
+            self.speaker_embedding.weight.data.normal_(0, 0.3)
+
+    def get_aux_input(self, **kwargs) -> Dict:
+        """Prepare and return `aux_input` used by `forward()`"""
+        return {"speaker_id": None, "style_wav": None, "d_vector": None, "language_id": None}
+
+    def get_aux_input_from_test_sentences(self, sentence_info):
+        if hasattr(self.config, "model_args"):
+            config = self.config.model_args
+        else:
+            config = self.config
+
+        # extract speaker and language info
+        text, speaker_name, style_wav, language_name = None, None, None, None
+
+        if isinstance(sentence_info, list):
+            if len(sentence_info) == 1:
+                text = sentence_info[0]
+            elif len(sentence_info) == 2:
+                text, speaker_name = sentence_info
+            elif len(sentence_info) == 3:
+                text, speaker_name, style_wav = sentence_info
+            elif len(sentence_info) == 4:
+                text, speaker_name, style_wav, language_name = sentence_info
+        else:
+            text = sentence_info
+
+        # get speaker  id/d_vector
+        speaker_id, d_vector, language_id = None, None, None
+        if self.speaker_manager is not None:
+            if config.use_d_vector_file:
+                if speaker_name is None:
+                    d_vector = self.speaker_manager.get_random_embedding()
+                else:
+                    d_vector = self.speaker_manager.get_d_vector_by_name(speaker_name)
+            elif config.use_speaker_embedding:
+                if speaker_name is None:
+                    speaker_id = self.speaker_manager.get_random_id()
+                else:
+                    speaker_id = self.speaker_manager.name_to_id[speaker_name]
+
+        # get language id
+        if self.language_manager is not None and config.use_language_embedding and language_name is not None:
+            language_id = self.language_manager.name_to_id[language_name]
+
+        return {
+            "text": text,
+            "speaker_id": speaker_id,
+            "style_wav": style_wav,
+            "d_vector": d_vector,
+            "language_id": language_id,
+        }
+
+    def format_batch(self, batch: Dict) -> Dict:
+        """Generic batch formatting for `VCDataset`.
+
+        You must override this if you use a custom dataset.
+
+        Args:
+            batch (Dict): [description]
+
+        Returns:
+            Dict: [description]
+        """
+        # setup input batch
+        text_input = batch["token_id"]
+        text_lengths = batch["token_id_lengths"]
+        speaker_names = batch["speaker_names"]
+        linear_input = batch["linear"]
+        mel_input = batch["mel"]
+        mel_lengths = batch["mel_lengths"]
+        stop_targets = batch["stop_targets"]
+        item_idx = batch["item_idxs"]
+        d_vectors = batch["d_vectors"]
+        speaker_ids = batch["speaker_ids"]
+        attn_mask = batch["attns"]
+        waveform = batch["waveform"]
+        pitch = batch["pitch"]
+        energy = batch["energy"]
+        language_ids = batch["language_ids"]
+        max_text_length = torch.max(text_lengths.float())
+        max_spec_length = torch.max(mel_lengths.float())
+
+        # compute durations from attention masks
+        durations = None
+        if attn_mask is not None:
+            durations = torch.zeros(attn_mask.shape[0], attn_mask.shape[2])
+            for idx, am in enumerate(attn_mask):
+                # compute raw durations
+                c_idxs = am[:, : text_lengths[idx], : mel_lengths[idx]].max(1)[1]
+                # c_idxs, counts = torch.unique_consecutive(c_idxs, return_counts=True)
+                c_idxs, counts = torch.unique(c_idxs, return_counts=True)
+                dur = torch.ones([text_lengths[idx]]).to(counts.dtype)
+                dur[c_idxs] = counts
+                # smooth the durations and set any 0 duration to 1
+                # by cutting off from the largest duration indeces.
+                extra_frames = dur.sum() - mel_lengths[idx]
+                largest_idxs = torch.argsort(-dur)[:extra_frames]
+                dur[largest_idxs] -= 1
+                assert (
+                    dur.sum() == mel_lengths[idx]
+                ), f" [!] total duration {dur.sum()} vs spectrogram length {mel_lengths[idx]}"
+                durations[idx, : text_lengths[idx]] = dur
+
+        # set stop targets wrt reduction factor
+        stop_targets = stop_targets.view(text_input.shape[0], stop_targets.size(1) // self.config.r, -1)
+        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
+        stop_target_lengths = torch.divide(mel_lengths, self.config.r).ceil_()
+
+        return {
+            "text_input": text_input,
+            "text_lengths": text_lengths,
+            "speaker_names": speaker_names,
+            "mel_input": mel_input,
+            "mel_lengths": mel_lengths,
+            "linear_input": linear_input,
+            "stop_targets": stop_targets,
+            "stop_target_lengths": stop_target_lengths,
+            "attn_mask": attn_mask,
+            "durations": durations,
+            "speaker_ids": speaker_ids,
+            "d_vectors": d_vectors,
+            "max_text_length": float(max_text_length),
+            "max_spec_length": float(max_spec_length),
+            "item_idx": item_idx,
+            "waveform": waveform,
+            "pitch": pitch,
+            "energy": energy,
+            "language_ids": language_ids,
+            "audio_unique_names": batch["audio_unique_names"],
+        }
+
+    def get_sampler(self, config: Coqpit, dataset: TTSDataset, num_gpus=1):
+        weights = None
+        data_items = dataset.samples
+
+        if getattr(config, "use_language_weighted_sampler", False):
+            alpha = getattr(config, "language_weighted_sampler_alpha", 1.0)
+            print(" > Using Language weighted sampler with alpha:", alpha)
+            weights = get_language_balancer_weights(data_items) * alpha
+
+        if getattr(config, "use_speaker_weighted_sampler", False):
+            alpha = getattr(config, "speaker_weighted_sampler_alpha", 1.0)
+            print(" > Using Speaker weighted sampler with alpha:", alpha)
+            if weights is not None:
+                weights += get_speaker_balancer_weights(data_items) * alpha
+            else:
+                weights = get_speaker_balancer_weights(data_items) * alpha
+
+        if getattr(config, "use_length_weighted_sampler", False):
+            alpha = getattr(config, "length_weighted_sampler_alpha", 1.0)
+            print(" > Using Length weighted sampler with alpha:", alpha)
+            if weights is not None:
+                weights += get_length_balancer_weights(data_items) * alpha
+            else:
+                weights = get_length_balancer_weights(data_items) * alpha
+
+        if weights is not None:
+            sampler = WeightedRandomSampler(weights, len(weights))
+        else:
+            sampler = None
+
+        # sampler for DDP
+        if sampler is None:
+            sampler = DistributedSampler(dataset) if num_gpus > 1 else None
+        else:  # If a sampler is already defined use this sampler and DDP sampler together
+            sampler = DistributedSamplerWrapper(sampler) if num_gpus > 1 else sampler
+
+        return sampler
+
+    def get_data_loader(
+        self,
+        config: Coqpit,
+        assets: Dict,
+        is_eval: bool,
+        samples: Union[List[Dict], List[List]],
+        verbose: bool,
+        num_gpus: int,
+        rank: int = None,
+    ) -> "DataLoader":
+        if is_eval and not config.run_eval:
+            loader = None
+        else:
+            # setup multi-speaker attributes
+            if self.speaker_manager is not None:
+                if hasattr(config, "model_args"):
+                    speaker_id_mapping = (
+                        self.speaker_manager.name_to_id if config.model_args.use_speaker_embedding else None
+                    )
+                    d_vector_mapping = self.speaker_manager.embeddings if config.model_args.use_d_vector_file else None
+                    config.use_d_vector_file = config.model_args.use_d_vector_file
+                else:
+                    speaker_id_mapping = self.speaker_manager.name_to_id if config.use_speaker_embedding else None
+                    d_vector_mapping = self.speaker_manager.embeddings if config.use_d_vector_file else None
+            else:
+                speaker_id_mapping = None
+                d_vector_mapping = None
+
+            # setup multi-lingual attributes
+            if self.language_manager is not None:
+                language_id_mapping = self.language_manager.name_to_id if self.args.use_language_embedding else None
+            else:
+                language_id_mapping = None
+
+            # init dataloader
+            dataset = TTSDataset(
+                outputs_per_step=config.r if "r" in config else 1,
+                compute_linear_spec=config.model.lower() == "tacotron" or config.compute_linear_spec,
+                compute_f0=config.get("compute_f0", False),
+                f0_cache_path=config.get("f0_cache_path", None),
+                compute_energy=config.get("compute_energy", False),
+                energy_cache_path=config.get("energy_cache_path", None),
+                samples=samples,
+                ap=self.ap,
+                return_wav=config.return_wav if "return_wav" in config else False,
+                batch_group_size=0 if is_eval else config.batch_group_size * config.batch_size,
+                min_text_len=config.min_text_len,
+                max_text_len=config.max_text_len,
+                min_audio_len=config.min_audio_len,
+                max_audio_len=config.max_audio_len,
+                phoneme_cache_path=config.phoneme_cache_path,
+                precompute_num_workers=config.precompute_num_workers,
+                use_noise_augment=False if is_eval else config.use_noise_augment,
+                verbose=verbose,
+                speaker_id_mapping=speaker_id_mapping,
+                d_vector_mapping=d_vector_mapping if config.use_d_vector_file else None,
+                tokenizer=None,
+                start_by_longest=config.start_by_longest,
+                language_id_mapping=language_id_mapping,
+            )
+
+            # wait all the DDP process to be ready
+            if num_gpus > 1:
+                dist.barrier()
+
+            # sort input sequences from short to long
+            dataset.preprocess_samples()
+
+            # get samplers
+            sampler = self.get_sampler(config, dataset, num_gpus)
+
+            loader = DataLoader(
+                dataset,
+                batch_size=config.eval_batch_size if is_eval else config.batch_size,
+                shuffle=config.shuffle if sampler is None else False,  # if there is no other sampler
+                collate_fn=dataset.collate_fn,
+                drop_last=config.drop_last,  # setting this False might cause issues in AMP training.
+                sampler=sampler,
+                num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
+                pin_memory=False,
+            )
+        return loader
+
+    def _get_test_aux_input(
+        self,
+    ) -> Dict:
+        d_vector = None
+        if self.config.use_d_vector_file:
+            d_vector = [self.speaker_manager.embeddings[name]["embedding"] for name in self.speaker_manager.embeddings]
+            d_vector = (random.sample(sorted(d_vector), 1),)
+
+        aux_inputs = {
+            "speaker_id": None
+            if not self.config.use_speaker_embedding
+            else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1),
+            "d_vector": d_vector,
+            "style_wav": None,  # TODO: handle GST style input
+        }
+        return aux_inputs
+
+    def test_run(self, assets: Dict) -> Tuple[Dict, Dict]:
+        """Generic test run for `vc` models used by `Trainer`.
+
+        You can override this for a different behaviour.
+
+        Args:
+            assets (dict): A dict of training assets. For `vc` models, it must include `{'audio_processor': ap}`.
+
+        Returns:
+            Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.
+        """
+        print(" | > Synthesizing test sentences.")
+        test_audios = {}
+        test_figures = {}
+        test_sentences = self.config.test_sentences
+        aux_inputs = self._get_test_aux_input()
+        for idx, sen in enumerate(test_sentences):
+            if isinstance(sen, list):
+                aux_inputs = self.get_aux_input_from_test_sentences(sen)
+                sen = aux_inputs["text"]
+            outputs_dict = synthesis(
+                self,
+                sen,
+                self.config,
+                "cuda" in str(next(self.parameters()).device),
+                speaker_id=aux_inputs["speaker_id"],
+                d_vector=aux_inputs["d_vector"],
+                style_wav=aux_inputs["style_wav"],
+                use_griffin_lim=True,
+                do_trim_silence=False,
+            )
+            test_audios["{}-audio".format(idx)] = outputs_dict["wav"]
+            test_figures["{}-prediction".format(idx)] = plot_spectrogram(
+                outputs_dict["outputs"]["model_outputs"], self.ap, output_fig=False
+            )
+            test_figures["{}-alignment".format(idx)] = plot_alignment(
+                outputs_dict["outputs"]["alignments"], output_fig=False
+            )
+        return test_figures, test_audios
+
+    def on_init_start(self, trainer):
+        """Save the speaker.pth and language_ids.json at the beginning of the training. Also update both paths."""
+        if self.speaker_manager is not None:
+            output_path = os.path.join(trainer.output_path, "speakers.pth")
+            self.speaker_manager.save_ids_to_file(output_path)
+            trainer.config.speakers_file = output_path
+            # some models don't have `model_args` set
+            if hasattr(trainer.config, "model_args"):
+                trainer.config.model_args.speakers_file = output_path
+            trainer.config.save_json(os.path.join(trainer.output_path, "config.json"))
+            print(f" > `speakers.pth` is saved to {output_path}.")
+            print(" > `speakers_file` is updated in the config.json.")
+
+        if self.language_manager is not None:
+            output_path = os.path.join(trainer.output_path, "language_ids.json")
+            self.language_manager.save_ids_to_file(output_path)
+            trainer.config.language_ids_file = output_path
+            if hasattr(trainer.config, "model_args"):
+                trainer.config.model_args.language_ids_file = output_path
+            trainer.config.save_json(os.path.join(trainer.output_path, "config.json"))
+            print(f" > `language_ids.json` is saved to {output_path}.")
+            print(" > `language_ids_file` is updated in the config.json.")

TTS/vc/models/freevc.py

@@ -0,0 +1,833 @@
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple, Union
+
+import librosa
+import numpy as np
+import torch
+from coqpit import Coqpit
+from torch import nn
+from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+import TTS.vc.modules.freevc.commons as commons
+import TTS.vc.modules.freevc.modules as modules
+from TTS.tts.utils.speakers import SpeakerManager
+from TTS.utils.io import load_fsspec, save_checkpoint
+from TTS.vc.configs.shared_configs import BaseVCConfig
+from TTS.vc.models.base_vc import BaseVC
+from TTS.vc.modules.freevc.commons import get_padding, init_weights
+from TTS.vc.modules.freevc.mel_processing import mel_spectrogram_torch
+from TTS.vc.modules.freevc.speaker_encoder.speaker_encoder import SpeakerEncoder as SpeakerEncoderEx
+from TTS.vc.modules.freevc.wavlm import get_wavlm
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows=4, gin_channels=0):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+                norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+                norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+                norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+                norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class SpeakerEncoder(torch.nn.Module):
+    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
+        super(SpeakerEncoder, self).__init__()
+        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
+        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, mels):
+        self.lstm.flatten_parameters()
+        _, (hidden, _) = self.lstm(mels)
+        embeds_raw = self.relu(self.linear(hidden[-1]))
+        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
+
+    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
+        mel_slices = []
+        for i in range(0, total_frames - partial_frames, partial_hop):
+            mel_range = torch.arange(i, i + partial_frames)
+            mel_slices.append(mel_range)
+
+        return mel_slices
+
+    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
+        mel_len = mel.size(1)
+        last_mel = mel[:, -partial_frames:]
+
+        if mel_len > partial_frames:
+            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
+            mels = list(mel[:, s] for s in mel_slices)
+            mels.append(last_mel)
+            mels = torch.stack(tuple(mels), 0).squeeze(1)
+
+            with torch.no_grad():
+                partial_embeds = self(mels)
+            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
+            # embed = embed / torch.linalg.norm(embed, 2)
+        else:
+            with torch.no_grad():
+                embed = self(last_mel)
+
+        return embed
+
+
+@dataclass
+class FreeVCAudioConfig(Coqpit):
+    """Audio configuration
+
+    Args:
+        max_wav_value (float):
+            The maximum value of the waveform.
+
+        input_sample_rate (int):
+            The sampling rate of the input waveform.
+
+        output_sample_rate (int):
+            The sampling rate of the output waveform.
+
+        filter_length (int):
+            The length of the filter.
+
+        hop_length (int):
+            The hop length.
+
+        win_length (int):
+            The window length.
+
+        n_mel_channels (int):
+            The number of mel channels.
+
+        mel_fmin (float):
+            The minimum frequency of the mel filterbank.
+
+        mel_fmax (Optional[float]):
+            The maximum frequency of the mel filterbank.
+    """
+
+    max_wav_value: float = field(default=32768.0)
+    input_sample_rate: int = field(default=16000)
+    output_sample_rate: int = field(default=24000)
+    filter_length: int = field(default=1280)
+    hop_length: int = field(default=320)
+    win_length: int = field(default=1280)
+    n_mel_channels: int = field(default=80)
+    mel_fmin: float = field(default=0.0)
+    mel_fmax: Optional[float] = field(default=None)
+
+
+@dataclass
+class FreeVCArgs(Coqpit):
+    """FreeVC model arguments
+
+    Args:
+        spec_channels (int):
+            The number of channels in the spectrogram.
+
+        inter_channels (int):
+            The number of channels in the intermediate layers.
+
+        hidden_channels (int):
+            The number of channels in the hidden layers.
+
+        filter_channels (int):
+            The number of channels in the filter layers.
+
+        n_heads (int):
+            The number of attention heads.
+
+        n_layers (int):
+            The number of layers.
+
+        kernel_size (int):
+            The size of the kernel.
+
+        p_dropout (float):
+            The dropout probability.
+
+        resblock (str):
+            The type of residual block.
+
+        resblock_kernel_sizes (List[int]):
+            The kernel sizes for the residual blocks.
+
+        resblock_dilation_sizes (List[List[int]]):
+            The dilation sizes for the residual blocks.
+
+        upsample_rates (List[int]):
+            The upsample rates.
+
+        upsample_initial_channel (int):
+            The number of channels in the initial upsample layer.
+
+        upsample_kernel_sizes (List[int]):
+            The kernel sizes for the upsample layers.
+
+        n_layers_q (int):
+            The number of layers in the quantization network.
+
+        use_spectral_norm (bool):
+            Whether to use spectral normalization.
+
+        gin_channels (int):
+            The number of channels in the global conditioning vector.
+
+        ssl_dim (int):
+            The dimension of the self-supervised learning embedding.
+
+        use_spk (bool):
+            Whether to use external speaker encoder.
+    """
+
+    spec_channels: int = field(default=641)
+    inter_channels: int = field(default=192)
+    hidden_channels: int = field(default=192)
+    filter_channels: int = field(default=768)
+    n_heads: int = field(default=2)
+    n_layers: int = field(default=6)
+    kernel_size: int = field(default=3)
+    p_dropout: float = field(default=0.1)
+    resblock: str = field(default="1")
+    resblock_kernel_sizes: List[int] = field(default_factory=lambda: [3, 7, 11])
+    resblock_dilation_sizes: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]])
+    upsample_rates: List[int] = field(default_factory=lambda: [10, 8, 2, 2])
+    upsample_initial_channel: int = field(default=512)
+    upsample_kernel_sizes: List[int] = field(default_factory=lambda: [16, 16, 4, 4])
+    n_layers_q: int = field(default=3)
+    use_spectral_norm: bool = field(default=False)
+    gin_channels: int = field(default=256)
+    ssl_dim: int = field(default=1024)
+    use_spk: bool = field(default=False)
+    num_spks: int = field(default=0)
+    segment_size: int = field(default=8960)
+
+
+class FreeVC(BaseVC):
+    """
+
+    Papaer::
+        https://arxiv.org/abs/2210.15418#
+
+    Paper Abstract::
+        Voice conversion (VC) can be achieved by first extracting source content information and target speaker
+        information, and then reconstructing waveform with these information. However, current approaches normally
+        either extract dirty content information with speaker information leaked in, or demand a large amount of
+        annotated data for training. Besides, the quality of reconstructed waveform can be degraded by the
+        mismatch between conversion model and vocoder. In this paper, we adopt the end-to-end framework of VITS for
+        high-quality waveform reconstruction, and propose strategies for clean content information extraction without
+        text annotation. We disentangle content information by imposing an information bottleneck to WavLM features,
+        and propose the spectrogram-resize based data augmentation to improve the purity of extracted content
+        information. Experimental results show that the proposed method outperforms the latest VC models trained with
+        annotated data and has greater robustness.
+
+    Original Code::
+        https://github.com/OlaWod/FreeVC
+
+    Examples:
+        >>> from TTS.vc.configs.freevc_config import FreeVCConfig
+        >>> from TTS.vc.models.freevc import FreeVC
+        >>> config = FreeVCConfig()
+        >>> model = FreeVC(config)
+    """
+
+    def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None):
+        super().__init__(config, None, speaker_manager, None)
+
+        self.init_multispeaker(config)
+
+        self.spec_channels = self.args.spec_channels
+        self.inter_channels = self.args.inter_channels
+        self.hidden_channels = self.args.hidden_channels
+        self.filter_channels = self.args.filter_channels
+        self.n_heads = self.args.n_heads
+        self.n_layers = self.args.n_layers
+        self.kernel_size = self.args.kernel_size
+        self.p_dropout = self.args.p_dropout
+        self.resblock = self.args.resblock
+        self.resblock_kernel_sizes = self.args.resblock_kernel_sizes
+        self.resblock_dilation_sizes = self.args.resblock_dilation_sizes
+        self.upsample_rates = self.args.upsample_rates
+        self.upsample_initial_channel = self.args.upsample_initial_channel
+        self.upsample_kernel_sizes = self.args.upsample_kernel_sizes
+        self.segment_size = self.args.segment_size
+        self.gin_channels = self.args.gin_channels
+        self.ssl_dim = self.args.ssl_dim
+        self.use_spk = self.args.use_spk
+
+        self.enc_p = Encoder(self.args.ssl_dim, self.inter_channels, self.hidden_channels, 5, 1, 16)
+        self.dec = Generator(
+            self.inter_channels,
+            self.resblock,
+            self.resblock_kernel_sizes,
+            self.resblock_dilation_sizes,
+            self.upsample_rates,
+            self.upsample_initial_channel,
+            self.upsample_kernel_sizes,
+            gin_channels=self.gin_channels,
+        )
+        self.enc_q = Encoder(
+            self.spec_channels, self.inter_channels, self.hidden_channels, 5, 1, 16, gin_channels=self.gin_channels
+        )
+        self.flow = ResidualCouplingBlock(
+            self.inter_channels, self.hidden_channels, 5, 1, 4, gin_channels=self.gin_channels
+        )
+        if not self.use_spk:
+            self.enc_spk = SpeakerEncoder(model_hidden_size=self.gin_channels, model_embedding_size=self.gin_channels)
+        else:
+            self.load_pretrained_speaker_encoder()
+
+        self.wavlm = get_wavlm()
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def load_pretrained_speaker_encoder(self):
+        """Load pretrained speaker encoder model as mentioned in the paper."""
+        print(" > Loading pretrained speaker encoder model ...")
+        self.enc_spk_ex = SpeakerEncoderEx(
+            "https://github.com/coqui-ai/TTS/releases/download/v0.13.0_models/speaker_encoder.pt"
+        )
+
+    def init_multispeaker(self, config: Coqpit):
+        """Initialize multi-speaker modules of a model. A model can be trained either with a speaker embedding layer
+        or with external `d_vectors` computed from a speaker encoder model.
+
+        You must provide a `speaker_manager` at initialization to set up the multi-speaker modules.
+
+        Args:
+            config (Coqpit): Model configuration.
+            data (List, optional): Dataset items to infer number of speakers. Defaults to None.
+        """
+        self.num_spks = self.args.num_spks
+        if self.speaker_manager:
+            self.num_spks = self.speaker_manager.num_spks
+
+    def forward(
+        self,
+        c: torch.Tensor,
+        spec: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        mel: Optional[torch.Tensor] = None,
+        c_lengths: Optional[torch.Tensor] = None,
+        spec_lengths: Optional[torch.Tensor] = None,
+    ) -> Tuple[
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
+    ]:
+        """
+        Forward pass of the model.
+
+        Args:
+            c: WavLM features. Shape: (batch_size, c_seq_len).
+            spec: The input spectrogram. Shape: (batch_size, spec_seq_len, spec_dim).
+            g: The speaker embedding. Shape: (batch_size, spk_emb_dim).
+            mel: The input mel-spectrogram for the speaker encoder. Shape: (batch_size, mel_seq_len, mel_dim).
+            c_lengths: The lengths of the WavLM features. Shape: (batch_size,).
+            spec_lengths: The lengths of the spectrogram. Shape: (batch_size,).
+
+        Returns:
+            o: The output spectrogram. Shape: (batch_size, spec_seq_len, spec_dim).
+            ids_slice: The slice indices. Shape: (batch_size, num_slices).
+            spec_mask: The spectrogram mask. Shape: (batch_size, spec_seq_len).
+            (z, z_p, m_p, logs_p, m_q, logs_q): A tuple of latent variables.
+        """
+
+        # If c_lengths is None, set it to the length of the last dimension of c
+        if c_lengths is None:
+            c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
+
+        # If spec_lengths is None, set it to the length of the last dimension of spec
+        if spec_lengths is None:
+            spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
+
+        # If use_spk is False, compute g from mel using enc_spk
+        g = None
+        if not self.use_spk:
+            g = self.enc_spk(mel).unsqueeze(-1)
+
+        # Compute m_p, logs_p, z, m_q, logs_q, and spec_mask using enc_p and enc_q
+        _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
+        z, m_q, logs_q, spec_mask = self.enc_q(spec.transpose(1, 2), spec_lengths, g=g)
+
+        # Compute z_p using flow
+        z_p = self.flow(z, spec_mask, g=g)
+
+        # Randomly slice z and compute o using dec
+        z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
+        o = self.dec(z_slice, g=g)
+
+        return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+    @torch.no_grad()
+    def inference(self, c, g=None, mel=None, c_lengths=None):
+        """
+        Inference pass of the model
+
+        Args:
+            c (torch.Tensor): Input tensor. Shape: (batch_size, c_seq_len).
+            g (torch.Tensor): Speaker embedding tensor. Shape: (batch_size, spk_emb_dim).
+            mel (torch.Tensor): Mel-spectrogram tensor. Shape: (batch_size, mel_seq_len, mel_dim).
+            c_lengths (torch.Tensor): Lengths of the input tensor. Shape: (batch_size,).
+
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+        if c_lengths == None:
+            c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
+        if not self.use_spk:
+            g = self.enc_spk.embed_utterance(mel)
+            g = g.unsqueeze(-1)
+        z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
+        z = self.flow(z_p, c_mask, g=g, reverse=True)
+        o = self.dec(z * c_mask, g=g)
+        return o
+
+    def extract_wavlm_features(self, y):
+        """Extract WavLM features from an audio tensor.
+
+        Args:
+            y (torch.Tensor): Audio tensor. Shape: (batch_size, audio_seq_len).
+        """
+
+        with torch.no_grad():
+            c = self.wavlm.extract_features(y)[0]
+        c = c.transpose(1, 2)
+        return c
+
+    def load_audio(self, wav):
+        """Read and format the input audio."""
+        if isinstance(wav, str):
+            wav, _ = librosa.load(wav, sr=self.config.audio.input_sample_rate)
+        if isinstance(wav, np.ndarray):
+            wav = torch.from_numpy(wav).to(self.device)
+        if isinstance(wav, torch.Tensor):
+            wav = wav.to(self.device)
+        if isinstance(wav, list):
+            wav = torch.from_numpy(np.array(wav)).to(self.device)
+        return wav.float()
+
+    @torch.inference_mode()
+    def voice_conversion(self, src, tgt):
+        """
+        Voice conversion pass of the model.
+
+        Args:
+            src (str or torch.Tensor): Source utterance.
+            tgt (str or torch.Tensor): Target utterance.
+
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+
+        wav_tgt = self.load_audio(tgt).cpu().numpy()
+        wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
+
+        if self.config.model_args.use_spk:
+            g_tgt = self.enc_spk_ex.embed_utterance(wav_tgt)
+            g_tgt = torch.from_numpy(g_tgt)[None, :, None].to(self.device)
+        else:
+            wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(self.device)
+            mel_tgt = mel_spectrogram_torch(
+                wav_tgt,
+                self.config.audio.filter_length,
+                self.config.audio.n_mel_channels,
+                self.config.audio.input_sample_rate,
+                self.config.audio.hop_length,
+                self.config.audio.win_length,
+                self.config.audio.mel_fmin,
+                self.config.audio.mel_fmax,
+            )
+        # src
+        wav_src = self.load_audio(src)
+        c = self.extract_wavlm_features(wav_src[None, :])
+
+        if self.config.model_args.use_spk:
+            audio = self.inference(c, g=g_tgt)
+        else:
+            audio = self.inference(c, mel=mel_tgt.transpose(1, 2))
+        audio = audio[0][0].data.cpu().float().numpy()
+        return audio
+
+    def eval_step():
+        ...
+
+    @staticmethod
+    def init_from_config(config: "VitsConfig", samples: Union[List[List], List[Dict]] = None, verbose=True):
+        model = FreeVC(config)
+        return model
+
+    def load_checkpoint(self, config, checkpoint_path, eval=False, strict=True, cache=False):
+        state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
+        self.load_state_dict(state["model"], strict=strict)
+        if eval:
+            self.eval()
+
+    def train_step():
+        ...
+
+
+@dataclass
+class FreeVCConfig(BaseVCConfig):
+    """Defines parameters for FreeVC End2End TTS model.
+
+    Args:
+        model (str):
+            Model name. Do not change unless you know what you are doing.
+
+        model_args (FreeVCArgs):
+            Model architecture arguments. Defaults to `FreeVCArgs()`.
+
+        audio (FreeVCAudioConfig):
+            Audio processing configuration. Defaults to `FreeVCAudioConfig()`.
+
+        grad_clip (List):
+            Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
+
+        lr_gen (float):
+            Initial learning rate for the generator. Defaults to 0.0002.
+
+        lr_disc (float):
+            Initial learning rate for the discriminator. Defaults to 0.0002.
+
+        lr_scheduler_gen (str):
+            Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to
+            `ExponentialLR`.
+
+        lr_scheduler_gen_params (dict):
+            Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
+
+        lr_scheduler_disc (str):
+            Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to
+            `ExponentialLR`.
+
+        lr_scheduler_disc_params (dict):
+            Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
+
+        scheduler_after_epoch (bool):
+            If true, step the schedulers after each epoch else after each step. Defaults to `False`.
+
+        optimizer (str):
+            Name of the optimizer to use with both the generator and the discriminator networks. One of the
+            `torch.optim.*`. Defaults to `AdamW`.
+
+        kl_loss_alpha (float):
+            Loss weight for KL loss. Defaults to 1.0.
+
+        disc_loss_alpha (float):
+            Loss weight for the discriminator loss. Defaults to 1.0.
+
+        gen_loss_alpha (float):
+            Loss weight for the generator loss. Defaults to 1.0.
+
+        feat_loss_alpha (float):
+            Loss weight for the feature matching loss. Defaults to 1.0.
+
+        mel_loss_alpha (float):
+            Loss weight for the mel loss. Defaults to 45.0.
+
+        return_wav (bool):
+            If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`.
+
+        compute_linear_spec (bool):
+            If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`.
+
+        use_weighted_sampler (bool):
+            If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`.
+
+        weighted_sampler_attrs (dict):
+            Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities
+            by overweighting `root_path` by 2.0. Defaults to `{}`.
+
+        weighted_sampler_multipliers (dict):
+            Weight each unique value of a key returned by the formatter for weighted sampling.
+            For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`.
+            It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`.
+
+        r (int):
+            Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`.
+
+        add_blank (bool):
+            If true, a blank token is added in between every character. Defaults to `True`.
+
+        test_sentences (List[List]):
+            List of sentences with speaker and language information to be used for testing.
+
+        language_ids_file (str):
+            Path to the language ids file.
+
+        use_language_embedding (bool):
+            If true, language embedding is used. Defaults to `False`.
+
+    Note:
+        Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
+
+    Example:
+
+        >>> from TTS.tts.configs.freevc_config import FreeVCConfig
+        >>> config = FreeVCConfig()
+    """
+
+    model: str = "freevc"
+    # model specific params
+    model_args: FreeVCArgs = FreeVCArgs()
+    audio: FreeVCAudioConfig = FreeVCAudioConfig()
+
+    # optimizer
+    # TODO with training support
+
+    # loss params
+    # TODO with training support
+
+    # data loader params
+    return_wav: bool = True
+    compute_linear_spec: bool = True
+
+    # sampler params
+    use_weighted_sampler: bool = False  # TODO: move it to the base config
+    weighted_sampler_attrs: dict = field(default_factory=lambda: {})
+    weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
+
+    # overrides
+    r: int = 1  # DO NOT CHANGE
+    add_blank: bool = True
+
+    # multi-speaker settings
+    # use speaker embedding layer
+    num_speakers: int = 0
+    speakers_file: str = None
+    speaker_embedding_channels: int = 256
+
+    # use d-vectors
+    use_d_vector_file: bool = False
+    d_vector_file: List[str] = None
+    d_vector_dim: int = None
+
+    def __post_init__(self):
+        for key, val in self.model_args.items():
+            if hasattr(self, key):
+                self[key] = val

TTS/vc/modules/freevc/commons.py

@@ -0,0 +1,170 @@
+import math
+
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def intersperse(lst, item):
+    result = [item] * (len(lst) * 2 + 1)
+    result[1::2] = lst
+    return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+    """KL(P||Q)"""
+    kl = (logs_q - logs_p) - 0.5
+    kl += 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
+    return kl
+
+
+def rand_gumbel(shape):
+    """Sample from the Gumbel distribution, protect from overflows."""
+    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+    return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+    return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+    ret = torch.zeros_like(x[:, :, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        ret[i] = x[i, :, idx_str:idx_end]
+    return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = x_lengths - segment_size + 1
+    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def rand_spec_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = x_lengths - segment_size
+    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
+    position = torch.arange(length, dtype=torch.float)
+    num_timescales = channels // 2
+    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (num_timescales - 1)
+    inv_timescales = min_timescale * torch.exp(
+        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
+    )
+    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+    signal = F.pad(signal, [0, 0, 0, channels % 2])
+    signal = signal.view(1, channels, length)
+    return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def shift_1d(x):
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+    return x
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+    device = duration.device
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = torch.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.view(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+    path = path.view(b, t_x, t_y)
+    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+    path = path.unsqueeze(1).transpose(2, 3) * mask
+    return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    if clip_value is not None:
+        clip_value = float(clip_value)
+
+    total_norm = 0
+    for p in parameters:
+        param_norm = p.grad.data.norm(norm_type)
+        total_norm += param_norm.item() ** norm_type
+        if clip_value is not None:
+            p.grad.data.clamp_(min=-clip_value, max=clip_value)
+    total_norm = total_norm ** (1.0 / norm_type)
+    return total_norm

TTS/vc/modules/freevc/mel_processing.py

@@ -0,0 +1,125 @@
+import torch
+import torch.utils.data
+from librosa.filters import mel as librosa_mel_fn
+
+MAX_WAV_VALUE = 32768.0
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+    output = dynamic_range_decompression_torch(magnitudes)
+    return output
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    if torch.min(y) < -1.0:
+        print("min value is ", torch.min(y))
+    if torch.max(y) > 1.0:
+        print("max value is ", torch.max(y))
+
+    global hann_window
+    dtype_device = str(y.dtype) + "_" + str(y.device)
+    wnsize_dtype_device = str(win_size) + "_" + dtype_device
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
+    )
+    y = y.squeeze(1)
+
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window[wnsize_dtype_device],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+    global mel_basis
+    dtype_device = str(spec.dtype) + "_" + str(spec.device)
+    fmax_dtype_device = str(fmax) + "_" + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+    return spec
+
+
+def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
+    if torch.min(y) < -1.0:
+        print("min value is ", torch.min(y))
+    if torch.max(y) > 1.0:
+        print("max value is ", torch.max(y))
+
+    global mel_basis, hann_window
+    dtype_device = str(y.dtype) + "_" + str(y.device)
+    fmax_dtype_device = str(fmax) + "_" + dtype_device
+    wnsize_dtype_device = str(win_size) + "_" + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
+    )
+    y = y.squeeze(1)
+
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window[wnsize_dtype_device],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec

TTS/vc/modules/freevc/modules.py

@@ -0,0 +1,391 @@
+import copy
+import math
+
+import numpy as np
+import scipy
+import torch
+from torch import nn
+from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+import TTS.vc.modules.freevc.commons as commons
+from TTS.vc.modules.freevc.commons import get_padding, init_weights
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+        assert n_layers > 1, "Number of layers should be larger than 0."
+
+        self.conv_layers = nn.ModuleList()
+        self.norm_layers = nn.ModuleList()
+        self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
+        self.norm_layers.append(LayerNorm(hidden_channels))
+        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
+        for _ in range(n_layers - 1):
+            self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
+            self.norm_layers.append(LayerNorm(hidden_channels))
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask):
+        x_org = x
+        for i in range(self.n_layers):
+            x = self.conv_layers[i](x * x_mask)
+            x = self.norm_layers[i](x)
+            x = self.relu_drop(x)
+        x = x_org + self.proj(x)
+        return x * x_mask
+
+
+class DDSConv(nn.Module):
+    """
+    Dialted and Depth-Separable Convolution
+    """
+
+    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
+        super().__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+
+        self.drop = nn.Dropout(p_dropout)
+        self.convs_sep = nn.ModuleList()
+        self.convs_1x1 = nn.ModuleList()
+        self.norms_1 = nn.ModuleList()
+        self.norms_2 = nn.ModuleList()
+        for i in range(n_layers):
+            dilation = kernel_size**i
+            padding = (kernel_size * dilation - dilation) // 2
+            self.convs_sep.append(
+                nn.Conv1d(channels, channels, kernel_size, groups=channels, dilation=dilation, padding=padding)
+            )
+            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+            self.norms_1.append(LayerNorm(channels))
+            self.norms_2.append(LayerNorm(channels))
+
+    def forward(self, x, x_mask, g=None):
+        if g is not None:
+            x = x + g
+        for i in range(self.n_layers):
+            y = self.convs_sep[i](x * x_mask)
+            y = self.norms_1[i](y)
+            y = F.gelu(y)
+            y = self.convs_1x1[i](y)
+            y = self.norms_2[i](y)
+            y = F.gelu(y)
+            y = self.drop(y)
+            x = x + y
+        return x * x_mask
+
+
+class WN(torch.nn.Module):
+    def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+        super(WN, self).__init__()
+        assert kernel_size % 2 == 1
+        self.hidden_channels = hidden_channels
+        self.kernel_size = (kernel_size,)
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+        self.p_dropout = p_dropout
+
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.drop = nn.Dropout(p_dropout)
+
+        if gin_channels != 0:
+            cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1)
+            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
+
+        for i in range(n_layers):
+            dilation = dilation_rate**i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = torch.nn.Conv1d(
+                hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding
+            )
+            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
+            self.in_layers.append(in_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * hidden_channels
+            else:
+                res_skip_channels = hidden_channels
+
+            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, x, x_mask, g=None, **kwargs):
+        output = torch.zeros_like(x)
+        n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+        if g is not None:
+            g = self.cond_layer(g)
+
+        for i in range(self.n_layers):
+            x_in = self.in_layers[i](x)
+            if g is not None:
+                cond_offset = i * 2 * self.hidden_channels
+                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
+            else:
+                g_l = torch.zeros_like(x_in)
+
+            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
+            acts = self.drop(acts)
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                res_acts = res_skip_acts[:, : self.hidden_channels, :]
+                x = (x + res_acts) * x_mask
+                output = output + res_skip_acts[:, self.hidden_channels :, :]
+            else:
+                output = output + res_skip_acts
+        return output * x_mask
+
+    def remove_weight_norm(self):
+        if self.gin_channels != 0:
+            torch.nn.utils.remove_weight_norm(self.cond_layer)
+        for l in self.in_layers:
+            torch.nn.utils.remove_weight_norm(l)
+        for l in self.res_skip_layers:
+            torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
+                ),
+                weight_norm(
+                    Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
+                ),
+                weight_norm(
+                    Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
+                ),
+            ]
+        )
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+            ]
+        )
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+            logdet = torch.sum(-y, [1, 2])
+            return y, logdet
+        else:
+            x = torch.exp(x) * x_mask
+            return x
+
+
+class Flip(nn.Module):
+    def forward(self, x, *args, reverse=False, **kwargs):
+        x = torch.flip(x, [1])
+        if not reverse:
+            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+            return x, logdet
+        else:
+            return x
+
+
+class ElementwiseAffine(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.channels = channels
+        self.m = nn.Parameter(torch.zeros(channels, 1))
+        self.logs = nn.Parameter(torch.zeros(channels, 1))
+
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = self.m + torch.exp(self.logs) * x
+            y = y * x_mask
+            logdet = torch.sum(self.logs * x_mask, [1, 2])
+            return y, logdet
+        else:
+            x = (x - self.m) * torch.exp(-self.logs) * x_mask
+            return x
+
+
+class ResidualCouplingLayer(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        p_dropout=0,
+        gin_channels=0,
+        mean_only=False,
+    ):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = WN(
+            hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels
+        )
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x

TTS/vc/modules/freevc/speaker_encoder/audio.py

@@ -0,0 +1,65 @@
+import struct
+from pathlib import Path
+from typing import Optional, Union
+
+# import webrtcvad
+import librosa
+import numpy as np
+from scipy.ndimage.morphology import binary_dilation
+
+from TTS.vc.modules.freevc.speaker_encoder.hparams import *
+
+int16_max = (2**15) - 1
+
+
+def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray], source_sr: Optional[int] = None):
+    """
+    Applies the preprocessing operations used in training the Speaker Encoder to a waveform
+    either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
+
+    :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not
+    just .wav), either the waveform as a numpy array of floats.
+    :param source_sr: if passing an audio waveform, the sampling rate of the waveform before
+    preprocessing. After preprocessing, the waveform's sampling rate will match the data
+    hyperparameters. If passing a filepath, the sampling rate will be automatically detected and
+    this argument will be ignored.
+    """
+    # Load the wav from disk if needed
+    if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
+        wav, source_sr = librosa.load(fpath_or_wav, sr=None)
+    else:
+        wav = fpath_or_wav
+
+    # Resample the wav if needed
+    if source_sr is not None and source_sr != sampling_rate:
+        wav = librosa.resample(wav, source_sr, sampling_rate)
+
+    # Apply the preprocessing: normalize volume and shorten long silences
+    wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
+    wav = trim_long_silences(wav)
+
+    return wav
+
+
+def wav_to_mel_spectrogram(wav):
+    """
+    Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
+    Note: this not a log-mel spectrogram.
+    """
+    frames = librosa.feature.melspectrogram(
+        y=wav,
+        sr=sampling_rate,
+        n_fft=int(sampling_rate * mel_window_length / 1000),
+        hop_length=int(sampling_rate * mel_window_step / 1000),
+        n_mels=mel_n_channels,
+    )
+    return frames.astype(np.float32).T
+
+
+def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
+    if increase_only and decrease_only:
+        raise ValueError("Both increase only and decrease only are set")
+    dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav**2))
+    if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
+        return wav
+    return wav * (10 ** (dBFS_change / 20))

TTS/vc/modules/freevc/speaker_encoder/hparams.py

@@ -0,0 +1,31 @@
+## Mel-filterbank
+mel_window_length = 25  # In milliseconds
+mel_window_step = 10  # In milliseconds
+mel_n_channels = 40
+
+
+## Audio
+sampling_rate = 16000
+# Number of spectrogram frames in a partial utterance
+partials_n_frames = 160  # 1600 ms
+
+
+## Voice Activation Detection
+# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
+# This sets the granularity of the VAD. Should not need to be changed.
+vad_window_length = 30  # In milliseconds
+# Number of frames to average together when performing the moving average smoothing.
+# The larger this value, the larger the VAD variations must be to not get smoothed out.
+vad_moving_average_width = 8
+# Maximum number of consecutive silent frames a segment can have.
+vad_max_silence_length = 6
+
+
+## Audio volume normalization
+audio_norm_target_dBFS = -30
+
+
+## Model parameters
+model_hidden_size = 256
+model_embedding_size = 256
+model_num_layers = 3

TTS/vc/modules/freevc/speaker_encoder/speaker_encoder.py

@@ -0,0 +1,175 @@
+from pathlib import Path
+from time import perf_counter as timer
+from typing import List, Union
+
+import numpy as np
+import torch
+from torch import nn
+
+from TTS.utils.io import load_fsspec
+from TTS.vc.modules.freevc.speaker_encoder import audio
+from TTS.vc.modules.freevc.speaker_encoder.hparams import *
+
+
+class SpeakerEncoder(nn.Module):
+    def __init__(self, weights_fpath, device: Union[str, torch.device] = None, verbose=True):
+        """
+        :param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda").
+        If None, defaults to cuda if it is available on your machine, otherwise the model will
+        run on cpu. Outputs are always returned on the cpu, as numpy arrays.
+        """
+        super().__init__()
+
+        # Define the network
+        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
+        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
+        self.relu = nn.ReLU()
+
+        # Get the target device
+        if device is None:
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        elif isinstance(device, str):
+            device = torch.device(device)
+        self.device = device
+
+        # Load the pretrained model'speaker weights
+        # weights_fpath = Path(__file__).resolve().parent.joinpath("pretrained.pt")
+        # if not weights_fpath.exists():
+        #     raise Exception("Couldn't find the voice encoder pretrained model at %s." %
+        #                     weights_fpath)
+
+        start = timer()
+        checkpoint = load_fsspec(weights_fpath, map_location="cpu")
+
+        self.load_state_dict(checkpoint["model_state"], strict=False)
+        self.to(device)
+
+        if verbose:
+            print("Loaded the voice encoder model on %s in %.2f seconds." % (device.type, timer() - start))
+
+    def forward(self, mels: torch.FloatTensor):
+        """
+        Computes the embeddings of a batch of utterance spectrograms.
+        :param mels: a batch of mel spectrograms of same duration as a float32 tensor of shape
+        (batch_size, n_frames, n_channels)
+        :return: the embeddings as a float 32 tensor of shape (batch_size, embedding_size).
+        Embeddings are positive and L2-normed, thus they lay in the range [0, 1].
+        """
+        # Pass the input through the LSTM layers and retrieve the final hidden state of the last
+        # layer. Apply a cutoff to 0 for negative values and L2 normalize the embeddings.
+        _, (hidden, _) = self.lstm(mels)
+        embeds_raw = self.relu(self.linear(hidden[-1]))
+        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
+
+    @staticmethod
+    def compute_partial_slices(n_samples: int, rate, min_coverage):
+        """
+        Computes where to split an utterance waveform and its corresponding mel spectrogram to
+        obtain partial utterances of <partials_n_frames> each. Both the waveform and the
+        mel spectrogram slices are returned, so as to make each partial utterance waveform
+        correspond to its spectrogram.
+
+        The returned ranges may be indexing further than the length of the waveform. It is
+        recommended that you pad the waveform with zeros up to wav_slices[-1].stop.
+
+        :param n_samples: the number of samples in the waveform
+        :param rate: how many partial utterances should occur per second. Partial utterances must
+        cover the span of the entire utterance, thus the rate should not be lower than the inverse
+        of the duration of a partial utterance. By default, partial utterances are 1.6s long and
+        the minimum rate is thus 0.625.
+        :param min_coverage: when reaching the last partial utterance, it may or may not have
+        enough frames. If at least <min_pad_coverage> of <partials_n_frames> are present,
+        then the last partial utterance will be considered by zero-padding the audio. Otherwise,
+        it will be discarded. If there aren't enough frames for one partial utterance,
+        this parameter is ignored so that the function always returns at least one slice.
+        :return: the waveform slices and mel spectrogram slices as lists of array slices. Index
+        respectively the waveform and the mel spectrogram with these slices to obtain the partial
+        utterances.
+        """
+        assert 0 < min_coverage <= 1
+
+        # Compute how many frames separate two partial utterances
+        samples_per_frame = int((sampling_rate * mel_window_step / 1000))
+        n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
+        frame_step = int(np.round((sampling_rate / rate) / samples_per_frame))
+        assert 0 < frame_step, "The rate is too high"
+        assert frame_step <= partials_n_frames, "The rate is too low, it should be %f at least" % (
+            sampling_rate / (samples_per_frame * partials_n_frames)
+        )
+
+        # Compute the slices
+        wav_slices, mel_slices = [], []
+        steps = max(1, n_frames - partials_n_frames + frame_step + 1)
+        for i in range(0, steps, frame_step):
+            mel_range = np.array([i, i + partials_n_frames])
+            wav_range = mel_range * samples_per_frame
+            mel_slices.append(slice(*mel_range))
+            wav_slices.append(slice(*wav_range))
+
+        # Evaluate whether extra padding is warranted or not
+        last_wav_range = wav_slices[-1]
+        coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
+        if coverage < min_coverage and len(mel_slices) > 1:
+            mel_slices = mel_slices[:-1]
+            wav_slices = wav_slices[:-1]
+
+        return wav_slices, mel_slices
+
+    def embed_utterance(self, wav: np.ndarray, return_partials=False, rate=1.3, min_coverage=0.75):
+        """
+        Computes an embedding for a single utterance. The utterance is divided in partial
+        utterances and an embedding is computed for each. The complete utterance embedding is the
+        L2-normed average embedding of the partial utterances.
+
+        TODO: independent batched version of this function
+
+        :param wav: a preprocessed utterance waveform as a numpy array of float32
+        :param return_partials: if True, the partial embeddings will also be returned along with
+        the wav slices corresponding to each partial utterance.
+        :param rate: how many partial utterances should occur per second. Partial utterances must
+        cover the span of the entire utterance, thus the rate should not be lower than the inverse
+        of the duration of a partial utterance. By default, partial utterances are 1.6s long and
+        the minimum rate is thus 0.625.
+        :param min_coverage: when reaching the last partial utterance, it may or may not have
+        enough frames. If at least <min_pad_coverage> of <partials_n_frames> are present,
+        then the last partial utterance will be considered by zero-padding the audio. Otherwise,
+        it will be discarded. If there aren't enough frames for one partial utterance,
+        this parameter is ignored so that the function always returns at least one slice.
+        :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If
+        <return_partials> is True, the partial utterances as a numpy array of float32 of shape
+        (n_partials, model_embedding_size) and the wav partials as a list of slices will also be
+        returned.
+        """
+        # Compute where to split the utterance into partials and pad the waveform with zeros if
+        # the partial utterances cover a larger range.
+        wav_slices, mel_slices = self.compute_partial_slices(len(wav), rate, min_coverage)
+        max_wave_length = wav_slices[-1].stop
+        if max_wave_length >= len(wav):
+            wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
+
+        # Split the utterance into partials and forward them through the model
+        mel = audio.wav_to_mel_spectrogram(wav)
+        mels = np.array([mel[s] for s in mel_slices])
+        with torch.no_grad():
+            mels = torch.from_numpy(mels).to(self.device)
+            partial_embeds = self(mels).cpu().numpy()
+
+        # Compute the utterance embedding from the partial embeddings
+        raw_embed = np.mean(partial_embeds, axis=0)
+        embed = raw_embed / np.linalg.norm(raw_embed, 2)
+
+        if return_partials:
+            return embed, partial_embeds, wav_slices
+        return embed
+
+    def embed_speaker(self, wavs: List[np.ndarray], **kwargs):
+        """
+        Compute the embedding of a collection of wavs (presumably from the same speaker) by
+        averaging their embedding and L2-normalizing it.
+
+        :param wavs: list of wavs a numpy arrays of float32.
+        :param kwargs: extra arguments to embed_utterance()
+        :return: the embedding as a numpy array of float32 of shape (model_embedding_size,).
+        """
+        raw_embed = np.mean([self.embed_utterance(wav, return_partials=False, **kwargs) for wav in wavs], axis=0)
+        return raw_embed / np.linalg.norm(raw_embed, 2)

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

@@ -0,0 +1,35 @@
+import os
+import urllib.request
+
+import torch
+
+from TTS.utils.generic_utils import get_user_data_dir
+from TTS.vc.modules.freevc.wavlm.wavlm import WavLM, WavLMConfig
+
+model_uri = "https://github.com/coqui-ai/TTS/releases/download/v0.13.0_models/WavLM-Large.pt"
+
+
+def get_wavlm(device="cpu"):
+    """Download the model and return the model object."""
+
+    output_path = get_user_data_dir("tts")
+
+    output_path = os.path.join(output_path, "wavlm")
+    if not os.path.exists(output_path):
+        os.makedirs(output_path)
+
+    output_path = os.path.join(output_path, "WavLM-Large.pt")
+    if not os.path.exists(output_path):
+        print(f" > Downloading WavLM model to {output_path} ...")
+        urllib.request.urlretrieve(model_uri, output_path)
+
+    checkpoint = torch.load(output_path, map_location=torch.device(device))
+    cfg = WavLMConfig(checkpoint["cfg"])
+    wavlm = WavLM(cfg).to(device)
+    wavlm.load_state_dict(checkpoint["model"])
+    wavlm.eval()
+    return wavlm
+
+
+if __name__ == "__main__":
+    wavlm = get_wavlm()

TTS/vc/modules/freevc/wavlm/config.json

@@ -0,0 +1,99 @@
+{
+    "_name_or_path": "./wavlm-large/",
+    "activation_dropout": 0.0,
+    "adapter_kernel_size": 3,
+    "adapter_stride": 2,
+    "add_adapter": false,
+    "apply_spec_augment": true,
+    "architectures": [
+      "WavLMModel"
+    ],
+    "attention_dropout": 0.1,
+    "bos_token_id": 1,
+    "classifier_proj_size": 256,
+    "codevector_dim": 768,
+    "contrastive_logits_temperature": 0.1,
+    "conv_bias": false,
+    "conv_dim": [
+      512,
+      512,
+      512,
+      512,
+      512,
+      512,
+      512
+    ],
+    "conv_kernel": [
+      10,
+      3,
+      3,
+      3,
+      3,
+      2,
+      2
+    ],
+    "conv_stride": [
+      5,
+      2,
+      2,
+      2,
+      2,
+      2,
+      2
+    ],
+    "ctc_loss_reduction": "sum",
+    "ctc_zero_infinity": false,
+    "diversity_loss_weight": 0.1,
+    "do_stable_layer_norm": true,
+    "eos_token_id": 2,
+    "feat_extract_activation": "gelu",
+    "feat_extract_dropout": 0.0,
+    "feat_extract_norm": "layer",
+    "feat_proj_dropout": 0.1,
+    "feat_quantizer_dropout": 0.0,
+    "final_dropout": 0.0,
+    "gradient_checkpointing": false,
+    "hidden_act": "gelu",
+    "hidden_dropout": 0.1,
+    "hidden_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "layer_norm_eps": 1e-05,
+    "layerdrop": 0.1,
+    "mask_channel_length": 10,
+    "mask_channel_min_space": 1,
+    "mask_channel_other": 0.0,
+    "mask_channel_prob": 0.0,
+    "mask_channel_selection": "static",
+    "mask_feature_length": 10,
+    "mask_feature_min_masks": 0,
+    "mask_feature_prob": 0.0,
+    "mask_time_length": 10,
+    "mask_time_min_masks": 2,
+    "mask_time_min_space": 1,
+    "mask_time_other": 0.0,
+    "mask_time_prob": 0.075,
+    "mask_time_selection": "static",
+    "max_bucket_distance": 800,
+    "model_type": "wavlm",
+    "num_adapter_layers": 3,
+    "num_attention_heads": 16,
+    "num_buckets": 320,
+    "num_codevector_groups": 2,
+    "num_codevectors_per_group": 320,
+    "num_conv_pos_embedding_groups": 16,
+    "num_conv_pos_embeddings": 128,
+    "num_ctc_classes": 80,
+    "num_feat_extract_layers": 7,
+    "num_hidden_layers": 24,
+    "num_negatives": 100,
+    "output_hidden_size": 1024,
+    "pad_token_id": 0,
+    "proj_codevector_dim": 768,
+    "replace_prob": 0.5,
+    "tokenizer_class": "Wav2Vec2CTCTokenizer",
+    "torch_dtype": "float32",
+    "transformers_version": "4.15.0.dev0",
+    "use_weighted_layer_sum": false,
+    "vocab_size": 32
+  }

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

@@ -0,0 +1,768 @@
+# --------------------------------------------------------
+# WavLM: Large-Scale Self-Supervised  Pre-training  for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
+# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Based on fairseq code bases
+# https://github.com/pytorch/fairseq
+# --------------------------------------------------------
+
+import math
+import warnings
+from typing import Dict, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn import Parameter
+
+
+class TransposeLast(nn.Module):
+    def __init__(self, deconstruct_idx=None):
+        super().__init__()
+        self.deconstruct_idx = deconstruct_idx
+
+    def forward(self, x):
+        if self.deconstruct_idx is not None:
+            x = x[self.deconstruct_idx]
+        return x.transpose(-2, -1)
+
+
+class Fp32LayerNorm(nn.LayerNorm):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, input):
+        output = F.layer_norm(
+            input.float(),
+            self.normalized_shape,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        )
+        return output.type_as(input)
+
+
+class Fp32GroupNorm(nn.GroupNorm):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, input):
+        output = F.group_norm(
+            input.float(),
+            self.num_groups,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        )
+        return output.type_as(input)
+
+
+class GradMultiply(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, scale):
+        ctx.scale = scale
+        res = x.new(x)
+        return res
+
+    @staticmethod
+    def backward(ctx, grad):
+        return grad * ctx.scale, None
+
+
+class SamePad(nn.Module):
+    def __init__(self, kernel_size, causal=False):
+        super().__init__()
+        if causal:
+            self.remove = kernel_size - 1
+        else:
+            self.remove = 1 if kernel_size % 2 == 0 else 0
+
+    def forward(self, x):
+        if self.remove > 0:
+            x = x[:, :, : -self.remove]
+        return x
+
+
+class Swish(nn.Module):
+    """Swish function"""
+
+    def __init__(self):
+        """Construct an MultiHeadedAttention object."""
+        super(Swish, self).__init__()
+        self.act = torch.nn.Sigmoid()
+
+    def forward(self, x):
+        return x * self.act(x)
+
+
+class GLU_Linear(nn.Module):
+    def __init__(self, input_dim, output_dim, glu_type="sigmoid", bias_in_glu=True):
+        super(GLU_Linear, self).__init__()
+
+        self.glu_type = glu_type
+        self.output_dim = output_dim
+
+        if glu_type == "sigmoid":
+            self.glu_act = torch.nn.Sigmoid()
+        elif glu_type == "swish":
+            self.glu_act = Swish()
+        elif glu_type == "relu":
+            self.glu_act = torch.nn.ReLU()
+        elif glu_type == "gelu":
+            self.glu_act = torch.nn.GELU()
+
+        if bias_in_glu:
+            self.linear = nn.Linear(input_dim, output_dim * 2, True)
+        else:
+            self.linear = nn.Linear(input_dim, output_dim * 2, False)
+
+    def forward(self, x):
+        # to be consistent with GLU_Linear, we assume the input always has the #channel (#dim) in the last dimension of the tensor, so need to switch the dimension first for 1D-Conv case
+        x = self.linear(x)
+
+        if self.glu_type == "bilinear":
+            x = x[:, :, 0 : self.output_dim] * x[:, :, self.output_dim : self.output_dim * 2]
+        else:
+            x = x[:, :, 0 : self.output_dim] * self.glu_act(x[:, :, self.output_dim : self.output_dim * 2])
+
+        return x
+
+
+def gelu_accurate(x):
+    if not hasattr(gelu_accurate, "_a"):
+        gelu_accurate._a = math.sqrt(2 / math.pi)
+    return 0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
+
+
+def gelu(x: torch.Tensor) -> torch.Tensor:
+    return torch.nn.functional.gelu(x.float()).type_as(x)
+
+
+def get_activation_fn(activation: str):
+    """Returns the activation function corresponding to `activation`"""
+
+    if activation == "relu":
+        return F.relu
+    elif activation == "gelu":
+        return gelu
+    elif activation == "gelu_fast":
+        warnings.warn("--activation-fn=gelu_fast has been renamed to gelu_accurate")
+        return gelu_accurate
+    elif activation == "gelu_accurate":
+        return gelu_accurate
+    elif activation == "tanh":
+        return torch.tanh
+    elif activation == "linear":
+        return lambda x: x
+    elif activation == "glu":
+        return lambda x: x
+    else:
+        raise RuntimeError("--activation-fn {} not supported".format(activation))
+
+
+def init_bert_params(module):
+    """
+    Initialize the weights specific to the BERT Model.
+    This overrides the default initializations depending on the specified arguments.
+        1. If normal_init_linear_weights is set then weights of linear
+           layer will be initialized using the normal distribution and
+           bais will be set to the specified value.
+        2. If normal_init_embed_weights is set then weights of embedding
+           layer will be initialized using the normal distribution.
+        3. If normal_init_proj_weights is set then weights of
+           in_project_weight for MultiHeadAttention initialized using
+           the normal distribution (to be validated).
+    """
+
+    def normal_(data):
+        # with FSDP, module params will be on CUDA, so we cast them back to CPU
+        # so that the RNG is consistent with and without FSDP
+        data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device))
+
+    if isinstance(module, nn.Linear):
+        normal_(module.weight.data)
+        if module.bias is not None:
+            module.bias.data.zero_()
+    if isinstance(module, nn.Embedding):
+        normal_(module.weight.data)
+        if module.padding_idx is not None:
+            module.weight.data[module.padding_idx].zero_()
+    if isinstance(module, MultiheadAttention):
+        normal_(module.q_proj.weight.data)
+        normal_(module.k_proj.weight.data)
+        normal_(module.v_proj.weight.data)
+
+
+def quant_noise(module, p, block_size):
+    """
+    Wraps modules and applies quantization noise to the weights for
+    subsequent quantization with Iterative Product Quantization as
+    described in "Training with Quantization Noise for Extreme Model Compression"
+
+    Args:
+        - module: nn.Module
+        - p: amount of Quantization Noise
+        - block_size: size of the blocks for subsequent quantization with iPQ
+
+    Remarks:
+        - Module weights must have the right sizes wrt the block size
+        - Only Linear, Embedding and Conv2d modules are supported for the moment
+        - For more detail on how to quantize by blocks with convolutional weights,
+          see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks"
+        - We implement the simplest form of noise here as stated in the paper
+          which consists in randomly dropping blocks
+    """
+
+    # if no quantization noise, don't register hook
+    if p <= 0:
+        return module
+
+    # supported modules
+    assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
+
+    # test whether module.weight has the right sizes wrt block_size
+    is_conv = module.weight.ndim == 4
+
+    # 2D matrix
+    if not is_conv:
+        assert module.weight.size(1) % block_size == 0, "Input features must be a multiple of block sizes"
+
+    # 4D matrix
+    else:
+        # 1x1 convolutions
+        if module.kernel_size == (1, 1):
+            assert module.in_channels % block_size == 0, "Input channels must be a multiple of block sizes"
+        # regular convolutions
+        else:
+            k = module.kernel_size[0] * module.kernel_size[1]
+            assert k % block_size == 0, "Kernel size must be a multiple of block size"
+
+    def _forward_pre_hook(mod, input):
+        # no noise for evaluation
+        if mod.training:
+            if not is_conv:
+                # gather weight and sizes
+                weight = mod.weight
+                in_features = weight.size(1)
+                out_features = weight.size(0)
+
+                # split weight matrix into blocks and randomly drop selected blocks
+                mask = torch.zeros(in_features // block_size * out_features, device=weight.device)
+                mask.bernoulli_(p)
+                mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
+
+            else:
+                # gather weight and sizes
+                weight = mod.weight
+                in_channels = mod.in_channels
+                out_channels = mod.out_channels
+
+                # split weight matrix into blocks and randomly drop selected blocks
+                if mod.kernel_size == (1, 1):
+                    mask = torch.zeros(
+                        int(in_channels // block_size * out_channels),
+                        device=weight.device,
+                    )
+                    mask.bernoulli_(p)
+                    mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
+                else:
+                    mask = torch.zeros(weight.size(0), weight.size(1), device=weight.device)
+                    mask.bernoulli_(p)
+                    mask = mask.unsqueeze(2).unsqueeze(3).repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])
+
+            # scale weights and apply mask
+            mask = mask.to(torch.bool)  # x.bool() is not currently supported in TorchScript
+            s = 1 / (1 - p)
+            mod.weight.data = s * weight.masked_fill(mask, 0)
+
+    module.register_forward_pre_hook(_forward_pre_hook)
+    return module
+
+
+class MultiheadAttention(nn.Module):
+    """Multi-headed attention.
+
+    See "Attention Is All You Need" for more details.
+    """
+
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        kdim=None,
+        vdim=None,
+        dropout=0.0,
+        bias=True,
+        add_bias_kv=False,
+        add_zero_attn=False,
+        self_attention=False,
+        encoder_decoder_attention=False,
+        q_noise=0.0,
+        qn_block_size=8,
+        has_relative_attention_bias=False,
+        num_buckets=32,
+        max_distance=128,
+        gru_rel_pos=False,
+        rescale_init=False,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
+
+        self.num_heads = num_heads
+        self.dropout_module = nn.Dropout(dropout)
+
+        self.has_relative_attention_bias = has_relative_attention_bias
+        self.num_buckets = num_buckets
+        self.max_distance = max_distance
+        if self.has_relative_attention_bias:
+            self.relative_attention_bias = nn.Embedding(num_buckets, num_heads)
+
+        self.head_dim = embed_dim // num_heads
+        self.q_head_dim = self.head_dim
+        self.k_head_dim = self.head_dim
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        self.scaling = self.head_dim**-0.5
+
+        self.self_attention = self_attention
+        self.encoder_decoder_attention = encoder_decoder_attention
+
+        assert not self.self_attention or self.qkv_same_dim, (
+            "Self-attention requires query, key and " "value to be of the same size"
+        )
+
+        k_bias = True
+        if rescale_init:
+            k_bias = False
+
+        k_embed_dim = embed_dim
+        q_embed_dim = embed_dim
+
+        self.k_proj = quant_noise(nn.Linear(self.kdim, k_embed_dim, bias=k_bias), q_noise, qn_block_size)
+        self.v_proj = quant_noise(nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size)
+        self.q_proj = quant_noise(nn.Linear(embed_dim, q_embed_dim, bias=bias), q_noise, qn_block_size)
+
+        self.out_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size)
+
+        if add_bias_kv:
+            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
+            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
+        else:
+            self.bias_k = self.bias_v = None
+
+        self.add_zero_attn = add_zero_attn
+
+        self.gru_rel_pos = gru_rel_pos
+        if self.gru_rel_pos:
+            self.grep_linear = nn.Linear(self.q_head_dim, 8)
+            self.grep_a = nn.Parameter(torch.ones(1, num_heads, 1, 1))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if self.qkv_same_dim:
+            # Empirically observed the convergence to be much better with
+            # the scaled initialization
+            nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
+            nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
+            nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
+        else:
+            nn.init.xavier_uniform_(self.k_proj.weight)
+            nn.init.xavier_uniform_(self.v_proj.weight)
+            nn.init.xavier_uniform_(self.q_proj.weight)
+
+        nn.init.xavier_uniform_(self.out_proj.weight)
+        if self.out_proj.bias is not None:
+            nn.init.constant_(self.out_proj.bias, 0.0)
+        if self.bias_k is not None:
+            nn.init.xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            nn.init.xavier_normal_(self.bias_v)
+        if self.has_relative_attention_bias:
+            nn.init.xavier_normal_(self.relative_attention_bias.weight)
+
+    def _relative_positions_bucket(self, relative_positions, bidirectional=True):
+        num_buckets = self.num_buckets
+        max_distance = self.max_distance
+        relative_buckets = 0
+
+        if bidirectional:
+            num_buckets = num_buckets // 2
+            relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets
+            relative_positions = torch.abs(relative_positions)
+        else:
+            relative_positions = -torch.min(relative_positions, torch.zeros_like(relative_positions))
+
+        max_exact = num_buckets // 2
+        is_small = relative_positions < max_exact
+
+        relative_postion_if_large = max_exact + (
+            torch.log(relative_positions.float() / max_exact)
+            / math.log(max_distance / max_exact)
+            * (num_buckets - max_exact)
+        ).to(torch.long)
+        relative_postion_if_large = torch.min(
+            relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
+        )
+
+        relative_buckets += torch.where(is_small, relative_positions, relative_postion_if_large)
+        return relative_buckets
+
+    def compute_bias(self, query_length, key_length):
+        context_position = torch.arange(query_length, dtype=torch.long)[:, None]
+        memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
+        relative_position = memory_position - context_position
+        relative_position_bucket = self._relative_positions_bucket(relative_position, bidirectional=True)
+        relative_position_bucket = relative_position_bucket.to(self.relative_attention_bias.weight.device)
+        values = self.relative_attention_bias(relative_position_bucket)
+        values = values.permute([2, 0, 1])
+        return values
+
+    def forward(
+        self,
+        query,
+        key: Optional[Tensor],
+        value: Optional[Tensor],
+        key_padding_mask: Optional[Tensor] = None,
+        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
+        need_weights: bool = True,
+        static_kv: bool = False,
+        attn_mask: Optional[Tensor] = None,
+        before_softmax: bool = False,
+        need_head_weights: bool = False,
+        position_bias: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
+        """Input shape: Time x Batch x Channel
+
+        Args:
+            key_padding_mask (ByteTensor, optional): mask to exclude
+                keys that are pads, of shape `(batch, src_len)`, where
+                padding elements are indicated by 1s.
+            need_weights (bool, optional): return the attention weights,
+                averaged over heads (default: False).
+            attn_mask (ByteTensor, optional): typically used to
+                implement causal attention, where the mask prevents the
+                attention from looking forward in time (default: None).
+            before_softmax (bool, optional): return the raw attention
+                weights and values before the attention softmax.
+            need_head_weights (bool, optional): return the attention
+                weights for each head. Implies *need_weights*. Default:
+                return the average attention weights over all heads.
+        """
+        if need_head_weights:
+            need_weights = True
+
+        is_tpu = query.device.type == "xla"
+
+        tgt_len, bsz, embed_dim = query.size()
+        src_len = tgt_len
+        assert embed_dim == self.embed_dim
+        assert list(query.size()) == [tgt_len, bsz, embed_dim]
+        if key is not None:
+            src_len, key_bsz, _ = key.size()
+            if not torch.jit.is_scripting():
+                assert key_bsz == bsz
+                assert value is not None
+                assert src_len, bsz == value.shape[:2]
+
+        if self.has_relative_attention_bias and position_bias is None:
+            position_bias = self.compute_bias(tgt_len, src_len)
+            position_bias = position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1).view(bsz * self.num_heads, tgt_len, src_len)
+
+        if (
+            not is_tpu  # don't use PyTorch version on TPUs
+            and incremental_state is None
+            and not static_kv
+            # A workaround for quantization to work. Otherwise JIT compilation
+            # treats bias in linear module as method.
+            and not torch.jit.is_scripting()
+            and self.q_head_dim == self.head_dim
+        ):
+            assert key is not None and value is not None
+            assert attn_mask is None
+
+            attn_mask_rel_pos = None
+            if position_bias is not None:
+                attn_mask_rel_pos = position_bias
+                if self.gru_rel_pos:
+                    query_layer = query.transpose(0, 1)
+                    new_x_shape = query_layer.size()[:-1] + (self.num_heads, -1)
+                    query_layer = query_layer.view(*new_x_shape)
+                    query_layer = query_layer.permute(0, 2, 1, 3)
+                    _B, _H, _L, __ = query_layer.size()
+
+                    gate_a, gate_b = torch.sigmoid(
+                        self.grep_linear(query_layer).view(_B, _H, _L, 2, 4).sum(-1, keepdim=False)
+                    ).chunk(2, dim=-1)
+                    gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0
+                    attn_mask_rel_pos = gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias
+
+                attn_mask_rel_pos = attn_mask_rel_pos.view((-1, tgt_len, tgt_len))
+            k_proj_bias = self.k_proj.bias
+            if k_proj_bias is None:
+                k_proj_bias = torch.zeros_like(self.q_proj.bias)
+
+            x, attn = F.multi_head_attention_forward(
+                query,
+                key,
+                value,
+                self.embed_dim,
+                self.num_heads,
+                torch.empty([0]),
+                torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
+                self.bias_k,
+                self.bias_v,
+                self.add_zero_attn,
+                self.dropout_module.p,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                self.training,
+                # self.training or self.dropout_module.apply_during_inference,
+                key_padding_mask,
+                need_weights,
+                attn_mask_rel_pos,
+                use_separate_proj_weight=True,
+                q_proj_weight=self.q_proj.weight,
+                k_proj_weight=self.k_proj.weight,
+                v_proj_weight=self.v_proj.weight,
+            )
+            return x, attn, position_bias
+
+        if incremental_state is not None:
+            saved_state = self._get_input_buffer(incremental_state)
+            if saved_state is not None and "prev_key" in saved_state:
+                # previous time steps are cached - no need to recompute
+                # key and value if they are static
+                if static_kv:
+                    assert self.encoder_decoder_attention and not self.self_attention
+                    key = value = None
+        else:
+            saved_state = None
+
+        if self.self_attention:
+            q = self.q_proj(query)
+            k = self.k_proj(query)
+            v = self.v_proj(query)
+        elif self.encoder_decoder_attention:
+            # encoder-decoder attention
+            q = self.q_proj(query)
+            if key is None:
+                assert value is None
+                k = v = None
+            else:
+                k = self.k_proj(key)
+                v = self.v_proj(key)
+
+        else:
+            assert key is not None and value is not None
+            q = self.q_proj(query)
+            k = self.k_proj(key)
+            v = self.v_proj(value)
+        q *= self.scaling
+
+        if self.bias_k is not None:
+            assert self.bias_v is not None
+            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
+            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
+            if attn_mask is not None:
+                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [
+                        key_padding_mask,
+                        key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
+                    ],
+                    dim=1,
+                )
+
+        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.q_head_dim).transpose(0, 1)
+        if k is not None:
+            k = k.contiguous().view(-1, bsz * self.num_heads, self.k_head_dim).transpose(0, 1)
+        if v is not None:
+            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+
+        if saved_state is not None:
+            # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
+            if "prev_key" in saved_state:
+                _prev_key = saved_state["prev_key"]
+                assert _prev_key is not None
+                prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
+                if static_kv:
+                    k = prev_key
+                else:
+                    assert k is not None
+                    k = torch.cat([prev_key, k], dim=1)
+                src_len = k.size(1)
+            if "prev_value" in saved_state:
+                _prev_value = saved_state["prev_value"]
+                assert _prev_value is not None
+                prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
+                if static_kv:
+                    v = prev_value
+                else:
+                    assert v is not None
+                    v = torch.cat([prev_value, v], dim=1)
+            prev_key_padding_mask: Optional[Tensor] = None
+            if "prev_key_padding_mask" in saved_state:
+                prev_key_padding_mask = saved_state["prev_key_padding_mask"]
+            assert k is not None and v is not None
+            key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
+                key_padding_mask=key_padding_mask,
+                prev_key_padding_mask=prev_key_padding_mask,
+                batch_size=bsz,
+                src_len=k.size(1),
+                static_kv=static_kv,
+            )
+
+            saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
+            saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
+            saved_state["prev_key_padding_mask"] = key_padding_mask
+            # In this branch incremental_state is never None
+            assert incremental_state is not None
+            incremental_state = self._set_input_buffer(incremental_state, saved_state)
+        assert k is not None
+        assert k.size(1) == src_len
+
+        # This is part of a workaround to get around fork/join parallelism
+        # not supporting Optional types.
+        if key_padding_mask is not None and key_padding_mask.dim() == 0:
+            key_padding_mask = None
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.size(0) == bsz
+            assert key_padding_mask.size(1) == src_len
+
+        if self.add_zero_attn:
+            assert v is not None
+            src_len += 1
+            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
+            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
+            if attn_mask is not None:
+                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [
+                        key_padding_mask,
+                        torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask),
+                    ],
+                    dim=1,
+                )
+
+        attn_weights = torch.bmm(q, k.transpose(1, 2))
+        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
+
+        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
+
+        if attn_mask is not None:
+            attn_mask = attn_mask.unsqueeze(0)
+            attn_weights += attn_mask
+
+        if key_padding_mask is not None:
+            # don't attend to padding symbols
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            if not is_tpu:
+                attn_weights = attn_weights.masked_fill(
+                    key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
+                    float("-inf"),
+                )
+            else:
+                attn_weights = attn_weights.transpose(0, 2)
+                attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
+                attn_weights = attn_weights.transpose(0, 2)
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if before_softmax:
+            return attn_weights, v, position_bias
+
+        if position_bias is not None:
+            if self.gru_rel_pos == 1:
+                query_layer = q.view(bsz, self.num_heads, tgt_len, self.q_head_dim)
+                _B, _H, _L, __ = query_layer.size()
+                gate_a, gate_b = torch.sigmoid(
+                    self.grep_linear(query_layer).view(_B, _H, _L, 2, 4).sum(-1, keepdim=False)
+                ).chunk(2, dim=-1)
+                gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0
+                position_bias = gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias
+
+            position_bias = position_bias.view(attn_weights.size())
+
+            attn_weights = attn_weights + position_bias
+
+        attn_weights_float = F.softmax(attn_weights, dim=-1)
+        attn_weights = attn_weights_float.type_as(attn_weights)
+        attn_probs = self.dropout_module(attn_weights)
+
+        assert v is not None
+        attn = torch.bmm(attn_probs, v)
+        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
+        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+        attn = self.out_proj(attn)
+        attn_weights: Optional[Tensor] = None
+        if need_weights:
+            attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
+            if not need_head_weights:
+                # average attention weights over heads
+                attn_weights = attn_weights.mean(dim=0)
+
+        return attn, attn_weights, position_bias
+
+    @staticmethod
+    def _append_prev_key_padding_mask(
+        key_padding_mask: Optional[Tensor],
+        prev_key_padding_mask: Optional[Tensor],
+        batch_size: int,
+        src_len: int,
+        static_kv: bool,
+    ) -> Optional[Tensor]:
+        # saved key padding masks have shape (bsz, seq_len)
+        if prev_key_padding_mask is not None and static_kv:
+            new_key_padding_mask = prev_key_padding_mask
+        elif prev_key_padding_mask is not None and key_padding_mask is not None:
+            new_key_padding_mask = torch.cat([prev_key_padding_mask.float(), key_padding_mask.float()], dim=1)
+        # During incremental decoding, as the padding token enters and
+        # leaves the frame, there will be a time when prev or current
+        # is None
+        elif prev_key_padding_mask is not None:
+            if src_len > prev_key_padding_mask.size(1):
+                filler = torch.zeros(
+                    (batch_size, src_len - prev_key_padding_mask.size(1)),
+                    device=prev_key_padding_mask.device,
+                )
+                new_key_padding_mask = torch.cat([prev_key_padding_mask.float(), filler.float()], dim=1)
+            else:
+                new_key_padding_mask = prev_key_padding_mask.float()
+        elif key_padding_mask is not None:
+            if src_len > key_padding_mask.size(1):
+                filler = torch.zeros(
+                    (batch_size, src_len - key_padding_mask.size(1)),
+                    device=key_padding_mask.device,
+                )
+                new_key_padding_mask = torch.cat([filler.float(), key_padding_mask.float()], dim=1)
+            else:
+                new_key_padding_mask = key_padding_mask.float()
+        else:
+            new_key_padding_mask = prev_key_padding_mask
+        return new_key_padding_mask
+
+    def _get_input_buffer(
+        self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
+    ) -> Dict[str, Optional[Tensor]]:
+        result = self.get_incremental_state(incremental_state, "attn_state")
+        if result is not None:
+            return result
+        else:
+            empty_result: Dict[str, Optional[Tensor]] = {}
+            return empty_result
+
+    def _set_input_buffer(
+        self,
+        incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
+        buffer: Dict[str, Optional[Tensor]],
+    ):
+        return self.set_incremental_state(incremental_state, "attn_state", buffer)
+
+    def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int):
+        return attn_weights

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

@@ -0,0 +1,719 @@
+# --------------------------------------------------------
+# WavLM: Large-Scale Self-Supervised  Pre-training  for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
+# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Based on fairseq code bases
+# https://github.com/pytorch/fairseq
+# --------------------------------------------------------
+
+import logging
+import math
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import LayerNorm
+
+from TTS.vc.modules.freevc.wavlm.modules import (
+    Fp32GroupNorm,
+    Fp32LayerNorm,
+    GLU_Linear,
+    GradMultiply,
+    MultiheadAttention,
+    SamePad,
+    TransposeLast,
+    get_activation_fn,
+    init_bert_params,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def compute_mask_indices(
+    shape: Tuple[int, int],
+    padding_mask: Optional[torch.Tensor],
+    mask_prob: float,
+    mask_length: int,
+    mask_type: str = "static",
+    mask_other: float = 0.0,
+    min_masks: int = 0,
+    no_overlap: bool = False,
+    min_space: int = 0,
+) -> np.ndarray:
+    """
+    Computes random mask spans for a given shape
+
+    Args:
+        shape: the the shape for which to compute masks.
+            should be of size 2 where first element is batch size and 2nd is timesteps
+        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
+        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
+            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
+            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
+        mask_type: how to compute mask lengths
+            static = fixed size
+            uniform = sample from uniform distribution [mask_other, mask_length*2]
+            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
+            poisson = sample from possion distribution with lambda = mask length
+        min_masks: minimum number of masked spans
+        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
+        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
+    """
+
+    bsz, all_sz = shape
+    mask = np.full((bsz, all_sz), False)
+
+    all_num_mask = int(
+        # add a random number for probabilistic rounding
+        mask_prob * all_sz / float(mask_length)
+        + np.random.rand()
+    )
+
+    all_num_mask = max(min_masks, all_num_mask)
+
+    mask_idcs = []
+    for i in range(bsz):
+        if padding_mask is not None:
+            sz = all_sz - padding_mask[i].long().sum().item()
+            num_mask = int(
+                # add a random number for probabilistic rounding
+                mask_prob * sz / float(mask_length)
+                + np.random.rand()
+            )
+            num_mask = max(min_masks, num_mask)
+        else:
+            sz = all_sz
+            num_mask = all_num_mask
+
+        if mask_type == "static":
+            lengths = np.full(num_mask, mask_length)
+        elif mask_type == "uniform":
+            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
+        elif mask_type == "normal":
+            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
+            lengths = [max(1, int(round(x))) for x in lengths]
+        elif mask_type == "poisson":
+            lengths = np.random.poisson(mask_length, size=num_mask)
+            lengths = [int(round(x)) for x in lengths]
+        else:
+            raise Exception("unknown mask selection " + mask_type)
+
+        if sum(lengths) == 0:
+            lengths[0] = min(mask_length, sz - 1)
+
+        if no_overlap:
+            mask_idc = []
+
+            def arrange(s, e, length, keep_length):
+                span_start = np.random.randint(s, e - length)
+                mask_idc.extend(span_start + i for i in range(length))
+
+                new_parts = []
+                if span_start - s - min_space >= keep_length:
+                    new_parts.append((s, span_start - min_space + 1))
+                if e - span_start - keep_length - min_space > keep_length:
+                    new_parts.append((span_start + length + min_space, e))
+                return new_parts
+
+            parts = [(0, sz)]
+            min_length = min(lengths)
+            for length in sorted(lengths, reverse=True):
+                lens = np.fromiter(
+                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
+                    np.int,
+                )
+                l_sum = np.sum(lens)
+                if l_sum == 0:
+                    break
+                probs = lens / np.sum(lens)
+                c = np.random.choice(len(parts), p=probs)
+                s, e = parts.pop(c)
+                parts.extend(arrange(s, e, length, min_length))
+            mask_idc = np.asarray(mask_idc)
+        else:
+            min_len = min(lengths)
+            if sz - min_len <= num_mask:
+                min_len = sz - num_mask - 1
+
+            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
+
+            mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])])
+
+        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
+
+    min_len = min([len(m) for m in mask_idcs])
+    for i, mask_idc in enumerate(mask_idcs):
+        if len(mask_idc) > min_len:
+            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
+        mask[i, mask_idc] = True
+
+    return mask
+
+
+class WavLMConfig:
+    def __init__(self, cfg=None):
+        self.extractor_mode: str = "default"  # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True)
+        self.encoder_layers: int = 12  # num encoder layers in the transformer
+
+        self.encoder_embed_dim: int = 768  # encoder embedding dimension
+        self.encoder_ffn_embed_dim: int = 3072  # encoder embedding dimension for FFN
+        self.encoder_attention_heads: int = 12  # num encoder attention heads
+        self.activation_fn: str = "gelu"  # activation function to use
+
+        self.layer_norm_first: bool = False  # apply layernorm first in the transformer
+        self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2"  # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]
+        self.conv_bias: bool = False  # include bias in conv encoder
+        self.feature_grad_mult: float = 1.0  # multiply feature extractor var grads by this
+
+        self.normalize: bool = False  # normalize input to have 0 mean and unit variance during training
+
+        # dropouts
+        self.dropout: float = 0.1  # dropout probability for the transformer
+        self.attention_dropout: float = 0.1  # dropout probability for attention weights
+        self.activation_dropout: float = 0.0  # dropout probability after activation in FFN
+        self.encoder_layerdrop: float = 0.0  # probability of dropping a tarnsformer layer
+        self.dropout_input: float = 0.0  # dropout to apply to the input (after feat extr)
+        self.dropout_features: float = 0.0  # dropout to apply to the features (after feat extr)
+
+        # masking
+        self.mask_length: int = 10  # mask length
+        self.mask_prob: float = 0.65  # probability of replacing a token with mask
+        self.mask_selection: str = "static"  # how to choose mask length
+        self.mask_other: float = (
+            0  # secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh
+        )
+        self.no_mask_overlap: bool = False  # whether to allow masks to overlap
+        self.mask_min_space: int = 1  # min space between spans (if no overlap is enabled)
+
+        # channel masking
+        self.mask_channel_length: int = 10  # length of the mask for features (channels)
+        self.mask_channel_prob: float = 0.0  # probability of replacing a feature with 0
+        self.mask_channel_selection: str = "static"  # how to choose mask length for channel masking
+        self.mask_channel_other: float = (
+            0  # secondary mask argument (used for more complex distributions), see help in compute_mask_indices
+        )
+        self.no_mask_channel_overlap: bool = False  # whether to allow channel masks to overlap
+        self.mask_channel_min_space: int = 1  # min space between spans (if no overlap is enabled)
+
+        # positional embeddings
+        self.conv_pos: int = 128  # number of filters for convolutional positional embeddings
+        self.conv_pos_groups: int = 16  # number of groups for convolutional positional embedding
+
+        # relative position embedding
+        self.relative_position_embedding: bool = False  # apply relative position embedding
+        self.num_buckets: int = 320  # number of buckets for relative position embedding
+        self.max_distance: int = 1280  # maximum distance for relative position embedding
+        self.gru_rel_pos: bool = False  # apply gated relative position embedding
+
+        if cfg is not None:
+            self.update(cfg)
+
+    def update(self, cfg: dict):
+        self.__dict__.update(cfg)
+
+
+class WavLM(nn.Module):
+    def __init__(
+        self,
+        cfg: WavLMConfig,
+    ) -> None:
+        super().__init__()
+        logger.info(f"WavLM Config: {cfg.__dict__}")
+
+        self.cfg = cfg
+        feature_enc_layers = eval(cfg.conv_feature_layers)
+        self.embed = feature_enc_layers[-1][0]
+
+        self.feature_extractor = ConvFeatureExtractionModel(
+            conv_layers=feature_enc_layers,
+            dropout=0.0,
+            mode=cfg.extractor_mode,
+            conv_bias=cfg.conv_bias,
+        )
+
+        self.post_extract_proj = (
+            nn.Linear(self.embed, cfg.encoder_embed_dim) if self.embed != cfg.encoder_embed_dim else None
+        )
+
+        self.mask_prob = cfg.mask_prob
+        self.mask_selection = cfg.mask_selection
+        self.mask_other = cfg.mask_other
+        self.mask_length = cfg.mask_length
+        self.no_mask_overlap = cfg.no_mask_overlap
+        self.mask_min_space = cfg.mask_min_space
+
+        self.mask_channel_prob = cfg.mask_channel_prob
+        self.mask_channel_selection = cfg.mask_channel_selection
+        self.mask_channel_other = cfg.mask_channel_other
+        self.mask_channel_length = cfg.mask_channel_length
+        self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
+        self.mask_channel_min_space = cfg.mask_channel_min_space
+
+        self.dropout_input = nn.Dropout(cfg.dropout_input)
+        self.dropout_features = nn.Dropout(cfg.dropout_features)
+
+        self.feature_grad_mult = cfg.feature_grad_mult
+
+        self.mask_emb = nn.Parameter(torch.FloatTensor(cfg.encoder_embed_dim).uniform_())
+
+        self.encoder = TransformerEncoder(cfg)
+        self.layer_norm = LayerNorm(self.embed)
+
+    def apply_mask(self, x, padding_mask):
+        B, T, C = x.shape
+        if self.mask_prob > 0:
+            mask_indices = compute_mask_indices(
+                (B, T),
+                padding_mask,
+                self.mask_prob,
+                self.mask_length,
+                self.mask_selection,
+                self.mask_other,
+                min_masks=2,
+                no_overlap=self.no_mask_overlap,
+                min_space=self.mask_min_space,
+            )
+            mask_indices = torch.from_numpy(mask_indices).to(x.device)
+            x[mask_indices] = self.mask_emb
+        else:
+            mask_indices = None
+
+        if self.mask_channel_prob > 0:
+            mask_channel_indices = compute_mask_indices(
+                (B, C),
+                None,
+                self.mask_channel_prob,
+                self.mask_channel_length,
+                self.mask_channel_selection,
+                self.mask_channel_other,
+                no_overlap=self.no_mask_channel_overlap,
+                min_space=self.mask_channel_min_space,
+            )
+            mask_channel_indices = torch.from_numpy(mask_channel_indices).to(x.device).unsqueeze(1).expand(-1, T, -1)
+            x[mask_channel_indices] = 0
+
+        return x, mask_indices
+
+    def forward_padding_mask(
+        self,
+        features: torch.Tensor,
+        padding_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        extra = padding_mask.size(1) % features.size(1)
+        if extra > 0:
+            padding_mask = padding_mask[:, :-extra]
+        padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1)
+        # padding_mask = padding_mask.all(-1)
+        padding_mask = padding_mask.any(-1)
+        return padding_mask
+
+    def extract_features(
+        self,
+        source: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        mask: bool = False,
+        ret_conv: bool = False,
+        output_layer: Optional[int] = None,
+        ret_layer_results: bool = False,
+    ):
+        if self.feature_grad_mult > 0:
+            features = self.feature_extractor(source)
+            if self.feature_grad_mult != 1.0:
+                features = GradMultiply.apply(features, self.feature_grad_mult)
+        else:
+            with torch.no_grad():
+                features = self.feature_extractor(source)
+
+        features = features.transpose(1, 2)
+        features = self.layer_norm(features)
+
+        if padding_mask is not None:
+            padding_mask = self.forward_padding_mask(features, padding_mask)
+
+        if self.post_extract_proj is not None:
+            features = self.post_extract_proj(features)
+
+        features = self.dropout_input(features)
+
+        if mask:
+            x, mask_indices = self.apply_mask(features, padding_mask)
+        else:
+            x = features
+
+        # feature: (B, T, D), float
+        # target: (B, T), long
+        # x: (B, T, D), float
+        # padding_mask: (B, T), bool
+        # mask_indices: (B, T), bool
+        x, layer_results = self.encoder(
+            x, padding_mask=padding_mask, layer=None if output_layer is None else output_layer - 1
+        )
+
+        res = {"x": x, "padding_mask": padding_mask, "features": features, "layer_results": layer_results}
+
+        feature = res["features"] if ret_conv else res["x"]
+        if ret_layer_results:
+            feature = (feature, res["layer_results"])
+        return feature, res["padding_mask"]
+
+
+class ConvFeatureExtractionModel(nn.Module):
+    def __init__(
+        self,
+        conv_layers: List[Tuple[int, int, int]],
+        dropout: float = 0.0,
+        mode: str = "default",
+        conv_bias: bool = False,
+        conv_type: str = "default",
+    ):
+        super().__init__()
+
+        assert mode in {"default", "layer_norm"}
+
+        def block(
+            n_in,
+            n_out,
+            k,
+            stride,
+            is_layer_norm=False,
+            is_group_norm=False,
+            conv_bias=False,
+        ):
+            def make_conv():
+                conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
+                nn.init.kaiming_normal_(conv.weight)
+                return conv
+
+            assert (is_layer_norm and is_group_norm) == False, "layer norm and group norm are exclusive"
+
+            if is_layer_norm:
+                return nn.Sequential(
+                    make_conv(),
+                    nn.Dropout(p=dropout),
+                    nn.Sequential(
+                        TransposeLast(),
+                        Fp32LayerNorm(dim, elementwise_affine=True),
+                        TransposeLast(),
+                    ),
+                    nn.GELU(),
+                )
+            elif is_group_norm:
+                return nn.Sequential(
+                    make_conv(),
+                    nn.Dropout(p=dropout),
+                    Fp32GroupNorm(dim, dim, affine=True),
+                    nn.GELU(),
+                )
+            else:
+                return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU())
+
+        self.conv_type = conv_type
+        if self.conv_type == "default":
+            in_d = 1
+            self.conv_layers = nn.ModuleList()
+            for i, cl in enumerate(conv_layers):
+                assert len(cl) == 3, "invalid conv definition: " + str(cl)
+                (dim, k, stride) = cl
+
+                self.conv_layers.append(
+                    block(
+                        in_d,
+                        dim,
+                        k,
+                        stride,
+                        is_layer_norm=mode == "layer_norm",
+                        is_group_norm=mode == "default" and i == 0,
+                        conv_bias=conv_bias,
+                    )
+                )
+                in_d = dim
+        elif self.conv_type == "conv2d":
+            in_d = 1
+            self.conv_layers = nn.ModuleList()
+            for i, cl in enumerate(conv_layers):
+                assert len(cl) == 3
+                (dim, k, stride) = cl
+
+                self.conv_layers.append(torch.nn.Conv2d(in_d, dim, k, stride))
+                self.conv_layers.append(torch.nn.ReLU())
+                in_d = dim
+        elif self.conv_type == "custom":
+            in_d = 1
+            idim = 80
+            self.conv_layers = nn.ModuleList()
+            for i, cl in enumerate(conv_layers):
+                assert len(cl) == 3
+                (dim, k, stride) = cl
+                self.conv_layers.append(torch.nn.Conv2d(in_d, dim, k, stride, padding=1))
+                self.conv_layers.append(torch.nn.LayerNorm([dim, idim]))
+                self.conv_layers.append(torch.nn.ReLU())
+                in_d = dim
+                if (i + 1) % 2 == 0:
+                    self.conv_layers.append(torch.nn.MaxPool2d(2, stride=2, ceil_mode=True))
+                    idim = int(math.ceil(idim / 2))
+        else:
+            pass
+
+    def forward(self, x, mask=None):
+        # BxT -> BxCxT
+        x = x.unsqueeze(1)
+        if self.conv_type == "custom":
+            for conv in self.conv_layers:
+                if isinstance(conv, nn.LayerNorm):
+                    x = x.transpose(1, 2)
+                    x = conv(x).transpose(1, 2)
+                else:
+                    x = conv(x)
+            x = x.transpose(2, 3).contiguous()
+            x = x.view(x.size(0), -1, x.size(-1))
+        else:
+            for conv in self.conv_layers:
+                x = conv(x)
+            if self.conv_type == "conv2d":
+                b, c, t, f = x.size()
+                x = x.transpose(2, 3).contiguous().view(b, c * f, t)
+        return x
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        self.dropout = args.dropout
+        self.embedding_dim = args.encoder_embed_dim
+
+        self.pos_conv = nn.Conv1d(
+            self.embedding_dim,
+            self.embedding_dim,
+            kernel_size=args.conv_pos,
+            padding=args.conv_pos // 2,
+            groups=args.conv_pos_groups,
+        )
+        dropout = 0
+        std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
+        nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
+        nn.init.constant_(self.pos_conv.bias, 0)
+
+        self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
+        self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())
+
+        if hasattr(args, "relative_position_embedding"):
+            self.relative_position_embedding = args.relative_position_embedding
+            self.num_buckets = args.num_buckets
+            self.max_distance = args.max_distance
+        else:
+            self.relative_position_embedding = False
+            self.num_buckets = 0
+            self.max_distance = 0
+
+        self.layers = nn.ModuleList(
+            [
+                TransformerSentenceEncoderLayer(
+                    embedding_dim=self.embedding_dim,
+                    ffn_embedding_dim=args.encoder_ffn_embed_dim,
+                    num_attention_heads=args.encoder_attention_heads,
+                    dropout=self.dropout,
+                    attention_dropout=args.attention_dropout,
+                    activation_dropout=args.activation_dropout,
+                    activation_fn=args.activation_fn,
+                    layer_norm_first=args.layer_norm_first,
+                    has_relative_attention_bias=(self.relative_position_embedding and i == 0),
+                    num_buckets=self.num_buckets,
+                    max_distance=self.max_distance,
+                    gru_rel_pos=args.gru_rel_pos,
+                )
+                for i in range(args.encoder_layers)
+            ]
+        )
+
+        self.layer_norm_first = args.layer_norm_first
+        self.layer_norm = LayerNorm(self.embedding_dim)
+        self.layerdrop = args.encoder_layerdrop
+
+        self.apply(init_bert_params)
+
+    def forward(self, x, padding_mask=None, streaming_mask=None, layer=None):
+        x, layer_results = self.extract_features(x, padding_mask, streaming_mask, layer)
+
+        if self.layer_norm_first and layer is None:
+            x = self.layer_norm(x)
+
+        return x, layer_results
+
+    def extract_features(self, x, padding_mask=None, streaming_mask=None, tgt_layer=None):
+        if padding_mask is not None:
+            x[padding_mask] = 0
+
+        x_conv = self.pos_conv(x.transpose(1, 2))
+        x_conv = x_conv.transpose(1, 2)
+        x += x_conv
+
+        if not self.layer_norm_first:
+            x = self.layer_norm(x)
+
+        x = F.dropout(x, p=self.dropout, training=self.training)
+
+        # B x T x C -> T x B x C
+        x = x.transpose(0, 1)
+
+        layer_results = []
+        z = None
+        if tgt_layer is not None:
+            layer_results.append((x, z))
+        r = None
+        pos_bias = None
+        for i, layer in enumerate(self.layers):
+            dropout_probability = np.random.random()
+            if not self.training or (dropout_probability > self.layerdrop):
+                x, z, pos_bias = layer(
+                    x,
+                    self_attn_padding_mask=padding_mask,
+                    need_weights=False,
+                    self_attn_mask=streaming_mask,
+                    pos_bias=pos_bias,
+                )
+            if tgt_layer is not None:
+                layer_results.append((x, z))
+            if i == tgt_layer:
+                r = x
+                break
+
+        if r is not None:
+            x = r
+
+        # T x B x C -> B x T x C
+        x = x.transpose(0, 1)
+
+        return x, layer_results
+
+
+class TransformerSentenceEncoderLayer(nn.Module):
+    """
+    Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
+    models.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: float = 768,
+        ffn_embedding_dim: float = 3072,
+        num_attention_heads: float = 8,
+        dropout: float = 0.1,
+        attention_dropout: float = 0.1,
+        activation_dropout: float = 0.1,
+        activation_fn: str = "relu",
+        layer_norm_first: bool = False,
+        has_relative_attention_bias: bool = False,
+        num_buckets: int = 0,
+        max_distance: int = 0,
+        rescale_init: bool = False,
+        gru_rel_pos: bool = False,
+    ) -> None:
+        super().__init__()
+        # Initialize parameters
+        self.embedding_dim = embedding_dim
+        self.dropout = dropout
+        self.activation_dropout = activation_dropout
+
+        # Initialize blocks
+        self.activation_name = activation_fn
+        self.activation_fn = get_activation_fn(activation_fn)
+        self.self_attn = MultiheadAttention(
+            self.embedding_dim,
+            num_attention_heads,
+            dropout=attention_dropout,
+            self_attention=True,
+            has_relative_attention_bias=has_relative_attention_bias,
+            num_buckets=num_buckets,
+            max_distance=max_distance,
+            rescale_init=rescale_init,
+            gru_rel_pos=gru_rel_pos,
+        )
+
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(self.activation_dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.layer_norm_first = layer_norm_first
+
+        # layer norm associated with the self attention layer
+        self.self_attn_layer_norm = LayerNorm(self.embedding_dim)
+
+        if self.activation_name == "glu":
+            self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish")
+        else:
+            self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
+        self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
+
+        # layer norm associated with the position wise feed-forward NN
+        self.final_layer_norm = LayerNorm(self.embedding_dim)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        self_attn_mask: torch.Tensor = None,
+        self_attn_padding_mask: torch.Tensor = None,
+        need_weights: bool = False,
+        pos_bias=None,
+    ):
+        """
+        LayerNorm is applied either before or after the self-attention/ffn
+        modules similar to the original Transformer imlementation.
+        """
+        residual = x
+
+        if self.layer_norm_first:
+            x = self.self_attn_layer_norm(x)
+            x, attn, pos_bias = self.self_attn(
+                query=x,
+                key=x,
+                value=x,
+                key_padding_mask=self_attn_padding_mask,
+                need_weights=False,
+                attn_mask=self_attn_mask,
+                position_bias=pos_bias,
+            )
+            x = self.dropout1(x)
+            x = residual + x
+
+            residual = x
+            x = self.final_layer_norm(x)
+            if self.activation_name == "glu":
+                x = self.fc1(x)
+            else:
+                x = self.activation_fn(self.fc1(x))
+            x = self.dropout2(x)
+            x = self.fc2(x)
+            x = self.dropout3(x)
+            x = residual + x
+        else:
+            x, attn, pos_bias = self.self_attn(
+                query=x,
+                key=x,
+                value=x,
+                key_padding_mask=self_attn_padding_mask,
+                need_weights=need_weights,
+                attn_mask=self_attn_mask,
+                position_bias=pos_bias,
+            )
+
+            x = self.dropout1(x)
+            x = residual + x
+
+            x = self.self_attn_layer_norm(x)
+
+            residual = x
+            if self.activation_name == "glu":
+                x = self.fc1(x)
+            else:
+                x = self.activation_fn(self.fc1(x))
+            x = self.dropout2(x)
+            x = self.fc2(x)
+            x = self.dropout3(x)
+            x = residual + x
+            x = self.final_layer_norm(x)
+
+        return x, attn, pos_bias

TTS/vocoder/models/base_vocoder.py

@@ -18,6 +18,8 @@ class BaseVocoder(BaseTrainerModel):
         - 1D tensors `batch x 1`
     """
 
+    MODEL_TYPE = "vocoder"
+
     def __init__(self, config):
         super().__init__()
         self._set_model_args(config)

docs/source/inference.md

@@ -36,8 +36,8 @@ Run a tts and a vocoder model from the released model list. Note that not every
 
 ```bash
 tts --text "Text for TTS" \
-    --model_name "<type>/<language>/<dataset>/<model_name>" \
-    --vocoder_name "<type>/<language>/<dataset>/<model_name>" \
+    --model_name "tts_models/<language>/<dataset>/<model_name>" \
+    --vocoder_name "vocoder_models/<language>/<dataset>/<model_name>" \
     --out_path folder/to/save/output.wav
 ```
 
@@ -64,8 +64,17 @@ tts --text "Text for TTS" \
 Run a multi-speaker TTS model from the released models list.
 
 ```bash
-tts --model_name "<type>/<language>/<dataset>/<model_name>"  --list_speaker_idxs  # list the possible speaker IDs.
-tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>"  --speaker_idx "<speaker_id>"
+tts --model_name "tts_models/<language>/<dataset>/<model_name>"  --list_speaker_idxs  # list the possible speaker IDs.
+tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "tts_models/<language>/<dataset>/<model_name>"  --speaker_idx "<speaker_id>"
+```
+
+Run a released voice conversion model
+
+```bash
+tts --model_name "voice_conversion/<language>/<dataset>/<model_name>"
+    --source_wav "my/source/speaker/audio.wav"
+    --target_wav "my/target/speaker/audio.wav"
+    --out_path folder/to/save/output.wav
 ```
 
 **Note:** You can use ```./TTS/bin/synthesize.py``` if you prefer running ```tts``` from the TTS project folder.
@@ -135,4 +144,23 @@ tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_
 tts.tts_to_file("This is voice cloning.", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
 tts.tts_to_file("C'est le clonage de la voix.", speaker_wav="my/cloning/audio.wav", language="fr", file_path="output.wav")
 tts.tts_to_file("Isso é clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt", file_path="output.wav")
+```
+
+Example voice conversion converting speaker of the `source_wav` to the speaker of the `target_wav`
+
+```python
+tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=True)
+tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav")
+```
+
+Example voice cloning by a single speaker TTS model combining with the voice conversion model. This way, you can
+clone voices by using any model in 🐸TTS.
+
+```python
+tts = TTS("tts_models/de/thorsten/tacotron2-DDC")
+tts.tts_with_vc_to_file(
+    "Wie sage ich auf Italienisch, dass ich dich liebe?",
+    speaker_wav="target/speaker.wav",
+    file_path="ouptut.wav"
+)
 ```

requirements.txt

@@ -14,6 +14,7 @@ tqdm
 anyascii
 pyyaml
 fsspec>=2021.04.0
+aiohttp
 packaging
 # deps for examples
 flask

tests/inference_tests/test_python_api.py

@@ -28,7 +28,7 @@ class TTSTest(unittest.TestCase):
 
     def test_multi_speaker_multi_lingual_model(self):
         tts = TTS()
-        tts.load_model_by_name(tts.models[0])  # YourTTS
+        tts.load_tts_model_by_name(tts.models[0])  # YourTTS
         tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path=OUTPUT_PATH)
 
         self.assertTrue(tts.is_multi_speaker)
@@ -38,5 +38,5 @@ class TTSTest(unittest.TestCase):
 
     def test_voice_cloning(self):  # pylint: disable=no-self-use
         tts = TTS()
-        tts.load_model_by_name("tts_models/multilingual/multi-dataset/your_tts")
+        tts.load_tts_model_by_name("tts_models/multilingual/multi-dataset/your_tts")
         tts.tts_to_file("Hello world!", speaker_wav=cloning_test_wav_path, language="en", file_path=OUTPUT_PATH)

tests/vc_tests/test_freevc.py

@@ -0,0 +1,135 @@
+import os
+import unittest
+
+import torch
+
+from tests import get_tests_input_path
+from TTS.vc.configs.freevc_config import FreeVCConfig
+from TTS.vc.models.freevc import FreeVC
+
+# pylint: disable=unused-variable
+# pylint: disable=no-self-use
+
+torch.manual_seed(1)
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+c = FreeVCConfig()
+
+WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
+BATCH_SIZE = 3
+
+
+def count_parameters(model):
+    r"""Count number of trainable parameters in a network"""
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+
+class TestFreeVC(unittest.TestCase):
+    def _create_inputs(self, config, batch_size=2):
+        input_dummy = torch.rand(batch_size, 30 * config.audio["hop_length"]).to(device)
+        input_lengths = torch.randint(100, 30 * config.audio["hop_length"], (batch_size,)).long().to(device)
+        input_lengths[-1] = 30 * config.audio["hop_length"]
+        spec = torch.rand(batch_size, 30, config.audio["filter_length"] // 2 + 1).to(device)
+        mel = torch.rand(batch_size, 30, config.audio["n_mel_channels"]).to(device)
+        spec_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
+        spec_lengths[-1] = spec.size(2)
+        waveform = torch.rand(batch_size, spec.size(2) * config.audio["hop_length"]).to(device)
+        return input_dummy, input_lengths, mel, spec, spec_lengths, waveform
+
+    @staticmethod
+    def _create_inputs_inference():
+        source_wav = torch.rand(16000)
+        target_wav = torch.rand(16000)
+        return source_wav, target_wav
+
+    @staticmethod
+    def _check_parameter_changes(model, model_ref):
+        count = 0
+        for param, param_ref in zip(model.parameters(), model_ref.parameters()):
+            assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
+                count, param.shape, param, param_ref
+            )
+            count += 1
+
+    def test_methods(self):
+        config = FreeVCConfig()
+        model = FreeVC(config).to(device)
+        model.load_pretrained_speaker_encoder()
+        model.init_multispeaker(config)
+        wavlm_feats = model.extract_wavlm_features(torch.rand(1, 16000))
+        assert wavlm_feats.shape == (1, 1024, 49), wavlm_feats.shape
+
+    def test_load_audio(self):
+        config = FreeVCConfig()
+        model = FreeVC(config).to(device)
+        wav = model.load_audio(WAV_FILE)
+        wav2 = model.load_audio(wav)
+        assert all(torch.isclose(wav, wav2))
+
+    def _test_forward(self, batch_size):
+        # create model
+        config = FreeVCConfig()
+        model = FreeVC(config).to(device)
+        model.train()
+        print(" > Num parameters for FreeVC model:%s" % (count_parameters(model)))
+
+        _, _, mel, spec, spec_lengths, waveform = self._create_inputs(config, batch_size)
+
+        wavlm_vec = model.extract_wavlm_features(waveform)
+        wavlm_vec_lengths = torch.ones(batch_size, dtype=torch.long)
+
+        y = model.forward(wavlm_vec, spec, None, mel, spec_lengths, wavlm_vec_lengths)
+        # TODO: assert with training implementation
+
+    def test_forward(self):
+        self._test_forward(1)
+        self._test_forward(3)
+
+    def _test_inference(self, batch_size):
+        config = FreeVCConfig()
+        model = FreeVC(config).to(device)
+        model.eval()
+
+        _, _, mel, _, _, waveform = self._create_inputs(config, batch_size)
+
+        wavlm_vec = model.extract_wavlm_features(waveform)
+        wavlm_vec_lengths = torch.ones(batch_size, dtype=torch.long)
+
+        output_wav = model.inference(wavlm_vec, None, mel, wavlm_vec_lengths)
+        assert (
+            output_wav.shape[-1] // config.audio.hop_length == wavlm_vec.shape[-1]
+        ), f"{output_wav.shape[-1] // config.audio.hop_length} != {wavlm_vec.shape}"
+
+    def test_inference(self):
+        self._test_inference(1)
+        self._test_inference(3)
+
+    def test_voice_conversion(self):
+        config = FreeVCConfig()
+        model = FreeVC(config).to(device)
+        model.eval()
+
+        source_wav, target_wav = self._create_inputs_inference()
+        output_wav = model.voice_conversion(source_wav, target_wav)
+        assert (
+            output_wav.shape[0] + config.audio.hop_length == source_wav.shape[0]
+        ), f"{output_wav.shape} != {source_wav.shape}"
+
+    def test_train_step(self):
+        ...
+
+    def test_train_eval_log(self):
+        ...
+
+    def test_test_run(self):
+        ...
+
+    def test_load_checkpoint(self):
+        ...
+
+    def test_get_criterion(self):
+        ...
+
+    def test_init_from_config(self):
+        ...

tests/zoo_tests/test_models.py

@@ -51,6 +51,13 @@ def run_models(offset=0, step=1):
             # remove downloaded models
             shutil.rmtree(local_download_dir)
             shutil.rmtree(get_user_data_dir("tts"))
+        elif "voice_conversion_models" in model_name:
+            speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")
+            reference_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0032.wav")
+            run_cli(
+                f"tts --model_name  {model_name} "
+                f'--out_path "{output_path}" --source_wav "{speaker_wav}" --target_wav "{reference_wav}" --progress_bar False'
+            )
         else:
             # only download the model
             manager.download_model(model_name)