Add emotion manager

TTS/tts/utils/emotions.py

@@ -0,0 +1,197 @@
+import json
+import os
+from typing import Any, Dict, List, Tuple, Union
+
+import fsspec
+import numpy as np
+import torch
+from coqpit import Coqpit
+
+from TTS.config import get_from_config_or_model_args_with_default
+from TTS.tts.utils.managers import EmbeddingManager
+
+class EmotionManager(EmbeddingManager):
+    """Manage the emotions for emotional TTS. Load a datafile and parse the information
+    in a way that can be queried by emotion or clip.
+
+    There are 3 different scenarios considered:
+
+    1. Models using emotion embedding layers. The datafile only maps emotion names to ids used by the embedding layer.
+    2. Models using embeddings. The datafile includes a dictionary in the following format.
+
+    ::
+
+        {
+            'clip_name.wav':{
+                'name': 'emotionA',
+                'embedding'[<embedding_values>]
+            },
+            ...
+        }
+
+
+    3. Computing the embeddings by the emotion encoder. It loads the emotion encoder model and
+    computes the embeddings for a given clip or emotion.
+
+    Args:
+        emo_embeddings_file_path (str, optional): Path to the metafile including x vectors. Defaults to "".
+        emotion_id_file_path (str, optional): Path to the metafile that maps emotion names to ids used by
+        TTS models. Defaults to "".
+        encoder_model_path (str, optional): Path to the emotion encoder model file. Defaults to "".
+        encoder_config_path (str, optional): Path to the spealer encoder config file. Defaults to "".
+
+    Examples:
+        >>> # load audio processor and emotion encoder
+        >>> ap = AudioProcessor(**config.audio)
+        >>> manager = EmotionManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path)
+        >>> # load a sample audio and compute embedding
+        >>> embedding = manager.compute_embedding_from_clip(sample_wav_path)
+    """
+
+    def __init__(
+        self,
+        emo_embeddings_file_path: str = "",
+        emotion_id_file_path: str = "",
+        encoder_model_path: str = "",
+        encoder_config_path: str = "",
+        use_cuda: bool = False,
+    ):
+        super().__init__(
+            external_emotions_ids_file_path=emo_embeddings_file_path,
+            id_file_path=emotion_id_file_path,
+            encoder_model_path=encoder_model_path,
+            encoder_config_path=encoder_config_path,
+            use_cuda=use_cuda
+            )
+
+    @property
+    def num_emotions(self):
+        return len(self.ids)
+
+    @property
+    def emotion_names(self):
+        return list(self.ids.keys())
+
+    @staticmethod
+    def parse_ids_from_data(items: List, parse_key: str) -> Any:
+        raise NotImplementedError
+
+    def set_ids_from_data(self, items: List, parse_key: str) -> Any:
+        raise NotImplementedError
+
+    def get_emotions(self) -> List:
+        return self.ids
+
+    @staticmethod
+    def init_from_config(config: "Coqpit") -> "EmotionManager":
+        """Initialize a emotion manager from config
+
+        Args:
+            config (Coqpit): Config object.
+
+        Returns:
+            EmotionEncoder: Emotion encoder object.
+        """
+        emotion_manager = None
+        if get_from_config_or_model_args_with_default(config, "use_emotion_embedding", False):
+            if get_from_config_or_model_args_with_default(config, "emotions_ids_file", None):
+                emotion_manager = EmotionManager(
+                    emotion_id_file_path=get_from_config_or_model_args_with_default(config, "emotions_ids_file", None)
+                )
+
+        if get_from_config_or_model_args_with_default(config, "use_external_emotion_embedding", False):
+            if get_from_config_or_model_args_with_default(config, "external_emotions_ids_file", None):
+                emotion_manager = EmotionManager(
+                    embeddings_file_path=get_from_config_or_model_args_with_default(config, "external_emotions_ids_file", None)
+                )
+
+        return emotion_manager
+
+
+def _set_file_path(path):
+    """Find the emotions.json under the given path or the above it.
+    Intended to band aid the different paths returned in restored and continued training."""
+    path_restore = os.path.join(os.path.dirname(path), "emotions.json")
+    path_continue = os.path.join(path, "emotions.json")
+    fs = fsspec.get_mapper(path).fs
+    if fs.exists(path_restore):
+        return path_restore
+    if fs.exists(path_continue):
+        return path_continue
+    raise FileNotFoundError(f" [!] `emotions.json` not found in {path}")
+
+
+def load_emotion_mapping(out_path):
+    """Loads emotion mapping if already present."""
+    if os.path.splitext(out_path)[1] == ".json":
+        json_file = out_path
+    else:
+        json_file = _set_file_path(out_path)
+    with fsspec.open(json_file, "r") as f:
+        return json.load(f)
+
+
+def save_emotion_mapping(out_path, emotion_mapping):
+    """Saves emotion mapping if not yet present."""
+    if out_path is not None:
+        emotions_json_path = _set_file_path(out_path)
+        with fsspec.open(emotions_json_path, "w") as f:
+            json.dump(emotion_mapping, f, indent=4)
+
+
+def get_emotion_manager(c: Coqpit, restore_path: str = None, out_path: str = None) -> EmotionManager:
+    """Initiate a `EmotionManager` instance by the provided config.
+
+    Args:
+        c (Coqpit): Model configuration.
+        restore_path (str): Path to a previous training folder.
+        out_path (str, optional): Save the generated emotion IDs to a output path. Defaults to None.
+
+    Returns:
+        EmotionManager: initialized and ready to use instance.
+    """
+    emotion_manager = EmotionManager()
+    if restore_path:
+        emotions_ids_file = _set_file_path(restore_path)
+        # restoring emotion manager from a previous run.
+        if c.use_external_emotions_embeddings:
+            # restore emotion manager with the embedding file
+            if not os.path.exists(emotions_ids_file):
+                print("WARNING: emotions.json was not found in restore_path, trying to use CONFIG.external_emotions_ids_file")
+                if not os.path.exists(c.external_emotions_ids_file):
+                    raise RuntimeError(
+                        "You must copy the file emotions.json to restore_path, or set a valid file in CONFIG.external_emotions_ids_file"
+                    )
+                emotion_manager.load_embeddings_from_file(c.external_emotions_ids_file)
+            emotion_manager.load_embeddings_from_file(emotions_ids_file)
+        elif not c.use_external_emotions_embeddings:  # restor emotion manager with emotion ID file.
+            emotion_manager.load_ids_from_file(emotions_ids_file)
+
+    elif c.use_external_emotions_embeddings and c.external_emotions_ids_file:
+        # new emotion manager with external emotion embeddings.
+        emotion_manager.load_embeddings_from_file(c.external_emotions_ids_file)
+    elif c.use_external_emotions_embeddings and not c.external_emotions_ids_file:
+        raise "use_external_emotions_embeddings is True, so you need pass a external emotion embedding file."
+    elif c.use_emotion_embedding:
+        if "emotions_ids_file" in c and c.emotions_ids_file:
+            emotion_manager.load_ids_from_file(c.emotions_ids_file)
+        else: # enable get ids from eternal embedding files
+            emotion_manager.load_embeddings_from_file(c.external_emotions_ids_file)
+
+    if emotion_manager.num_emotions > 0:
+        print(
+            " > Emotion manager is loaded with {} emotions: {}".format(
+                emotion_manager.num_emotions, ", ".join(emotion_manager.ids)
+            )
+        )
+
+    # save file if path is defined
+    if out_path:
+        out_file_path = os.path.join(out_path, "emotions.json")
+        print(f" > Saving `emotions.json` to {out_file_path}.")
+        if c.use_external_emotions_embeddings and c.external_emotions_ids_file:
+            emotion_manager.save_embeddings_to_file(out_file_path)
+        else:
+            emotion_manager.save_ids_to_file(out_file_path)
+    return emotion_manager
+