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