import json
import os
from typing import Any, List
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:
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,
embeddings_file_path: str = "",
emotion_id_file_path: str = "",
encoder_model_path: str = "",
encoder_config_path: str = "",
use_cuda: bool = False,
):
super().__init__(
embedding_file_path=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)
)
elif get_from_config_or_model_args_with_default(config, "external_emotions_embs_file", None):
emotion_manager = EmotionManager(
embeddings_file_path=get_from_config_or_model_args_with_default(
config, "external_emotions_embs_file", None
)
)
if get_from_config_or_model_args_with_default(config, "use_external_emotions_embeddings", False):
if get_from_config_or_model_args_with_default(config, "external_emotions_embs_file", None):
emotion_manager = EmotionManager(
embeddings_file_path=get_from_config_or_model_args_with_default(
config, "external_emotions_embs_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_embs_file"
)
if not os.path.exists(c.external_emotions_embs_file):
raise RuntimeError(
"You must copy the file emotions.json to restore_path, or set a valid file in CONFIG.external_emotions_embs_file"
)
emotion_manager.load_embeddings_from_file(c.external_emotions_embs_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_embs_file:
# new emotion manager with external emotion embeddings.
emotion_manager.load_embeddings_from_file(c.external_emotions_embs_file)
elif c.use_external_emotions_embeddings and not c.external_emotions_embs_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_embs_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_embs_file:
emotion_manager.save_embeddings_to_file(out_file_path)
else:
emotion_manager.save_ids_to_file(out_file_path)
return emotion_manager
def get_speech_style_balancer_weights(items: list):
style_names = np.array([item["speech_style"] for item in items])
unique_style_names = np.unique(style_names).tolist()
style_ids = [unique_style_names.index(s) for s in style_names]
style_count = np.array([len(np.where(style_names == s)[0]) for s in unique_style_names])
weight_style = 1.0 / style_count
# get weight for each sample
dataset_samples_weight = np.array([weight_style[s] for s in style_ids])
# normalize
dataset_samples_weight = dataset_samples_weight / np.linalg.norm(dataset_samples_weight)
return torch.from_numpy(dataset_samples_weight).float()