Spaces:
Runtime error
Runtime error
File size: 4,379 Bytes
9b2107c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
import os
from typing import Any, Dict, List
import fsspec
import numpy as np
import torch
from coqpit import Coqpit
from TTS.config import check_config_and_model_args
from TTS.tts.utils.managers import BaseIDManager
class LanguageManager(BaseIDManager):
"""Manage the languages for multi-lingual 🐸TTS models. Load a datafile and parse the information
in a way that can be queried by language.
Args:
language_ids_file_path (str, optional): Path to the metafile that maps language names to ids used by
TTS models. Defaults to "".
config (Coqpit, optional): Coqpit config that contains the language information in the datasets filed.
Defaults to None.
Examples:
>>> manager = LanguageManager(language_ids_file_path=language_ids_file_path)
>>> language_id_mapper = manager.language_ids
"""
def __init__(
self,
language_ids_file_path: str = "",
config: Coqpit = None,
):
super().__init__(id_file_path=language_ids_file_path)
if config:
self.set_language_ids_from_config(config)
@property
def num_languages(self) -> int:
return len(list(self.name_to_id.keys()))
@property
def language_names(self) -> List:
return list(self.name_to_id.keys())
@staticmethod
def parse_language_ids_from_config(c: Coqpit) -> Dict:
"""Set language id from config.
Args:
c (Coqpit): Config
Returns:
Tuple[Dict, int]: Language ID mapping and the number of languages.
"""
languages = set({})
for dataset in c.datasets:
if "language" in dataset:
languages.add(dataset["language"])
else:
raise ValueError(f"Dataset {dataset['name']} has no language specified.")
return {name: i for i, name in enumerate(sorted(list(languages)))}
def set_language_ids_from_config(self, c: Coqpit) -> None:
"""Set language IDs from config samples.
Args:
c (Coqpit): Config.
"""
self.name_to_id = self.parse_language_ids_from_config(c)
@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 save_ids_to_file(self, file_path: str) -> None:
"""Save language IDs to a json file.
Args:
file_path (str): Path to the output file.
"""
self._save_json(file_path, self.name_to_id)
@staticmethod
def init_from_config(config: Coqpit) -> "LanguageManager":
"""Initialize the language manager from a Coqpit config.
Args:
config (Coqpit): Coqpit config.
"""
language_manager = None
if check_config_and_model_args(config, "use_language_embedding", True):
if config.get("language_ids_file", None):
language_manager = LanguageManager(language_ids_file_path=config.language_ids_file)
language_manager = LanguageManager(config=config)
return language_manager
def _set_file_path(path):
"""Find the language_ids.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), "language_ids.json")
path_continue = os.path.join(path, "language_ids.json")
fs = fsspec.get_mapper(path).fs
if fs.exists(path_restore):
return path_restore
if fs.exists(path_continue):
return path_continue
return None
def get_language_balancer_weights(items: list):
language_names = np.array([item["language"] for item in items])
unique_language_names = np.unique(language_names).tolist()
language_ids = [unique_language_names.index(l) for l in language_names]
language_count = np.array([len(np.where(language_names == l)[0]) for l in unique_language_names])
weight_language = 1.0 / language_count
# get weight for each sample
dataset_samples_weight = np.array([weight_language[l] for l in language_ids])
# normalize
dataset_samples_weight = dataset_samples_weight / np.linalg.norm(dataset_samples_weight)
return torch.from_numpy(dataset_samples_weight).float()
|