mirror of
https://github.com/Nighthawk42/MioTTS.git
synced 2026-09-04 09:32:29 +00:00
384 lines
13 KiB
Python
384 lines
13 KiB
Python
import json
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import os
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import random
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from typing import Any
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import fsspec
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import numpy as np
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import torch
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from TTS.config import load_config
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from TTS.encoder.models.base_encoder import BaseEncoder
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from TTS.encoder.utils.generic_utils import setup_encoder_model
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.generic_utils import is_pytorch_at_least_2_4
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def load_file(path: str | os.PathLike[Any]) -> Any:
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path = str(path)
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if path.endswith(".json"):
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with fsspec.open(path, "r") as f:
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return json.load(f)
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elif path.endswith(".pth"):
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with fsspec.open(path, "rb") as f:
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return torch.load(f, map_location="cpu", weights_only=is_pytorch_at_least_2_4())
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else:
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raise ValueError("Unsupported file type")
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def save_file(obj: Any, path: str | os.PathLike[Any]):
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path = str(path)
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if path.endswith(".json"):
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with fsspec.open(path, "w") as f:
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json.dump(obj, f, indent=4)
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elif path.endswith(".pth"):
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with fsspec.open(path, "wb") as f:
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torch.save(obj, f)
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else:
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raise ValueError("Unsupported file type")
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class BaseIDManager:
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"""Base `ID` Manager class. Every new `ID` manager must inherit this.
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Defines common `ID` manager specific functions.
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"""
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def __init__(self, id_file_path: str | os.PathLike[Any] = ""):
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self.name_to_id: dict[str, int] = {}
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if id_file_path:
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self.load_ids_from_file(id_file_path)
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def set_ids_from_data(self, items: list[dict[str, Any]], parse_key: str) -> None:
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"""Set IDs from data samples.
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Args:
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items (List): Data sampled returned by `load_tts_samples()`.
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"""
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self.name_to_id = self.parse_ids_from_data(items, parse_key=parse_key)
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def load_ids_from_file(self, file_path: str | os.PathLike[Any]) -> None:
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"""Set IDs from a file.
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Args:
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file_path (str): Path to the file.
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"""
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self.name_to_id = load_file(file_path)
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def save_ids_to_file(self, file_path: str | os.PathLike[Any]) -> None:
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"""Save IDs to a json file.
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Args:
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file_path (str): Path to the output file.
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"""
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save_file(self.name_to_id, file_path)
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def get_random_id(self) -> int | None:
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"""Get a random ID.
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Returns:
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Integer ID or None if there are no IDs.
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"""
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if self.name_to_id:
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return self.name_to_id[random.choices(list(self.name_to_id.keys()))[0]]
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return None
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@staticmethod
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def parse_ids_from_data(items: list[dict[str, Any]], parse_key: str) -> dict[str, int]:
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"""Parse IDs from data samples retured by `load_tts_samples()`.
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Args:
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items (list): Data sampled returned by `load_tts_samples()`.
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parse_key (str): The key to being used to parse the data.
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Returns:
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Tuple[Dict]: speaker IDs.
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"""
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classes = sorted({item[parse_key] for item in items})
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ids = {name: i for i, name in enumerate(classes)}
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return ids
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class EmbeddingManager(BaseIDManager):
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"""Base `Embedding` Manager class. Every new `Embedding` manager must inherit this.
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It defines common `Embedding` manager specific functions.
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It expects embeddings files in the following format:
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::
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{
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'audio_file_key':{
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'name': 'category_name',
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'embedding'[<embedding_values>]
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},
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...
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}
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`audio_file_key` is a unique key to the audio file in the dataset. It can be the path to the file or any other unique key.
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`embedding` is the embedding vector of the audio file.
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`name` can be name of the speaker of the audio file.
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"""
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def __init__(
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self,
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embedding_file_path: str | os.PathLike[Any] | list[str | os.PathLike[Any]] | None = None,
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id_file_path: str | os.PathLike[Any] = "",
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encoder_model_path: str | os.PathLike[Any] = "",
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encoder_config_path: str | os.PathLike[Any] = "",
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use_cuda: bool = False,
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):
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super().__init__(id_file_path=id_file_path)
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self.embeddings = {}
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self.embeddings_by_names = {}
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self.clip_ids = []
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self.encoder: BaseEncoder | None = None
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self.encoder_ap: AudioProcessor | None = None
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self.use_cuda = use_cuda
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if embedding_file_path:
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if isinstance(embedding_file_path, list):
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self.load_embeddings_from_list_of_files(embedding_file_path)
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else:
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self.load_embeddings_from_file(embedding_file_path)
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if encoder_model_path and encoder_config_path:
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self.init_encoder(encoder_model_path, encoder_config_path, use_cuda)
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@property
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def num_embeddings(self) -> int:
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"""Get number of embeddings."""
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return len(self.embeddings)
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@property
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def num_names(self) -> int:
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"""Get number of embeddings."""
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return len(self.embeddings_by_names)
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@property
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def embedding_dim(self) -> int:
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"""Dimensionality of embeddings. If embeddings are not loaded, returns zero."""
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if self.embeddings:
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return len(self.embeddings[list(self.embeddings.keys())[0]]["embedding"])
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return 0
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@property
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def embedding_names(self) -> list[str]:
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"""Get embedding names."""
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return list(self.embeddings_by_names.keys())
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def save_embeddings_to_file(self, file_path: str | os.PathLike[Any]) -> None:
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"""Save embeddings to a json file.
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Args:
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file_path (str): Path to the output file.
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"""
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save_file(self.embeddings, file_path)
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@staticmethod
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def read_embeddings_from_file(
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file_path: str | os.PathLike[Any],
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) -> tuple[dict[str, int], list[str], Any, dict[str, Any]]:
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"""Load embeddings from a json file.
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Args:
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file_path (str): Path to the file.
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"""
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embeddings = load_file(file_path)
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speakers = sorted({x["name"] for x in embeddings.values()})
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name_to_id = {name: i for i, name in enumerate(speakers)}
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clip_ids = list(set(clip_name for clip_name in embeddings.keys()))
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# cache embeddings_by_names for fast inference using a bigger speakers.json
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embeddings_by_names = {}
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for x in embeddings.values():
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if x["name"] not in embeddings_by_names.keys():
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embeddings_by_names[x["name"]] = [x["embedding"]]
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else:
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embeddings_by_names[x["name"]].append(x["embedding"])
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return name_to_id, clip_ids, embeddings, embeddings_by_names
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def load_embeddings_from_file(self, file_path: str | os.PathLike[Any]) -> None:
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"""Load embeddings from a json file.
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Args:
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file_path (str): Path to the target json file.
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"""
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self.name_to_id, self.clip_ids, self.embeddings, self.embeddings_by_names = self.read_embeddings_from_file(
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file_path
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)
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def load_embeddings_from_list_of_files(self, file_paths: list[str | os.PathLike[Any]]) -> None:
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"""Load embeddings from a list of json files and don't allow duplicate keys.
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Args:
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file_paths (List[str]): List of paths to the target json files.
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"""
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self.name_to_id = {}
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self.clip_ids = []
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self.embeddings_by_names = {}
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self.embeddings = {}
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for file_path in file_paths:
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ids, clip_ids, embeddings, embeddings_by_names = self.read_embeddings_from_file(file_path)
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# check colliding keys
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duplicates = set(self.embeddings.keys()) & set(embeddings.keys())
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if duplicates:
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raise ValueError(f" [!] Duplicate embedding names <{duplicates}> in {file_path}")
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# store values
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self.name_to_id.update(ids)
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self.clip_ids.extend(clip_ids)
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self.embeddings_by_names.update(embeddings_by_names)
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self.embeddings.update(embeddings)
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# reset name_to_id to get the right speaker ids
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self.name_to_id = {name: i for i, name in enumerate(self.name_to_id)}
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def get_embedding_by_clip(self, clip_idx: str) -> list:
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"""Get embedding by clip ID.
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Args:
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clip_idx (str): Target clip ID.
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Returns:
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List: embedding as a list.
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"""
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return self.embeddings[clip_idx]["embedding"]
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def get_embeddings_by_name(self, idx: str) -> list[list]:
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"""Get all embeddings of a speaker.
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Args:
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idx (str): Target name.
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Returns:
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List[List]: all the embeddings of the given speaker.
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"""
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return self.embeddings_by_names[idx]
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def get_embeddings_by_names(self) -> dict[str, Any]:
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"""Get all embeddings by names.
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Returns:
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Dict: all the embeddings of each speaker.
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"""
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embeddings_by_names = {}
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for x in self.embeddings.values():
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if x["name"] not in embeddings_by_names.keys():
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embeddings_by_names[x["name"]] = [x["embedding"]]
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else:
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embeddings_by_names[x["name"]].append(x["embedding"])
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return embeddings_by_names
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def get_mean_embedding(self, idx: str, num_samples: int | None = None, randomize: bool = False) -> np.ndarray:
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"""Get mean embedding of a idx.
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Args:
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idx (str): Target name.
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num_samples (int, optional): Number of samples to be averaged. Defaults to None.
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randomize (bool, optional): Pick random `num_samples` of embeddings. Defaults to False.
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Returns:
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np.ndarray: Mean embedding.
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"""
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embeddings = self.get_embeddings_by_name(idx)
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if num_samples is None:
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embeddings = np.stack(embeddings).mean(0)
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else:
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assert len(embeddings) >= num_samples, f" [!] {idx} has number of samples < {num_samples}"
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if randomize:
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embeddings = np.stack(random.choices(embeddings, k=num_samples)).mean(0)
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else:
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embeddings = np.stack(embeddings[:num_samples]).mean(0)
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return embeddings
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def get_random_embedding(self) -> Any:
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"""Get a random embedding.
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Args:
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Returns:
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np.ndarray: embedding.
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"""
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if self.embeddings:
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return self.embeddings[random.choices(list(self.embeddings.keys()))[0]]["embedding"]
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return None
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def get_clips(self) -> list[str]:
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return sorted(self.embeddings.keys())
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def init_encoder(
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self, model_path: str | os.PathLike[Any], config_path: str | os.PathLike[Any], use_cuda: bool = False
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) -> None:
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"""Initialize a speaker encoder model.
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Args:
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model_path (str): Model file path.
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config_path (str): Model config file path.
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use_cuda (bool, optional): Use CUDA. Defaults to False.
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"""
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self.use_cuda = use_cuda
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self.encoder_config = load_config(config_path)
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self.encoder = setup_encoder_model(self.encoder_config)
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self.encoder_criterion = self.encoder.load_checkpoint(
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self.encoder_config, str(model_path), eval=True, use_cuda=use_cuda, cache=True
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)
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self.encoder_ap = AudioProcessor(**self.encoder_config.audio)
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@torch.inference_mode()
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def compute_embedding_from_clip(
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self, wav_file: str | os.PathLike[Any] | list[str | os.PathLike[Any]]
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) -> list[float]:
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"""Compute a embedding from a given audio file.
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Args:
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wav_file (Union[str, List[str]]): Target file path.
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Returns:
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list: Computed embedding.
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"""
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def _compute(wav_file: str | os.PathLike[Any]) -> torch.Tensor:
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if self.encoder_ap is None or self.encoder is None:
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msg = "You must first initialize the encoder with init_encoder()"
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raise RuntimeError(msg)
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waveform = self.encoder_ap.load_wav(wav_file, sr=self.encoder_ap.sample_rate)
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if not self.encoder_config.model_params.get("use_torch_spec", False):
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m_input = self.encoder_ap.melspectrogram(waveform)
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m_input = torch.from_numpy(m_input)
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else:
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m_input = torch.from_numpy(waveform)
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if self.use_cuda:
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m_input = m_input.cuda()
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m_input = m_input.unsqueeze(0)
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embedding = self.encoder.compute_embedding(m_input)
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return embedding
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if isinstance(wav_file, list):
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# compute the mean embedding
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embeddings = torch.stack([_compute(wf) for wf in wav_file])
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return embeddings.mean(dim=0)[0].tolist()
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embedding = _compute(wav_file)
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return embedding[0].tolist()
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def compute_embeddings(self, feats: torch.Tensor | np.ndarray) -> torch.Tensor:
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"""Compute embedding from features.
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Args:
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feats (Union[torch.Tensor, np.ndarray]): Input features.
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Returns:
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List: computed embedding.
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"""
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if isinstance(feats, np.ndarray):
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feats = torch.from_numpy(feats)
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if feats.ndim == 2:
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feats = feats.unsqueeze(0)
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if self.use_cuda:
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feats = feats.cuda()
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if self.encoder is None:
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msg = "You must first initialize the encoder with init_encoder()"
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raise RuntimeError(msg)
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return self.encoder.compute_embedding(feats)
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