mirror of https://github.com/coqui-ai/TTS.git
refactor `SpeakerManager`
Eren Gölge
parent
421194880d
commit
f840268181
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@ -1,6 +1,7 @@
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import json
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import os
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import random
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from typing import Any, List, Union
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from typing import Any, Dict, List, Tuple, Union
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import numpy as np
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import torch
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@ -10,6 +11,71 @@ from TTS.speaker_encoder.utils.generic_utils import setup_model
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from TTS.utils.audio import AudioProcessor
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def make_speakers_json_path(out_path):
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"""Returns conventional speakers.json location."""
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return os.path.join(out_path, "speakers.json")
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def load_speaker_mapping(out_path):
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"""Loads speaker 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 = make_speakers_json_path(out_path)
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with open(json_file) as f:
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return json.load(f)
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def save_speaker_mapping(out_path, speaker_mapping):
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"""Saves speaker mapping if not yet present."""
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if out_path is not None:
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speakers_json_path = make_speakers_json_path(out_path)
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with open(speakers_json_path, "w") as f:
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json.dump(speaker_mapping, f, indent=4)
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def get_speaker_manager(c, args, meta_data_train):
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"""Inititalize and return a `SpeakerManager` based on config values"""
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speaker_manager = SpeakerManager()
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if c.use_speaker_embedding:
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speaker_manager.set_speaker_ids_from_data(meta_data_train)
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if args.restore_path:
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# restoring speaker manager from a previous run.
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if c.use_external_speaker_embedding_file:
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# restore speaker manager with the embedding file
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speakers_file = os.path.dirname(args.restore_path)
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if not os.path.exists(speakers_file):
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print(
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"WARNING: speakers.json was not found in restore_path, trying to use CONFIG.external_speaker_embedding_file"
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)
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if not os.path.exists(c.external_speaker_embedding_file):
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raise RuntimeError(
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"You must copy the file speakers.json to restore_path, or set a valid file in CONFIG.external_speaker_embedding_file"
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)
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speaker_manager.load_x_vectors_file(c.external_speaker_embedding_file)
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speaker_manager.set_x_vectors_from_file(speakers_file)
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elif not c.use_external_speaker_embedding_file: # restor speaker manager with speaker ID file.
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speakers_file = os.path.dirname(args.restore_path)
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speaker_ids_from_data = speaker_manager.speaker_ids
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speaker_manager.set_speaker_ids_from_file(speakers_file)
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assert all(
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speaker in speaker_manager.speaker_ids for speaker in speaker_ids_from_data
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), " [!] You cannot introduce new speakers to a pre-trained model."
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elif c.use_external_speaker_embedding_file and c.external_speaker_embedding_file:
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# new speaker manager with external speaker embeddings.
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speaker_manager.set_x_vectors_from_file(c.external_speaker_embedding_file)
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elif (
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c.use_external_speaker_embedding_file and not c.external_speaker_embedding_file
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): # new speaker manager with speaker IDs file.
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raise "use_external_speaker_embedding_file is True, so you need pass a external speaker embedding file, run GE2E-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb or AngularPrototypical-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb notebook in notebooks/ folder"
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print(
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" > Training with {} speakers: {}".format(
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speaker_manager.num_speakers, ", ".join(speaker_manager.speaker_ids)
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)
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)
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return speaker_manager
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class SpeakerManager:
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"""It manages the multi-speaker setup for 🐸TTS models. It loads the speaker files and parses the information
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in a way that you can query. There are 3 different scenarios considered.
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@ -64,24 +130,24 @@ class SpeakerManager:
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self.speaker_encoder_ap = None
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if data_items:
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self.speaker_ids = self.parse_speakers()
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self.speaker_ids, _ = self.parse_speakers_from_data(self.data_items)
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if x_vectors_file_path:
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self.load_x_vectors_file(x_vectors_file_path)
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self.set_x_vectors_from_file(x_vectors_file_path)
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if speaker_id_file_path:
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self.load_ids_file(speaker_id_file_path)
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self.set_speaker_ids_from_file(speaker_id_file_path)
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if encoder_model_path and encoder_config_path:
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self.init_speaker_encoder(encoder_model_path, encoder_config_path)
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@staticmethod
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def _load_json(json_file_path: str):
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def _load_json(json_file_path: str) -> Dict:
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with open(json_file_path) as f:
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return json.load(f)
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@staticmethod
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def _save_json(json_file_path: str, data: dict):
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def _save_json(json_file_path: str, data: dict) -> None:
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with open(json_file_path, "w") as f:
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json.dump(data, f, indent=4)
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@ -91,35 +157,101 @@ class SpeakerManager:
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@property
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def x_vector_dim(self):
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return len(self.x_vectors[list(self.x_vectors.keys())[0]]["embedding"])
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"""Dimensionality of x_vectors. If x_vectors are not loaded, returns zero."""
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if self.x_vectors:
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return len(self.x_vectors[list(self.x_vectors.keys())[0]]["embedding"])
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return 0
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def parse_speakers_from_items(self, items: list):
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@staticmethod
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def parse_speakers_from_data(items: list) -> Tuple[Dict, int]:
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"""Parse speaker IDs from data samples retured by `load_meta_data()`.
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Args:
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items (list): Data sampled returned by `load_meta_data()`.
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Returns:
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Tuple[Dict, int]: speaker IDs and number of speakers.
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"""
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speakers = sorted({item[2] for item in items})
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self.speaker_ids = {name: i for i, name in enumerate(speakers)}
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num_speakers = len(self.speaker_ids)
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return self.speaker_ids, num_speakers
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speaker_ids = {name: i for i, name in enumerate(speakers)}
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num_speakers = len(speaker_ids)
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return speaker_ids, num_speakers
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def save_ids_file(self, file_path: str):
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self._save_json(file_path, self.speaker_ids)
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def set_speaker_ids_from_data(self, items: List) -> None:
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"""Set speaker IDs from data samples.
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def load_ids_file(self, file_path: str):
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Args:
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items (List): Data sampled returned by `load_meta_data()`.
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"""
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self.speaker_ids, _ = self.parse_speakers_from_data(items)
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def set_speaker_ids_from_file(self, file_path: str) -> None:
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"""Set speaker 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.speaker_ids = self._load_json(file_path)
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def save_x_vectors_file(self, file_path: str):
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def save_speaker_ids_to_file(self, file_path: str) -> None:
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"""Save speaker 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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self._save_json(file_path, self.speaker_ids)
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def save_x_vectors_to_file(self, file_path: str) -> None:
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"""Save x_vectors 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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self._save_json(file_path, self.x_vectors)
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def load_x_vectors_file(self, file_path: str):
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def set_x_vectors_from_file(self, file_path: str) -> None:
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"""Load x_vectors 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.x_vectors = self._load_json(file_path)
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self.speaker_ids = list(set(sorted(x["name"] for x in self.x_vectors.values())))
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self.clip_ids = list(set(sorted(clip_name for clip_name in self.x_vectors.keys())))
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def get_x_vector_by_clip(self, clip_idx: str):
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def get_x_vector_by_clip(self, clip_idx: str) -> List:
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"""Get x_vector 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: x_vector as a list.
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"""
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return self.x_vectors[clip_idx]["embedding"]
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def get_x_vectors_by_speaker(self, speaker_idx: str):
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def get_x_vectors_by_speaker(self, speaker_idx: str) -> List[List]:
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"""Get all x_vectors of a speaker.
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Args:
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speaker_idx (str): Target speaker ID.
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Returns:
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List[List]: all the x_vectors of the given speaker.
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"""
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return [x["embedding"] for x in self.x_vectors.values() if x["name"] == speaker_idx]
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def get_mean_x_vector(self, speaker_idx: str, num_samples: int = None, randomize: bool = False):
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def get_mean_x_vector(self, speaker_idx: str, num_samples: int = None, randomize: bool = False) -> np.Array:
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"""Get mean x_vector of a speaker ID.
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Args:
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speaker_idx (str): Target speaker ID.
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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 x_vectors. Defaults to False.
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Returns:
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np.Array: Mean x_vector.
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"""
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x_vectors = self.get_x_vectors_by_speaker(speaker_idx)
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if num_samples is None:
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x_vectors = np.stack(x_vectors).mean(0)
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@ -131,13 +263,19 @@ class SpeakerManager:
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x_vectors = np.stack(x_vectors[:num_samples]).mean(0)
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return x_vectors
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def get_speakers(self):
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def get_speakers(self) -> List:
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return self.speaker_ids
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def get_clips(self):
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def get_clips(self) -> List:
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return sorted(self.x_vectors.keys())
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def init_speaker_encoder(self, model_path: str, config_path: str) -> 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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"""
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self.speaker_encoder_config = load_config(config_path)
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self.speaker_encoder = setup_model(self.speaker_encoder_config)
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self.speaker_encoder.load_checkpoint(config_path, model_path, True)
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@ -147,6 +285,15 @@ class SpeakerManager:
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self.speaker_encoder_ap.do_trim_silence = True
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def compute_x_vector_from_clip(self, wav_file: Union[str, list]) -> list:
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"""Compute a x_vector from a given audio file.
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Args:
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wav_file (Union[str, list]): Target file path.
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Returns:
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list: Computed x_vector.
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"""
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def _compute(wav_file: str):
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waveform = self.speaker_encoder_ap.load_wav(wav_file, sr=self.speaker_encoder_ap.sample_rate)
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spec = self.speaker_encoder_ap.melspectrogram(waveform)
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@ -168,7 +315,15 @@ class SpeakerManager:
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x_vector = _compute(wav_file)
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return x_vector[0].tolist()
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def compute_x_vector(self, feats):
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def compute_x_vector(self, feats: Union[torch.Tensor, np.Array]) -> List:
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"""Compute x_vector from features.
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Args:
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feats (Union[torch.Tensor, np.Array]): Input features.
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Returns:
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List: computed x_vector.
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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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