import json import os from typing import Any, Dict, List, Union 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 SpeakerManager(EmbeddingManager): """Manage the speakers for multi-speaker 🐸TTS models. Load a datafile and parse the information in a way that can be queried by speaker or clip. There are 3 different scenarios considered: 1. Models using speaker embedding layers. The datafile only maps speaker names to ids used by the embedding layer. 2. Models using d-vectors. The datafile includes a dictionary in the following format. :: { 'clip_name.wav':{ 'name': 'speakerA', 'embedding'[] }, ... } 3. Computing the d-vectors by the speaker encoder. It loads the speaker encoder model and computes the d-vectors for a given clip or speaker. Args: d_vectors_file_path (str, optional): Path to the metafile including x vectors. Defaults to "". speaker_id_file_path (str, optional): Path to the metafile that maps speaker names to ids used by TTS models. Defaults to "". encoder_model_path (str, optional): Path to the speaker encoder model file. Defaults to "". encoder_config_path (str, optional): Path to the spealer encoder config file. Defaults to "". Examples: >>> # load audio processor and speaker encoder >>> ap = AudioProcessor(**config.audio) >>> manager = SpeakerManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path) >>> # load a sample audio and compute embedding >>> waveform = ap.load_wav(sample_wav_path) >>> mel = ap.melspectrogram(waveform) >>> d_vector = manager.compute_embeddings(mel.T) """ def __init__( self, data_items: List[List[Any]] = None, d_vectors_file_path: str = "", speaker_id_file_path: str = "", encoder_model_path: str = "", encoder_config_path: str = "", use_cuda: bool = False, ): super().__init__( embedding_file_path=d_vectors_file_path, id_file_path=speaker_id_file_path, encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path, use_cuda=use_cuda, ) if data_items: self.set_ids_from_data(data_items, parse_key="speaker_name") @property def num_speakers(self): return len(self.name_to_id) @property def speaker_names(self): return list(self.name_to_id.keys()) def get_speakers(self) -> List: return self.name_to_id @staticmethod def init_from_config(config: "Coqpit", samples: Union[List[List], List[Dict]] = None) -> "SpeakerManager": """Initialize a speaker manager from config Args: config (Coqpit): Config object. samples (Union[List[List], List[Dict]], optional): List of data samples to parse out the speaker names. Defaults to None. Returns: SpeakerEncoder: Speaker encoder object. """ speaker_manager = None if get_from_config_or_model_args_with_default(config, "use_speaker_embedding", False): if samples: speaker_manager = SpeakerManager(data_items=samples) if get_from_config_or_model_args_with_default(config, "speaker_file", None): speaker_manager = SpeakerManager( speaker_id_file_path=get_from_config_or_model_args_with_default(config, "speaker_file", None) ) if get_from_config_or_model_args_with_default(config, "speakers_file", None): speaker_manager = SpeakerManager( speaker_id_file_path=get_from_config_or_model_args_with_default(config, "speakers_file", None) ) if get_from_config_or_model_args_with_default(config, "use_d_vector_file", False): speaker_manager = SpeakerManager() if get_from_config_or_model_args_with_default(config, "d_vector_file", None): speaker_manager = SpeakerManager( d_vectors_file_path=get_from_config_or_model_args_with_default(config, "d_vector_file", None) ) return speaker_manager def _set_file_path(path): """Find the speakers.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), "speakers.json") path_continue = os.path.join(path, "speakers.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" [!] `speakers.json` not found in {path}") def load_speaker_mapping(out_path): """Loads speaker 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_speaker_mapping(out_path, speaker_mapping): """Saves speaker mapping if not yet present.""" if out_path is not None: speakers_json_path = _set_file_path(out_path) with fsspec.open(speakers_json_path, "w") as f: json.dump(speaker_mapping, f, indent=4) def get_speaker_manager(c: Coqpit, data: List = None, restore_path: str = None, out_path: str = None) -> SpeakerManager: """Initiate a `SpeakerManager` instance by the provided config. Args: c (Coqpit): Model configuration. restore_path (str): Path to a previous training folder. data (List): Data samples used in training to infer speakers from. It must be provided if speaker embedding layers is used. Defaults to None. out_path (str, optional): Save the generated speaker IDs to a output path. Defaults to None. Returns: SpeakerManager: initialized and ready to use instance. """ speaker_manager = SpeakerManager() if c.use_speaker_embedding: if data is not None: speaker_manager.set_ids_from_data(data, parse_key="speaker_name") if restore_path: speakers_file = _set_file_path(restore_path) # restoring speaker manager from a previous run. if c.use_d_vector_file: # restore speaker manager with the embedding file if not os.path.exists(speakers_file): print("WARNING: speakers.json was not found in restore_path, trying to use CONFIG.d_vector_file") if not os.path.exists(c.d_vector_file): raise RuntimeError( "You must copy the file speakers.json to restore_path, or set a valid file in CONFIG.d_vector_file" ) speaker_manager.load_embeddings_from_file(c.d_vector_file) speaker_manager.load_embeddings_from_file(speakers_file) elif not c.use_d_vector_file: # restor speaker manager with speaker ID file. speaker_ids_from_data = speaker_manager.name_to_id speaker_manager.load_ids_from_file(speakers_file) assert all( speaker in speaker_manager.name_to_id for speaker in speaker_ids_from_data ), " [!] You cannot introduce new speakers to a pre-trained model." elif c.use_d_vector_file and c.d_vector_file: # new speaker manager with external speaker embeddings. speaker_manager.load_embeddings_from_file(c.d_vector_file) elif c.use_d_vector_file and not c.d_vector_file: raise "use_d_vector_file is True, so you need pass a external speaker embedding file." elif c.use_speaker_embedding and "speakers_file" in c and c.speakers_file: # new speaker manager with speaker IDs file. speaker_manager.load_ids_from_file(c.speakers_file) if speaker_manager.num_speakers > 0: print( " > Speaker manager is loaded with {} speakers: {}".format( speaker_manager.num_speakers, ", ".join(speaker_manager.name_to_id) ) ) # save file if path is defined if out_path: out_file_path = os.path.join(out_path, "speakers.json") print(f" > Saving `speakers.json` to {out_file_path}.") if c.use_d_vector_file and c.d_vector_file: speaker_manager.save_embeddings_to_file(out_file_path) else: speaker_manager.save_ids_to_file(out_file_path) return speaker_manager def get_speaker_balancer_weights(items: list): speaker_names = np.array([item["speaker_name"] for item in items]) unique_speaker_names = np.unique(speaker_names).tolist() speaker_ids = [unique_speaker_names.index(l) for l in speaker_names] speaker_count = np.array([len(np.where(speaker_names == l)[0]) for l in unique_speaker_names]) weight_speaker = 1.0 / speaker_count dataset_samples_weight = np.array([weight_speaker[l] for l in speaker_ids]) # normalize dataset_samples_weight = dataset_samples_weight / np.linalg.norm(dataset_samples_weight) return torch.from_numpy(dataset_samples_weight).float()