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