mirror of https://github.com/coqui-ai/TTS.git
382 lines
14 KiB
Python
382 lines
14 KiB
Python
import json
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import os
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import random
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from typing import Any, Dict, List, Tuple, 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 load_config
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from TTS.speaker_encoder.utils.generic_utils import setup_model
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from TTS.utils.audio import AudioProcessor
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class SpeakerManager:
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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_d_vector(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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self.data_items = []
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self.d_vectors = {}
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self.speaker_ids = {}
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self.clip_ids = []
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self.speaker_encoder = None
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self.speaker_encoder_ap = None
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self.use_cuda = use_cuda
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if data_items:
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self.speaker_ids, self.speaker_names, _ = self.parse_speakers_from_data(self.data_items)
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if d_vectors_file_path:
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self.set_d_vectors_from_file(d_vectors_file_path)
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if 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) -> Dict:
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with fsspec.open(json_file_path, "r") 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) -> None:
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with fsspec.open(json_file_path, "w") as f:
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json.dump(data, f, indent=4)
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@property
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def num_speakers(self):
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return len(self.speaker_ids)
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@property
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def speaker_names(self):
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return list(self.speaker_ids.keys())
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@property
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def d_vector_dim(self):
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"""Dimensionality of d_vectors. If d_vectors are not loaded, returns zero."""
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if self.d_vectors:
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return len(self.d_vectors[list(self.d_vectors.keys())[0]]["embedding"])
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return 0
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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_tts_samples()`.
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Args:
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items (list): Data sampled returned by `load_tts_samples()`.
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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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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 set_speaker_ids_from_data(self, items: List) -> None:
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"""Set speaker 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.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_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_d_vectors_to_file(self, file_path: str) -> None:
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"""Save d_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.d_vectors)
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def set_d_vectors_from_file(self, file_path: str) -> None:
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"""Load d_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.d_vectors = self._load_json(file_path)
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speakers = sorted({x["name"] for x in self.d_vectors.values()})
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self.speaker_ids = {name: i for i, name in enumerate(speakers)}
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self.clip_ids = list(set(sorted(clip_name for clip_name in self.d_vectors.keys())))
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def get_d_vector_by_clip(self, clip_idx: str) -> List:
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"""Get d_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: d_vector as a list.
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"""
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return self.d_vectors[clip_idx]["embedding"]
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def get_d_vectors_by_speaker(self, speaker_idx: str) -> List[List]:
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"""Get all d_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 d_vectors of the given speaker.
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"""
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return [x["embedding"] for x in self.d_vectors.values() if x["name"] == speaker_idx]
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def get_mean_d_vector(self, speaker_idx: str, num_samples: int = None, randomize: bool = False) -> np.ndarray:
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"""Get mean d_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 d_vectors. Defaults to False.
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Returns:
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np.ndarray: Mean d_vector.
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"""
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d_vectors = self.get_d_vectors_by_speaker(speaker_idx)
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if num_samples is None:
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d_vectors = np.stack(d_vectors).mean(0)
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else:
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assert len(d_vectors) >= num_samples, f" [!] speaker {speaker_idx} has number of samples < {num_samples}"
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if randomize:
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d_vectors = np.stack(random.choices(d_vectors, k=num_samples)).mean(0)
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else:
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d_vectors = np.stack(d_vectors[:num_samples]).mean(0)
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return d_vectors
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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) -> List:
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return sorted(self.d_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, eval=True, use_cuda=self.use_cuda)
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self.speaker_encoder_ap = AudioProcessor(**self.speaker_encoder_config.audio)
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# normalize the input audio level and trim silences
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# self.speaker_encoder_ap.do_sound_norm = True
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# self.speaker_encoder_ap.do_trim_silence = True
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def compute_d_vector_from_clip(self, wav_file: Union[str, list]) -> list:
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"""Compute a d_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 d_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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spec = torch.from_numpy(spec.T)
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if self.use_cuda:
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spec = spec.cuda()
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spec = spec.unsqueeze(0)
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d_vector = self.speaker_encoder.compute_embedding(spec)
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return d_vector
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if isinstance(wav_file, list):
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# compute the mean d_vector
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d_vectors = None
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for wf in wav_file:
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d_vector = _compute(wf)
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if d_vectors is None:
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d_vectors = d_vector
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else:
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d_vectors += d_vector
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return (d_vectors / len(wav_file))[0].tolist()
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d_vector = _compute(wav_file)
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return d_vector[0].tolist()
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def compute_d_vector(self, feats: Union[torch.Tensor, np.ndarray]) -> List:
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"""Compute d_vector 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 d_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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feats = feats.unsqueeze(0)
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if self.use_cuda:
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feats = feats.cuda()
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return self.speaker_encoder.compute_embedding(feats)
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def run_umap(self):
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# TODO: implement speaker encoder
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raise NotImplementedError
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def plot_embeddings(self):
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# TODO: implement speaker encoder
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raise NotImplementedError
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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_speaker_ids_from_data(data)
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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_d_vectors_file(c.d_vector_file)
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speaker_manager.set_d_vectors_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.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_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.set_d_vectors_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.set_speaker_ids_from_file(c.speakers_file)
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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.speaker_ids)
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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_d_vectors_to_file(out_file_path)
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else:
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speaker_manager.save_speaker_ids_to_file(out_file_path)
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return speaker_manager
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