import json
import os
import random
from typing import Any, Dict, List, Tuple, Union
import fsspec
import numpy as np
import torch
from coqpit import Coqpit
from TTS.config import load_config
from TTS.speaker_encoder.utils.generic_utils import setup_model
from TTS.utils.audio import AudioProcessor
class SpeakerManager:
"""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'[<d_vector_values>]
},
...
}
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_d_vector(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,
):
self.data_items = []
self.d_vectors = {}
self.speaker_ids = {}
self.clip_ids = []
self.speaker_encoder = None
self.speaker_encoder_ap = None
self.use_cuda = use_cuda
if data_items:
self.speaker_ids, self.speaker_names, _ = self.parse_speakers_from_data(self.data_items)
if d_vectors_file_path:
self.set_d_vectors_from_file(d_vectors_file_path)
if speaker_id_file_path:
self.set_speaker_ids_from_file(speaker_id_file_path)
if encoder_model_path and encoder_config_path:
self.init_speaker_encoder(encoder_model_path, encoder_config_path)
@staticmethod
def _load_json(json_file_path: str) -> Dict:
with fsspec.open(json_file_path, "r") as f:
return json.load(f)
@staticmethod
def _save_json(json_file_path: str, data: dict) -> None:
with fsspec.open(json_file_path, "w") as f:
json.dump(data, f, indent=4)
@property
def num_speakers(self):
return len(self.speaker_ids)
@property
def speaker_names(self):
return list(self.speaker_ids.keys())
@property
def d_vector_dim(self):
"""Dimensionality of d_vectors. If d_vectors are not loaded, returns zero."""
if self.d_vectors:
return len(self.d_vectors[list(self.d_vectors.keys())[0]]["embedding"])
return 0
@staticmethod
def parse_speakers_from_data(items: list) -> Tuple[Dict, int]:
"""Parse speaker IDs from data samples retured by `load_tts_samples()`.
Args:
items (list): Data sampled returned by `load_tts_samples()`.
Returns:
Tuple[Dict, int]: speaker IDs and number of speakers.
"""
speakers = sorted({item[2] for item in items})
speaker_ids = {name: i for i, name in enumerate(speakers)}
num_speakers = len(speaker_ids)
return speaker_ids, num_speakers
def set_speaker_ids_from_data(self, items: List) -> None:
"""Set speaker IDs from data samples.
Args:
items (List): Data sampled returned by `load_tts_samples()`.
"""
self.speaker_ids, _ = self.parse_speakers_from_data(items)
def set_speaker_ids_from_file(self, file_path: str) -> None:
"""Set speaker IDs from a file.
Args:
file_path (str): Path to the file.
"""
self.speaker_ids = self._load_json(file_path)
def save_speaker_ids_to_file(self, file_path: str) -> None:
"""Save speaker IDs to a json file.
Args:
file_path (str): Path to the output file.
"""
self._save_json(file_path, self.speaker_ids)
def save_d_vectors_to_file(self, file_path: str) -> None:
"""Save d_vectors to a json file.
Args:
file_path (str): Path to the output file.
"""
self._save_json(file_path, self.d_vectors)
def set_d_vectors_from_file(self, file_path: str) -> None:
"""Load d_vectors from a json file.
Args:
file_path (str): Path to the target json file.
"""
self.d_vectors = self._load_json(file_path)
speakers = sorted({x["name"] for x in self.d_vectors.values()})
self.speaker_ids = {name: i for i, name in enumerate(speakers)}
self.clip_ids = list(set(sorted(clip_name for clip_name in self.d_vectors.keys())))
def get_d_vector_by_clip(self, clip_idx: str) -> List:
"""Get d_vector by clip ID.
Args:
clip_idx (str): Target clip ID.
Returns:
List: d_vector as a list.
"""
return self.d_vectors[clip_idx]["embedding"]
def get_d_vectors_by_speaker(self, speaker_idx: str) -> List[List]:
"""Get all d_vectors of a speaker.
Args:
speaker_idx (str): Target speaker ID.
Returns:
List[List]: all the d_vectors of the given speaker.
"""
return [x["embedding"] for x in self.d_vectors.values() if x["name"] == speaker_idx]
def get_mean_d_vector(self, speaker_idx: str, num_samples: int = None, randomize: bool = False) -> np.ndarray:
"""Get mean d_vector of a speaker ID.
Args:
speaker_idx (str): Target speaker ID.
num_samples (int, optional): Number of samples to be averaged. Defaults to None.
randomize (bool, optional): Pick random `num_samples` of d_vectors. Defaults to False.
Returns:
np.ndarray: Mean d_vector.
"""
d_vectors = self.get_d_vectors_by_speaker(speaker_idx)
if num_samples is None:
d_vectors = np.stack(d_vectors).mean(0)
else:
assert len(d_vectors) >= num_samples, f" [!] speaker {speaker_idx} has number of samples < {num_samples}"
if randomize:
d_vectors = np.stack(random.choices(d_vectors, k=num_samples)).mean(0)
else:
d_vectors = np.stack(d_vectors[:num_samples]).mean(0)
return d_vectors
def get_speakers(self) -> List:
return self.speaker_ids
def get_clips(self) -> List:
return sorted(self.d_vectors.keys())
def init_speaker_encoder(self, model_path: str, config_path: str) -> None:
"""Initialize a speaker encoder model.
Args:
model_path (str): Model file path.
config_path (str): Model config file path.
"""
self.speaker_encoder_config = load_config(config_path)
self.speaker_encoder = setup_model(self.speaker_encoder_config)
self.speaker_encoder.load_checkpoint(config_path, model_path, eval=True, use_cuda=self.use_cuda)
self.speaker_encoder_ap = AudioProcessor(**self.speaker_encoder_config.audio)
# normalize the input audio level and trim silences
# self.speaker_encoder_ap.do_sound_norm = True
# self.speaker_encoder_ap.do_trim_silence = True
def compute_d_vector_from_clip(self, wav_file: Union[str, list]) -> list:
"""Compute a d_vector from a given audio file.
Args:
wav_file (Union[str, list]): Target file path.
Returns:
list: Computed d_vector.
"""
def _compute(wav_file: str):
waveform = self.speaker_encoder_ap.load_wav(wav_file, sr=self.speaker_encoder_ap.sample_rate)
spec = self.speaker_encoder_ap.melspectrogram(waveform)
spec = torch.from_numpy(spec.T)
if self.use_cuda:
spec = spec.cuda()
spec = spec.unsqueeze(0)
d_vector = self.speaker_encoder.compute_embedding(spec)
return d_vector
if isinstance(wav_file, list):
# compute the mean d_vector
d_vectors = None
for wf in wav_file:
d_vector = _compute(wf)
if d_vectors is None:
d_vectors = d_vector
else:
d_vectors += d_vector
return (d_vectors / len(wav_file))[0].tolist()
d_vector = _compute(wav_file)
return d_vector[0].tolist()
def compute_d_vector(self, feats: Union[torch.Tensor, np.ndarray]) -> List:
"""Compute d_vector from features.
Args:
feats (Union[torch.Tensor, np.ndarray]): Input features.
Returns:
List: computed d_vector.
"""
if isinstance(feats, np.ndarray):
feats = torch.from_numpy(feats)
if feats.ndim == 2:
feats = feats.unsqueeze(0)
if self.use_cuda:
feats = feats.cuda()
return self.speaker_encoder.compute_embedding(feats)
def run_umap(self):
# TODO: implement speaker encoder
raise NotImplementedError
def plot_embeddings(self):
# TODO: implement speaker encoder
raise NotImplementedError
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_speaker_ids_from_data(data)
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_d_vectors_file(c.d_vector_file)
speaker_manager.set_d_vectors_from_file(speakers_file)
elif not c.use_d_vector_file: # restor speaker manager with speaker ID file.
speaker_ids_from_data = speaker_manager.speaker_ids
speaker_manager.set_speaker_ids_from_file(speakers_file)
assert all(
speaker in speaker_manager.speaker_ids 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.set_d_vectors_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.set_speaker_ids_from_file(c.speakers_file)
print(
" > Speaker manager is loaded with {} speakers: {}".format(
speaker_manager.num_speakers, ", ".join(speaker_manager.speaker_ids)
)
)
# 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_d_vectors_to_file(out_file_path)
else:
speaker_manager.save_speaker_ids_to_file(out_file_path)
return speaker_manager