mirror of https://github.com/coqui-ai/TTS.git
480 lines
18 KiB
Python
480 lines
18 KiB
Python
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 get_from_config_or_model_args_with_default, load_config
|
|
from TTS.encoder.utils.generic_utils import setup_encoder_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_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,
|
|
):
|
|
|
|
self.embeddings = {}
|
|
self.ids = {}
|
|
self.embeddings_by_names = {}
|
|
self.clip_ids = []
|
|
self.encoder = None
|
|
self.encoder_ap = None
|
|
self.use_cuda = use_cuda
|
|
|
|
if data_items:
|
|
self.ids, _ = self.parse_from_data(data_items)
|
|
|
|
if d_vectors_file_path:
|
|
self.set_embeddings_from_file(d_vectors_file_path)
|
|
|
|
if speaker_id_file_path:
|
|
self.set_ids_from_file(speaker_id_file_path)
|
|
|
|
if encoder_model_path and encoder_config_path:
|
|
self.init_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.ids)
|
|
|
|
@property
|
|
def speaker_names(self):
|
|
return list(self.ids.keys())
|
|
|
|
@property
|
|
def embedding_dim(self):
|
|
"""Dimensionality of embeddings. If embeddings are not loaded, returns zero."""
|
|
if self.embeddings:
|
|
return len(self.embeddings[list(self.embeddings.keys())[0]]["embedding"])
|
|
return 0
|
|
|
|
@staticmethod
|
|
def parse_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["speaker_name"] 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_ids_from_data(self, items: List) -> None:
|
|
"""Set IDs from data samples.
|
|
|
|
Args:
|
|
items (List): Data sampled returned by `load_tts_samples()`.
|
|
"""
|
|
self.ids, _ = self.parse_from_data(items)
|
|
|
|
def set_ids_from_file(self, file_path: str) -> None:
|
|
"""Set speaker IDs from a file.
|
|
|
|
Args:
|
|
file_path (str): Path to the file.
|
|
"""
|
|
self.ids = self._load_json(file_path)
|
|
|
|
def save_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.ids)
|
|
|
|
def save_embeddings_to_file(self, file_path: str) -> None:
|
|
"""Save embeddings to a json file.
|
|
|
|
Args:
|
|
file_path (str): Path to the output file.
|
|
"""
|
|
self._save_json(file_path, self.embeddings)
|
|
|
|
def set_embeddings_from_file(self, file_path: str) -> None:
|
|
"""Load embeddings from a json file.
|
|
|
|
Args:
|
|
file_path (str): Path to the target json file.
|
|
"""
|
|
self.embeddings = self._load_json(file_path)
|
|
|
|
speakers = sorted({x["name"] for x in self.embeddings.values()})
|
|
self.ids = {name: i for i, name in enumerate(speakers)}
|
|
|
|
self.clip_ids = list(set(sorted(clip_name for clip_name in self.embeddings.keys())))
|
|
# cache embeddings_by_names for fast inference using a bigger speakers.json
|
|
self.embeddings_by_names = self.get_embeddings_by_names()
|
|
|
|
def get_embedding_by_clip(self, clip_idx: str) -> List:
|
|
"""Get embedding by clip ID.
|
|
|
|
Args:
|
|
clip_idx (str): Target clip ID.
|
|
|
|
Returns:
|
|
List: embedding as a list.
|
|
"""
|
|
return self.embeddings[clip_idx]["embedding"]
|
|
|
|
def get_embeddings_by_name(self, idx: str) -> List[List]:
|
|
"""Get all embeddings of a speaker.
|
|
|
|
Args:
|
|
idx (str): Target name.
|
|
|
|
Returns:
|
|
List[List]: all the embeddings of the given speaker.
|
|
"""
|
|
return self.embeddings_by_names[idx]
|
|
|
|
def get_embeddings_by_names(self) -> Dict:
|
|
"""Get all embeddings by names.
|
|
|
|
Returns:
|
|
Dict: all the embeddings of each speaker.
|
|
"""
|
|
embeddings_by_names = {}
|
|
for x in self.embeddings.values():
|
|
if x["name"] not in embeddings_by_names.keys():
|
|
embeddings_by_names[x["name"]] = [x["embedding"]]
|
|
else:
|
|
embeddings_by_names[x["name"]].append(x["embedding"])
|
|
return embeddings_by_names
|
|
|
|
def get_mean_embedding(self, idx: str, num_samples: int = None, randomize: bool = False) -> np.ndarray:
|
|
"""Get mean embedding of a idx.
|
|
|
|
Args:
|
|
idx (str): Target name.
|
|
num_samples (int, optional): Number of samples to be averaged. Defaults to None.
|
|
randomize (bool, optional): Pick random `num_samples` of embeddings. Defaults to False.
|
|
|
|
Returns:
|
|
np.ndarray: Mean embedding.
|
|
"""
|
|
embeddings = self.get_embeddings_by_name(idx)
|
|
if num_samples is None:
|
|
embeddings = np.stack(embeddings).mean(0)
|
|
else:
|
|
assert len(embeddings) >= num_samples, f" [!] {idx} has number of samples < {num_samples}"
|
|
if randomize:
|
|
embeddings = np.stack(random.choices(embeddings, k=num_samples)).mean(0)
|
|
else:
|
|
embeddings = np.stack(embeddings[:num_samples]).mean(0)
|
|
return embeddings
|
|
|
|
def get_random_speaker_id(self) -> Any:
|
|
"""Get a random embedding.
|
|
|
|
Args:
|
|
|
|
Returns:
|
|
np.ndarray: embedding.
|
|
"""
|
|
if self.ids:
|
|
return self.ids[random.choices(list(self.ids.keys()))[0]]
|
|
|
|
return None
|
|
|
|
def get_random_embedding(self) -> Any:
|
|
"""Get a random embedding.
|
|
|
|
Args:
|
|
|
|
Returns:
|
|
np.ndarray: embedding.
|
|
"""
|
|
if self.embeddings:
|
|
return self.embeddings[random.choices(list(self.embeddings.keys()))[0]]["embedding"]
|
|
|
|
return None
|
|
|
|
def get_speakers(self) -> List:
|
|
return self.ids
|
|
|
|
def get_clips(self) -> List:
|
|
return sorted(self.embeddings.keys())
|
|
|
|
def init_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.encoder_config = load_config(config_path)
|
|
self.encoder = setup_encoder_model(self.encoder_config)
|
|
self.speaker_encoder_criterion = self.speaker_encoder.load_checkpoint(
|
|
self.encoder_config, model_path, eval=True, use_cuda=self.use_cuda
|
|
)
|
|
self.encoder_ap = AudioProcessor(**self.encoder_config.audio)
|
|
|
|
def compute_embedding_from_clip(self, wav_file: Union[str, List[str]]) -> list:
|
|
"""Compute a embedding from a given audio file.
|
|
|
|
Args:
|
|
wav_file (Union[str, List[str]]): Target file path.
|
|
|
|
Returns:
|
|
list: Computed embedding.
|
|
"""
|
|
|
|
def _compute(wav_file: str):
|
|
waveform = self.encoder_ap.load_wav(wav_file, sr=self.encoder_ap.sample_rate)
|
|
if not self.encoder_config.model_params.get("use_torch_spec", False):
|
|
m_input = self.encoder_ap.melspectrogram(waveform)
|
|
m_input = torch.from_numpy(m_input)
|
|
else:
|
|
m_input = torch.from_numpy(waveform)
|
|
|
|
if self.use_cuda:
|
|
m_input = m_input.cuda()
|
|
m_input = m_input.unsqueeze(0)
|
|
embedding = self.encoder.compute_embedding(m_input)
|
|
return embedding
|
|
|
|
if isinstance(wav_file, list):
|
|
# compute the mean embedding
|
|
embeddings = None
|
|
for wf in wav_file:
|
|
embedding = _compute(wf)
|
|
if embeddings is None:
|
|
embeddings = embedding
|
|
else:
|
|
embeddings += embedding
|
|
return (embeddings / len(wav_file))[0].tolist()
|
|
embedding = _compute(wav_file)
|
|
return embedding[0].tolist()
|
|
|
|
def compute_embedding(self, feats: Union[torch.Tensor, np.ndarray]) -> List:
|
|
"""Compute embedding from features.
|
|
|
|
Args:
|
|
feats (Union[torch.Tensor, np.ndarray]): Input features.
|
|
|
|
Returns:
|
|
List: computed embedding.
|
|
"""
|
|
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.encoder.compute_embedding(feats)
|
|
|
|
def run_umap(self):
|
|
# TODO: implement speaker encoder
|
|
raise NotImplementedError
|
|
|
|
def plot_embeddings(self):
|
|
# TODO: implement speaker encoder
|
|
raise NotImplementedError
|
|
|
|
@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):
|
|
if get_from_config_or_model_args_with_default(config, "speakers_file", None):
|
|
speaker_manager = SpeakerManager(
|
|
d_vectors_file_path=get_from_config_or_model_args_with_default(config, "speaker_file", None)
|
|
)
|
|
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)
|
|
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_file(c.d_vector_file)
|
|
speaker_manager.set_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.ids
|
|
speaker_manager.set_ids_from_file(speakers_file)
|
|
assert all(
|
|
speaker in speaker_manager.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_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.set_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.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_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()
|