mirror of https://github.com/coqui-ai/TTS.git
Update `speaker_manager`
This commit is contained in:
Eren Gölge
parent
8182f5168f
commit
0e01c2594f
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@ -5,91 +5,13 @@ from typing import Any, Dict, List, Tuple, Union
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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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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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if os.path.exists(path_restore):
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return path_restore
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if os.path.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 open(json_file) 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 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, restore_path, meta_data_train, out_path=None):
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"""Inititalize and return a `SpeakerManager` based on config values"""
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speaker_manager = SpeakerManager()
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if c.use_speaker_embedding:
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speaker_manager.set_speaker_ids_from_data(meta_data_train)
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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_external_speaker_embedding_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(
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"WARNING: speakers.json was not found in restore_path, trying to use CONFIG.external_speaker_embedding_file"
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)
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if not os.path.exists(c.external_speaker_embedding_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.external_speaker_embedding_file"
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)
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speaker_manager.load_d_vectors_file(c.external_speaker_embedding_file)
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speaker_manager.set_d_vectors_from_file(speakers_file)
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elif not c.use_external_speaker_embedding_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_external_speaker_embedding_file and c.external_speaker_embedding_file:
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# new speaker manager with external speaker embeddings.
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speaker_manager.set_d_vectors_from_file(c.external_speaker_embedding_file)
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elif (
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c.use_external_speaker_embedding_file and not c.external_speaker_embedding_file
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): # new speaker manager with speaker IDs file.
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raise "use_external_speaker_embedding_file is True, so you need pass a external speaker embedding file, run GE2E-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb or AngularPrototypical-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb notebook in notebooks/ folder"
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print(
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" > Training 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_external_speaker_embedding_file and c.external_speaker_embedding_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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class SpeakerManager:
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"""It manages the multi-speaker setup for 🐸TTS models. It loads the speaker files and parses the information
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in a way that you can query. There are 3 different scenarios considered.
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@ -356,3 +278,90 @@ class SpeakerManager:
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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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if os.path.exists(path_restore):
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return path_restore
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if os.path.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 open(json_file) 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 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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"""Create a SpeakerManager instance based on provided configuration.
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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:
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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: # new speaker manager with speaker IDs file.
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raise "use_d_vector_file is True, so you need pass a external speaker embedding file, run GE2E-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb or AngularPrototypical-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb notebook in notebooks/ folder"
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print(
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" > Training 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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