mirror of https://github.com/coqui-ai/TTS.git
Improve runtime of __parse_items() from O(|speakers|*|items|) to O(|items|)
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mueller
parent
d733b90255
commit
c909ca3855
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@ -1,6 +1,6 @@
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{
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"run_name": "Model compatible to CorentinJ/Real-Time-Voice-Cloning",
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"run_name": "mueller91",
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"run_description": "train speaker encoder with voxceleb1, voxceleb2 and libriSpeech ",
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"audio":{
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// Audio processing parameters
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@ -41,7 +41,7 @@
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
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"print_step": 1, // Number of steps to log traning on console.
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"output_path": "../../checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
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"output_path": "../../MozillaTTSOutput/checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
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"model": {
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"input_dim": 40,
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"proj_dim": 256,
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@ -52,8 +52,38 @@
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"datasets":
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[
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{
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"name": "vctk",
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"path": "../../../datasets/VCTK-Corpus-removed-silence/",
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"name": "voxceleb1",
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"path": "../../audio-datasets/en/voxceleb1/",
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"meta_file_train": null,
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"meta_file_val": null
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},
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// {
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// "name": "voxceleb2",
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// "path": "../../audio-datasets/en/voxceleb2/",
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// "meta_file_train": null,
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// "meta_file_val": null
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// },
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// {
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// "name": "vctk",
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// "path": "../../audio-datasets/en/VCTK-Corpus/",
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// "meta_file_train": null,
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// "meta_file_val": null
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// },
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// {
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// "name": "libri_tts",
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// "path": "../../audio-datasets/en/LibriTTS/train-clean-100",
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// "meta_file_train": null,
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// "meta_file_val": null
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// },
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// {
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// "name": "libri_tts",
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// "path": "../../audio-datasets/en/LibriTTS/train-clean-360",
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// "meta_file_train": null,
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// "meta_file_val": null
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// },
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{
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"name": "libri_tts",
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"path": "../../audio-datasets/en/LibriTTS/train-other-500",
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"meta_file_train": null,
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"meta_file_val": null
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}
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@ -2,6 +2,7 @@ import numpy as np
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import torch
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import random
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from torch.utils.data import Dataset
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from tqdm import tqdm
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class MyDataset(Dataset):
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@ -53,6 +54,7 @@ class MyDataset(Dataset):
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def __parse_items(self):
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self.speaker_to_utters = {}
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for i in self.items:
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text_ = i[0]
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path_ = i[1]
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speaker_ = i[2]
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if speaker_ in self.speaker_to_utters.keys():
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@ -60,11 +62,11 @@ class MyDataset(Dataset):
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else:
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self.speaker_to_utters[speaker_] = [path_, ]
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if self.skip_speakers:
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self.speaker_to_utters = {k: v for (k, v) in self.speaker_to_utters.items() if
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len(v) >= self.num_utter_per_speaker}
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if self.skip_speakers:
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self.speaker_to_utters = {k: v for (k, v) in self.speaker_to_utters.items() if
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len(v) >= self.num_utter_per_speaker}
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self.speakers = [k for (k, v) in self.speaker_to_utters]
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self.speakers = [k for (k, v) in self.speaker_to_utters.items()]
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# def __parse_items(self):
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# """
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@ -2,6 +2,10 @@ import os
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from glob import glob
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import re
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import sys
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from pathlib import Path
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from tqdm import tqdm
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from TTS.tts.utils.generic_utils import split_dataset
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@ -16,6 +20,7 @@ def load_meta_data(datasets):
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preprocessor = get_preprocessor_by_name(name)
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meta_data_train = preprocessor(root_path, meta_file_train)
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print(f"Found {len(meta_data_train)} files in {Path(root_path).absolute()}")
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if meta_file_val is None:
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meta_data_eval, meta_data_train = split_dataset(meta_data_train)
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else:
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@ -187,7 +192,7 @@ def libri_tts(root_path, meta_files=None):
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cols = line.split('\t')
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wav_file = os.path.join(_root_path, cols[0] + '.wav')
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text = cols[1]
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items.append([text, wav_file, speaker_name])
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items.append([text, wav_file, 'LTTS_' + speaker_name])
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for item in items:
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assert os.path.exists(
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item[1]), f" [!] wav files don't exist - {item[1]}"
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@ -235,8 +240,7 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
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"""homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
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test_speakers = meta_files
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items = []
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meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt",
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recursive=True)
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meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
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for meta_file in meta_files:
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_, speaker_id, txt_file = os.path.relpath(meta_file,
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root_path).split(os.sep)
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@ -247,8 +251,50 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
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continue
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with open(meta_file) as file_text:
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text = file_text.readlines()[0]
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wav_file = os.path.join(root_path, wavs_path, speaker_id,
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wav_file = os.path.join(root_path, wavs_path, 'VCTK_' + speaker_id,
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file_id + '.wav')
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items.append([text, wav_file, speaker_id])
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return items
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# ======================================== VOX CELEB ===========================================
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def voxceleb2(root_path, meta_file):
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"""
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:param meta_file Used only for consistency with load_meta_data api
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"""
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return _voxcel_x(root_path, voxcel_idx="2")
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def voxceleb1(root_path, meta_file):
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"""
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:param meta_file Used only for consistency with load_meta_data api
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"""
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return _voxcel_x(root_path, voxcel_idx="1")
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def _voxcel_x(root_path, voxcel_idx):
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assert voxcel_idx in ["1", "2"]
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expected_count = 148_000 if voxcel_idx == "1" else 1_000_000
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voxceleb_path = Path(root_path)
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cache_to = voxceleb_path / f"metafile_voxceleb{voxcel_idx}.csv"
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cache_to.parent.mkdir(exist_ok=True)
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# if not exists meta file, crawl recursively for 'wav' files
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if not cache_to.exists():
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cnt = 0
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meta_data = ""
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wav_files = voxceleb_path.rglob("**/*.wav")
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for path in tqdm(wav_files, desc=f"Building VoxCeleb {voxcel_idx} Meta file ... this needs to be done only once.",
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total=expected_count):
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speaker_id = str(Path(path).parent.parent.stem)
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assert speaker_id.startswith('id')
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text = None # VoxCel does not provide transciptions, and they are not needed for training the SE
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meta_data += f"{text}|{path}|voxcel{voxcel_idx}_{speaker_id}\n"
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cnt += 1
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with open(str(cache_to), 'w') as f:
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f.write(meta_data)
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if cnt < expected_count:
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raise ValueError(f"Found too few instances for Voxceleb. Should be around {expected_count}, is: {cnt}")
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with open(str(cache_to), 'r') as f:
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return [x.strip().split('|') for x in f.readlines()]
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