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e9a0669 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | import json
import os
import random
import pandas as pd
import numpy as np
import torch
import torchaudio
from tqdm import tqdm
from scipy.io.wavfile import write
import argparse
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
device = "cuda:0" if torch.cuda.is_available() else "cpu"
lang_codes = {
'English': 'en',
'Estonian': 'et',
'Russian': 'ru',
}
ref_metas = {
'en': '/scratch/project_465001704/data/eng/commonvoice/metadata.csv',
'et': '/scratch/project_465001704/data/est/commonvoice-14.0/metadata.csv',
'ru': '/scratch/project_465001704/data/rus/commonvoice-20.0/metadata.csv',
}
refs = {}
def load_ref_list(languages):
for language in languages:
refs[language] = pd.read_csv(ref_metas[language], sep='|')['audio_file'].tolist()
def create_xtts_trainer_parser():
parser = argparse.ArgumentParser(description="Arguments for XTTS runner")
parser.add_argument("--model_folder", type=str, default='/scratch/project_465001704/output/xtts-gpt/run/training/GPT_XTTS_FT-November-24-2025_01+29AM-8e59ec3', #required=True,
help="Path of model file")
parser.add_argument("--model_name", type=str, default='best_model', # required=True,
help="Name of model file")
parser.add_argument("--vocab_path", type=str, default='/project/project_465001704/rlellep/repos/XTTSv2-Finetuning-for-New-Languages/vocabs/vocab_et-100.json', # required=True,
help="Path of vocab file")
parser.add_argument("--languages", nargs='+', type=str, default=["en", "et"], # required=True,
help="language1 language2")
parser.add_argument("--dataset_meta", type=str, default='/scratch/project_465001704/data/to_synth_split/en_et-EOPC_00.jsonl', # required=True,
help="Path of metadata file")
parser.add_argument("--output_folder", type=str, default='/scratch/project_465001704/output/synth/en_et_EOPC_00', # required=True,
help="Path of output folder")
parser.add_argument("--stream", type=bool, default=True,
help="Run model in stream mode.")
parser.add_argument("--start_id", type=int, default=0)
return parser
def load_model(model_folder, model_name, vocab_file):
# Model paths
xtts_checkpoint = os.path.join(model_folder, f"{model_name}.pth")
xtts_config = os.path.join(model_folder, "config.json")
# Load model
config = XttsConfig()
config.load_json(xtts_config)
XTTS_MODEL = Xtts.init_from_config(config)
XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=vocab_file, use_deepspeed=False)
XTTS_MODEL.to(device)
return XTTS_MODEL
def get_random_reference(language):
while True:
ref_file = random.choice(refs[language])
try:
metadata = torchaudio.info(ref_file)
duration = metadata.num_frames / metadata.sample_rate
if duration > 1:
return ref_file
except Exception as e:
continue
def reference_latents_and_embedding(language):
ref_clip = get_random_reference(language)
return model.get_conditioning_latents(
audio_path=ref_clip,
gpt_cond_len=model.config.gpt_cond_len,
max_ref_length=model.config.max_ref_len,
sound_norm_refs=model.config.sound_norm_refs,
)
def perform_synthesis(model, gpt_cond_latent, speaker_embedding, text, language, stream=True):
wav_chunks = []
if stream:
for chunk in model.inference_stream(
text=text,
language=language,
gpt_cond_latent=gpt_cond_latent,
speaker_embedding=speaker_embedding,
temperature=0.1,
length_penalty=1.0,
repetition_penalty=10.0,
top_k=10,
top_p=0.3,
):
if chunk is not None:
wav_chunks.append(chunk)
else:
wav_chunk = model.inference(
text=text,
language=language,
gpt_cond_latent=gpt_cond_latent,
speaker_embedding=speaker_embedding,
temperature=0.1,
length_penalty=1.0,
repetition_penalty=10.0,
top_k=10,
top_p=0.3,
)
wav_chunks.append(torch.tensor(wav_chunk["wav"]))
out_wav = torch.cat(wav_chunks, dim=0).unsqueeze(0)[0].detach().cpu().numpy()
return out_wav
def write_output(clip, output_folder, language, id):
relative_path = os.path.join(language, f'{language}_{id:07}.wav')
write(os.path.join(output_folder, relative_path), 24000, clip)
return relative_path
if __name__ == "__main__":
parser = create_xtts_trainer_parser()
args = parser.parse_args()
src_metadata_1 = os.path.join(args.output_folder, f'{args.languages[0]}-src.csv')
src_metadata_2 = os.path.join(args.output_folder, f'{args.languages[1]}-src.csv')
tgt_metadata_1 = os.path.join(args.output_folder, f'{args.languages[0]}-tgt.csv')
tgt_metadata_2 = os.path.join(args.output_folder, f'{args.languages[1]}-tgt.csv')
need_header_src_1 = not os.path.exists(src_metadata_1)
need_header_src_2 = not os.path.exists(src_metadata_2)
need_header_tgt_1 = not os.path.exists(tgt_metadata_1)
need_header_tgt_2 = not os.path.exists(tgt_metadata_2)
ref_clips = load_ref_list(args.languages)
meta_paths = {}
for language in args.languages:
os.makedirs(os.path.join(args.output_folder, language), exist_ok=True)
model = load_model(model_folder=args.model_folder, model_name=args.model_name, vocab_file=args.vocab_path)
outer_id = int(args.output_folder[-2:]) * 100000
# 1. Open the file (using utf-8 to handle special characters like "õ")
with open(args.dataset_meta, 'r', encoding='utf-8', buffering=1) as source_file, \
open(src_metadata_1, 'a', encoding='utf-8', buffering=1) as f_src_1, \
open(src_metadata_2, 'a', encoding='utf-8', buffering=1) as f_src_2, \
open(tgt_metadata_1, 'a', encoding='utf-8', buffering=1) as f_tgt_1, \
open(tgt_metadata_2, 'a', encoding='utf-8', buffering=1) as f_tgt_2:
if need_header_src_1:
f_src_1.write("audio_file|text\n")
if need_header_src_2:
f_src_2.write("audio_file|text\n")
if need_header_tgt_1:
f_tgt_1.write("audio_file|text\n")
if need_header_tgt_2:
f_tgt_2.write("audio_file|text\n")
# 2. Iterate through the file line by line
id = 1
line = source_file.readline()
with tqdm() as pbar:
while line:
try:
if id <= args.start_id:
continue
# 3. Parse the current line into a dictionary
data = json.loads(line)
# 4. Assign values to variables as requested
src_segm = data.get('src_segm')
tgt_segm = data.get('tgt_segm')
if len(src_segm.split(" ")) < 3 or max(len(src_segm), len(tgt_segm)) > 400:
continue
src_lang = lang_codes[data.get('src_lang')]
tgt_lang = lang_codes[data.get('tgt_lang')]
gpt_cond_latent, speaker_embedding = reference_latents_and_embedding(src_lang)
# print(f"Source language: {src_lang}, source text: {src_segm}")
src_clip = perform_synthesis(model, gpt_cond_latent, speaker_embedding, src_segm, src_lang)
src_path = write_output(src_clip, args.output_folder, src_lang, id + outer_id)
if src_lang == args.languages[0]:
f_src_1.write('|'.join([src_path, src_segm]) + '\n')
else:
f_src_2.write('|'.join([src_path, src_segm]) + '\n')
# print(f"Target language: {tgt_lang}, target text: {tgt_segm}")
tgt_clip = perform_synthesis(model, gpt_cond_latent, speaker_embedding, tgt_segm, tgt_lang)
tgt_path = write_output(tgt_clip, args.output_folder, tgt_lang, id + outer_id)
if tgt_lang == args.languages[0]:
f_tgt_1.write('|'.join([tgt_path, tgt_segm]) + '\n')
else:
f_tgt_2.write('|'.join([tgt_path, tgt_segm]) + '\n')
except json.JSONDecodeError:
print(f"Skipping invalid JSON line: {line}")
finally:
if not id <= args.start_id and id % 100 == 0:
for f in (f_src_1, f_src_2, f_tgt_1, f_tgt_2):
f.flush()
id += 1
line = source_file.readline()
pbar.update(1)
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