File size: 4,137 Bytes
70f229b | 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 | import torch
import torchaudio
import argparse
import os
import sys
from tqdm import tqdm
from underthesea import sent_tokenize
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
def main():
# Parse command line arguments
parser = argparse.ArgumentParser(description='Text-to-Speech using XTTS model')
parser.add_argument('--text', '-t', type=str, required=True,
help='Text to synthesize')
parser.add_argument('--speaker', '-s', type=str, required=True,
help='Path to speaker audio file')
parser.add_argument('--language', '-l', type=str, required=True,
help='Language code (e.g., "multi", "en", "es", etc.)')
parser.add_argument('--output', '-o', type=str, default='output.wav',
help='Output audio file name (default: output.wav)')
parser.add_argument('--model-checkpoint', type=str,
default='../export_checkpoint/best_model.pth',
help='Path to model checkpoint')
parser.add_argument('--model-config', type=str,
default='../export_checkpoint/XTTS_v2.0_original_model_files/config.json',
help='Path to model config file')
parser.add_argument('--model-vocab', type=str,
default='../export_checkpoint/XTTS_v2.0_original_model_files/vocab.json',
help='Path to model vocabulary file')
args = parser.parse_args()
# Validate inputs
if not os.path.exists(args.speaker):
print(f"Error: Speaker audio file not found: {args.speaker}")
sys.exit(1)
if not os.path.exists(args.model_checkpoint):
print(f"Error: Model checkpoint not found: {args.model_checkpoint}")
sys.exit(1)
if not os.path.exists(args.model_config):
print(f"Error: Model config not found: {args.model_config}")
sys.exit(1)
if not os.path.exists(args.model_vocab):
print(f"Error: Model vocab not found: {args.model_vocab}")
sys.exit(1)
# Device configuration
device = "cuda:0" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
# Load model
print("Loading model...")
config = XttsConfig()
config.load_json(args.model_config)
XTTS_MODEL = Xtts.init_from_config(config)
XTTS_MODEL.load_checkpoint(config, checkpoint_path=args.model_checkpoint,
vocab_path=args.model_vocab, use_deepspeed=False)
XTTS_MODEL.to(device)
print("Model loaded successfully!")
# Get conditioning latents from speaker audio
print("Processing speaker audio...")
gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(
audio_path=args.speaker,
gpt_cond_len=XTTS_MODEL.config.gpt_cond_len,
max_ref_length=XTTS_MODEL.config.max_ref_len,
sound_norm_refs=XTTS_MODEL.config.sound_norm_refs,
)
# Tokenize text into sentences
tts_texts = sent_tokenize(args.text)
print(f"Processing {len(tts_texts)} sentences...")
# Generate audio for each sentence
wav_chunks = []
for text in tqdm(tts_texts, desc="Generating audio"):
wav_chunk = XTTS_MODEL.inference(
text=text,
language=args.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"]))
# Concatenate all audio chunks
out_wav = torch.cat(wav_chunks, dim=0).unsqueeze(0).cpu()
# Save the output wav
print(f"Saving audio to: {args.output}")
torchaudio.save(
args.output,
out_wav,
XTTS_MODEL.config.audio.output_sample_rate,
encoding="PCM_S",
bits_per_sample=16,
)
print("Audio generation completed successfully!")
if __name__ == "__main__":
main() |