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39da493 | 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 | #!/usr/bin/env python3
"""
Fine-tune XTTS v2 for Nigerian Languages (Yoruba, Hausa, Igbo, Pidgin).
This script uses Coqui TTS to fine-tune the XTTS model for better
Nigerian language support.
"""
import os
import sys
import json
from pathlib import Path
import torch
# Check GPU
print("=" * 60)
print("XTTS Nigerian Languages Fine-tuning")
print("=" * 60)
print(f"\nPyTorch: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"GPU: {torch.cuda.get_device_name(0)}")
mem = torch.cuda.get_device_properties(0).total_memory / 1024**3
print(f"GPU Memory: {mem:.1f} GB")
else:
print("WARNING: No GPU found. Training will be very slow on CPU.")
BASE_DIR = Path.home() / "voice-training"
PREPARED_DIR = BASE_DIR / "prepared_data"
OPENSLR_DIR = BASE_DIR / "datasets" / "openslr_yoruba"
OUTPUT_DIR = BASE_DIR / "output"
def check_data():
"""Check available training data."""
print("\n=== Available Training Data ===")
total_files = 0
# Check Nigerian CV data
for lang in ["yoruba", "hausa", "igbo"]:
manifest_file = PREPARED_DIR / lang / "manifest.json"
if manifest_file.exists():
with open(manifest_file) as f:
data = json.load(f)
print(f" {lang.upper()}: {len(data)} samples")
total_files += len(data)
# Check OpenSLR Yoruba
if OPENSLR_DIR.exists():
wav_count = len(list(OPENSLR_DIR.glob("*.wav")))
print(f" OpenSLR Yoruba: {wav_count} high-quality WAV files")
total_files += wav_count
print(f"\nTotal audio files: {total_files}")
return total_files > 0
def prepare_xtts_dataset():
"""Prepare dataset in XTTS format."""
print("\n=== Preparing XTTS Dataset ===")
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
all_samples = []
# Load Nigerian CV manifests
for lang in ["yoruba", "hausa", "igbo"]:
manifest_file = PREPARED_DIR / lang / "manifest.json"
if manifest_file.exists():
with open(manifest_file) as f:
samples = json.load(f)
for s in samples:
s['lang_code'] = lang[:2] # yo, ha, ig
all_samples.extend(samples)
# Load OpenSLR Yoruba
if OPENSLR_DIR.exists():
tsv_file = OPENSLR_DIR / "line_index.tsv"
if tsv_file.exists():
with open(tsv_file, 'r', encoding='utf-8') as f:
for line in f:
parts = line.strip().split('\t')
if len(parts) >= 2:
wav_file = OPENSLR_DIR / f"{parts[0]}.wav"
if wav_file.exists():
all_samples.append({
"audio_file": str(wav_file),
"text": parts[1],
"language": "yoruba",
"lang_code": "yo"
})
# Save combined dataset
dataset_file = OUTPUT_DIR / "nigerian_tts_dataset.json"
with open(dataset_file, 'w', encoding='utf-8') as f:
json.dump(all_samples, f, indent=2, ensure_ascii=False)
print(f" Created dataset with {len(all_samples)} samples")
print(f" Saved to: {dataset_file}")
return all_samples
def run_xtts_finetuning():
"""Run XTTS fine-tuning using Coqui TTS."""
print("\n=== Starting XTTS Fine-tuning ===")
try:
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
from TTS.utils.manage import ModelManager
print(" TTS modules loaded successfully")
# Download base XTTS model
print(" Downloading base XTTS v2 model...")
model_manager = ModelManager()
# The model will be downloaded to ~/.local/share/tts/
model_path = model_manager.download_model("tts_models/multilingual/multi-dataset/xtts_v2")
print(f" Model path: {model_path}")
print("\n To fine-tune XTTS, use the Coqui TTS training recipes:")
print(" https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech/xtts_v2")
print("\n Or use the XTTS fine-tuning demo:")
print(" python -m TTS.demos.xtts_ft_demo")
return True
except Exception as e:
print(f" Error: {e}")
return False
def main():
if not check_data():
print("ERROR: No training data found!")
sys.exit(1)
samples = prepare_xtts_dataset()
if samples:
print("\n" + "=" * 60)
print("Dataset prepared! Next steps:")
print("=" * 60)
print(f"1. Dataset: {OUTPUT_DIR / 'nigerian_tts_dataset.json'}")
print(f"2. Total samples: {len(samples)}")
print("\nTo start training:")
print(" python -m TTS.demos.xtts_ft_demo")
print("\nOr for voice cloning (no training needed):")
print(" from TTS.api import TTS")
print(" tts = TTS('tts_models/multilingual/multi-dataset/xtts_v2')")
print(" tts.tts_to_file('Hello', speaker_wav='your_voice.wav', language='en')")
run_xtts_finetuning()
if __name__ == "__main__":
main()
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