test.py

import os
import torch
import torchaudio
import subprocess

# Set environment variables for CPU-only usage
os.environ['COQUI_TOS_AGREED'] = '1'
os.environ['NUMBA_DISABLE_JIT'] = '1'
os.environ['FORCE_CPU'] = 'true'
os.environ['CUDA_VISIBLE_DEVICES'] = ''

# Fix PyTorch weights_only issue for XTTS
import torch.serialization
from TTS.tts.configs.xtts_config import XttsConfig
torch.serialization.add_safe_globals([XttsConfig])

from TTS.api import TTS
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
from TTS.utils.generic_utils import get_user_data_dir

print("Testing XTTS C3PO voice cloning...")

# C3PO model path
model_path = "XTTS-v2_C3PO/"
config_path = "XTTS-v2_C3PO/config.json"

# Check if model files exist, if not download them
if not os.path.exists(config_path):
    print("C3PO model not found locally, downloading...")
    try:
        subprocess.run([
            "git", "clone", 
            "https://huggingface.co/Borcherding/XTTS-v2_C3PO",
            "XTTS-v2_C3PO"
        ], check=True)
        print("C3PO model downloaded successfully")
    except subprocess.CalledProcessError as e:
        print(f"Failed to download C3PO model: {e}")
        exit(1)

# Load configuration
config = XttsConfig()
config.load_json(config_path)

# Initialize and load model
model = Xtts.init_from_config(config)
model.load_checkpoint(
    config,
    checkpoint_path=os.path.join(model_path, "model.pth"),
    vocab_path=os.path.join(model_path, "vocab.json"),
    eval=True,
)

device = "cpu"  # Force CPU usage
print(f"C3PO model loaded on {device} (forced CPU mode)")

# Text to convert to speech
text = "Hello there! I am C-3PO, human-cyborg relations. How may I assist you today?"

# Look for reference audio in the C3PO model directory
reference_audio_path = None
for file in os.listdir(model_path):
    if file.endswith(('.wav', '.mp3', '.m4a')):
        reference_audio_path = os.path.join(model_path, file)
        print(f"Found C3PO reference audio: {file}")
        break

# If no reference audio found, create a simple test reference
if reference_audio_path is None:
    print("No reference audio found in C3PO model, creating test reference...")
    reference_audio_path = "test_reference.wav"
    
    # Generate a simple sine wave as placeholder
    import numpy as np
    sample_rate = 24000
    duration = 3  # seconds
    frequency = 440  # Hz
    t = np.linspace(0, duration, int(sample_rate * duration))
    audio_data = 0.3 * np.sin(2 * np.pi * frequency * t)
    
    # Save as WAV
    torchaudio.save(reference_audio_path, torch.tensor(audio_data).unsqueeze(0), sample_rate)
    print(f"Test reference audio created: {reference_audio_path}")

try:
    # Generate conditioning latents
    print("Processing reference audio...")
    gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
        audio_path=reference_audio_path,
        gpt_cond_len=30,
        gpt_cond_chunk_len=4,
        max_ref_length=60
    )
    
    # Generate speech
    print("Generating C3PO speech...")
    out = model.inference(
        text,
        "en",  # language
        gpt_cond_latent,
        speaker_embedding,
        repetition_penalty=5.0,
        temperature=0.75,
    )
    
    # Save output
    output_path = "c3po_test_output.wav"
    torchaudio.save(output_path, torch.tensor(out["wav"]).unsqueeze(0), 24000)
    print(f"C3PO speech generated successfully! Saved as: {output_path}")
    
    # Test multilingual capabilities
    print("\nTesting multilingual C3PO...")
    multilingual_tests = [
        ("es", "Hola, soy C-3PO. Domino más de seis millones de formas de comunicación."),
        ("fr", "Bonjour, je suis C-3PO. Je maîtrise plus de six millions de formes de communication."),
        ("de", "Hallo, ich bin C-3PO. Ich beherrsche über sechs Millionen Kommunikationsformen."),
    ]
    
    for lang, test_text in multilingual_tests:
        print(f"Generating {lang.upper()} speech...")
        out = model.inference(
            test_text,
            lang,
            gpt_cond_latent,
            speaker_embedding,
            repetition_penalty=5.0,
            temperature=0.75,
        )
        
        output_path = f"c3po_test_{lang}.wav"
        torchaudio.save(output_path, torch.tensor(out["wav"]).unsqueeze(0), 24000)
        print(f"C3PO {lang.upper()} speech saved as: {output_path}")
    
except Exception as e:
    print(f"Error during speech generation: {e}")
    import traceback
    traceback.print_exc()

print("XTTS C3PO test completed!")
print("\nGenerated files:")
for file in os.listdir("."):
    if file.startswith("c3po_test") and file.endswith(".wav"):
        print(f"  - {file}")