Spaces:
Running
on
Zero
Running
on
Zero
File size: 17,311 Bytes
240a407 5cbfdab deb04b6 240a407 91d6893 240a407 189eecb 240a407 91d6893 deb04b6 74dbc75 91d6893 74dbc75 deb04b6 ee39abc 74dbc75 91d6893 74dbc75 91d6893 b32e924 74dbc75 91d6893 b32e924 91d6893 74dbc75 91d6893 74dbc75 91d6893 74dbc75 91d6893 240a407 91d6893 240a407 74dbc75 ee39abc 240a407 74dbc75 240a407 74dbc75 240a407 74dbc75 240a407 189eecb 240a407 74dbc75 91d6893 240a407 74dbc75 240a407 74dbc75 240a407 ee39abc 240a407 ee39abc 74dbc75 91d6893 ee39abc 7be21d2 91d6893 240a407 91d6893 74dbc75 91d6893 ee39abc 91d6893 ee39abc 91d6893 ee39abc 91d6893 ee39abc 91d6893 240a407 deb04b6 240a407 deb04b6 91d6893 240a407 91d6893 240a407 91d6893 ee39abc 91d6893 240a407 91d6893 deb04b6 91d6893 74dbc75 91d6893 ee39abc 91d6893 ee39abc 91d6893 240a407 ee39abc 240a407 deb04b6 240a407 91d6893 5961c78 7be21d2 91d6893 7be21d2 5961c78 91d6893 7be21d2 91d6893 7be21d2 240a407 5961c78 74dbc75 240a407 91d6893 240a407 91d6893 240a407 91d6893 5961c78 240a407 5961c78 5cbfdab deb04b6 74dbc75 ee39abc 74dbc75 ee39abc 74dbc75 ee39abc 74dbc75 ee39abc 74dbc75 5cbfdab 91d6893 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 |
import gradio as gr
import os
import traceback
import torch
import gc
from huggingface_hub import hf_hub_download
import shutil
import spaces
try:
from config import MODEL_REPO_ID, MODEL_FILES, LOCAL_MODEL_PATH
except ImportError:
MODEL_REPO_ID = "ramimu/chatterbox-voice-cloning-model"
LOCAL_MODEL_PATH = "./chatterbox_model_files"
MODEL_FILES = ["s3gen.pt", "t3_cfg.pt", "ve.pt", "tokenizer.json"]
try:
from chatterbox.tts import ChatterboxTTS
chatterbox_available = True
print("Chatterbox TTS imported successfully")
except ImportError as e:
print(f"Failed to import ChatterboxTTS: {e}")
chatterbox_available = False
# Global model variable - will be loaded inside GPU function
model = None
model_loaded = False
# Text length limits for the model
MAX_CHARS_PER_GENERATION = 1000 # Safe limit for single generation
MAX_CHARS_TOTAL = 5000 # Maximum we'll accept via API
def download_model_files():
"""Download model files with error handling."""
print(f"Checking for model files in {LOCAL_MODEL_PATH}...")
os.makedirs(LOCAL_MODEL_PATH, exist_ok=True)
for filename in MODEL_FILES:
local_path = os.path.join(LOCAL_MODEL_PATH, filename)
if not os.path.exists(local_path):
print(f"Downloading {filename} from {MODEL_REPO_ID}...")
try:
downloaded_path = hf_hub_download(
repo_id=MODEL_REPO_ID,
filename=filename,
cache_dir="./cache",
force_download=False
)
shutil.copy2(downloaded_path, local_path)
print(f"β Downloaded and copied {filename}")
except Exception as e:
print(f"β Failed to download {filename}: {e}")
raise e
else:
print(f"β {filename} already exists locally")
print("All model files are ready!")
def load_model_on_gpu():
"""Load model inside GPU context - only called within @spaces.GPU decorated function."""
global model, model_loaded
if model_loaded and model is not None:
return True
if not chatterbox_available:
print("ERROR: Chatterbox TTS library not available")
return False
try:
print("Loading model inside GPU context...")
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Loading model on device: {device}")
# Try different loading methods
try:
model = ChatterboxTTS.from_local(LOCAL_MODEL_PATH, device)
print("β Model loaded successfully using from_local method.")
except Exception as e1:
print(f"from_local failed: {e1}")
try:
model = ChatterboxTTS.from_pretrained(device)
print("β Model loaded successfully with from_pretrained.")
except Exception as e2:
print(f"from_pretrained failed: {e2}")
model = load_model_manually(device)
if model and hasattr(model, 'to'):
model = model.to(device)
if model and hasattr(model, 'eval'):
model.eval()
model_loaded = True
print("β Model loaded successfully in GPU context")
return True
except Exception as e:
print(f"ERROR: Failed to load model in GPU context: {e}")
traceback.print_exc()
model = None
model_loaded = False
return False
def load_model_manually(device):
"""Manual model loading with proper error handling."""
import pathlib
import json
model_path = pathlib.Path(LOCAL_MODEL_PATH)
print("Manual loading with correct constructor signature...")
s3gen_path = model_path / "s3gen.pt"
ve_path = model_path / "ve.pt"
tokenizer_path = model_path / "tokenizer.json"
t3_cfg_path = model_path / "t3_cfg.pt"
s3gen = torch.load(s3gen_path, map_location='cpu')
ve = torch.load(ve_path, map_location='cpu')
t3_cfg = torch.load(t3_cfg_path, map_location='cpu')
with open(tokenizer_path, 'r') as f:
tokenizer_data = json.load(f)
try:
from chatterbox.models.tokenizers.tokenizer import EnTokenizer
tokenizer = EnTokenizer.from_dict(tokenizer_data)
except Exception:
tokenizer = tokenizer_data
model = ChatterboxTTS(
t3=t3_cfg,
s3gen=s3gen,
ve=ve,
tokenizer=tokenizer,
device=device
)
print("β Model loaded successfully with manual constructor.")
return model
def cleanup_gpu_memory():
"""Clean up GPU memory - only call within GPU context."""
try:
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.synchronize()
gc.collect()
except Exception as e:
print(f"Warning: GPU cleanup failed: {e}")
def truncate_text_safely(text, max_chars=MAX_CHARS_PER_GENERATION):
"""Truncate text to safe length while preserving sentence boundaries."""
if len(text) <= max_chars:
return text, False
# Find the last sentence ending before the limit
truncated = text[:max_chars]
# Look for sentence endings
for ending in ['. ', '! ', '? ']:
last_sentence = truncated.rfind(ending)
if last_sentence > max_chars * 0.7: # Don't truncate too aggressively
return text[:last_sentence + 1].strip(), True
# Fallback to word boundary
last_space = truncated.rfind(' ')
if last_space > max_chars * 0.8:
return text[:last_space].strip(), True
# Last resort: hard truncate
return truncated.strip(), True
# Download model files during startup (CPU only)
if chatterbox_available:
try:
download_model_files()
print("Model files downloaded. Model will be loaded on first GPU request.")
except Exception as e:
print(f"ERROR during model file download: {e}")
@spaces.GPU
def clone_voice(text_to_speak, reference_audio_path, exaggeration=0.6, cfg_pace=0.3, random_seed=0, temperature=0.6):
"""Main voice cloning function - runs on GPU."""
global model, model_loaded
# Input validation
if not chatterbox_available:
return None, "Error: Chatterbox TTS library not available. Please check installation."
if not text_to_speak or text_to_speak.strip() == "":
return None, "Error: Please enter some text to speak."
if reference_audio_path is None:
return None, "Error: Please upload a reference audio file (.wav or .mp3)."
# Check text length and truncate if necessary
original_length = len(text_to_speak)
if original_length > MAX_CHARS_TOTAL:
return None, f"Error: Text is too long ({original_length:,} characters). Maximum allowed is {MAX_CHARS_TOTAL:,} characters. Please use the chunked generation API for longer texts."
# Truncate to safe generation length
text_to_use, was_truncated = truncate_text_safely(text_to_speak, MAX_CHARS_PER_GENERATION)
try:
# Load model if not already loaded
if not model_loaded:
print("Loading model for the first time...")
if not load_model_on_gpu():
return None, "Error: Failed to load model. Please check the logs for details."
if model is None:
return None, "Error: Model not loaded. Please check the logs for details."
print(f"Processing request:")
print(f" Original text length: {original_length:,} characters")
print(f" Processing length: {len(text_to_use):,} characters")
print(f" Truncated: {was_truncated}")
print(f" Audio: '{reference_audio_path}'")
print(f" Parameters: exag={exaggeration}, cfg={cfg_pace}, seed={random_seed}, temp={temperature}")
# Clean GPU memory before generation
cleanup_gpu_memory()
# Set random seed if specified
if random_seed > 0:
torch.manual_seed(random_seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(random_seed)
# Check CUDA availability and memory
if torch.cuda.is_available():
print(f"CUDA memory before generation: {torch.cuda.memory_allocated() / 1024**2:.1f} MB")
# Generate audio with error handling
try:
with torch.no_grad():
output_wav_data = model.generate(
text=text_to_use,
audio_prompt_path=reference_audio_path,
exaggeration=exaggeration,
cfg_weight=cfg_pace,
temperature=temperature
)
except RuntimeError as e:
if "CUDA" in str(e) or "out of memory" in str(e) or "device-side assert" in str(e):
print(f"CUDA error during generation: {e}")
cleanup_gpu_memory()
return None, f"CUDA error: Text may be too long for single generation. Try shorter text (under {MAX_CHARS_PER_GENERATION} characters) or use the chunked generation API for longer content."
else:
raise e
# Get sample rate
try:
sample_rate = model.sr
except:
sample_rate = 24000
# Process output
if isinstance(output_wav_data, str):
result = output_wav_data
else:
import numpy as np
if hasattr(output_wav_data, 'cpu'):
output_wav_data = output_wav_data.cpu().numpy()
if output_wav_data.ndim > 1:
output_wav_data = output_wav_data.squeeze()
result = (sample_rate, output_wav_data)
# Clean up GPU memory after generation
cleanup_gpu_memory()
if torch.cuda.is_available():
print(f"CUDA memory after generation: {torch.cuda.memory_allocated() / 1024**2:.1f} MB")
print("β Audio generated successfully")
# Prepare success message
success_msg = "Success: Audio generated successfully!"
if was_truncated:
success_msg += f" Note: Text was truncated from {original_length:,} to {len(text_to_use):,} characters for optimal generation. Use the chunked generation API for longer texts."
return result, success_msg
except Exception as e:
print(f"ERROR during audio generation: {e}")
traceback.print_exc()
# Clean up on error
try:
cleanup_gpu_memory()
except:
pass
# Provide specific error messages
error_msg = str(e)
if "CUDA" in error_msg or "device-side assert" in error_msg:
return None, f"CUDA error: {error_msg}. Try shorter text (under {MAX_CHARS_PER_GENERATION} characters) or use the chunked generation API."
elif "out of memory" in error_msg:
return None, f"GPU memory error: {error_msg}. Please try with shorter text."
else:
return None, f"Error during audio generation: {error_msg}. Check logs for more details."
def clone_voice_api(text_to_speak, reference_audio_url, exaggeration=0.6, cfg_pace=0.3, random_seed=0, temperature=0.6):
"""API wrapper function."""
import requests
import tempfile
import os
import base64
temp_audio_path = None
try:
# Handle different audio input formats
if reference_audio_url.startswith('data:audio'):
header, encoded = reference_audio_url.split(',', 1)
audio_data = base64.b64decode(encoded)
ext = '.mp3' if 'mp3' in header else '.wav'
with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as temp_file:
temp_file.write(audio_data)
temp_audio_path = temp_file.name
elif reference_audio_url.startswith('http'):
response = requests.get(reference_audio_url, timeout=30)
response.raise_for_status()
ext = '.mp3' if reference_audio_url.endswith('.mp3') else '.wav'
with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as temp_file:
temp_file.write(response.content)
temp_audio_path = temp_file.name
else:
temp_audio_path = reference_audio_url
# Call the GPU function
audio_output, status = clone_voice(text_to_speak, temp_audio_path, exaggeration, cfg_pace, random_seed, temperature)
return audio_output, status
except Exception as e:
print(f"API Error: {e}")
return None, f"API Error: {str(e)}"
finally:
# Clean up temporary file
if temp_audio_path and temp_audio_path != reference_audio_url:
try:
os.unlink(temp_audio_path)
except:
pass
def main():
print("Starting Advanced Gradio interface...")
with gr.Blocks(title="ποΈ Advanced Chatterbox Voice Cloning") as demo:
gr.Markdown("# ποΈ Advanced Chatterbox Voice Cloning")
gr.Markdown("Clone any voice using advanced AI technology with fine-tuned controls.")
# Add warning about text length
gr.Markdown(f"""
**β οΈ Text Length Limits:**
- **Single Generation**: Up to {MAX_CHARS_PER_GENERATION:,} characters (optimal quality)
- **API Maximum**: Up to {MAX_CHARS_TOTAL:,} characters (may be truncated)
- **For longer texts**: Use the chunked generation API in your application
""")
with gr.Row():
with gr.Column(scale=2):
text_input = gr.Textbox(
label=f"Text to Speak (max {MAX_CHARS_TOTAL:,} characters)",
placeholder="Enter the text you want the cloned voice to say...",
lines=5,
max_lines=10
)
audio_input = gr.Audio(
type="filepath",
label="Reference Audio (Upload a short .wav or .mp3 clip)",
sources=["upload", "microphone"]
)
with gr.Accordion("π§ Advanced Settings", open=False):
with gr.Row():
exaggeration_input = gr.Slider(
minimum=0.25, maximum=1.0, value=0.6, step=0.05,
label="Exaggeration", info="Controls voice characteristic emphasis"
)
cfg_pace_input = gr.Slider(
minimum=0.2, maximum=1.0, value=0.3, step=0.05,
label="CFG/Pace", info="Classifier-free guidance weight"
)
with gr.Row():
seed_input = gr.Number(
value=0, label="Random Seed", info="Set to 0 for random results", precision=0
)
temperature_input = gr.Slider(
minimum=0.05, maximum=2.0, value=0.6, step=0.05,
label="Temperature", info="Controls randomness in generation"
)
generate_btn = gr.Button("π΅ Generate Voice Clone", variant="primary", size="lg")
with gr.Column(scale=1):
audio_output = gr.Audio(label="Generated Audio", type="numpy")
status_output = gr.Textbox(label="Status", lines=3)
# Connect the interface
generate_btn.click(
fn=clone_voice_api,
inputs=[text_input, audio_input, exaggeration_input, cfg_pace_input, seed_input, temperature_input],
outputs=[audio_output, status_output],
api_name="predict"
)
# API endpoint for external calls
def clone_voice_base64_api(text_to_speak, reference_audio_b64, exaggeration=0.6, cfg_pace=0.3, random_seed=0, temperature=0.6):
return clone_voice_api(text_to_speak, reference_audio_b64, exaggeration, cfg_pace, random_seed, temperature)
# Hidden API interface
with gr.Row(visible=False):
api_text_input = gr.Textbox()
api_audio_input = gr.Textbox()
api_exaggeration_input = gr.Slider(minimum=0.25, maximum=1.0, value=0.6)
api_cfg_pace_input = gr.Slider(minimum=0.2, maximum=1.0, value=0.3)
api_seed_input = gr.Number(value=0, precision=0)
api_temperature_input = gr.Slider(minimum=0.05, maximum=2.0, value=0.6)
api_audio_output = gr.Audio(type="numpy")
api_status_output = gr.Textbox()
api_btn = gr.Button()
api_btn.click(
fn=clone_voice_base64_api,
inputs=[api_text_input, api_audio_input, api_exaggeration_input, api_cfg_pace_input, api_seed_input, api_temperature_input],
outputs=[api_audio_output, api_status_output],
api_name="clone_voice"
)
demo.launch(
server_name="0.0.0.0",
server_port=7860,
show_error=True,
quiet=False,
share=False
)
if __name__ == "__main__":
main()
|