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| import gradio as gr | |
| from transformers import AutoProcessor, BarkModel | |
| import scipy.io.wavfile | |
| import torch | |
| import os | |
| import numpy as np | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| # Basic device setup | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| print(f"Using device: {DEVICE}") | |
| # Model initialization with basic settings | |
| processor = AutoProcessor.from_pretrained( | |
| "suno/bark", | |
| trust_remote_code=True | |
| ) | |
| model = BarkModel.from_pretrained( | |
| "suno/bark", | |
| torch_dtype=torch.float32, # Using float32 for stability | |
| trust_remote_code=True | |
| ) | |
| # Basic model optimization | |
| model.to(DEVICE) | |
| model.eval() | |
| # Define cache directory in the allowed space | |
| CACHE_DIR = "audio_cache" | |
| os.makedirs(CACHE_DIR, exist_ok=True) | |
| def clean_text(text): | |
| """Clean and prepare text for processing.""" | |
| if not isinstance(text, str): | |
| return "" | |
| return text.strip() | |
| def get_cache_path(text: str, voice_preset: str) -> str: | |
| """Generate a unique cache path.""" | |
| import hashlib | |
| hash_key = hashlib.md5(f"{text}_{voice_preset}".encode()).hexdigest() | |
| return os.path.join(CACHE_DIR, f"{hash_key}.wav") | |
| def text_to_speech(text: str, voice_preset: str = "v2/hi_speaker_2"): | |
| """Convert text to speech using Bark model.""" | |
| try: | |
| # Clean and validate input | |
| text = clean_text(text) | |
| if not text: | |
| return None | |
| # Generate cache path | |
| cache_path = get_cache_path(text, voice_preset) | |
| # Process the text | |
| inputs = processor(text, voice_preset=voice_preset) | |
| # Move inputs to device | |
| inputs = {k: v.to(DEVICE) if isinstance(v, torch.Tensor) else v | |
| for k, v in inputs.items()} | |
| # Generate audio | |
| with torch.inference_mode(): | |
| audio_array = model.generate( | |
| **inputs, | |
| do_sample=True, | |
| temperature=0.7 | |
| ) | |
| # Process the audio | |
| audio_array = audio_array.cpu().numpy().squeeze() | |
| audio_array = np.clip(audio_array, -1, 1) | |
| # Save the audio | |
| sample_rate = model.generation_config.sample_rate | |
| scipy.io.wavfile.write(cache_path, rate=sample_rate, data=audio_array) | |
| return cache_path | |
| except Exception as e: | |
| print(f"Error in text_to_speech: {str(e)}") | |
| return None | |
| # Voice presets | |
| voice_presets = [ | |
| "v2/hi_speaker_1", | |
| "v2/hi_speaker_2", | |
| "v2/hi_speaker_3", | |
| "v2/hi_speaker_4", | |
| "v2/hi_speaker_5" | |
| ] | |
| # Create Gradio interface | |
| demo = gr.Interface( | |
| fn=text_to_speech, | |
| inputs=[ | |
| gr.Textbox( | |
| label="Enter text (Hindi or English)", | |
| placeholder="Type your text here...", | |
| lines=3 | |
| ), | |
| gr.Dropdown( | |
| choices=voice_presets, | |
| value="v2/hi_speaker_2", | |
| label="Select Voice" | |
| ) | |
| ], | |
| outputs=gr.Audio(label="Generated Speech"), | |
| title="Bark Text-to-Speech", | |
| description="Convert text to speech using the Bark model. Supports Hindi and English text." | |
| ) | |
| # Launch the app | |
| demo.launch() |