import spaces from snac import SNAC import torch import gradio as gr from transformers import AutoModelForCausalLM, AutoTokenizer from huggingface_hub import snapshot_download from dotenv import load_dotenv load_dotenv() # Check if CUDA is available device = "cuda" if torch.cuda.is_available() else "cpu" print("Loading SNAC model...") snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz") snac_model = snac_model.to(device) model_name = "syvai/tts-v1-finetuned" # Download only model config and safetensors snapshot_download( repo_id=model_name, allow_patterns=[ "config.json", "*.safetensors", "model.safetensors.index.json", ], ignore_patterns=[ "optimizer.pt", "pytorch_model.bin", "training_args.bin", "scheduler.pt", "tokenizer.json", "tokenizer_config.json", "special_tokens_map.json", "vocab.json", "merges.txt", "tokenizer.*" ] ) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16) model.to(device) tokenizer = AutoTokenizer.from_pretrained(model_name) print(f"Orpheus model loaded to {device}") # Process text prompt def process_prompt(prompt, voice, tokenizer, device): prompt = f"{voice}: {prompt}" input_ids = tokenizer(prompt, return_tensors="pt").input_ids start_token = torch.tensor([[128259]], dtype=torch.int64) # Start of human end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64) # End of text, End of human modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1) # SOH SOT Text EOT EOH # No padding needed for single input attention_mask = torch.ones_like(modified_input_ids) return modified_input_ids.to(device), attention_mask.to(device) # Parse output tokens to audio def parse_output(generated_ids): token_to_find = 128257 token_to_remove = 128258 token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True) if len(token_indices[1]) > 0: last_occurrence_idx = token_indices[1][-1].item() cropped_tensor = generated_ids[:, last_occurrence_idx+1:] else: cropped_tensor = generated_ids processed_rows = [] for row in cropped_tensor: masked_row = row[row != token_to_remove] processed_rows.append(masked_row) code_lists = [] for row in processed_rows: row_length = row.size(0) new_length = (row_length // 7) * 7 trimmed_row = row[:new_length] trimmed_row = [t - 128266 for t in trimmed_row] code_lists.append(trimmed_row) return code_lists[0] # Return just the first one for single sample # Redistribute codes for audio generation def redistribute_codes(code_list, snac_model): device = next(snac_model.parameters()).device # Get the device of SNAC model layer_1 = [] layer_2 = [] layer_3 = [] for i in range((len(code_list)+1)//7): layer_1.append(code_list[7*i]) layer_2.append(code_list[7*i+1]-4096) layer_3.append(code_list[7*i+2]-(2*4096)) layer_3.append(code_list[7*i+3]-(3*4096)) layer_2.append(code_list[7*i+4]-(4*4096)) layer_3.append(code_list[7*i+5]-(5*4096)) layer_3.append(code_list[7*i+6]-(6*4096)) # Move tensors to the same device as the SNAC model codes = [ torch.tensor(layer_1, device=device).unsqueeze(0), torch.tensor(layer_2, device=device).unsqueeze(0), torch.tensor(layer_3, device=device).unsqueeze(0) ] audio_hat = snac_model.decode(codes) return audio_hat.detach().squeeze().cpu().numpy() # Always return CPU numpy array # Main generation function @spaces.GPU() def generate_speech(text, voice, temperature, top_p, repetition_penalty, max_new_tokens, progress=gr.Progress()): if not text.strip(): return None try: progress(0.1, "Processing text...") input_ids, attention_mask = process_prompt(text, voice, tokenizer, device) progress(0.3, "Generating speech tokens...") with torch.no_grad(): generated_ids = model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, num_return_sequences=1, eos_token_id=128258, ) progress(0.6, "Processing speech tokens...") code_list = parse_output(generated_ids) progress(0.8, "Converting to audio...") audio_samples = redistribute_codes(code_list, snac_model) return (24000, audio_samples) # Return sample rate and audio except Exception as e: print(f"Error generating speech: {e}") return None # Examples for the UI examples = [ ["Spørger man lykke friis, der er tysklandskender og direktør i Tænketanken europa, så kan man kun gætte på årsagerne, men er ikke gode venner med alle i regeringen.", "mic", 0.2, 0.95, 1.1, 1200], ["Det burde have været en formssag i Den Tyske Forbundsdag, men det endte som alt andet end det. For første gang i Forbundsrepublikkens historie fik kanslerkandidaten ikke nok stemmer til at sikre sig den fornemme titel som kansler, da der skulle stemmes i parlamentet.", "nic", 0.2, 0.95, 1.1, 2000], ] # Available voices VOICES = ["nic", "mic"] # Available Emotive Tags EMOTIVE_TAGS = [] # Create Gradio interface with gr.Blocks(title="Syv.ai TTS v0.1") as demo: gr.Markdown(f""" # 🎵 [Syv.ai TTS v0.1](https://huggingface.co/syvai/tts-v1-finetuned) Skriv din tekst (gerne kortere end 200 tegn) nedenfor og hør hvad den kan. Vi har pt. kun 2 stemmer, og ingen måde at styre tone, grin eller andre paralinguistiske elementer. Vi arbejder dog på at udgive en model med bedre stemmestying. Syvai TTS er trænet på +1000 timer af dansk tale og bygger ovenpå en model fra [Orpheus TTS](https://huggingface.co/canopyai/Orpheus-TTS). """) with gr.Row(): with gr.Column(scale=3): text_input = gr.Textbox( label="Tekst at tale", placeholder="Indtast din tekst her...", lines=5 ) voice = gr.Dropdown( choices=VOICES, value="mic", label="Stemme" ) with gr.Accordion("Advanced Settings", open=False): temperature = gr.Slider( minimum=0.1, maximum=1.5, value=0.6, step=0.05, label="Temperature", info="Higher values (0.7-1.0) create more expressive but less stable speech" ) top_p = gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top P", info="Nucleus sampling threshold" ) repetition_penalty = gr.Slider( minimum=1.0, maximum=2.0, value=1.1, step=0.05, label="Repetition Penalty", info="Higher values discourage repetitive patterns" ) max_new_tokens = gr.Slider( minimum=100, maximum=2000, value=1200, step=100, label="Max Length", info="Maximum length of generated audio (in tokens)" ) with gr.Row(): submit_btn = gr.Button("Generer tale", variant="primary") clear_btn = gr.Button("Ryd") with gr.Column(scale=2): audio_output = gr.Audio(label="Genereret tale", type="numpy") # Set up examples gr.Examples( examples=examples, inputs=[text_input, voice, temperature, top_p, repetition_penalty, max_new_tokens], outputs=audio_output, fn=generate_speech, cache_examples=True, ) # Set up event handlers submit_btn.click( fn=generate_speech, inputs=[text_input, voice, temperature, top_p, repetition_penalty, max_new_tokens], outputs=audio_output ) clear_btn.click( fn=lambda: (None, None), inputs=[], outputs=[text_input, audio_output] ) # Launch the app if __name__ == "__main__": demo.queue().launch(share=False, ssr_mode=False)