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import gradio as gr
import torch
import soundfile as sf
from snac import SNAC
from transformers import AutoTokenizer, AutoModelForCausalLM
# Ensure the code uses NVIDIA GPUs
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def find_last_instance_of_separator(lst, element=50258):
reversed_list = lst[::-1]
try:
reversed_index = reversed_list.index(element)
return len(lst) - 1 - reversed_index
except ValueError:
raise ValueError
def reconstruct_tensors(flattened_output):
def count_elements_between_hashes(lst):
try:
first_index = lst.index(50258)
second_index = lst.index(50258, first_index + 1)
return second_index - first_index - 1
except ValueError:
return "List does not contain two '#' symbols"
def remove_elements_before_hash(flattened_list):
try:
first_hash_index = flattened_list.index(50258)
return flattened_list[first_hash_index:]
except ValueError:
return "List does not contain the symbol '#'"
def list_to_torch_tensor(tensor1):
tensor = torch.tensor(tensor1)
tensor = tensor.unsqueeze(0)
return tensor
flattened_output = remove_elements_before_hash(flattened_output)
last_index = find_last_instance_of_separator(flattened_output)
flattened_output = flattened_output[:last_index]
codes = []
tensor1 = []
tensor2 = []
tensor3 = []
tensor4 = []
n_tensors = count_elements_between_hashes(flattened_output)
if n_tensors == 7:
for i in range(0, len(flattened_output), 8):
tensor1.append(flattened_output[i+1])
tensor2.append(flattened_output[i+2])
tensor3.append(flattened_output[i+3])
tensor3.append(flattened_output[i+4])
tensor2.append(flattened_output[i+5])
tensor3.append(flattened_output[i+6])
tensor3.append(flattened_output[i+7])
codes = [list_to_torch_tensor(tensor1).to(device), list_to_torch_tensor(tensor2).to(device), list_to_torch_tensor(tensor3).to(device)]
if n_tensors == 15:
for i in range(0, len(flattened_output), 16):
tensor1.append(flattened_output[i+1])
tensor2.append(flattened_output[i+2])
tensor3.append(flattened_output[i+3])
tensor4.append(flattened_output[i+4])
tensor4.append(flattened_output[i+5])
tensor3.append(flattened_output[i+6])
tensor4.append(flattened_output[i+7])
tensor4.append(flattened_output[i+8])
tensor2.append(flattened_output[i+9])
tensor3.append(flattened_output[i+10])
tensor4.append(flattened_output[i+11])
tensor4.append(flattened_output[i+12])
tensor3.append(flattened_output[i+13])
tensor4.append(flattened_output[i+14])
tensor4.append(flattened_output[i+15])
codes = [list_to_torch_tensor(tensor1).to(device), list_to_torch_tensor(tensor2).to(device), list_to_torch_tensor(tensor3).to(device), list_to_torch_tensor(tensor4).to(device)]
return codes
def load_model():
tokenizer = AutoTokenizer.from_pretrained("Lwasinam/voicera-jenny-finetune")
model = AutoModelForCausalLM.from_pretrained("Lwasinam/voicera-jenny-finetune").to(device)
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().to(device)
return model, tokenizer, snac_model
def SpeechDecoder(codes, snac_model):
codes = codes.squeeze(0).tolist()
reconstructed_codes = reconstruct_tensors(codes)
audio_hat = snac_model.decode(reconstructed_codes)
audio_path = "reconstructed_audio.wav"
sf.write(audio_path, audio_hat.squeeze().cpu().detach().numpy(), 24000)
return audio_path
def generate_audio(text, tokenizer, model, snac_model):
output_codes = []
with torch.no_grad():
input_text = text
input_ids = tokenizer(input_text, return_tensors='pt').to(device)
output_codes = model.generate(input_ids['input_ids'], attention_mask=input_ids['attention_mask'], max_length=1024,
num_beams=5, top_p=0.95, temperature=0.8, do_sample=True, repetition_penalty=2.0)
audio_path = SpeechDecoder(output_codes, snac_model)
return audio_path
def main(text):
model, tokenizer, snac_model = load_model()
audio_path = generate_audio(text, tokenizer, model, snac_model)
return audio_path
# Define the Gradio interface
iface = gr.Interface(
fn=main,
inputs='textbox',
outputs="audio",
title="Voicera TTS",
description="Generate speech from text using Voicera TTS model."
)
if __name__ == "__main__":
iface.launch()
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