File size: 1,550 Bytes
badff1c 828d42b badff1c 828d42b badff1c 828d42b badff1c 828d42b badff1c 828d42b badff1c 828d42b badff1c 828d42b badff1c |
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 |
import gradio as gr
import torch
from datasets import load_dataset
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
import soundfile as sf
import spaces
device = "cuda" if torch.cuda.is_available() else "cpu"
def load_models_and_data():
model_name = "microsoft/speecht5_tts"
processor = SpeechT5Processor.from_pretrained(model_name)
model = SpeechT5ForTextToSpeech.from_pretrained("emirhanbilgic/speecht5_finetuned_emirhan_tr").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0).to(device)
return model, processor, vocoder, speaker_embeddings
model, processor, vocoder, speaker_embeddings = load_models_and_data()
@spaces.GPU(duration = 60)
def text_to_speech(text):
inputs = processor(text=text, return_tensors="pt").to(device)
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
sf.write("output.wav", speech.cpu().numpy(), samplerate=16000)
return "output.wav"
iface = gr.Interface(
fn=text_to_speech,
inputs=gr.Textbox(label="Enter Turkish text to convert to speech"),
outputs=gr.Audio(label="Generated Speech"),
title="Turkish SpeechT5 Text-to-Speech Demo",
description="Enter Turkish text and listen to the generated speech using the fine-tuned SpeechT5 model."
)
iface.launch() |