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import gradio as gr
import torch
from datasets import load_dataset
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
import soundfile as sf
# Load the fine-tuned model, processor, and vocoder
model_name = "microsoft/speecht5_tts"
processor = SpeechT5Processor.from_pretrained(model_name)
model = SpeechT5ForTextToSpeech.from_pretrained("emirhanbilgic/speecht5_finetuned_emirhan_tr")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
# Load the Turkish dataset
turkish_dataset = load_dataset("erenfazlioglu/turkishvoicedataset", split="train")
# Get an example text and its corresponding audio
example_item = turkish_dataset[0]
example_text = example_item['text']
example_audio = example_item['audio']['array']
# Create speaker embedding from the example audio
with torch.no_grad():
speaker_embeddings = model.get_speaker_embeddings(torch.tensor(example_audio).unsqueeze(0))
def text_to_speech(text):
inputs = processor(text=text, return_tensors="pt")
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
sf.write("output.wav", speech.numpy(), samplerate=16000)
return "output.wav"
# Create Gradio interface
iface = gr.Interface(
fn=text_to_speech,
inputs=gr.Textbox(label="Enter Turkish text to convert to speech", value=example_text),
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."
)
# Launch the demo
iface.launch() |