File size: 1,997 Bytes
08a2633 |
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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
# Step 1: Import necessary libraries
import gradio as gr
import json
import torch
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
import soundfile as sf
# Step 2: Load the models and the pronunciation dictionary
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
# Load pronunciation dictionary from JSON file
with open("pronunciation_dict.json", "r") as f:
pronunciation_dict = json.load(f)
# Function to preprocess the input text
def preprocess_text(text):
for term, phonetic in pronunciation_dict.items():
text = text.replace(term, phonetic)
return text
# Step 3: Define the TTS function
def text_to_speech(input_text):
# Preprocess the text
processed_text = preprocess_text(input_text)
# Convert the processed text to model inputs
inputs = processor(text=processed_text, return_tensors="pt")
# Load xvector embeddings from dataset for speaker voice characteristics
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
# Generate speech using the model and vocoder
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
# Save the generated speech as a .wav file
output_file = "speech_output.wav"
sf.write(output_file, speech.numpy(), samplerate=16000)
return output_file
# Step 4: Create Gradio interface
iface = gr.Interface(fn=text_to_speech,
inputs="text",
outputs="audio",
title="Text-to-Speech (TTS) Application",
description="Enter text with technical jargon for TTS conversion.")
# Step 5: Launch the app
iface.launch(share=True)
|