Create app.py

app.py

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+# 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)