Update app.py

app.py

@@ -1,32 +1,20 @@
-import gradio as gr
-from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor
 import torch
-import torchaudio
-import tempfile
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
+import soundfile as sf
 
-# Load model and processor
+# Initialize the model and processor from Hugging Face
 processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
 model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
 
-# Load a voice embedding (necessary for the SpeechT5 model)
-speaker_embedding, _ = torchaudio.load("https://huggingface.co/microsoft/speecht5_tts/blob/main/speaker_embeddings/english/vctk_speaker_0.pt")
-
 def text_to_speech(text):
+    # Process the input text into tokens
     inputs = processor(text, return_tensors="pt")
-    speech = model.generate_speech(inputs["input_ids"], speaker_embedding)
-
-    # Save the output to a temporary file
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
-        torchaudio.save(f.name, speech, 16000)
-        return f.name
-
-# Gradio interface
-interface = gr.Interface(
-    fn=text_to_speech,
-    inputs="text",
-    outputs="audio",
-    title="Text to Speech",
-    description="Convert text to speech using the microsoft/speecht5_tts model"
-)
+    
+    # Generate speech
+    with torch.no_grad():
+        speech = model.generate_speech(inputs.input_ids)
+    
+    # Save the generated speech as a WAV file
+    sf.write('output.wav', speech.squeeze().cpu().numpy(), 16000)
 
-interface.launch()
+    return "output.wav"