Update app.py

app.py

@@ -1,52 +1,46 @@
 import streamlit as st
-from transformers import pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
-from datasets import load_dataset
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
 import torch
 import soundfile as sf
-import os
-
-# Function to generate speech using the pipeline method
-def generate_speech_pipeline(text, speaker_embedding):
-    synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts")
-    speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding})
-    return speech["audio"], speech["sampling_rate"]
-
-# Function to generate speech using the processor + generate method
-def generate_speech_processor(text, speaker_embedding):
-    processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
-    model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
-    vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-
-    inputs = processor(text=text, return_tensors="pt")
-    speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
-    return speech.numpy(), 16000
-
-def main():
-    st.title("Text-to-Speech with SpeechT5")
-    
-    st.write("Enter the text you want to convert to speech:")
-
-    text = st.text_area("Text", "Hello, my dog is cooler than you!")
-    
-    if st.button("Generate Speech"):
-        st.write("Generating speech...")
-
-        # Load xvector containing speaker's voice characteristics from a dataset
-        embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
-        speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
-
-        # Choose the method to generate speech
-        method = st.selectbox("Choose the method for generating speech", ["Pipeline", "Processor + Generate"])
-
-        if method == "Pipeline":
-            audio, samplerate = generate_speech_pipeline(text, speaker_embedding)
-        else:
-            audio, samplerate = generate_speech_processor(text, speaker_embedding)
-
-        # Save and play the generated speech
-        output_path = "speech.wav"
-        sf.write(output_path, audio, samplerate=samplerate)
-        st.audio(output_path)
-
-if __name__ == "__main__":
-    main()
+from datasets import load_dataset
+
+# Initialize the processor and model
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+
+# Load the vocoder
+vocoder = torch.hub.load("s3prl/s3prl", "mb_melgan")
+
+# Initialize session state
+if 'text' not in st.session_state:
+    st.session_state['text'] = "Hello, my dog is cooler than you!"
+
+# Function to update session state
+def update_text():
+    st.session_state['text'] = st.text_area("Text", st.session_state['text'])
+
+st.title("Text-to-Speech with SpeechT5")
+st.write("Enter the text you want to convert to speech:")
+
+# Use session state to store text
+update_text()
+
+if st.button("Generate Speech"):
+    st.write("Generating speech...")
+
+    # Process the input text
+    inputs = processor(text=st.session_state['text'], return_tensors="pt")
+
+    # Generate speech
+    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings=None)
+
+    # Use the vocoder to convert the generated speech to audio
+    with torch.no_grad():
+        audio = vocoder(speech)
+
+    # Save the audio to a file
+    sf.write("output.wav", audio.cpu().numpy(), samplerate=16000)
+
+    # Provide a download link for the generated speech
+    st.audio("output.wav", format="audio/wav")
+    st.write("Speech generation complete. You can listen to the generated speech above.")