Create app.py

app.py

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+import os
+import torch
+import whisper
+import streamlit as st
+from groq import Groq
+from TTS.api import TTS
+from tempfile import NamedTemporaryFile
+from streamlit_webrtc import webrtc_streamer, WebRtcMode, ClientSettings
+import av
+
+# LLM Response Function
+def get_llm_response(api_key, user_input):
+    client = Groq(api_key=api_key)
+    prompt = (
+        "IMPORTANT: You are an AI assistant that MUST provide responses in 25 words or less.\n"
+        "CRITICAL RULES:\n"
+        "1. NEVER exceed 25 words unless absolutely necessary.\n"
+        "2. Always give a complete sentence with full context.\n"
+        "3. Answer directly and precisely.\n"
+        "4. Use clear, simple language.\n"
+        "5. Maintain a polite, professional tone.\n"
+        "6. NO lists, bullet points, or multiple paragraphs.\n"
+        "7. NEVER apologize for brevity - embrace it.\n"
+        "Your response will be converted to speech. Maximum 25 words."
+    )
+    
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": prompt},
+            {"role": "user", "content": user_input}
+        ],
+        model="llama3-8b-8192",
+        temperature=0.5,
+        top_p=1,
+        stream=False,
+    )
+    return chat_completion.choices[0].message.content
+
+# Transcribe Audio
+def transcribe_audio(audio_path, model_size="base"):
+    model = whisper.load_model(model_size)
+    result = model.transcribe(audio_path)
+    return result["text"]
+
+# Generate Speech
+def generate_speech(text, output_file, speaker_wav, language="en", use_gpu=True):
+    if not os.path.exists(speaker_wav):
+        raise FileNotFoundError("Reference audio file not found. Please upload or record a valid audio.")
+    
+    tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=use_gpu)
+    tts.tts_to_file(
+        text=text,
+        file_path=output_file,
+        speaker_wav=speaker_wav,
+        language=language,
+    )
+
+# Audio Frame Processing
+class AudioProcessor:
+    def __init__(self):
+        self.audio_frames = []
+
+    def recv(self, frame):
+        self.audio_frames.append(frame.to_ndarray().tobytes())
+        return frame
+
+    def save_audio(self, file_path):
+        with open(file_path, "wb") as f:
+            for frame in self.audio_frames:
+                f.write(frame)
+        return file_path
+
+# Streamlit App
+def main():
+    st.set_page_config(page_title="Vocal AI", layout="wide")
+    st.sidebar.title("Vocal-AI Settings")
+
+    # User option for reference audio (Record or Upload)
+    ref_audio_choice = st.sidebar.radio("Reference Audio", ("Upload", "Record"))
+
+    ref_audio_path = None
+    reference_audio_processor = None
+
+    if ref_audio_choice == "Upload":
+        reference_audio = st.sidebar.file_uploader("Upload Reference Audio", type=["wav", "mp3", "ogg"])
+        if reference_audio:
+            with NamedTemporaryFile(delete=False, suffix=".wav") as temp_ref_audio:
+                temp_ref_audio.write(reference_audio.read())
+                ref_audio_path = temp_ref_audio.name
+    else:
+        st.sidebar.write("Record your reference audio:")
+        reference_audio_processor = AudioProcessor()
+        webrtc_streamer(
+            key="ref_audio",
+            mode=WebRtcMode.SENDRECV,
+            client_settings=ClientSettings(rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]}),
+            audio_receiver_size=1024,
+            video_processor_factory=None,
+            audio_processor_factory=lambda: reference_audio_processor,
+        )
+
+    st.title("Welcome to VocaL AI")
+    st.write("### How to Use")
+    st.write("1. Upload or record a reference audio file.")
+    st.write("2. Choose between text or audio input.")
+    st.write("3. If audio input is selected, record and submit your audio.")
+    st.write("4. Click 'Generate Speech' to hear the AI response in your cloned voice.")
+
+    # User Input (Text or Audio)
+    input_type = st.radio("Choose Input Type", ("Text", "Audio"))
+    user_input = None
+    user_audio_processor = None
+
+    if input_type == "Text":
+        user_input = st.text_area("Enter your text here")
+    else:
+        st.write("Record your voice:")
+        user_audio_processor = AudioProcessor()
+        webrtc_streamer(
+            key="user_audio",
+            mode=WebRtcMode.SENDRECV,
+            client_settings=ClientSettings(rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]}),
+            audio_receiver_size=1024,
+            video_processor_factory=None,
+            audio_processor_factory=lambda: user_audio_processor,
+        )
+
+    if st.button("Generate Speech"):
+        # Handle Reference Audio
+        if reference_audio_processor:
+            with NamedTemporaryFile(delete=False, suffix=".wav") as temp_ref_audio:
+                reference_audio_processor.save_audio(temp_ref_audio.name)
+                ref_audio_path = temp_ref_audio.name
+
+        if not ref_audio_path:
+            st.error("Please upload or record reference audio.")
+            return
+
+        # Handle User Input
+        if input_type == "Audio":
+            if user_audio_processor:
+                with NamedTemporaryFile(delete=False, suffix=".wav") as temp_user_audio:
+                    user_audio_processor.save_audio(temp_user_audio.name)
+                    user_input = transcribe_audio(temp_user_audio.name)
+                    os.unlink(temp_user_audio.name)
+
+        if not user_input:
+            st.error("Please enter text or record audio.")
+            return
+
+        # Get AI Response
+        api_key = st.secrets["GROQ_API_KEY"]
+        response_text = get_llm_response(api_key, user_input)
+
+        # Generate Speech
+        output_audio_path = "output_speech.wav"
+        try:
+            generate_speech(response_text, output_audio_path, ref_audio_path)
+            os.unlink(ref_audio_path)
+            st.audio(output_audio_path, format="audio/wav")
+        except FileNotFoundError as e:
+            st.error(str(e))
+
+if __name__ == "__main__":
+    main()