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import gradio as gr
from transformers import pipeline
import soundfile as sf
from huggingface_hub import InferenceClient
# Initialize Facebook MMS ASR model
asr_model = pipeline("automatic-speech-recognition", model="facebook/mms-1b-all")
# Initialize Facebook MMS TTS model
tts_model = pipeline("text-to-speech", model="facebook/mms-tts")
# Initialize the Chat Model (Gemma-2-9B or Futuresony.gguf)
chat_client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf") # Change if needed
def asr_chat_tts(audio):
"""
1. Convert Speech to Text (ASR)
2. Process text through Chat Model (LLM)
3. Convert response to Speech (TTS)
"""
# Step 1: Transcribe speech using Facebook MMS ASR
transcription = asr_model(audio)["text"]
# Step 2: Process text through the chat model
messages = [{"role": "system", "content": "You are a helpful AI assistant."}]
messages.append({"role": "user", "content": transcription})
response = ""
for msg in chat_client.chat_completion(messages, max_tokens=512, stream=True):
token = msg.choices[0].delta.content
response += token
# Step 3: Convert response to speech using Facebook MMS TTS
speech = tts_model(response)
output_file = "generated_speech.wav"
sf.write(output_file, speech["audio"], samplerate=speech["sampling_rate"])
return transcription, response, output_file
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("<h2 style='text-align: center;'>ASR → Chatbot → TTS</h2>")
with gr.Row():
audio_input = gr.Audio(source="microphone", type="filepath", label="🎤 Speak Here")
text_transcription = gr.Textbox(label="📝 Transcription", interactive=False)
text_response = gr.Textbox(label="🤖 Chatbot Response", interactive=False)
audio_output = gr.Audio(label="🔊 Generated Speech")
submit_button = gr.Button("Process Speech 🔄")
submit_button.click(fn=asr_chat_tts, inputs=[audio_input], outputs=[text_transcription, text_response, audio_output])
# Run the App
if __name__ == "__main__":
demo.launch()
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