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from transformers import pipeline,Conversation
import gradio as gr
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-base",
)
model= pipeline("conversational", model="facebook/blenderbot-400M-distill")
# Get a response
# Initialize the conversational model
# conversational_model = pipeline("conversational", model="microsoft/DialoGPT-medium")
# Mock implementation of transcribe_speech for demonstration
def transcribe_speech(filepath):
output = pipe(
filepath,
max_new_tokens=256,
generate_kwargs={
"task": "transcribe",
"language": "english",
}, # update with the language you've fine-tuned on
chunk_length_s=30,
batch_size=8,
)
return output["text"]
def handle_audio_input(audio_file):
try:
# Step 1: Transcribe the audio
transcribed_text = transcribe_speech(audio_file)
print(f"Transcribed text: {transcribed_text}")
# Step 2: Create a conversation and generate a response from transcribed text
conversation = Conversation(transcribed_text)
response = model(conversation)
chatbot_response = response.generated_responses[-1]
print(f"Chatbot response: {chatbot_response}")
return transcribed_text, chatbot_response
except Exception as e:
print(f"Error: {e}")
return "Error in processing audio", str(e)
# Create the Gradio Blocks container
with gr.Blocks() as demo:
gr.Markdown("## Customer query audio to text chatbot")
with gr.Tab("Microphone"):
mic_transcribe = gr.Interface(
fn=handle_audio_input,
inputs=gr.Audio(sources="microphone", type="filepath"),
outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Chatbot Response")],
)
mic_transcribe.render()
with gr.Tab("File Upload"):
file_transcribe = gr.Interface(
fn=handle_audio_input,
inputs=gr.Audio(sources="upload", type="filepath"),
outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Chatbot Response")],
)
file_transcribe.render()
# Launch the Gradio app
demo.launch(share=True) |