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import gradio as gr
import torch
from faster_whisper import WhisperModel
import tempfile
import logging
import os
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Mapping of model names to Whisper model sizes
MODELS = {
"Faster Whisper Medium": "Systran/faster-whisper-medium", # Use the medium model
}
def transcribe_live_audio(audio):
"""Transcribe live audio from the microphone."""
try:
# Save the live audio to a temporary file
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
temp_audio_path = temp_audio.name
audio.save(temp_audio_path) # Save the audio file
# Load the appropriate model
device = "cuda" if torch.cuda.is_available() else "cpu"
compute_type = "float32" if device == "cuda" else "int8"
model = WhisperModel(MODELS["Faster Whisper Medium"], device=device, compute_type=compute_type)
# Transcribe the live audio
segments, info = model.transcribe(
temp_audio_path,
task="transcribe",
word_timestamps=True,
repetition_penalty=1.1,
temperature=[0.0, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0],
)
transcription = " ".join([segment.text for segment in segments])
# Clean up temporary audio file
os.remove(temp_audio_path)
return transcription
except Exception as e:
logger.error(f"Error in transcribe_live_audio: {str(e)}")
return f"Error: {str(e)}"
# Define the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Live Audio Transcription")
gr.Markdown("Transcribe live audio from your microphone using the **Systran/faster-whisper-medium** model.")
# Live audio input
live_audio_input = gr.Audio(type="filepath", label="Speak into the microphone")
# Transcription output
live_transcription_output = gr.Textbox(label="Live Transcription")
# Button to start transcription
live_transcribe_button = gr.Button("Start Transcription")
# Link button to function
live_transcribe_button.click(
transcribe_live_audio,
inputs=[live_audio_input],
outputs=live_transcription_output
)
# Launch the Gradio interface
demo.launch() |