Spaces:
Sleeping
Sleeping
File size: 4,101 Bytes
c569b48 81e4ee2 aaac499 81e4ee2 f36e52e aaac499 81e4ee2 aaac499 f36e52e 81e4ee2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
import gradio as gr
from audio_processing import process_audio, print_results
from transformers import pipeline
import spaces
import torch
# Check if CUDA is available
cuda_available = torch.cuda.is_available()
# Initialize the summarization and question-answering models
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
qa_model = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
# Move models to GPU if available
if cuda_available:
summarizer.to('cuda')
qa_model.to('cuda')
@spaces.GPU
def transcribe_audio(audio_file, translate, model_size):
language_segments, final_segments = process_audio(audio_file, translate=translate, model_size=model_size)
output = "Detected language changes:\n\n"
for segment in language_segments:
output += f"Language: {segment['language']}\n"
output += f"Time: {segment['start']:.2f}s - {segment['end']:.2f}s\n\n"
output += f"Transcription with language detection and speaker diarization (using {model_size} model):\n\n"
full_text = ""
for segment in final_segments:
output += f"[{segment['start']:.2f}s - {segment['end']:.2f}s] ({segment['language']}) {segment['speaker']}:\n"
output += f"Original: {segment['text']}\n"
if translate:
output += f"Translated: {segment['translated']}\n"
full_text += segment['translated'] + " "
else:
full_text += segment['text'] + " "
output += "\n"
return output, full_text
@spaces.GPU
def summarize_text(text):
summary = summarizer(text, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
return summary
@spaces.GPU
def answer_question(context, question):
result = qa_model(question=question, context=context)
return result['answer']
@spaces.GPU
def process_and_summarize(audio_file, translate, model_size):
transcription, full_text = transcribe_audio(audio_file, translate, model_size)
summary = summarize_text(full_text)
return transcription, summary
@spaces.GPU
def qa_interface(audio_file, translate, model_size, question):
_, full_text = transcribe_audio(audio_file, translate, model_size)
answer = answer_question(full_text, question)
return answer
# Main interface
with gr.Blocks() as iface:
gr.Markdown("# WhisperX Audio Transcription, Translation, Summarization, and QA (with ZeroGPU support)")
with gr.Tab("Transcribe and Summarize"):
audio_input = gr.Audio(type="filepath")
translate_checkbox = gr.Checkbox(label="Enable Translation")
model_dropdown = gr.Dropdown(choices=["tiny", "base", "small", "medium", "large", "large-v2", "large-v3"], label="Whisper Model Size", value="small")
transcribe_button = gr.Button("Transcribe and Summarize")
transcription_output = gr.Textbox(label="Transcription")
summary_output = gr.Textbox(label="Summary")
transcribe_button.click(
process_and_summarize,
inputs=[audio_input, translate_checkbox, model_dropdown],
outputs=[transcription_output, summary_output]
)
with gr.Tab("Question Answering"):
qa_audio_input = gr.Audio(type="filepath")
qa_translate_checkbox = gr.Checkbox(label="Enable Translation")
qa_model_dropdown = gr.Dropdown(choices=["tiny", "base", "small", "medium", "large", "large-v2", "large-v3"], label="Whisper Model Size", value="small")
question_input = gr.Textbox(label="Ask a question about the audio")
qa_button = gr.Button("Get Answer")
answer_output = gr.Textbox(label="Answer")
qa_button.click(
qa_interface,
inputs=[qa_audio_input, qa_translate_checkbox, qa_model_dropdown, question_input],
outputs=answer_output
)
gr.Markdown(
"""
## ZeroGPU Support
This application supports ZeroGPU for Hugging Face Spaces pro users.
GPU-intensive tasks are automatically optimized for better performance.
"""
)
iface.launch() |