Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,194 Bytes
f11f43f 754df64 f11f43f 754df64 f11f43f 754df64 f11f43f |
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 |
import torch
import gradio as gr
from transformers import pipeline
import concurrent.futures
import time
# Load both models
MODEL_NAME_TURBO = "openai/whisper-large-v3-turbo"
MODEL_NAME_STANDARD = "openai/whisper-large-v3"
device = 0 if torch.cuda.is_available() else "cpu"
# Set up the pipeline for both models
pipe_turbo = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME_TURBO,
chunk_length_s=30,
device=device,
)
pipe_standard = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME_STANDARD,
chunk_length_s=30,
device=device,
)
# Function to transcribe audio using the turbo model
@spaces.GPU
def transcribe_turbo(audio):
start_time = time.time()
text_turbo = pipe_turbo(audio)["text"]
elapsed_time = time.time() - start_time
return text_turbo, elapsed_time
# Function to transcribe audio using the standard model
@spaces.GPU
def transcribe_standard(audio):
start_time = time.time()
text_standard = pipe_standard(audio)["text"]
elapsed_time = time.time() - start_time
return text_standard, elapsed_time
# Function to compare transcriptions and speed
@spaces.GPU
def compare_transcriptions(audio):
if audio is None:
raise gr.Error("No audio file submitted! Please record an audio before submitting your request.")
# Run both transcriptions in parallel
with concurrent.futures.ThreadPoolExecutor() as executor:
future_turbo = executor.submit(transcribe_turbo, audio)
future_standard = executor.submit(transcribe_standard, audio)
# Get the results
text_turbo, time_turbo = future_turbo.result()
text_standard, time_standard = future_standard.result()
# Return both transcriptions and processing times
return (text_standard, f"{time_standard:.2f} seconds"), (text_turbo, f"{time_turbo:.2f} seconds")
css = """
h1 {
text-align: center;
display:block;
}
"""
# Gradio Interface
with gr.Blocks(css=css) as demo:
# Title and description
gr.Markdown("# Whisper large-v3-turbo ...vs... Whisper large-v3")
gr.Markdown("This app compares the transcription performance and processing time between openAI 'Whisper large-v3' and 'Whisper large-v3-turbo' models")
with gr.Column():
with gr.Row():
with gr.Group():
audio_input = gr.Audio(sources=["microphone"], type="filepath")
transcribe_button = gr.Button("Start transcription", variant="primary")
with gr.Row():
with gr.Row():
with gr.Group():
gr.Markdown("### 📝 **Standard model**")
standard_output = gr.Textbox(label="Transcription")
standard_time = gr.Textbox(label="Processing Time")
with gr.Group():
gr.Markdown("### ⚡ **Turbo model**")
turbo_output = gr.Textbox(label="Transcription")
turbo_time = gr.Textbox(label="Processing Time")
# Set up the interaction
transcribe_button.click(fn=compare_transcriptions, inputs=audio_input, outputs=[standard_output, standard_time, turbo_output, turbo_time])
# Launch the demo
demo.launch()
|