Spaces:
Running
Running
File size: 1,411 Bytes
7b18d60 502159a eb134bd 7b18d60 68a9c43 eb134bd 68a9c43 a4f93b2 68a9c43 a4f93b2 eb134bd 7b18d60 ebd3d99 65129d9 ebd3d99 6aaee7d ebd3d99 eb134bd 542278b eb134bd ebd3d99 eb134bd 972bbda eb134bd 92d60f9 7b18d60 eb134bd 65129d9 eb134bd 7b18d60 7006793 4644447 d0fb6f8 7b18d60 77f489c 92d60f9 ad0f8bc 92d60f9 |
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 |
import gradio as gr
import time
from transformers import pipeline
import torch
# Check if GPU is available
use_gpu = torch.cuda.is_available()
# Configure the pipeline to use the GPU if available
if use_gpu:
p = pipeline("automatic-speech-recognition",
model="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h", device=0)
else:
p = pipeline("automatic-speech-recognition",
model="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h")
def transcribe(audio, state="", uploaded_audio=None):
if uploaded_audio is not None:
audio = uploaded_audio
if not audio:
return state, state # Return a meaningful message
try:
time.sleep(3)
text = p(audio, chunk_length_s= 50)["text"]
state += text + "\n"
return state, state
except Exception as e:
return "An error occurred during transcription.", state # Handle other exceptions
def reset(state):
state = ''
return state
demo = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath"),
'state',
gr.inputs.Audio(label="Upload Audio File", type="filepath", source="upload")
],
outputs=[
"textbox",
"state",
gr.Button('Reset').click(reset, inputs=["state"], outputs=["state"])
],
live=True)
demo.launch()
|