Spaces:
Sleeping
Sleeping
File size: 1,819 Bytes
7b18d60 502159a eb134bd 0659665 7b18d60 68a9c43 12974e0 68a9c43 12974e0 68a9c43 12974e0 0659665 12974e0 0659665 ebd3d99 65129d9 0659665 ebd3d99 12974e0 0659665 ebd3d99 eb134bd 542278b eb134bd 12974e0 ebd3d99 12974e0 |
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 |
import gradio as gr
import time
from transformers import pipeline
import torch
import ffmpeg # Make sure it's ffmpeg-python
# 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 extract_audio_from_m3u8(url):
try:
output_file = "output_audio.aac"
ffmpeg.input(url).output(output_file).run(overwrite_output=True)
return output_file
except Exception as e:
return f"An error occurred: {e}"
def transcribe(audio, state="", uploaded_audio=None, m3u8_url=""):
if m3u8_url:
audio = extract_audio_from_m3u8(m3u8_url)
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.components.Audio(source="microphone", type="filepath"),
'state',
gr.components.Audio(label="Upload Audio File", type="filepath", source="upload"),
gr.components.Textbox(label="m3u8 URL | E.g.: from kvf.fo or logting.fo")
],
outputs=[
gr.components.Textbox(type="text"),
"state"
],
live=True)
demo.launch()
|