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import gradio as gr
import time
from transformers import pipeline
import torch
import ffmpeg
def main():
# 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"Hendan villan hendi: {e}"
def transcribe_function(audio, state, m3u8_url):
if m3u8_url:
audio = extract_audio_from_m3u8(m3u8_url)
if not audio:
# Return a meaningful message; no audio found
return state, "Einki ljóð er til talukenning."
try:
time.sleep(3)
text = p(audio, chunk_length_s=50)["text"]
state += text + "\n"
return state, text
except Exception as e:
return state, "Okkurt riggaði ikki í talukenningini."
def reset_output(transcription, state):
"""Function to reset the state to an empty string."""
return "", ""
with gr.Blocks() as demo:
state_var = gr.State("")
with gr.Row():
with gr.Column():
microphone = gr.Audio(
type="filepath",
label="Mikrofon ella ljóðfíla"
)
m3u8_url = gr.Textbox(
label="m3u8-leinki (t.d. frá kvf.fo ella logting.fo)"
)
with gr.Column():
transcription_var = gr.Textbox(
type="text",
label="Tekstur frá talukennara",
interactive=False
)
with gr.Row():
transcribe_button = gr.Button("Byrja talukenning")
reset_button = gr.Button("Strika tekst frá talukennara")
transcribe_button.click(
transcribe_function,
[microphone, state_var, m3u8_url], # Removed uploaded_audio
[state_var, transcription_var]
)
reset_button.click(
reset_output,
[transcription_var, state_var],
[transcription_var, state_var]
)
# Launch with the latest Gradio features
demo.launch()
if __name__ == "__main__":
main()
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