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import os
from datasets import load_dataset, Audio
from transformers import pipeline
import gradio as gr
############### HF ###########################
HF_TOKEN = os.getenv("HF_TOKEN")
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "Urdu-ASR-flags")
############## DVC ################################
Model = "Model"
if os.path.isdir(".dvc"):
print("Running DVC")
# os.system("dvc config cache.type copy")
# os.system("dvc config core.no_scm true")
if os.system(f"dvc pull {Model} -r origin") != 0:
exit("dvc pull failed")
# os.system("rm -r .dvc")
# .apt/usr/lib/dvc
############## Inference ##############################
def asr(audio):
asr = pipeline("automatic-speech-recognition", model=Model)
prediction = asr(audio, chunk_length_s=5, stride_length_s=1)
return prediction
################### Gradio Web APP ################################
title = "Urdu Automatic Speech Recognition"
description = """
<p>
<center>
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
<img src="https://huggingface.co/spaces/kingabzpro/Urdu-ASR-SOTA/blob/main/Images/cover.jpg" alt="logo" width="250"/>
</center>
</p>
"""
article = """<p style='text-align: center'>
<a href='https://dagshub.com/kingabzpro/Urdu-ASR-SOTA' target='_blank'>
Source Code on DagsHub</a>
</center></p>"""
examples = [["Sample/sample1.mp3"], ["Sample/sample2.mp3"], ["Sample/sample3.mp3"]]
Input = gr.inputs.Audio(
source="microphone",
type="filepath",
optional=True,
label="Please Record Your Voice",
)
Output = gr.outputs.Textbox(label="Urdu Script")
def main():
iface = gr.Interface(
asr,
Input,
Output,
title=title,
flagging_options=["incorrect", "worst", "ambiguous"],
allow_flagging="manual",
flagging_callback=hf_writer,
description=description,
article=article,
examples=examples,
theme="peach",
)
iface.launch(enable_queue=True)
# enable_queue=True,auth=("admin", "pass1234")
if __name__ == "__main__":
main()
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