app.py

import gradio as gr
import torch
from faster_whisper import WhisperModel
import pandas as pd

model_size = "large-v2"

# get device
device = "cuda:0" if torch.cuda.is_available() else "cpu"

if device == "cuda:0":
 # Run on GPU with FP16
 model_whisper = WhisperModel(model_size, device="cuda", compute_type="float16")
 # or Run on GPU with INT8
 # model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
else:
 # Run on CPU with INT8
 model_whisper = WhisperModel(model_size, device="cpu", compute_type="int8")

def get_filename(file_obj):
 return file_obj.name.split("/")[-1]

def audio_to_transcript(file_obj):
 # get all audio segments
 segments, _ = model_whisper.transcribe(file_obj.name, beam_size=5, vad_filter=True)
 print("start")
 start_segments, end_segments, text_segments = list(), list(), list()
 for segment in segments:
 start, end, text = segment.start, segment.end, segment.text
 start_segments.append(start)
 end_segments.append(end)
 text_segments.append(text)

 # save transcript into csv
 df = pd.DataFrame()
 df["start"] = start_segments
 df["end"] = end_segments
 df["text"] = text_segments

 print(df)

 return get_filename(file_obj), df

## Gradio interface
headers = ["start", "end", "text"]
iface = gr.Interface(fn=audio_to_transcript,
 inputs=gr.File(label="Audio file"),
 outputs=[
 gr.Textbox(label="Name of the audio file"),
 gr.DataFrame(label="Transcript", headers=headers),
 ],
 allow_flagging="never",
 title="Audio to Transcript",
 description="Just paste any audio file and get its corresponding transcript with timeline.",
 )
iface.launch()